Molecular Genetics and Personalized Medicine

Molecular and Translational Medicine Series Editors William B. Coleman Gregory J. Tsongalis For further volumes: http...

Author: D. Hunter Best | Jeffrey J. Swensen

133 downloads 1908 Views 4MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

Molecular and Translational Medicine

Series Editors William B. Coleman Gregory J. Tsongalis

For further volumes: http://www.springer.com/series/8176

D. Hunter Best • Jeffrey J. Swensen Editors

Molecular Genetics and Personalized Medicine

Editors D. Hunter Best, Ph.D., FACMG Department of Pathology University of Utah School of Medicine Salt Lake City, UT 84112-0565 [email protected]

Jeffrey J. Swensen, Ph.D., FACMG Department of Pathology University of Utah School of Medicine Salt Lake City, UT 84112-0565, USA [email protected]

ISBN 978-1-61779-529-9 e-ISBN 978-1-61779-530-5 DOI 10.1007/978-1-61779-530-5 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011943887 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

Preface

Just under 10 years ago, the first draft of the human genome sequence was completed, officially starting the era of genomic medicine. In the decade that has followed, this knowledge has fueled revolutionary technological advances that allow medicine to be personalized to the individual patient. Genetic testing has become commonplace, and clinicians are frequently able to use knowledge of an individual’s specific genetic differences to guide their course of action. However, understanding the complexities involved in molecular genetic testing is difficult and can be intimidating. In this volume, we have sought to simplify some of the complex issues that arise when dealing with molecular genetic testing. Topics covered include everything from a description of the basic molecular methods used to perform molecular testing to genetic counseling and presymptomatic genetic testing. Each chapter is written by an expert in their field in a manner that is accessible to individuals with very little background in genetics. In addition, the authors have tried to focus on practical patient-related issues that commonly present themselves to today’s practicing physician. While we realize that this text is by no means a comprehensive review of the field of molecular genetics, we do feel it will serve as a useful reference for physicians hoping to better understand the role of molecular medicine in clinical practice. Furthermore, we hope it will prove to be an invaluable resource for the basic scientist that wants to better understand how advances in the laboratory are being moved from the bench to the bedside. Sincerely, D. Hunter Best Jeffrey J. Swensen

v

Contents

1

Molecular Genetic Testing in the Genomic Era ................................... Charles J. Sailey and Ferrin C. Wheeler

2

Array Comparative Genomic Hybridization in Cytogenetics and Molecular Genetics .......................................................................... S. Hussain Askree and Madhuri R. Hegde

1

21

3

Pharmacogenomics: Tailoring Treatment Based on Genotype........... Alan H.B. Wu

37

4

Somatic Alterations and Targeted Therapy ......................................... Allison M. Cushman-Vokoun

51

5

Genome-Wide Association Studies in Disease Risk Calculation: The Role of Bioinformatics in Patient Care.......................................... 103 Todd L. Edwards, Digna R. Velez Edwards, and Marylyn DeRiggi Ritchie

6

Presymptomatic Genetic Testing: Shifting the Emphasis from Reaction to Prevention .................................................................. 131 Irene H. Hung and John C. Carey

7

Prenatal Testing: Screening, Diagnosis, and Preimplantation Genetic Diagnosis .................................................................................... 147 Eugene Pergament

8

Newborn Screening for Metabolic Disorders ....................................... 163 Marzia Pasquali and Nicola Longo

9

The Role of Genetic Counseling in Everyday Medical Practice ......... 199 Kimberly J. Hart, Erin E. Baldwin, and D. Hunter Best

10

Direct-to-Consumer Genetic Testing ..................................................... 215 Caroline F. Wright and Daniel G. MacArthur

Index ................................................................................................................. 237 vii

Contributors

S. Hussain Askree, Ph.D., M.B.B.S. Emory Genetics Laboratory, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA Erin E. Baldwin, M.S. Department of Genetics, ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA D. Hunter Best, Ph.D., FACMG Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA John C. Carey, M.D., M.P.H. Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA Allison M. Cushman-Vokoun, M.D., Ph.D. Department of Pathology & Microbiology, University of Nebraska Medical Center, Omaha, NE, USA Todd L. Edwards, Ph.D. Center for Human Genetics Research, Vanderbilt Medical Center, Vanderbilt University, Nashville, TN, USA Digna R. Velez Edwards, Ph.D. Division of Epidemiology, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA Kimberly J. Hart, M.S. Department of Genetics, ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA Madhuri R. Hegde, Ph.D., FACMG Emory Genetics Laboratory, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA Irene H. Hung, M.D. Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA

ix

x

Contributors

Nicola Longo, M.D., Ph.D. Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA Daniel G. MacArthur, Ph.D. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK Marzia Pasquali, Ph.D., FACMG Department of Pathology, University of Utah and ARUP Laboratories, Salt Lake City, UT, USA Eugene Pergament, M.D., Ph.D., FACMG Northwestern Reproductive Genetics, Inc., and Department of Obstetrics and Gynecology Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Marylyn DeRiggi Ritchie, Ph.D., M.S. Molecular Physiology & Biophysics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA Charles J. Sailey, M.D., M.S. Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Molecular Genetic Pathology, Arkansas Children’s Hospital, Little Rock, AR, USA Jeffrey J. Swensen, Ph.D., FACMG Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA Ferrin C. Wheeler, Ph.D., FACMG Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA Department of Pathology, Microbiology and Immunology, Vanderbilt University, Nashville, TN, USA Caroline F. Wright, Ph.D. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK Alan H.B. Wu, Ph.D. Department of Laboratory Medicine, San Francisco General Hospital, University of California, San Francisco, CA, USA

Chapter 1

Molecular Genetic Testing in the Genomic Era Charles J. Sailey and Ferrin C. Wheeler

Introduction Molecular Genetic Testing in the Genomic Era Technological advancements have allowed molecular-based testing that was once done on a research-only basis to be adopted for routine use in clinical laboratories. Initially, these types of techniques were labor intensive and highly complex. The introduction of automated processes combined with an improved understanding of human genetic variation has allowed molecular testing to expand into clinical diagnostics, where it is now considered an essential aspect of patient care. Completion of the Human Genome Project has provided the scientific and medical communities with a multitude of genomic targets for diagnostic analysis, including single-nucleotide polymorphisms (SNPs) that have been associated with specific diseases and conditions. Indeed, as a better understanding of genetics and human genetic variation has reached the general public, the demand for genetic testing from both clinicians and patients has increased. Knowledge of the genetic basis of human disease has the potential to affect patient care on multiple levels, including

C.J. Sailey, M.D., M.S. (*) Department of Pathology and Laboratory Medicine, University of North Carolina, 101 Manning Drive, Chapel Hill, NC 27514, USA Molecular Genetic Pathology, Arkansas Children’s Hospital, 1 Children’s Way, Little Rock, AR 72202, USA e-mail: [email protected] F. C. Wheeler, Ph.D., FACMG Department of Pathology and Laboratory Medicine, University of North Carolina, 101 Manning Drive, Chapel Hill, NC 27514, USA Department of Pathology, Microbiology and Immunology, Vanderbilt University, 1211 Medical Center Drive, Nashville, TN 37232, USA D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_1, © Springer Science+Business Media, LLC 2012

1

2

C.J. Sailey and F.C. Wheeler

accurate diagnosis, optimal treatment, risk to other family members, as well as prognosis and outcome prediction. The increased use and availability of genetic diagnostic testing has provided improvements to the clinical management of patients. Pharmacogenetic studies on drug metabolism genes have shown that drug responsiveness varies depending on the genetic background of the patient, as in the case of the CYP2C19 polymorphisms and clopidogrel metabolism [1, 2]. Clinical cancer research studies have also shown that the drug responsiveness of some kinds of tumors is often based on specific acquired mutations that can be assayed by molecular testing strategies, as in the case of EGFR mutations in lung cancer and responsiveness to anti-EGFR chemotherapy [3]. In this chapter, we discuss the current state of technology in the clinical diagnostic laboratory setting, with an emphasis on what kinds of testing are best suited for particular medical questions, and also highlight advances in technology that have the potential to transform the field of molecular diagnostics in the near future.

Laboratory Molecular Analysis Methodologies Nucleic Acid Extraction The majority of diagnostic tests performed in clinical molecular laboratories are DNA-based, with RNA-based assays making up a distinctly smaller proportion of all testing. While once done solely on a manual basis, extraction of DNA and RNA from biological specimens is typically done using a highly automated process that employs a robotic instrument such as the Qiagen QIAcube or the Roche MagNA Pure System. DNA and RNA can be extracted from different specimen types, though peripheral blood is the most commonly used sample type in most laboratories. For oncology testing, nucleic acid is often isolated from bone marrow or formalin-fixed, paraffin-embedded (FFPE) tumor tissue blocks. The relative instability of RNA requires a fresh or freshly frozen sample for efficient extraction, while DNA can generally be extracted from samples that have been stored for longer periods of time, including FFPE tissue. In many cases, tumors that have been surgically removed from patients several years prior to the availability of genetic tests can be analyzed using current techniques.

Polymerase Chain Reaction No other method has revolutionized molecular biology, and in turn, molecular diagnostics, to the degree that polymerase chain reaction (PCR) did in the 1980s and 1990s [4], though certainly, next-generation sequencing has the potential to cause another such revolution in the coming years. Until the 1980s, Southern blotting, a time-consuming and labor-intensive technique, was the primary method used by

1 Molecular Genetic Testing in the Genomic Era

3

most research and clinical laboratories. The advantages offered by PCR included improved sensitivity and versatility, combined with a reduction in turnaround time for result availability, increasing the utility of molecular testing in a clinical setting. It is difficult to overemphasize the importance of PCR as it serves as the basis of nearly all genetic diagnostic testing. The discovery of PCR is generally credited to Dr. Kary Mullis, who was awarded the 1993 Nobel Prize in Chemistry in recognition of the improvements he made to a procedure first described many years earlier [5, 6]. Despite its status as an essential technique in molecular biology, PCR is a relatively simple method for generating large amounts of DNA from a small amount of starting material. PCR is a three-step process that is repeated many times to achieve an exponential increase in the quantity of nucleic acid. To perform basic PCR, a genomic DNA template is combined with two or more oligonucleotide primers, which are small pieces of single-stranded DNA that recognize a specific part of the genome. In an appropriately buffered reaction, DNA polymerase and deoxynucleotide triphosphates (dNTPs), the four nucleotide bases that are needed to make the new strands of DNA, are added to the DNA template and oligonucleotide primers. The three steps of PCR are denaturation, annealing, and extension. In denaturation, the reaction is heated (typically to 94–95°C), and the double-stranded DNA denatures into single strands. In the second step, annealing, the temperature is lowered to approximately 60°C, allowing the oligonucleotide primers to anneal to the complementary sequence in the template DNA. Extension, the final step, occurs at 72°C, the optimal temperature for DNA polymerase. In the extension step, nucleotides are added to form new DNA strands. These three steps are repeated 30–40 times to yield an amplification of the desired piece of DNA, which is often a specific segment of a gene. A representation of the first three cycles of PCR shows the initial exponential amplification from one molecule to eight molecules (Fig. 1.1). The discovery of the thermostable Taq polymerase, isolated from Thermus aquaticus, a thermophilic bacterium found in hot springs [7], and the availability of programmable thermocyclers have improved the utility of PCR as a clinical method. Many biotechnology companies offer thermocyclers for traditional PCR, and several companies have developed proprietary reagents that make the PCR process more easily adapted to the clinical laboratory, such as “master mixes” that contain all the reaction components except for the DNA template and the primers. The ability to generate large amounts of a specific DNA fragment allows for the analysis of many types of genetic variation in human and nonhuman biological specimens. Most laboratory testing methodologies utilize PCR, and it can be adapted for both quantitative and qualitative assays. One of the most powerful aspects of PCR is the incredible versatility of assays that it makes possible, and more recently, adaptations have been introduced that provide even greater flexibility. Some of these variations include allele-specific PCR, multiplex ligation-dependent probe amplification (MLPA), and real-time PCR, all of which will be discussed in subsequent sections of this chapter. The most significant of these modifications is real-time capability, allowing for the simultaneous amplification and detection of nucleic acid targets, eliminating the need for post-PCR processing steps. Real-time PCR has

4


Fig. 1.1 Schematic representation of the first three cycles of a basic PCR amplification. The three steps (denaturation, annealing, and extension) are shown in cycle 1. The target sequence to be amplified is indicated in blue, with flanking sequence in black and primers in red. The products generated during the next two cycles are shown, with eight molecules produced from a single template molecule after three cycles. This exponential amplification generates millions of target molecules after 30 cycles

become the method of choice for many laboratories and for many types of tests based on its ability to perform in both qualitative and quantitative applications.

Qualitative Analysis Most of the clinical testing done in modern molecular genetics laboratories is done to determine the presence or absence of a particular genetic sequence. This qualitative testing can involve genetic variation including inherited and de novo mutations associated with a clinical phenotype, polymorphisms that alter drug metabolism, or acquired somatic alterations associated with the development of neoplasia.


5

The choice of method for detection of genetic variation is largely dependent on the type of alteration one expects to detect, and whether the changes are frequent and recurrent or unique and highly variable. Certain methods work well for the identification of single-nucleotide changes, epigenetic alterations, and small insertions or deletions, while others are better suited for the detection of large deletions, duplications, or other genomic rearrangements.

Real-Time PCR Real-time (quantitative) PCR combines traditional PCR with the ability to quantify newly synthesized DNA as it accumulates during each cycle of amplification [8]. The nomenclature for this technique varies, but it is generally accepted that qPCR be used to denote quantitative PCR (real-time PCR), while RT-PCR is reserved for reverse transcription PCR, conversion of RNA to complementary DNA (cDNA), followed by PCR amplification. Real-time PCR is one of the most sensitive tools we have for genetic analysis. The cycle at which amplification crosses a threshold level is directly proportional to the concentration of starting material, allowing for a systematic method of quantification. This is in contrast to traditional PCR, where it is impossible to calculate the starting DNA concentration by measurement of endpoint concentration. With qPCR, reporter molecules bind to DNA as it accumulates, which in turn emit a signal that can be measured. The reaction occurs within a chamber that facilitates real-time measurement of the signal, with data uploaded to a computer for recording and plotting. Reporter molecules typically consist of either nonspecific DNA-binding dyes or target-specific primers/probes [9]. A typical PCR reaction starts with reagents in excess of template, which promotes primer-template binding over template renaturation. The exponential phase of PCR is extremely reproducible and is the phase at which data is collected and quantified. The reaction eventually enters a linear phase, which is variable from run to run, followed by cessation of amplification in the plateau phase [8]. In qPCR, the fluorescence of the reaction is measured during each cycle of amplification. More specifically, an “amplification plot” will show the change in fluorescence plotted against cycle number. Since background noise is inevitable, a threshold level of fluorescent signal is set at a point that is determined to be significant (i.e., above background). The cycle at which the measured fluorescence crosses the threshold is called the cycle threshold, or Ct (Fig. 1.2). Normally, amplification is carried out for 35–40 cycles. A logarithmic scale depicts the exponential growth phase as a linear plot which can be used for direct comparison of the amount of DNA to cycle number. Theoretically, the concentration of DNA doubles during every cycle in the exponential phase of a PCR reaction. Therefore, we can calculate the relative concentration of DNA based on the Ct. For example, if sample A has a Ct of 9 and sample B a Ct of 14, then sample A crossed the threshold five cycles earlier than B and has 2^5 (32×) more starting material. In actuality, the reactions are not 100% efficient, so the numbers require some modification, as defined by validation protocols. The specificity of the technique depends largely on good primer design. For example, primer dimers (primers binding to each other rather

6


Fig. 1.2 The fold increase over background of fluorescence is plotted on the y-axis, and the cycle number on the x-axis. The point at which the fluorescence crosses the threshold (red horizontal line) is called the Ct. The Ct value is inversely proportional to the concentration of starting DNA. The jagged lines to the left and middle of the graph are nonspecific background noise

than target DNA) and nonspecific amplification permit nonspecific dye binding and can result in falsely increased values and possibly false-positive results. DNA-Binding Dyes DNA-binding dyes provide a nonspecific method of detecting the concentration of double-stranded DNA as it increases with each PCR cycle. Originally, this was done using ethidium bromide, which intercalates in double-stranded DNA [9]. When DNA treated with ethidium bromide is exposed to ultraviolet light (300–360 nm), a measureable orange florescence (590 nm) is emitted. As the concentration of DNA increases, so does the relative amount of intercalated ethidium bromide and, therefore, the fluorescence. SYBR Green, which preferentially binds to the minor groove of double-stranded DNA, is an example of a modern version of this technique [10]. Introduced in the 1990s, SYBR Green allows for a cleaner signal than ethidium bromide, which exhibits background noise from free dye in solution. In addition, modern dyes have been shown to be less mutagenic than ethidium bromide, making them safer alternatives [11].

Target-Specific Methods Target-specific methods are those that are designed to identify a particular DNA alteration. An important advantage of target-specific methods is that multiplexing is


7

possible. Multiple primers can be labeled with distinguishable fluorescent signals to detect amplification of different targets simultaneously. This technique also produces a more specific signal, reducing background noise and detection of nonspecific amplification products. An example of a target-specific methodology utilizing qPCR is called TaqMan® [12]. In this method, target-specific PCR primers are created to flank the region of interest. Subsequently, a probe specific for the region of interest hybridizes to the template. The probe contains a fluorescent reporter dye attached to the 5¢ end and a quencher moiety coupled to the 3¢ end. The proximity of the reporter to the quencher prevents fluorescence emission due to Förster resonance energy transfer (FRET). During the extension phase of PCR, the 5¢–3¢ exonuclease activity of the Taq DNA polymerase cleaves the 5¢ reporter dye, separating the reporter from the quencher, allowing the reporter to fluoresce, with the signal increasing with each successive cycle. Molecular beacons rely on the same basic principles as TaqMan® probes. The probe itself is a hairpin-shaped molecule, which in the unbound form holds the reporter and quencher in close proximity. The stem of the probe is composed of a sequence that is complementary to the target sequence [13]. Upon hybridization, the hairpin unfolds, separating the reporter from the quencher and allowing for the emission of fluorescence.

Sequencing Analysis The ability to precisely determine the nucleotide sequence of a particular genomic segment has enabled the detection of many types of genetic variation, including novel mutations that alter gene function, and polymorphic variants that are present in the population at some measurable frequency. Conventional chain-terminator sequencing, also known as Sanger sequencing, after its developer, Frederick Sanger, is commonly used to determine the order of nucleotide bases in a DNA fragment [14]. This type of sequencing utilizes modified nucleotides, known as dideoxyNTPs (ddNTPs), to terminate chain elongation, resulting in fragments of different lengths that give sequence information for each fragment, based on the incorporated ddNTP. The modified ddNTPs are typically fluorescently labeled, each with a different fluorophore, so all four nucleotide bases of DNA (adenosine, thymine, guanine, and cytosine) can be detected and differentiated. Chain-terminator sequencing has evolved over the past decades to become highly automated, due largely to the use of capillary electrophoresis platforms such as the 3130 Genetic Analyzer and the 3730 DNA Analyzer from Applied Biosystems. Additionally, commercially available dye-terminator reagents such as the BigDye® Terminator Cycle Sequencing kit, also from Applied Biosystems, have made DNA sequencing a method that is both rapid and simple to perform on a clinical basis. DNA sequencing as a diagnostic tool is most appropriate in situations in which the clinical phenotype is known to be associated with a particular gene, but the

8


Fig. 1.3 Sequencing chromatograms showing sequence data from patients that are homozygous normal (top), heterozygous (middle), or homozygous mutant (bottom) for two different GJB2 (connexin 26) mutations. The c.101T>C mutation is shown on the left, and the c.35delG frameshift mutation is shown on the right. The arrows indicate the position of the mutation. Note that the heterozygote for c.101T>C shows two overlapping peaks, one for the T and one for the C. The heterozygous c.35delG chromatogram indicates the frameshifted nature of this mutation, with two sequence traces beginning at the position of the T nucleotide deletion (arrow). The homozygous c.35delG chromatogram shows that both alleles are lacking one of the G nucleotides from the polyG tract

alterations can be located in many different segments of the gene. This is exemplified by mutations in the GJB2 gene in nonsyndromic autosomal recessive sensorineural hearing loss [15]. GJB2 encodes the connexin-26 protein, which is expressed in the inner ear and forms ion channels that regulate intercellular communication. Although certain mutations, such as c.35delG (the deletion of a single guanine nucleotide), occur more frequently than others, mutations have been identified throughout the coding region (exon 2) and flanking intronic regions of GJB2. For this reason, sequencing of the coding region is the most efficient method of mutation detection. Sequencing can detect point mutations that change one nucleotide for another, such as the missense mutation c.101T>C [p.M34T], and can also detect small insertion/ deletion (indel) mutations, such as c.35delG, that cause a shift in the sequence that is easily visible. Sequencing electropherograms showing these two mutations are in Fig. 1.3. Some tumors are also associated with a spectrum of mutations, such as nonsmall-cell lung cancer and mutations in the epidermal growth factor receptor (EGFR) gene. Mutations in the kinase domain of EGFR, which spans exons 18 through 21, have been shown to confer responsiveness to tyrosine kinase inhibitors such as gefitinib, erlotinib, and cetuximab [3].


9

Fig. 1.4 The synthesis of ATP drives the conversion of luciferin to oxyluciferin, producing a detectable signal. Apyrase continuously degrades unincorporated nucleotides in preparation for the next dNTP injection

Pyrosequencing Unlike Sanger sequencing, which relies on “sequencing by termination,” Pyrosequencing® (Qiagen) relies on a “sequencing by synthesis” methodology [16]. The reaction mixture consists of a template (the sequence to be interrogated), a set of primers, enzymes (DNA polymerase, ATP sulfurylase, luciferase, and apyrase), and substrates (adenosine 5¢-phosphosulfate and luciferin). As the reaction progresses, a deoxynucleotide triphosphate (dNTP) is injected into the reaction chamber. If it is complementary to the next base in the template strand, DNA polymerase will incorporate it, releasing one pyrophosphate molecule. ATP sulfurylase will then convert the pyrophosphate to ATP in the presence of adenosine 5¢-phosphosulfate. The newly synthesized ATP fuels the luciferase-mediated conversion of luciferin to oxyluciferin, which generates a measureable signal (Fig. 1.4). If the nucleotide is not incorporated, then no signal is produced. Apyrase, a nucleotidedegrading enzyme, continuously degrades unincorporated nucleotides and ATP. When degradation is complete, another nucleotide is injected into the reaction [17]. This series continues sequentially, creating what is called a pyrogram (Fig. 1.5). It has been reported that pyrosequencing has an analytical sensitivity that allows detection of 5% mutant allele; this high sensitivity makes it ideal for situations where tumor cells are admixed with abundant nonneoplastic tissue [18]. For example, solid tumors sent for KRAS or BRAF gene mutation testing typically

10


Fig. 1.5 Pyrogram. Addition of dNTPs is performed sequentially. As the process continues, the complementary DNA strand is built up, and the nucleotide sequence is determined from the signal peaks in the pyrogram trace

require dissection of carcinoma from formalin-fixed paraffin-embedded tissue (FFPE) and separation from background stromal cells, making them ideal candidates for pyrosequencing.

Promoter Hypermethylation Alterations in gene expression due to epigenetic changes can have pathological implications similar to the effects of mutations in gene coding sequences. DNA methylation is one mechanism of epigenetic gene silencing. The addition of a methyl group to the carbon-5 of the cytosine pyrimidine ring creates 5-methylcytosine (5-mC). This conversion is an in vivo process which typically occurs in the context of a CpG dinucleotide. CpG islands are often found in gene promoter regions, where methylation (both normal and aberrant) may result in transcriptional silencing of the gene. It has been suggested that methylation has evolved to suppress transcription of regions of the genome consisting of inserted viral sequences, transposons, and repeat elements, all of which may be harmful to a cell if expressed [9]. However, the very same system has been implicated in silencing crucial tumor suppressor genes in various carcinomas, such as the VHL gene in renal carcinoma or the MLH1 gene in colorectal and other carcinomas [19]. There are examples of other types of genes, such as repair enzymes, being silenced through methylation which confer an advantage under certain circumstances. Recently, methylation of the promoter of the O6-methylguanine methyltransferase (MGMT) gene has been reported in some cases of glioblastoma and is associated with prolonged survival in patients treated with temozolomide [20]. MGMT is a DNA repair enzyme that reverses alkylation of guanine by transferring the alkyl group to the active site of the enzyme. Diminished MGMT expression due to methylation of CpG sites in the 5¢ region of the MGMT gene allows accumulation of alkylguanine DNA which, following incorrect base pairing with thymine, triggers DNA damage signaling and cell death.

1 Molecular Genetic Testing in the Genomic Era NH2

NH2

HSO3-

+

O

H2O

+

HN O

11

HN

HN O

N

N

SO3-

NH3

O

HN N

2

1 [C]

O

[C-SO3- ]

SO3-

HSO3-

O

N

3 [U-SO3- ]

[U]

Fig. 1.6 Bisulfite conversion of cytosine to uracil. Reaction 1: The reversible addition of bisulfite (HSO3−) results in an equilibrium between C and C-SO3−. Acidic conditions shift the equilibrium toward C-SO3− side. Reaction 2: The deamination of C-SO3− is the rate-limiting step, and the velocity increases in proportion to bisulfite concentration. Reaction 3: The final step results in the conversion of U-SO3− to U and recovery of the 5,6-double bond

MGMT methylation is associated with increased sensitivity of glioblastoma to alkylating agents such as BCNU (carmustine), and it also correlates with prolonged progression-free survival in glioma patients treated with temozolomide [21]. In vitro, treatment of DNA with bisulfite converts unmethylated cytosine residues to uracil (Fig. 1.6), while methylated nucleotides are protected from this modification [22]. By sequencing the promoter region of a gene both before and after bisulfite treatment, one can calculate the percent methylation to determine if a gene is transcriptionally silent (Fig. 1.7).

Fragment Analysis In situations in which the genetic alteration affects the size of the DNA fragment, either by the removal or addition of nucleotides, fragment analysis is the simplest method of detection. Conveniently, the same capillary electrophoresis platforms used for DNA sequencing can also be used for DNA fragment analysis. An example of this methodology is the detection of mutations in two different genes commonly mutated in patients with acute myelogenous leukemia (AML) with normal cytogenetics: FLT3 and NPM1 [23, 24]. In the FLT3 gene, the most commonly observed alteration is an in-frame internal tandem duplication (ITD) within the coding sequence of the juxtamembrane domain found in exons 13 and 14. This mutation results in the constitutive activation of the FLT3 tyrosine kinase, and its presence confers a high risk of relapse and an overall poorer prognosis than that observed in AML patients that do not harbor this mutation. In the NPM1 gene, the most common mutation observed in AML patients is a 4-bp insertion in exon 12 of the gene that interrupts the gene’s nuclear localization signal and results in the aberrant cytoplasmic localization of the nucleophosmin protein. This mutation is associated with an overall favorable prognosis in the absence of a concomitant FLT3 ITD mutation, and an intermediate prognosis if the FLT3 ITD is present. Examples of fragment analysis of PCR products showing amplification of FLT3 from 3 AML samples with different sizes of the ITD mutation are shown in Fig. 1.8a. A peak representing the

12


Fig. 1.7 Pyrograms of two different patient samples are shown (top and bottom panes) after bisulfite treatment. The sequence of the region interrogated is: CGA CAG CGA TAC GCG, with cytosines of interest underlined and what is called the “natural control” position italicized. Note: U residues are detected as T residues in DNA sequencing. Top – All C residues (blue shading) were converted, that is, the segment of DNA is not methylated; bottom – first position – 57% did not convert (i.e., 57% remained as a cytosine). The next position shows the natural control (tan shading), where the C residue fully converted as expected. (It is not a CpG dinucleotide; therefore, it is never methylated and always converts.) The following three positions are calculated the same way. The average of the four positions (not including the natural control) is calculated at 58% methylated, enough to consider the gene silenced (the actual cutoff value is determined during validation procedures in the lab)

normal-sized allele is present as well as a peak representing a larger mutant fragment in all three samples. Figure 1.8b shows amplification of the NPM1 gene in the area of the four-base-pair insertion. Two samples are positive for the mutation, and one does not have the mutation present (bottom panel).

Multiplex PCR and Fragment Analysis Multiplex PCR is the process whereby multiple sets of PCR primers are utilized to amplify several areas of interest in a single PCR reaction. To differentiate the amplicons, each primer set can be labeled with a unique fluorescent tag, or the amplicons


13

Fig. 1.8 Fragment analysis results for two tests done for AML. In a, three patients with different FLT3 ITD mutations are shown. The normal-sized peak is the same in all three, and different-sized mutant peaks are present in each sample, indicating that this mutation is of a variable size. In b, the results for NPM1 mutation analysis are shown for three patients. All three show the normal peak. The top two panels are from patients that are positive for the NPM1 four-base-pair insertion, visible as a second, larger peak. The bottom panel is a sample from a patient lacking the NPM1 mutation and only shows a single peak. The less prominent peaks surrounding the high peaks are commonly observed in fragment analysis and represent PCR products that differ by one base in size and are generally due to slippage during PCR

can be of different lengths such that they can be differentiated by electrophoresis. One example of the use of multiplex PCR in the clinical laboratory is in microsatellite instability (MSI) analysis. Microsatellites, tracts of short repeated elements that typically range from 1 to 7 nucleotides in length, vary in length between individuals. However, in patients with defects in mismatch repair genes (e.g., MLH1, MSH2, MSH6, and PMS2), variation in the length of microsatellites can vary between normal tissue and that taken from a tumor sample in the same individual. MSI analysis is able to detect these differences by amplifying the same microsatellites in both normal and tumor tissue and then visualizing the results by capillary electrophoresis (or an equivalent method). The detection of MSI can suggest the diagnosis of Lynch syndrome (also known as hereditary nonpolyposis colon cancer or HNPCC), a dominantly inherited cancer predisposition syndrome associated with an increased risk for multiple cancer types including colorectal, endometrial, ovarian, urothelial, and small intestine cancers. One of the hallmarks of Lynch syndrome–associated tumors is an increase in DNA replication errors, and MSI, due to mismatch repair deficiency. Microsatellites used in clinical testing are typically mononucleotide repeat sequences, and in tumors with mismatch repair defects, the errors are visible as the presence of new alleles in the tumor sample. If a patient is found to have MSI in at

14


Fig. 1.9 Microsatellite instability analysis of matched tumor and normal tissue from two patients. Panel A is the normal, and B is the tumor from a patient with colorectal cancer, and the tumor panel (B) shows high microsatellite instability, with shifts in microsatellite size in all five markers examined (arrows indicate new alleles not present in normal tissue). Panels C and D are from a patient with a colorectal cancer that is microsatellite stable, and no new alleles are present in the tumor (D)

least 2 of the 5 microsatellites tested, it is considered MSI high. For patients with MSI-high tumors, further testing for Lynch syndrome can be performed. A diagnosis of Lynch syndrome in a family can have a dramatic impact on cancer risk estimates for the other members of the family that have inherited the gene defect and result in increased screening with the goal of early detection. Figure 1.9 shows the typical results in an MSI-high tumor and a microsatellite stable tumor.

Melting Curve Analysis Sanger sequencing of DNA is the gold standard for identifying germline mutations; however, it is time consuming and labor intensive. As such, methods have been developed to “scan” DNA to quickly determine if a region of interest is likely to contain a mutation. One such method is high-resolution melt analysis (HRM) [25]. HRM is performed by monitoring the melting temperature (Tm) of double-stranded DNA (called duplexes) during controlled heating. Homoduplexes are strands of DNA that are perfectly complementary to one another, whereas heteroduplexes contain at least one mismatched base pair. These duplexes create a melt curve signature over a range of temperatures. The process is usually carried out in a light cycler


15

Fig. 1.10 A pyrimidine to purine base change in a segment of DNA will change the temperature at which the strands denature or “melt.” The change in Tm is noted as a shift on the graph. The shift represents denaturing of double-stranded DNA at a different temperature and reduced signal emission from the double-stranded DNA-binding dye

Fig. 1.11 There are three scenarios: wild-type, heterozygous, or homozygous. Each gives a melt curve that is slightly different. With a high-quality HRM assay, it is possible to distinguish between all three of these scenarios

following PCR. Melting curve analysis exploits the fact that mismatched strands of DNA (even at a single nucleotide) will have a lower Tm than perfectly complementary strands. The simplest method to monitor this reaction involves the use of a double-stranded DNA-binding dye at saturating concentrations. Figure 1.10 shows a shift in Tm that is attributed to a single base change of a pyrimidine to a purine between 2 homoduplexes, normal (wild-type) and mutant. As with most screening tools, possibly significant results identified using HRM are investigated further by another more specific method (i.e., DNA sequencing). HRM can be used to screen for a wide range of mutation types but is often used for the detection of point mutations or single-nucleotide polymorphisms (SNPs). If a patient’s DNA is mixed with a known sequence of DNA, HRM can determine if there are mismatched nucleotides (heteroduplexes) by comparing the melt curve to that of known heteroduplexes or homoduplexes (Fig. 1.11).

16


Multiplex Ligation-Dependent Probe Amplification One of the variations of PCR that can effectively and easily detect large genomic deletions and duplications that affect one or more whole exons of a gene is multiplex ligation-dependent probe amplification (MLPA) [26]. Large deletions/duplications comprise a significant proportion of the mutations responsible for some genetic conditions, and MLPA provides a rapid, reliable, and inexpensive way to detect them. For example, Duchenne muscular dystrophy (DMD) is a severe X-linked recessive form of muscular dystrophy caused by mutations of the dystrophin (DMD) gene. Often, the causative mutations in patients with DMD are large-scale genomic alterations, including deletion (~65%) and duplication (~7–10%) of one or more exons. The remaining ~25% of cases are associated with point mutations or small insertions and deletions that can be detected by sequence-based methods [27]. MLPA has advantages over traditional multiplex PCR in that only a single PCR primer pair that amplifies multiple probes hybridized to the tested gene. The MLPA probes consist of two separate oligonucleotides, each containing one of the PCR primer sequences. It is only when these two hemiprobes are both hybridized to their adjacent targets and ligated that they can be amplified by PCR in a quantitative fashion (Fig. 1.12, reproduced with permission from MRC-Holland). For a diagnostic case, a patient sample is compared to a reference sample, and copy number changes can be assessed for each target sequence (usually an individual exon). Conveniently, the biotechnology company MRC-Holland has developed many commercially available kits for MLPA that are commonly used in clinical laboratories [28].

The Next Generation of Molecular Diagnostics The increased demand for low-cost, high-throughput sequencing has led to advances in technology in recent years that are likely to dramatically alter the landscape of clinical molecular diagnostic testing in the same way PCR transformed the field of molecular biology over two decades ago. The term “next-generation sequencing” (NGS) refers broadly to platforms that allow massively parallel DNA sequencing. The different technologies utilized are all fundamentally different than traditional Sanger sequencing [29]. Unlike Sanger sequencing, which generates a consensus sequence from a pool of DNA molecules, NGS methods produce individual sequence reads from a single strand of DNA, and on average, each nucleotide is sequenced many times (typically 20–60 times). The sequences are subsequently combined using computational methods. NGS has many advantages over traditional sequencing methods (reduced cost per sequenced nucleotide, potential for automation, and a wider variety of alterations that can be identified) that will ultimately result in its widespread adoption in clinical diagnostics laboratories. Next-generation sequencing has the potential to replace many of the currently utilized diagnostic tools due to its ability to detect large-scale genomic rearrangements including translocations, inversions, deletions, and duplications in addition to singlenucleotide alterations and small insertions and deletions.


17

Fig. 1.12 Outline of the MLPA® technique. After hybridization to their target sequence in the sample DNA, the probe oligonucleotides are enzymatically ligated. One probe oligonucleotide contains a nonhybridizing stuffer sequence of variable length. Ligation products can be amplified using PCR primer sequences X and Y; amplification product of each probe has a unique length (130–500 nt). Amplification products are separated by electrophoresis. Relative amounts of probe amplification products, as compared to a reference DNA sample, reflect the relative copy number of target sequences

Clinical Applications of Next-Generation Sequencing The potential clinical uses of next-generation sequencing include sequencing of the whole genome, exome (all gene coding exons), or transcriptome (all expressed sequences). To date, these applications have primarily been used to identify novel genes associated with genetic disorders in basic research and translational studies. However, several clinical laboratories have started to use this technology as part of patient care, and it will likely become the method of choice for most laboratories in the very near future.

18


References 1. Simon T, Verstuyft C, Mary-Krause M, French Registry of Acute ST-Elevation and Non-STElevation Myocardial Infarction (FAST-MI) Investigators, et al. Genetic determinants of response to clopidogrel and cardiovascular events. N Engl J Med. 2009;360(4):363–75. 2. Mega JL, Close SL, Wiviott SD, et al. Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med. 2009;360(4):354–62. 3. Linardou H, Dahabreh IJ, Bafaloukos D, et al. Somatic EGFR mutations and efficacy of tyrosine kinase inhibitors in NSCLC. Nat Rev Clin Oncol. 2009;6:352–66. 4. Bartlett JMS, Stirling D. A short history of the polymerase chain reaction. Methods Mol Biol. 2003;226:3–6. Humana Press. 5. Saiki RK, Scharf S, Faloona F, et al. Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science. 1985;230(4732):1350–4. 6. Kleppe K, Ohtsuka E, Kleppe R, et al. Studies on polynucleotides. XCVI. Repair replications of short synthetic DNA’s as catalyzed by DNA polymerases. J Mol Biol. 1971;56(2):341–61. 7. Innis MA, Myambo KB, Gelfand DH, et al. DNA sequencing with Thermus aquaticus DNA polymerase and direct sequencing of polymerase chain reaction-amplified DNA. Proc Natl Acad Sci USA. 1988;85(24):9436–40. 8. Udvardi MK, Czechowski T, Scheible WR. Eleven golden rules of quantitative RT-PCR. Plant Cell. 2008;20(7):1736–7. 9. Logan J, Edwards K, Saunders N. Real-time PCR: current technology and applications. In: Applied and functional genomics. Norfolk: Caister Academic Press; 2009. 10. Karlsen F, Steen HB, Nesland JM. SYBR green I DNA staining increases the detection sensitivity of viruses by polymerase chain reaction. J Virol Methods. 1995;55(1):153–6. 11. Singer VL, Lawlor TE, Yue S. Comparison of SYBR Green I nucleic acid gel stain mutagenicity and ethidium bromide mutagenicity in the Salmonella/mammalian microsome reverse mutation assay (Ames test). Mutat Res. 1999;439(1):37–47. 12. Holland PM, Abramson RD, Watson R, Gelfand DH. Detection of specific polymerase chain reaction product by utilizing the 5¢––3¢ exonuclease activity of Thermus aquaticus DNA polymerase. Proc Natl Acad Sci USA. 1991;88(16):7276–80. 13. Tyagi S, Kramer FR. Molecular beacons: probes that fluoresce upon hybridization. Nat Biotechnol. 1996;14:303–8. 14. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA. 1977;74(12):5463–7. 15. Kelsell DP, Dunlop J, Stevens HP, et al. Connexin 26 mutations in hereditary non-syndromic sensorineural deafness. Nature. 1997;387:80–3. 16. Ronaghi M, Uhlén M, Nyrén P. A sequencing method based on real-time pyrophosphate. Science. 1998;281(5375):363–5. 17. Ahmadian A, Ehn M, Hober S. Pyrosequencing: history, biochemistry and future. Clin Chim Acta. 2006;363(1–2):83–94. 18. Ogino S, Kawasaki T, Brahmandam M, et al. Sensitive sequencing method for KRAS mutation detection by pyrosequencing. J Mol Diagn. 2005;7(3):413–21. 19. Herman J, Baylin S. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 2003;349:2042–54. 20. Hegi ME, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997. 21. Dunn J, Baborie A, Alam F, et al. Extent of MGMT promoter methylation correlates with outcome in glioblastomas given temozolomide and radiotherapy. Br J Cancer. 2009;101:124–31. 22. Grunau C, Clark S, Rosenthal A. Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res. 2001;29(13):e65. 23. Gale RE, Green C, Allen C, et al. The impact of FLT3 internal tandem duplication mutant level, number, size, and interaction with NPM1 mutations in a large cohort of young adult patients with acute myeloid leukemia. Blood. 2008;111:2776.


19

24. Schlenk RF, Dohner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med. 1909;2008:358. 25. Reed G, Kent J, Wittwer C. High-resolution DNA melting analysis for simple and efficient molecular diagnostics. Pharmacogenomics. 2007;8(6):597–608. 26. Yau SC, Bobrow M, Mathew CG, Abbs SJ. Accurate diagnosis of carriers of deletions and duplications in Duchenne/Becker muscular dystrophy by fluorescent dosage analysis. J Med Genet. 1996;33(7):550–8. 27. GeneReviews: Dystrophinopathies. http://www.ncbi.nlm.nih.gov/books/NBK1119/. Accessed 24 Mar 2011. 28. Schouten JP, McElgunn CJ, Waaijer R, et al. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res. 2002;30:e57. 29. Metzker ML. Sequencing technologies-the next generation. Nat Rev Genet. 2010;11(1): 31–46.

Chapter 2

Array Comparative Genomic Hybridization in Cytogenetics and Molecular Genetics S. Hussain Askree and Madhuri R. Hegde

Human Mutations and Testing Methodologies Variations in the human genome range from single nucleotide changes to whole chromosomal aneuploidies. De novo single nucleotide changes are estimated to occur at a rate of 1.7 × 10−8 per nucleotide in every generation [1]. These sequence variations include coding and noncoding nucleotide changes that may or may not be important in disease. At the opposite end of the spectrum are large segmental genomic rearrangements (inversions and translocations) and copy number changes (CNCs), that is, aneuploidies, marker chromosomes, and large interstitial deletions and duplications. The portion of the human genome that shows variations in segmental genomic copy numbers between individuals accounts for 12% of the DNA. Moreover, de novo copy number changes in newborns are expected to occur once in every 8 births for deletions and once in every 50 births for duplications [2]. Several methodologies have been developed to detect these changes. Changes at the small end of the spectrum can be detected with traditional Sanger sequencing or targeted with allele-specific PCR-based or restriction fragment analysis methodologies (discussed in detail in Chap. 1 of this volume). Other major categories of common

S.H. Askree, Ph.D., M.B.B.S. Emory Genetics Laboratory, Department of Human Genetics, Emory University School of Medicine, 615 Michael St. Suite 301, Atlanta, GA 30322, USA Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA M.R. Hegde, Ph.D., FACMG (*) Emory Genetics Laboratory, Department of Human Genetics, Emory University School of Medicine, 615 Michael St. Suite 301, Atlanta, GA 30322, USA e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_2, © Springer Science+Business Media, LLC 2012

21

22

S.H. Askree and M.R. Hegde

mutations are short-tandem repeat expansions, methylation aberrations, and retrotransposon insertions. The scope of this chapter is limited to detection of large segmental CNCs responsible for disease. Traditional cytogenetic analysis with G-banding (also termed karyotyping) serves well for detecting large chromosomal and segmental abnormalities larger than ~5 Mb. In this technique, Giemsa-stained metaphase chromosomes are ordered and numbered under a microscope according to morphology, size, arm-length ratio, and banding pattern. This technique efficiently detects aneuploidies, marker chromosomes, balanced and unbalanced microscopic rearrangements, and CNCs. It has been very useful in the clinic, as aneuploidy alone has been calculated to affect approximately three in every thousand live births [3]. However, routine karyotyping is time consuming and labor intensive and has limited sensitivity due to low resolution. A second molecular cytogenetic technique used in conjunction with G-banding is fluorescence in situ hybridization (FISH). In this method, metaphase chromosomes or interphase nuclei are denatured on a slide along with a fluorescently labeled DNA probe that targets a region of interest. The probe and the chromosomes are then hybridized, and the slides are washed, counterstained, and analyzed by fluorescent microscopy. With the selection of the appropriate probes, FISH has broad applications. Probes targeting specific sequences can be used to detect microdeletions and duplications responsible for common syndromes, for example, the ~4-Mb deletion of 15q11-13 in Prader-Willi and Angelman syndromes [4], the 5-Mb deletion of 17p11.2 in Smith-Magenis syndrome [5], the ~1.6-Mb deletion of 7q11.23 in Williams syndrome [6], and all except the smallest of the 130-kb to 9-Mb deletions in 22q11.2 deletion syndrome (DiGeorge/ velocardiofacial syndrome) [7–9]. In addition, FISH probes targeting repetitive sequences (centromeric alpha satellite probes) can detect aneuploidy, and probes targeting pathogenic fusion sequences (e.g., the fusion BCR/ABL gene in t(9;22)) can detect common translocations and rearrangements. The disadvantage of FISH is that probes can only target a region of interest and cannot be used for a genomewide screen. Changes that are submicroscopic and indiscernible even with the highest resolution karyotype, but too large to be sequenced, contribute substantially to the mutation spectrum seen in a diagnostic genetics laboratory. Two molecular techniques that have been extensively used for the detection of these CNCs are multiplex ligation-dependent probe amplification (MLPA) and real-time PCR (Fig. 2.1). Assays developed with these methods are specifically designed to detect CNCs within a gene or a particular locus. Gene-specific MLPA assays are common compliments to full-gene sequencing assays. Unfortunately, these assays carry a high cost, together with low information yield, and cannot be employed to scan large genomic regions in a high-throughput assay. In the diagnostic arena of genetic testing, the methods have also proven useful as confirmatory tools for CNCs detected with array-based comparative genome hybridization (aCGH) (Fig. 2.1) [10].

2

Array Comparative Genomic Hybridization… Cytogenetics Testing

> 5 MB

up to 10 kb

G-banding Karyotype

23

Molecular genetics Testing

up to 500 bp

Down to 1 bp

Constitutive Targeted Genomic Sanger array-CGH array-CGH Sequencing

FISH

MLPA

SNP array

RT-PCR

Allele-specific PCR

Range of detectable genomic changes Conventional methods In searching for mutations

Methods used in assaying for (or confirming) known mutations

Breakpoint Mapping

Fig. 2.1 The common methods used in the detection of mutations. The methods listed in the top row represent the conventional methods being the first tier of testing for identification of mutations, and the methods listed below being used primarily as a means to confirm presence of mutations for intragenic CNCs

Array-Based Comparative Genome Hybridization (aCGH) Comparative genome hybridization (CGH) is a methodology used to detect copy number differences in a query DNA sample relative to a reference sample. The first description of the technique was a cytogenetics method in cancer biology where relative hybridization to a normal metaphase spread was used to detect gains and losses in tumor DNA [11]. In this metaphase-CGH, biotinylated total tumor DNA and digoxigenin-labeled normal reference DNA were co-hybridized to a normal metaphase spread on a microscope slide. Tumor and reference DNA were visualized with green-fluorescing fluorescein isothiocyanate (FITC)–avidin and red-fluorescing rhodamine-antidigoxigenin, respectively. Gains and losses in the tumor DNA were visible through a microscope as changes in relative fluorescence intensities: genomic losses in the tumor produced a decrease in green-to-red ratio, and an amplification or chromosomal duplication in the tumor DNA produced an increase in green-to-red ratio. The use of this method was very short-lived because it had several technical difficulties and very low resolution [12]. However, it paved the way to aCGH by substituting the metaphase chromosomes with arrayed probes on glass slides. Microarray-based comparative genomic hybridization can be viewed as a Southern blot for high-throughput analysis, where hybridization of probes to target DNA sequences is used to detect CNCs. However, in contrast to Southern blotting where sample DNAs are cross-linked to a nylon membrane and hybridized to one probe at a time, aCGH has several thousand probes immobilized in an array on a glass slide. Equal amounts of differentially labeled query sample and reference genomic DNA are co-hybridized to this array of probes (Fig. 2.2). In diagnostic genetics laboratories,

24


Fig. 2.2 The steps used in an array-CGH protocol. The array depicted is an 8× and can be used to test for samples from 8 different individuals. The colors of the wells illustrate the florescence detected from concomitant hybridization of differentially labeled control and patient DNA

the reference DNA, or control DNA, is often made with pooled controls. In the query sample or patient, DNA is matched with same-sex or opposite-sex control, depending upon the laboratory. Most commonly, Cyanine 3 (Cy3) and Cyanine 5 (Cy5) fluorescent dyes are used to label patient and control DNA samples. The ratio of the hybridization is proportional to the ratio of the copy numbers of DNA sequences between the two samples. After hybridization, plates are washed and a microarray scanner captures the signal generated by relative hybridization to each probe. If the intensities of the two dyes for a specific DNA probe are equal, that is, a Cy3:Cy5 ratio of ~1, the query sample is interpreted to have a normal copy number in that region. An altered Cy3:Cy5 ratio indicates a gain or a loss for the patient DNA (Fig. 2.2). The results are generally confirmed via a second method, for example, FISH, MLPA, qPCR, or PCR-based breakpoint mapping.

Array Design The application of any array is dictated by its design. The length of the probes and their coverage of the genome represent the resolution of the aCGH. The probes can represent one or more specific regions of interest, or they can correspond to sequences evenly spaced across the entire genome. The former design is termed “targeted,” and the latter is termed “whole genome” or “constitutive” aCGH (Fig. 2.3). In each of these methods, the length of the probes utilized is dependent upon how they were generated. Probes generated with cloned human sequences are dependent upon the vectors that carry them. Bacterial artificial chromosomes (BACs) clones had been

2

Array Comparative Genomic Hybridization…

25

Fig. 2.3 The three basic designs in aCGH. DNA probes in a whole genome or constitutive array represent sequences that are approximately evenly spaced across the length of a chromosome. Targeted arrays have probes corresponding to genes of interest and disproportionately represent exons or coding sequences. The lower panel shows a combination design that is a constitutive array with limited targeted genes. The widely spaced probes along the chromosomes are termed “backbone” probes

used extensively in FISH and were easily adapted to aCGH. Probes generated with BACs range in size from 75 to 200 kb. Smaller inserts in cosmid and fosmid vectors, ranging in size from 25 to 50 kb, were also developed. One major issue with the initial arrays was the lack of genome-wide screening. To this end, arrays were designed with widely spaced probes that tiled the entire genome. For example, genome-wide coverage was initially achieved using BAC clone probes spaced at 1-Mb intervals across the entire genome; in regions of interest, a higher density of probes provided better sensitivity. This design is termed “targeted array with a backbone” (Fig. 2.3). The sensitivity of detection is also dependent upon the density of probes at the site of the CNC. Using probes that overlap was one method to increase coverage over a region of interest, and signifies another important aspect of array design: the tiling-path (overlapping) probes. In a quest for increasing resolution with more probes, arrays were manufactured with synthesized oligonucleotides ranging from 25 to 80 bp in length. In this chapter, BAC and oligonucleotide arrays will be discussed in further detail due to their success and continual applications in clinical genetics.

BAC Arrays The very first array constructed for CGH was made with DNA sequences taken from cosmids (30–40 kb) and plasmids, immobilized in a chip format on a glass slide [13]. This was followed by arrays made with several BAC clones (75–200 kb) that

26


corresponded to genomic regions deleted or duplicated in genetic diseases [14–17]. For example, clones with sequences that are lost in the pathogenic deletion associated with Prader-Willi/Angelman syndromes (PWAS). In an attempt to uncover novel pathogenic CNCs, BAC arrays were designed that targeted similar repeat regions [18, 19]. There are several examples of novel pathogenic microdeletions and microduplications being uncovered with BAC arrays. For example, the recurrent 478–650kb deletion on 17q21.31 that is commonly observed in patients of intellectual disability was discovered with this method [18, 20, 21]. In addition, screening with BAC arrays also identified a recurrent 1.5-Mb deletion at 15q13 that extended distal from a breakpoint within the PWAS region. Distinct from Prader-Willi and Angelman syndromes, this deletion results in intellectual disability, epilepsy, and dysmorphic features [22, 23]. Furthermore, a deletion of another region of the same chromosome, 15q24, was identified in patients with microcephaly, hypospadias, intellectual disability, and dysmorphic facies [19, 23]. When several overlapping probes target the same locus, the sensitivity of detection can increase considerably. For example, a whole genome BAC aCGH platform with ~2,500 clones of 120 kb in size each, spanning the genome with an average resolution of ~1.4 Mb, yielded a 16p11.2 microdeletion syndrome [24]. These examples highlight the success of BAC arrays in identification of pathogenic CNCs that were previously undetectable by routine karyotyping [25–27]. BAC arrays, although robust, do carry certain disadvantages. The most limiting factor in the use of BACs in aCGH is that clones can only be selected from existing libraries and often do not ideally represent the target sequences. The ability to detect CNCs of smaller size is also limited due to the large size and spacing of BAC probes. These problems can be overcome with the use of oligonucleotide probes.

Oligonucleotide aCGH With the success of BAC arrays, there was an obvious incentive to tile an increasing number of probes for improved resolution. This was achieved through the use of synthetic single-stranded oligonucleotides as probes for aCGH. Oligonucleotide arrays have allowed aCGH to be performed using a wide variety of array designs and resolutions. There are several proprietary methodologies to construct these arrays. One method is termed spotting or printing and involves first synthesizing the oligonucleotides, 25–85 bp in length, followed by placing them onto the surface of a chip via robotic pins. NimbleGen and Affymetrix use photolithography to directly synthesize oligonucleotides on the chip using a proprietary light mask technology. Agilent utilizes an inkjet technology for in situ synthesis. Oligoarrays are significant improvement over BAC arrays in terms of the resolution. The ability to synthesize oligonucleotides in situ on the arrays allows higher probe density and a more comprehensive whole genome scan in a single experiment. It is extremely simple to select target sequences, avoiding repetitive sequences, hence decreasing noise and increasing reproducibility. The customization of the arrays includes the ability keep improving the design of an array by eliminating poor performing probes and selecting high-performance reliable probes. And lastly, the better coverage possible

2


27

with oligonucleotide arrays allows detection of CNCs of significantly smaller size as well as refinement of the breakpoints of previously known CNCs of larger size.

Applications of aCGH Cancer Pathogenic CNCs contribute to both the initiation and progression of tumorigenesis. Amplification and/or loss of certain loci containing cancer-related genes are fundamental mechanisms utilized by transformed cells to alter normal proliferation control and promote cellular growth and metastasis. Such copy number changes are efficiently detected by aCGH [28, 29]. It has been argued that aCGH can be used to both monitor progression and determine prognosis by assessing the genomic instability that is often associated with disease progression [30]. Although loss of heterozygosity (LOH) in tumors that results from mitotic recombination is not detectable by standard oligonucleotide arrays, it can be detected with SNP arrays (discussed below). Often, translocations that form fusion genes are not detectable via aCGH.

Constitutive Array in Genetic Testing of Intellectual Disability, Autism Spectrum Disorder, and Dysmorphia Excluding Down syndrome, G-banding is able to identify a genetic cause for intellectual disability (ID) in less than 4% of affected individuals tested [31]. The yield for identification of pathogenic genetic alteration increases with the addition subtelomeric FISH analysis [32]. However, the cumulative yield of combined karyotype and subtelomeric FISH testing in these patients is still below 10% [33, 34]. This success rate is easily doubled with whole genome analysis of CNCs using aCGH. The absolute yield is dependent upon the design of the array. Autism spectrum disorders (ASDs), although extremely heterogeneous in their clinical presentations, have a strong genetic component. Common genetic aberrations observed in patients with ASDs include sex chromosome abnormalities, 15q11-q12 gains, FMR1 gene CGG repeat expansions (fragile X syndrome), MECP2 gene mutations (Rett syndrome), and mutations in a number of other single genes, for example, SHANK3, NLGN3, and NLGN4 [35]. Still, the genetic etiology remains unknown in the vast majority of ASD patients. Arrays designed with widely spaced probes covering the entire genome have yielded novel genetic loci where pathogenic CNCs are associated with ASDs that present with additional features [36, 37]. In one study of a cohort of patients with isolated ASDs, aCGH identified a strong candidate for a pathogenic CNC in 7% of the individuals tested [38]. Recently, recurrent microdeletions that were identified with aCGH in ID were also found to be associated with ASDs, namely the 16p11.2 and 17q12 deletion syndromes [39, 40].

28


Due to the higher yield and technical ease with which aCGH is performed, many clinicians are using this technology as a first-tier test for all individuals with ID, ASD, developmental delay, and congenital anomalies [10]. The biggest weakness of this technology is the inability to detect balanced translocations, for which G-banding continues to be the gold standard.

Prenatal Testing Fetal samples, typically isolated from cultured amniocytes or chorionic villi, do not provide large amounts of high-quality DNA, resulting in poor resolution of chromosomes in a traditional karyotype. As such, aCGH is a good alternative to traditional prenatal cytogenetic testing methods and has been successfully used for accurate, high-resolution testing in prenatal samples [41]. In addition, aCGH has also been successfully used to identify genomic imbalances in products of conception [42]. As mentioned previously, the main limitation of aCGH is its inability to detect balanced translocations; however, an additional weakness is that it is not a method that can be routinely used for the detection of polyploidy or mosaicism. With these limitations in mind, the use of aCGH is becoming increasingly more significant in prenatal testing.

Gene Identification There are several candidate genes located within recurrent pathogenic CNCs that are either entirely or partially contributory to the phenotype of that observed in disease. In this regard, aCGH has served as a powerful tool in localizing dosage-sensitive genes that contribute to the molecular basis of the syndrome. As the size of CNCs observed may vary among patients with the same syndrome, the high-resolution mapping afforded by aCGH makes it possible to determine the minimum defect shared by all affected individuals, and therefore the locus of the disease-associated gene(s). For example, testing with a constitutive BAC array in patients with CHARGE (coloboma of the eye, heart defects, atresia of nasal choanae, growth retardation, genital and ear abnormalities) syndrome led to the identification of the disease-causing gene, CHD7 [43]. In yet another example, a targeted oligonucleotide array was used to identify that deletion of the PORCN gene at Xp11.23 is responsible for Goltz syndrome (focal dermal hypoplasia) [44].

High-Density Targeted Oligoarray for Intragenic CNC Detection One of the biggest achievements of oligoarrays has been the ability to detect of pathogenic intragenic CNCs with the use of gene-targeted high-density arrays.

2


29

Such arrays are custom-designed for the detection of pathogenic CNCs within or encompassing a single gene by molecular genetics diagnostic laboratories. For example, two common deletions encompassing exons 1 and 2 of the GJB6 gene result in nonsyndromic hearing impairment (DFNB1) [45, 46]. Similarly, a 30-kb deletion encompassing exons 11–17 of the GALC gene is a frequent cause of Krabbe disease [47]. Deletions and duplications within the DMD gene account for approximately 70% of pathogenic mutations responsible for Duchenne muscular dystrophy. Undoubtedly, the method with the highest yield for the detection of pathogenic DMD CNCs has been custom-designed oligoarrays targeting the gene [48, 49]. The yield of targeted aCGH is dependent upon the mutational spectrum of the specific gene. Gene-targeted high-density aCGH is currently being used routinely in the molecular diagnosis of several well-known diseases. As a result, many companies are producing commercially available products. One such platform is the NimbleGen custom manufactured 385 K array that has an exon-centric design and has been used for several genes in patients with different syndromes [50, 51].

SNP and Hybrid Arrays for Genotyping One class of array-based technology, genotyping arrays, were also developed to study genomic variation. Like aCGH, genotyping arrays also rely on the basic principle of hybridization of target and probe DNA. However, in genotyping arrays, no comparison is drawn with a reference control DNA. In fact, a specific genotype is queried and inference is drawn based on the intensity of the signal produced by hybridization. In contrast to aCGH that detects alterations in copy number, genotyping arrays detect single nucleotide changes. A new generation of hybrid array design combines aCGH technology with that of SNP arrays. Initially, these were developed by Affymetrix and allowed genotyping and the concomitant detection of copy number variations. More recently, Illumina has developed a similar hybrid array. Yet the technology used in making arrays differs between the two companies as Affymetrix is using Agilent’s photolithographic technology while Illumina is using their proprietary BeadChips method. Regardless, the application of each company’s platform is similar. The main advantage of the hybrid arrays in the clinical diagnostics laboratory is the ability to detect uniparental disomy through observation of the absence of expected heterozygosity at the SNPs along a chromosome.

Interpretation of CNCs Detected on Constitutive aCGH Figure 2.4 illustrates a typical result from a whole genome constitutive aCGH. In this case, the testing was ordered for a newborn female with no additional clinical information provided. In most cases, aCGH is ordered when clinical presentation does not point to a specific, previously defined, genetic disease or syndrome

30


Fig. 2.4 Data from a whole genome or constitutive aCGH. Only data that correspond to probes from chromosome 2 are shown. Each probe is represented as a dot and is plotted as patient versus reference ratio of the hybridization signal (Cy3/Cy5 ratios). Averaging of segments is derived from the mean of the log2 of the fluorescence ratios with the CBS algorithm. The smaller image is a zoomed-in view of a 228-kb deletion at 2q22.3 that encompasses the ZEB2 gene, consistent with a diagnosis of Mowat-Wilson syndrome. All annotations are with NCBI build 36.1, and all coordinates are according to UCSC hg 18 build (March 2006)

(see indications in Table 2.1). In the example in Fig. 2.4, only data from chromosome 2 are shown where a 228-kb deletion encompassing the ZEB2 gene was detected. Mutations in this gene cause Mowat-Wilson syndrome (MWS), an autosomal dominant disorder characterized by developmental delay, ID, dysmorphic facial features, and Hirschsprung disease. The question may be asked why targeted mutation analysis for ZEB2 was not ordered when MWS has such a distinctive phenotype. However, these features usually become more obvious with age. As the patient being tested here was a newborn that may have had a generalized nonspecific clinical presentation. In this example, aCGH was informative and lead to a definitive diagnosis. Therefore, the interpretation was simple. Still, testing with whole genome aCGH does not always detect an obvious disease-causing CNC. Several considerations must be evaluated when interpreting aberrations detected by aCGH (Table 2.1).

2


Table 2.1 Differences in constitutive versus targeted arrays Constitutive array Indications Nonspecific presentation, e.g., ID, ASDs, dysmorphia, congenital anomalies Common G-banding companion tests Considerations in 1. Size interpretation of CNC detected 2. Gene content 3. De novo versus inherited (parental testing) Methods FISH of confirmation

31

Targeted array Clinical presentation suggestive of specific gene/s associated with disease Sequencing Biochemical assays 1. Involvement of coding region: encompasses exons, splice junction, or not 2. In frame versus out of frame 3. Inheritance: e.g., parental testing for carriers in recessive disease 4. Clinical presentation Real-time PCR/MLPA/ breakpoint mapping

The size of the deletion/duplication and the number of genes encompassed are among the most important factors. If a gene, known to cause disease in a dominant pattern, is deleted, then the CNC can be interpreted as pathogenic with relative ease. However, if the deleted gene is associated with a recessively inherited disease, the detection of a second mutation may be necessary to confirm a diagnosis. In this case, traditional Sanger sequencing may have to be ordered for the specific gene. Inheritance of the CNC is also a very important criterion in the interpretation of any variant detected. Parental testing can help clarify if a mutation occurred de novo in the patient or was inherited. If the parent and child share the same CNC, but the parent does not share any symptoms with the affected child, then the CNC may be interpreted as possibly not related to the disease. However, incomplete penetrance or imprinting may make a familial mutation disease causing in certain individuals and not in others. If a CNC is undetectable in DNA from parental samples (assuming paternity is confirmed), it is considered to have occurred de novo in the affected child. Such a de novo CNC in the proband increases the likelihood that the event is pathogenic. Determining the mode of inheritance is extremely important in quoting a recurrence risk for the family. However, evidence of a de novo event does not entirely remove the risk of recurrence, and the patient’s family has to be carefully counseled about the possibility of germ line mosaicism in the parents. In some cases, additional testing is required to rule out other common genomic events. Conventional G-banding performed concomitantly to this aCGH can be useful to rule out a balanced translocation. If both techniques yield no obvious mutation or genomic rearrangement, clinicians can proceed to sequencing of potential diseaseassociated genes.

32


Interpretation of CNC Detected on Targeted aCGH Figure 2.5 represents data seen when a gene-targeted aCGH is used to test for a deletion or duplication. In the case presented, a sample from a 2-year-old boy that had a biochemically and clinically established diagnosis of maple syrup urine disease (MSUD) was received in a molecular diagnostic laboratory for mutation analysis within the three genes implicated in the etiology of this metabolic disorder: BCKDHA, BCKDHB, and DBT. Upon sequencing of all coding exons and flanking intronic regions, the only mutation that was detected was a single copy (heterozygous) of a nonsense mutation in exon 7 of the DBT gene. Since MSUD has an autosomal recessive mode of inheritance, finding two pathogenic mutations is essential for diagnosis. In a quest to find a second mutation, aCGH was requested and detected a 3.7-kb deletion encompassing exon 5 of the DBT gene (Fig. 2.5).

Fig. 2.5 Data from a high-density gene-targeted aCGH. The image is taken from Cytosure software, where each probe is represented as a dot and is plotted as patient versus reference ratio of the hybridization signal (Cy3/Cy5 ratios). A horizontal thick blue line represents the averaging of segments derived from the mean of the log2 of the fluorescence ratios with the CBS algorithm. Thresholds for deletion and duplication calls at log2 ratios of +0.4 and −0.6, respectively, are shown with horizontal thin blue lines. The Cytosure display also demonstrates the aCGH data across the entire chromosome on the top portion of the schematic shown. The view shown is of the targeted gene data for the patient tested. At the bottom is a tract marking the location of each exon. All annotations are with NCBI build 36.1, and all coordinates are according to UCSC hg 18 build (March 2006). Data show a ~3.7-kb deletion encompassing exon 5 of the DBT gene in a 2-year-old male with an established diagnosis of maple syrup urine disease

2


33

Testing the patient’s parents was required to confirm that the deletion mutation and the nonsense mutation were located on opposite chromosomes. However, the definitive biochemical and clinical diagnosis for this patient leaves little doubt that the mutations detected are both pathogenic.

Conclusions The use of aCGH in clinical diagnoses has revolutionized mutation detection. The ease and power of whole genome survey for CNC detection with this technology is unprecedented. Constitutive aCGH is quickly replacing the laborious G-banding techniques in routine testing for mutation detection in cases of ID, ASDs, dysmorphia, congenital anomalies, prenatal testing, and even gene identification. Meanwhile, gene-targeted aCGH in the molecular genetics laboratory is changing the known mutational spectrum of genes by uncovering increasing numbers of pathogenic CNCs. Additionally, uniparental disomy and aneuploidies are easily detected by hybrid arrays that utilize SNPs for genotyping. However, limitations do exist with this technology as it is incapable of detecting balanced translocations and other rearrangements that do not lead to a detectable change in copy number.

References 1. Kondrashov AS. Direct estimates of human per nucleotide mutation rates at 20 loci causing Mendelian diseases. Hum Mutat. 2003;21(1):12–27. 2. van Ommen GJ. Frequency of new copy number variation in humans. Nat Genet. 2005;37(4):333–4. 3. Hassold T, Hunt P. To err (meiotically) is human: the genesis of human aneuploidy. Nat Rev Genet. 2001;2(4):280–91. 4. Christian SL, Robinson WP, Huang B, et al. Molecular characterization of two proximal deletion breakpoint regions in both Prader-Willi and Angelman syndrome patients. Am J Hum Genet. 1995;57(1):40–8. 5. Chen KS, Manian P, Koeuth T, et al. Homologous recombination of a flanking repeat gene cluster is a mechanism for a common contiguous gene deletion syndrome. Nat Genet. 1997;17(2):154–63. 6. Dutly F, Schinzel A. Unequal interchromosomal rearrangements may result in elastin gene deletions causing the Williams-Beuren syndrome. Hum Mol Genet. 1996;5(12):1893–8. 7. Lindsay EA. Chromosomal microdeletions: dissecting del22q11 syndrome. Nat Rev Genet. 2001;2(11):858–68. 8. Scambler PJ. The 22q11 deletion syndromes. Hum Mol Genet. 2000;9(16):2421–6. 9. Bittel DC, Yu S, Newkirk H, et al. Refining the 22q11.2 deletion breakpoints in DiGeorge syndrome by aCGH. Cytogenet Genome Res. 2009;124(2):113–20. 10. Miller DT, Adam MP, Aradhya S, et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet. 2010;86(5):749–64. 11. Kallioniemi A, Kallioniemi OP, Sudar D, et al. Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors. Science. 1992;258(5083):818–21.

34


12. Kirchhoff M, Gerdes T, Maahr J, et al. Deletions below 10 megabasepairs are detected in comparative genomic hybridization by standard reference intervals. Genes Chromosomes Cancer. 1999;25(4):410–3. 13. Solinas-Toldo S, Lampel S, Stilgenbauer S, et al. Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer. 1997;20(4):399–407. 14. Cheung SW, Shaw CA, Yu W, et al. Development and validation of a CGH microarray for clinical cytogenetic diagnosis. Genet Med. 2005;7(6):422–32. 15. Shaffer LG, Kashork CD, Saleki R, et al. Targeted genomic microarray analysis for identification of chromosome abnormalities in 1500 consecutive clinical cases. J Pediatr. 2006;149(1):98–102. 16. Pinkel D, Segraves R, Sudar D, et al. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet. 1998;20(2):207–11. 17. Pollack JR, Perou CM, Alizadeh AA, et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet. 1999;23(1):41–6. 18. Koolen DA, Vissers LE, Pfundt R, et al. A new chromosome 17q21.31 microdeletion syndrome associated with a common inversion polymorphism. Nat Genet. 2006;38(9): 999–1001. 19. Sharp AJ, Hansen S, Selzer RR, et al. Discovery of previously unidentified genomic disorders from the duplication architecture of the human genome. Nat Genet. 2006;38(9):1038–42. 20. Shaw-Smith C, Pittman AM, Willatt L, et al. Microdeletion encompassing MAPT at chromosome 17q21.3 is associated with developmental delay and learning disability. Nat Genet. 2006;38(9):1032–7. 21. Varela MC, Krepischi-Santos AC, Paz JA, et al. A 17q21.31 microdeletion encompassing the MAPT gene in a mentally impaired patient. Cytogenet Genome Res. 2006;114(1):89–92. 22. Sharp AJ, Mefford HC, Li K, et al. A recurrent 15q13.3 microdeletion syndrome associated with mental retardation and seizures. Nat Genet. 2008;40(3):322–8. 23. Sharp AJ, Selzer RR, Veltman JA, et al. Characterization of a recurrent 15q24 microdeletion syndrome. Hum Mol Genet. 2007;16(5):567–72. 24. Ghebranious N, Giampietro PF, Wesbrook FP, Rezkalla SH. A novel microdeletion at 16p11.2 harbors candidate genes for aortic valve development, seizure disorder, and mild mental retardation. Am J Med Genet A. 2007;143A(13):1462–71. 25. Shaffer LG, Bejjani BA, Torchia B, Kirkpatrick S, Coppinger J, Ballif BC. The identification of microdeletion syndromes and other chromosome abnormalities: cytogenetic methods of the past, new technologies for the future. Am J Med Genet C Semin Med Genet. 2007;145C(4): 335–45. 26. Slavotinek AM. Novel microdeletion syndromes detected by chromosome microarrays. Hum Genet. 2008;124(1):1–17. 27. Berg JS, Potocki L, Bacino CA. Common recurrent microduplication syndromes: diagnosis and management in clinical practice. Am J Med Genet A. 2010;152A(5):1066–78. 28. Kallioniemi A. CGH microarrays and cancer. Curr Opin Biotechnol. 2008;19(1):36–40. 29. Michels E, De Preter K, Van Roy N, Speleman F. Detection of DNA copy number alterations in cancer by array comparative genomic hybridization. Genet Med. 2007;9(9):574–84. 30. Lai LA, Paulson TG, Li X, et al. Increasing genomic instability during premalignant neoplastic progression revealed through high resolution array-CGH. Genes Chromosomes Cancer. 2007;46(6):532–42. 31. Shevell M, Ashwal S, Donley D, et al. Practice parameter: evaluation of the child with global developmental delay: report of the Quality Standards Subcommittee of the American Academy of Neurology and The Practice Committee of the Child Neurology Society. Neurology. 2003;60(3):367–80. 32. Martin CL, Waggoner DJ, Wong A, et al. “Molecular rulers” for calibrating phenotypic effects of telomere imbalance. J Med Genet. 2002;39(10):734–40.

2


35

33. Flint J, Knight S. The use of telomere probes to investigate submicroscopic rearrangements associated with mental retardation. Curr Opin Genet Dev. 2003;13(3):310–6. 34. Ravnan JB, Tepperberg JH, Papenhausen P, et al. Subtelomere FISH analysis of 11 688 cases: an evaluation of the frequency and pattern of subtelomere rearrangements in individuals with developmental disabilities. J Med Genet. 2006;43(6):478–89. 35. Muhle R, Trentacoste SV, Rapin I. The genetics of autism. Pediatrics. 2004;113(5):e472–86. 36. Jacquemont ML, Sanlaville D, Redon R, et al. Array-based comparative genomic hybridisation identifies high frequency of cryptic chromosomal rearrangements in patients with syndromic autism spectrum disorders. J Med Genet. 2006;43(11):843–9. 37. Miles JH, Takahashi TN, Bagby S, et al. Essential versus complex autism: definition of fundamental prognostic subtypes. Am J Med Genet A. 2005;135(2):171–80. 38. Marshall CR, Noor A, Vincent JB, et al. Structural variation of chromosomes in autism spectrum disorder. Am J Hum Genet. 2008;82(2):477–88. 39. Weiss LA, Shen Y, Korn JM, et al. Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med. 2008;358(7):667–75. 40. Moreno-De-Luca D, Mulle JG, Kaminsky EB, et al. Deletion 17q12 is a recurrent copy number variant that confers high risk of autism and schizophrenia. Am J Hum Genet. 2010;87(5):618–30. 41. Sahoo T, Cheung SW, Ward P, et al. Prenatal diagnosis of chromosomal abnormalities using array-based comparative genomic hybridization. Genet Med. 2006;8(11):719–27. 42. Schaeffer AJ, Chung J, Heretis K, Wong A, Ledbetter DH, Lese Martin C. Comparative genomic hybridization-array analysis enhances the detection of aneuploidies and submicroscopic imbalances in spontaneous miscarriages. Am J Hum Genet. 2004;74(6):1168–74. 43. Vissers LE, van Ravenswaaij CM, Admiraal R, et al. Mutations in a new member of the chromodomain gene family cause CHARGE syndrome. Nat Genet. 2004;36(9):955–7. 44. Wang X, Reid Sutton V, Omar Peraza-Llanes J, et al. Mutations in X-linked PORCN, a putative regulator of Wnt signaling, cause focal dermal hypoplasia. Nat Genet. 2007;39(7):836–8. 45. del Castillo FJ, Rodriguez-Ballesteros M, Alvarez A, et al. A novel deletion involving the connexin-30 gene, del(GJB6-d13s1854), found in trans with mutations in the GJB2 gene (connexin-26) in subjects with DFNB1 non-syndromic hearing impairment. J Med Genet. 2005;42(7):588–94. 46. del Castillo I, Villamar M, Moreno-Pelayo MA, et al. A deletion involving the connexin 30 gene in nonsyndromic hearing impairment. N Engl J Med. 2002;346(4):243–9. 47. Luzi P, Rafi MA, Wenger DA. Characterization of the large deletion in the GALC gene found in patients with Krabbe disease. Hum Mol Genet. 1995;4(12):2335–8. 48. Hegde MR, Chin EL, Mulle JG, Okou DT, Warren ST, Zwick ME. Microarray-based mutation detection in the dystrophin gene. Hum Mutat. 2008;29(9):1091–9. 49. del Gaudio D, Yang Y, Boggs BA, et al. Molecular diagnosis of Duchenne/Becker muscular dystrophy: enhanced detection of dystrophin gene rearrangements by oligonucleotide arraycomparative genomic hybridization. Hum Mutat. 2008;29(9):1100–7. 50. Tayeh MK, Chin EL, Miller VR, Bean LJ, Coffee B, Hegde M. Targeted comparative genomic hybridization array for the detection of single- and multiexon gene deletions and duplications. Genet Med. 2009;11(4):232–40. 51. Boone PM, Bacino CA, Shaw CA, et al. Detection of clinically relevant exonic copy-number changes by array CGH. Hum Mutat. 2010;31(12):1326–42.

Chapter 3

Pharmacogenomics: Tailoring Treatment Based on Genotype Alan H.B. Wu

Introduction Pharmacogenomics is a medical science that promotes personalized medicine, i.e., providing the right drug at the right concentration at the right time. An individual’s genetic makeup is a determinant as to how pharmacologic medications are absorbed, transported, metabolized, and excreted. Currently, genetic polymorphisms in the genes that produce drug-metabolizing enzymes cause the greatest variance in drug response. Therefore to date, most of the focus for pharmacogenomic testing has been for these genes. There are no pharmacogenomic tests for variances in transport proteins that are in routine clinical practice. The approval of medications in the United States is under the auspices of the Food and Drug Administration (FDA). Results of clinical trials in their various phases are reported to the FDA as part of the approval process. Pharmaceutical manufacturers must demonstrate the drug’s efficacy and potential for developing toxic effects. Drugs that fail to meet therapeutic claims or are associated with unacceptable adverse effects are not cleared. Recommendations for optimum dosing for the intended population and its intended clinical indication are also established through clinical trials. The recommended dosage suggested by the manufacturer is based on the effect that is achieved for the majority of the population tested. Drug dosages for individuals who have been shown to be overly resistant or sensitive to the recommended amount should be appropriately altered. Pharmacogenomic testing enables prediction of individuals who have poor, intermediate, or ultrarapid metabolism. In order to be in the proper therapeutic concentrations for maximum efficacy, individuals who are poor metabolizers will likely require lower doses for active drugs, and higher doses for “prodrugs,” i.e., those that require metabolism to

A.H.B. Wu, Ph.D. (*) Department of Laboratory Medicine, San Francisco General Hospital, University of California, 1001 Potrero Ave., Room 2M27, San Francisco, CA 94110, USA e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_3, © Springer Science+Business Media, LLC 2012

37

38

A.H.B. Wu

Table 3.1 Comparison of pharmacogenomic vs. companion testing Parameter Pharmacogenomics Companion Mutation type Germ line Somatic DNA or RNA expression Nucleic acid tested DNA DNA and/or RNA Tissue source Blood, saliva, and buccal cells Tumor tissue FDA approval Recommended for testing Often co-approved with drug Drug formulation Small molecules Small molecules and antibodies or Fab fragments Disease application Many diseases Largely oncology Test examples CYP2D6, UGT1A1 K-ras, Her-2-neu

the biologically active compounds. For individuals who are ultrarapid metabolizers, the reverse is true – increased and decreased dosages for active and prodrug formulations, respectively. Genotyping for detection of genetic polymorphism can commence prior to the initiation of the drug itself. However, genetic factors are not the sole criteria for metabolic status. Nongenetic factors such as age, body mass index, ethnicity, diet, and especially co-medications can influence drug metabolism rates. While some of these variables can be accounted for by dosing algorithms, it is not possible to account for all of the genetic and nongenetic variables. Therefore for many drugs, there is a need to characterize the phenotype in an individual, i.e., measurement of the drug concentrations in serum or plasma. While results of therapeutic drug monitoring can supersede information obtained from genotyping, it does require the patient to be on the drug long enough to reach steady state (typically 5–7 times the drug’s half-life). For some drugs with long half lives, such as tamoxifen, the induction period can be several months. In the interim, a patient who has a variant genotype may not benefit from the intended effects of the drug or worse, may suffer unwanted toxicity. In this chapter, “pharmacogenomic” testing is defined as testing for mutations and polymorphisms in an individual’s germ line. DNA can be obtained from buccal swabs, oral fluids, or leukocytes from blood collections. The next chapter describes testing for mutations and polymorphisms that occur somatically and are tested in DNA and RNA extracted directly from tumor tissues. This type of testing is often termed “companion diagnostics” as therapeutic strategies can be specifically tailored to the presence of these mutations. A summary of the differences between these types of tests is listed in Table 3.1.

Barriers to Implementation of Pharmacogenomic Testing While there is great promise for pharmacogenomics to improve the effectiveness of therapeutic drugs, there are many barriers to the widespread adoption into routine clinical practice. Some of these barriers are listed in Table 3.2 and have been

3

Pharmacogenomics: Tailoring Treatment Based on Genotype

39

Table 3.2 Barriers for implementation of pharmacogenomics Barrier Potential solution Education Incorporation into undergraduate medical curricula and implementation of continuing physician education programs Reimbursement Demonstration of efficacy through randomized clinical trials Lack of accepted guidelines Enlistment of medical “champions” for authoring pharmacogenomic testing documents Assay standardization May require involvement/enforcement of manufacturers by the FDA Lack of commercial assays Self-limiting; universal adoption of pharmacogenomics will drive commercialization

summarized previously [1]. Perhaps the biggest barrier is the lack of education as to what pharmacogenomics is and how it can improve clinical outcomes. Molecular diagnostics has only recently been introduced into some undergraduate medical curricula, where the major focus is currently on detection of infectious and genetic diseases. From a clinical laboratory standpoint, molecular tests are widely available to support physicians treating and counseling these patients. Geneticists and infectious disease specialists, respectively, are well versed in molecular testing and request testing services to be routinely available. In contrast, the availability of pharmacogenomic testing is limited due to the lack of demand for such testing by physicians. Part of the problem is that pharmacogenomics crosses multiple medical disciplines, e.g., oncology, cardiovascular, psychiatry, neurology, pharmacology, and laboratory medicine. Another barrier is the cost associated with molecular testing. Genetic testing for genes that have large genetic variances can be expensive (>$100). Furthermore, the American Medical Association has not established any Current Procedural Terminology (CPT) codes for pharmacogenomic tests. Therefore, reimbursement for clinical testing requires assembly of a series of appropriate general codes. Irrespective to the cost of testing, there is a paucity of studies demonstrating that pharmacogenomic testing is cost effective. Finally, clinical data demonstrating that pharmacogenomic testing improves outcomes is lacking. Trials to demonstrate testing efficacy are difficult to conduct because, unlike drug trials, there is no funding mechanism to support such studies. For example, there is no financial incentive for the pharmaceutical industry to support a pharmacogenomics trial for a drug already cleared by the FDA. Moreover, there is a perception that if routine pharmacogenomic testing were a requisite for drug usage, it would limit the population of subjects who could and should take these drugs. Finally, pharmacogenomic testing is designed to detect individuals who are outliers from those who are normal or wild type. Depending on the allele frequency, the number of subjects affected by testing is small, thereby necessitating large numbers of subjects in a clinical study, further increasing the costs of such trials. Clinical validations for cancer markers are particularly difficult, as the clinical outcomes of disease progression require many years of follow-up observation. Therefore, most pharmacogenomic studies for cancer drugs are retrospective, with testing of existing sample banks.

40

A.H.B. Wu

Drivers for Pharmacogenomic Testing While there are barriers for pharmacogenomic testing, there are also many proponents. Principal among those in favor of pharmacogenomics is the Center for Drug Education and Research (CDER) of the FDA. Through careful review of the existing literature, the CDER has begun requiring manufacturers to relabel certain drug products recommending or suggesting that pharmacogenomic testing be conducted prior to initial drug dosing. The label is very specific as to the target gene and polymorphism to be tested. Table 3.3 lists the drugs, target genotype, and clinical indications for some of the drugs for which the FDA has new labeling requirements. For clopidogrel, the FDA has taken a further step in issuing a “black box warning.” These labeling requirements have stimulated manufacturers of molecular diagnostic tests to produce testing products which have been submitted and cleared by the FDA for routine clinical use in certified laboratories. It is also likely that awareness of relabeling may stimulate more clinical testing for medicolegal purposes. For example, an adverse event in a patient who is a poor or ultrarapid metabolizer may result in medical malpractice litigation against the physician if testing was not conducted as recommended by the FDA. As pharmacogenomic testing has not reached the level of routine standard of medical care, such lawsuits may not result in an award for the plaintiff. Nevertheless these suits will likely be time consuming and expensive to litigate.

Specific Drugs Targeted for Routine Pharmacogenomic Testing Based in large part on the list of drugs that has been relabeled by the FDA, there has been a substantial amount of research for the following drugs and their genotype targets. While routine pharmacogenomic testing is not widespread yet for any of these drugs, clinical testing has begun in selected clinical situations.

Table 3.3 US FDA Center for Drug Education and Research drug relabeling requirement Drug Genomic target Drug indication 6-Mercaptopurine Thiopurine methyltransferase Autoimmune, cancer and azathioprine Atomoxetine CYP2D6 Attention deficit disorder Irinotecan UGT1A1 Cancer Warfarin CYP2C9, VKORC1 Anticoagulation Abacavir HLA-B*5701 HIV Allopurinol HLA-B*5801 Gout Carbamazepine, phenytoin, HLA-B*1502 Epilepsy and fosphenytoin Codeine CYP2D6 Analgesia Clopidogrela CYP2C19 Cardiovascular disease Tamoxifen CYP2D6 Breast cancer a Black box warning issued by the FDA

3


41

Fig. 3.1 Difficulties of controlling INR. A 65-yearold African American male with atrial fibrillation, CYP2C9 *1/*2, and VKORC1 -1639G/G genotype. After what appears to be INRs that are reaching the target level (yellow zone), the patient stops coming in for INR testing. He returns 10 days later with a very high (7.4) INR and high risk for bleeding. Dotted lines indicate possible routes to this high level. This prompts admission to the hospital and discontinuance of warfarin until the INR stabilizes

Warfarin Warfarin is an anticoagulant drug widely used to prevent thrombosis in patients who are at high risk due to atrial fibrillation, venous thrombotic diseases, arterial thrombotic diseases, and heart valve disorders or replacement. Warfarin exists as a racemic mixture of R and S stereoisomers with the latter form having the most anticoagulant potency. Prior to the advent of pharmacogenomics for warfarin, the standard warfarin maintenance dose was 5 mg/day. The desired degree of anticoagulation is determined by regular measurement of the prothrombin time and calculation of the International Normalized Ratio (INR). The target is an INR between 2.0 and 3.0 (normal 1.0). Over-anticoagulation can lead to cerebral bleeding. Therefore, it is necessary to adjust the warfarin dose based on the degree of anticoagulation achieved at steady state. Figure 3.1 demonstrates the problem of overanticoagulation with a standard dose. The metabolism of the S isomer is largely catalyzed by cytochrome P450 2C9. The wild type is designated as *1/*1. Individuals who have one or more copies of either the *2 or *3 genotype have reduced rate of metabolism for warfarin. As a result, the serum concentration of warfarin in these patients is higher than for wild-type patients. The allele frequency for CYP2C9 *2 and *3 are 15% and 8%, respectively, for Caucasians; 0% and 6% Asians; and 1% and 6% African Americans. A second gene, VKORC1 (vitamin K epoxide reductase complex, subunit 1), affects the pharmacodynamics of warfarin. Warfarin blocks the activity of vitamin K epoxide reductase, which is necessary to recycle oxidized vitamin D back to the reduced form. This latter form is necessary as a cofactor for the activation of clotting factors

42

A.H.B. Wu

II, VII, VIII, and X and proteins C, S, and Z. The wild type for the VKORC1 gene at position -1639 is GG. One or more copies of the A allele at -1639 render the enzyme more responsive to warfarin, leading to increased anticoagulation. The A allele frequency for VKORC1 is 38%, 86%, and 16% for Caucasians, Asians, and African Americans, respectively. The required warfarin dose for individuals who are homozygous for CYP2C9 *2/*3 and VKORC1 -1639 AA is approximately 1 mg of warfarin, and use of the standard 5-mg dose leads to over-anticoagulation. Initial dosing algorithms have been created using genetic (CYP2C9 and VKORC1 genotypes) and nongenetic factors (age, gender, body mass index, ethnicity, and comedications). A widely used algorithm is publically available through Washington University (www.warfarindosing.org). This algorithm derived from over 1,000 patients predicts about 53% of the variability in warfarin dosing. Warfarin dosing algorithms do not supersede the need to perform regular INR monitoring and adjustments to meet the target INR. The efficacy of pharmacogenomic testing has been evaluated by several randomized clinical trials. None of these studies have been sufficiently powered to determine if pharmacogenomic testing can improve clinical outcomes. However, they have shown that dosing based on an algorithm can reduce the time needed for patients to achieve the target INR [2, 3]. Given that abnormal INR results are strongly correlated with adverse outcomes, improved INR compliance should lead to improved clinical outcomes. Based on the few reports available from these trials, economic models have also been created to determine if pharmacogenomic testing is cost effective. In the Eckman et al. model for warfarin [4], the incremental costeffectiveness ratio (ICER), the amount needed to achieve complete health benefit for a particular intervention, was estimated to be $170,000. This estimate, which takes into account the cost of testing versus savings in healthcare costs that are avoided by the intervention, is higher than the $50,000 threshold that medical economists consider to be cost effective. However, should future trials demonstrate a higher degree of therapeutic efficacy with warfarin pharmacogenomic testing coupled with lower costs for testing, the ICER will decrease.

Tamoxifen Tamoxifen is drug that blocks estrogen receptors which is used for treating patients with breast cancer in the adjuvant setting. Full biologic activity of tamoxifen requires its conversion to endoxifen, which has a much higher affinity for the estrogen receptors. Endoxifen is produce through two pathways (Fig. 3.2). In the minor pathway, endoxifen is produced from 4-hydroxytamoxifen, which has high biologic activity but is present in low concentrations in blood and tissues. In the major pathway, endoxifen is produced from desmethyltamoxifen, which is present in high concentrations but has low antiestrogen potency. Therefore, the major action of tamoxifen is mediated through endoxifen. The metabolism of the desmethyl intermediate to endoxifen is mediated by CYP2D6. This enzyme is highly polymorphic, with multiple SNPs that

3


a

43

CYP 2D6 Tamoxifen

4-hydroxy tamoxifen

CYP 3A4/5

CYP 3A4/5 CYP 2D6

N-desmethyl tamoxifen

Endoxifen

Fig. 3.2 Pathway for the enzymatic metabolism of tamoxifen to endoxifen via N-desmethyltamoxifen (major pathway) and 4-hydroxytamoxifen (minor pathway). CYP2D6 is the rate-limiting step for the major pathway. LC/MS/MS analysis of serum for tamoxifen and these metabolites

are associated with no activity (e.g., *3 through *8), reduced activity (e.g., *9, *10, *17, *36, and *41), and increased activity (gene duplications (*1, *2, and *35)). The allele frequency for 2D6 *4 is 25% for Caucasians and 5% for Asians and African Americans. For the *10 allele, the corresponding frequencies are 2%, 40%, and 35%, respectively. The pharmacogenomic interest in CYP2D6 was generated by retrospective studies that showed that women with breast cancer who were poor metabolizers (*4/*4) had shorter disease-free survival and freedom from relapse than patients with the wild type (*1/*1) [5]. Similar results were observed for the CYP2D6 *10/*10 genotypes among Asian women [6]. There have been other studies that showed no correlation between genotypes and outcomes. As with most pharmacogenomic markers, there are other factors that influence the effectiveness of therapeutic medications. In the case of CYP2D6, the use of other drugs that inhibit the enzyme activity of 2D6 will result in endoxifen concentrations that are as low as what is seen among women who are poor metabolizers [7]. For example, serotonin selective reuptake inhibitors

A.H.B. Wu

44 OCH3

O

CYP 2C19

N S

O

Cl

OCH3

HOOC N HS

Cl

Fig. 3.3 Conversion of clopidogrel to the active metabolite through CYP2C19

are sometimes used to treat depression and hot flashes in patients on estrogen receptor blockers. Drugs that are not inhibitors of 2D6, such as sertraline or venlafaxine, should be considered as alternatives. While genotyping may be predictive of therapeutic success, the concentration of the active metabolite that is actually achieved in any given patient will likely correlate best to clinical outcomes. A recent study showed that there is a “threshold effect” for endoxifen, i.e., a minimum concentration for therapeutic efficacy, above which the drug offers women no additional protection [8]. Measurement of serum endoxifen and tamoxifen concentrations and the calculation of a ratio will also facilitate an assessment of drug compliance. Tamoxifen and its metabolites can all be simultaneously measured by liquid chromatography/mass spectrometry (Fig. 3.2). The disadvantage of phenotyping is the requirement that drug be administered and levels measured at steady state. Tamoxifen has a long half-life of 210 h, and steadystate levels are not achieved for 2 months. Therefore, an effective strategy might be to initially use pharmacogenomic testing to identify poor metabolizers who should be treated with aromatase inhibitors. For those who are wild type or intermediate metabolizers, tamoxifen could be initiated with therapeutic drug monitoring for the metabolite to take place after 2 months. Therapeutic drug monitoring will also be helpful to assess patients who take other medications, such as antidepressants and drugs that minimize tamoxifen side effects.

Clopidogrel Clopidogrel binds to the P2Y12 receptor of platelets and is a potent inhibitor of aggregation. It is used to prevent thrombosis in patients with cardiovascular disease including acute coronary syndromes, cerebrovascular disease, and in patients undergoing revascularization (e.g., percutaneous coronary intervention, PCI). The standard dose is 75 mg/day. Clopidogrel is a prodrug that must be metabolized to an active metabolite, principally by the enzyme CYP2C19 (Fig. 3.3). Individuals who have the *2 or *3 genotype have reduced capacity to metabolize clopidogrel. The allele frequencies for *2 and *3 are 15% and A in the second position of the codon. (c) Example of a BRAF p.V600E (c.1799T>A) mutation identified by pyrosequencing analysis of DNA from tumor-enriched FFPE tissue. Top panel, wild-type sequence; bottom panel, BRAF-mutated sequence in which an abnormal adenine appears (red arrow) due to a transversion of a T>A in the second position of the codon

54

A.M. Cushman-Vokoun

Fig. 4.1 (continued)

The goal of many targeted therapies is to inhibit the constitutively activated protein in order to control the downstream cellular effects. Alternatively, analysis of activating mutations in various genes may also be performed in order to decide if a tumor may be unresponsive to a therapy targeting a signaling mediator upstream of the mediator in which the mutation resides. These activated targets for clinical evaluation fall into many classes of signaling mediators. The most commonly involved class is that of tyrosine kinases (TKs). Tyrosine kinases can either be receptors (RTKs) or intracellular tyrosine kinases. When activated, TKs phosphorylate other proteins by transfer of a phosphate group from an ATP molecule onto tyrosine residues, usually activating those proteins.

4

Somatic Alterations and Targeted Therapy

55

Fig. 4.2 HER2 positivity is necessary for treatment with targeted HER2 inhibitor therapy. (a) The HER2 receptor is a member of the EGFR superfamily that dimerizes with other EGFR monomers and activates proliferation cascades. The antibody inhibitor of HER2, trastuzumab (Y), is clinically available for treatment of metastatic invasive breast carcinoma. (b) 3+ HER2 staining by IHC staining; strong and complete membranous staining is identified in most cells. Such staining can be compromised by prolonged tissue fixation in formalin, and thus, stringent tissue handling guidelines are required. (c) Amplification of HER2 (ERBB2) on chromosome 17q12 by FISH analysis (red signals). A centromeric probe is also utilized (CEP17, green signal) in order to identify polysomy of chromosome 17 which can alter interpretation of HER2 amplification. FISH is employed in cases with indeterminate IHC (2+ staining), but some advocate using FISH as the initial test for evaluating HER2 (FISH image courtesy of Dr. Warren Sanger, Human Genetics Laboratory, University of Nebraska Medical Center, Omaha, NE)

56


A.M. Cushman-Vokoun

4


57

Fig. 4.3 KIT and PDGFRA mutations in GISTs. (a) Structure of a class III dimerized RTK family member such as PDGFR, KIT, and FLT3 (EGFR is a class I RTK). The domains include the extracellular domain (ECD), juxtamembrane domain (JMD), tyrosine kinase domain 1 (TKD1), tyrosine kinase domain 2 (TKD2), and the tyrosine kinase insert (TKI). The triangles represent tyrosines which become phosphorylated upon ligand activation and dimerization of the receptor. The numbered exons containing activating GIST mutations are shown relative to the location of the corresponding affected domain for each gene. (b, c) Examples of KIT and PDGFRA mutations detected by PCR amplification and direct sequencing. Horizontal and vertical arrows indicate the beginning of the deletion and single-nucleotide substitution, respectively. (b) A c.1690_1695delTGGAAG mutation (p.Trp557_Lys558del) in KIT. Trp557_Lys558del mutants are sensitive to imatinib in vitro and in vivo. (c) A missense mutation c.2664A>T (p.Asp842Val) in PDGFRA. p.Asp842Val mutants are resistant to imatinib in vitro and in vivo (Sequencing figures courtesy of Dr. Jerzy Lasota, National Cancer Institute; Reprinted from Bridge JA, Cushman-Vokoun AM. Molecular diagnostics of soft tissue tumors. Arch Pathol Lab Med. 2011;135(5):588–601 with permission from Archives of Pathology & Laboratory Medicine. Copyright 2011. College of American Pathologists)

58

A.M. Cushman-Vokoun

b G A

c

C

T

A

G

G

T A

G C

C

C A

A

G

G

70

G

150 A G

T

T

N C

G T

A

T

T

G

C A

A G

T

G


RTKs dimerize upon ligand binding and autophosphorylate within their tyrosine kinase domains (TKDs), stimulating downstream pathways. Examples of RTKs that are clinically relevant targets of therapies include the epidermal growth factor receptor (EGFR), the platelet-derived growth factor receptor (PDGFR), FLT3, and KIT. Intracellular TK therapeutic targets include JAK2 and ABL. A second class of kinases includes serine-threonine kinases (STKs) which also serve in many signaling cascades. BRAF is the most targeted STK in many types of cancers. Another class of signaling molecules is that of the small guanosine triphosphate hydrolases (GTPases). The best characterized GTPase is the RAS molecule. Multiple isoforms of RAS exist including KRAS, NRAS, and HRAS, all of which have three highly conserved codons (12, 13, 61) that contain a majority of the activating mutations. KRAS mutational analysis has become a mainstay in therapeutic decision making in both colorectal and lung adenocarcinomas (see below). Unique cell surface antigens are also often targeted. For example, the B cell marker, CD20, is targeted by rituximab (Rituxan®, Genentech, South San Francisco, CA) therapy in B cell lymphomas. Many other signaling molecules involved in nuclear-cytoplasmic shuttling, cell development, lipid signaling [phosphatidyl inositol 3-kinase (PI3K), phosphatase and tensin homolog (PTEN)], cell adhesion (e-cadherin, beta-catenin), cell metabolism [isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2)], and transcriptional activation are also being considered as the next generation of targets for therapeutic investigation. Additionally, epigenetic silencing by promoter methylation [O6-methylguanine-DNA methyltransferase (MGMT) promoter analysis] or

4


59

Fig. 4.4 COL1A1-PDGFB fusion in DFSP is a molecular target for imatinib. (a) The COL1A1PDGFB fusion results in oncogenic promoter activation of the PDGFB gene and increased PDGFB ligand production (green ovals). Increased ligand overstimulates PDGFR activation, phosphorylation (red circles), and downstream pathways such as RAS-MAPK and PI3K-AKT. Imatinib (yellow pies) prevents phosphorylation of the PDGFR and downstream activation of these pathways. (b) DFSP is a spindle cell neoplasm of the dermis and subcutis with a low risk of metastasis but high risk of recurrence. (c) The characteristic anomaly in DFSP is a fusion of the COL1A1 and PDGFB genes corresponding to juxtapositioning of chromosomes 17 and 22 material within a supernumerary ring chromosome or a t(17;22)(q21;q13) (inset). Custom-designed clinical FISH

60

A.M. Cushman-Vokoun

Fig. 4.4 (continued) probes that span each locus are utilized to identify the fusion. In this DFSP, the reciprocal translocation results in two fusions, on both the derivative 17 and the derivative 22 chromosomes (COL1A1-PDGFB), which can be identified as a juxtapositioned red and green signal or as fused yellow signals (arrows) (Karyotype and FISH image courtesy of Dr. Julia Bridge, Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE)

4


61

chromatin modulation, or mutations in other genes involved in DNA repair [mismatch repair enzymes (MMR) causing microsatellite instability (MSI)] are also being evaluated in order to better understand and therapeutically target various tumors. A caveat of targeted therapy is that secondary resistance to the therapy can, and often does, develop within the tumor. The best characterized form of resistance occurs in the tyrosine kinase inhibitor (TKI) class of targets, many of which will be discussed below. Such resistance is usually in the form of missense mutations within the kinase or activation domain. These mutations allow the molecule to overcome inhibition by the therapy through an activating change in its own conformation or interaction with other important signaling molecules. Other mechanisms of drug resistance may include drug efflux mechanisms such as the ATP-binding cassette receptors (MDR-1; reviewed in [1]). It is also possible that subclones within the tumor exist that have primary resistance mutations resulting in persistence of a subclone despite inhibition of a majority of the primary tumor. In many patients, because of resistance, one single drug will usually not suffice for long-term treatment of a disease with a therapeutic target. Therefore, variation in targeted therapies is needed (i.e., chemical or structural modification) in order to create secondary treatments if an initial primary treatment fails or resistance develops. An example discussed below is the use of imatinib mesylate (Gleevec®, Novartis, East Hanover, NJ) and secondgeneration TKIs in the treatment of chronic myelogenous leukemia. As more targeted therapies enter therapeutic treatment algorithms, expanding clinical molecular diagnostic testing also becomes vital in order to detect the specific genetic variations that delineate the targeted need. Many molecular tests enter the clinical realm as diagnostic tests, only to be employed further as prognostic and therapeutically driven tests as therapies develop. Thus, molecular diagnostics has become increasingly important to proper cancer therapy. These tests have become more complex as more oncogenic gene targets and mutations within these targets are discovered. As such, many companies are developing clinical molecular tests for molecular diagnostics laboratories to employ in their labs so that they can maintain pace with therapeutic advances. A newer trend is the concept of companion diagnostic tests (CDTs). CDTs result from a partnership between a biotechnology company that develops a test and a pharmaceutical company that is creating a targeted therapy. The purpose of CDTs is to create a molecular test for evaluating a target for a pharmaceutical company’s therapy and to utilize these tests and therapies together in clinical studies. The goal is for both the drug and test to gain FDA approval and to standardize molecular diagnosis and therapy. In this chapter, many of the signaling molecules, which have been clinically targeted for therapy and are regularly analyzed in clinical molecular diagnostics laboratories, are outlined. This discussion will be limited to oncologic targets as human malignancy is the prime area where targeted therapy is employed currently. Discussion of such molecules is organized by family of molecules since mutations in many of these molecules can be identified in multiple types of tumors. Both the molecular biology of the molecules and clinical relevance of important mutations will be provided. Finally, development of targeted therapies, and pitfalls of resistance, for each molecule will be briefly reviewed when applicable.

62

A.M. Cushman-Vokoun

Receptor Tyrosine Kinases The RTK superfamily is a large group of receptors that, when ligand bound, activate multiple cellular signaling pathways involved in proliferation, migration, growth, differentiation, survival, and metabolic function. There are multiple classes of RTKs, each with varying themes on a similar structure. Simplistically, these receptors have an extracellular domain for ligand binding, a single transmembrane domain, a juxtamembrane domain which often functions in autoregulation of the receptor activity, and an intracellular TKD with intrinsic tyrosine kinase activity. Characteristic of these receptors is dimerization of two receptor monomers and cross phosphorylation of the TKDs upon activation. After phosphorylation of the tyrosines, various intracellular signaling proteins and/or adaptor molecules bind and become activated, eliciting a cascade of signaling events, often leading to transcriptional activation within the nucleus. Such important signaling cascades include the RAS-RAF-mitogenactivated protein kinase (MAPK) pathway, Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, and the PI3K-AKT pathway, discussed elsewhere in this chapter. As discussed in the following sections, mutations within these receptors, most often activating, can be found in various malignant diseases and have served as important targets for therapy (extensively reviewed in [2]).

Epidermal Growth Factor Family of Receptors The ERBB/EGFR family of receptors is a group of 4 RTK subtypes (ERBB1ERBB4) that bind various ligands (reviewed in [3]). Two of these subtypes, ERBB1EGFR (gene EGFR at 7p12) and ERBB2-HER2 (gene ERBB2 at 17q11.2-q12), are commonly implicated in the pathogenesis of three of the most common cancers [EGFR in lung and colorectal carcinoma (CRC) and HER2 in breast]. Depending on the clinical setting, alterations in these receptors may take the form of gene amplification, increased mRNA or protein expression, and/or activating mutations in the gene. These two subtypes as they relate to targeted therapy currently used in these three malignancies will be discussed further. A variant EGFR (EGFR variant III) in the CNS neoplasm, glioblastoma multiforme, is also briefly addressed.

EGFR (ERBB1) In the past decade, FDA-approved monoclonal antibodies that inhibit the EGFR (cetuximab, Erbitux®, ImClone, Bridgewater, NJ and panitumumab, Vectibix®, Amgen Inc., Thousand Oaks, CA) have been developed as second- or third-line agents in metastatic CRC refractory to other therapies (reviewed in [4]). While these therapies are not curative, and mainly palliative, these therapies do provide for tumor stabilization and response [5, 6]. Cetuximab is more effective with irinotecan

4


63

coadministration [5]. The EGFR signaling pathway is the canonical RTK pathway leading to cell growth, proliferation, and angiogenesis (growth of new blood vessels within a tumor). The EGFR activates many commonly used downstream signaling pathways including the RAS-MAPK pathway and the PI3K-AKT pathway, providing a useful target for therapy (Fig. 4.1a). The activity and involvement of the EGFR in the pathogenesis of CRC has been somewhat complicated. EGFR activating mutations are not commonly identified in CRC [7]. However, the EGFR gene can be amplified in CRC (approximately 10–50%) and the EGFR protein is expressed in 50–80% of CRC [7–9]. Gene amplification does not always correlate with protein expression [9]. Additionally, studies have shown that EGFR protein expression does not necessarily correlate with response to the EGFR antibody inhibitors, as patients lacking protein expression of the EGFR may still respond to the inhibitors [6, 7, 10, 11]. There are multiple theories as to why this occurs including variability in immunohistochemistry (IHC) technique or antibodies used, tissue storage and fixation conditions, testing of the primary versus metastatic site, or heterogeneity in affinity of EGFR binding [11]. Such variability in factors can be problematic when using IHC to quantify receptor expression as discussed below with HER2. As a result, EGFR IHC is not routinely employed in evaluation of tumor responsiveness for cetuximab or panitumumab therapy in CRC. As discussed below, analysis of other biomarkers (KRAS, BRAF) have been shown to be of utmost importance in evaluating whether a CRC will respond to an EGFR inhibitor. Unlike CRC, activating mutations within the EGFR kinase domain, in addition to amplification of the EGFR gene, are somewhat more common in non-small cell lung carcinoma (NSCLC), mainly adenocarcinoma. Studies have indicated that analysis of EGFR mutations is more beneficial than assessing EGFR gene amplification when assessing response to EGFR inhibitors [12]. Approximately 80% of these EGFR mutations involve deletions in exon 19, leading to common in-frame removal of 4 amino acids (LREA), or common missense mutations (p.L858R or p.L861Q) in exon 21 [13–15]. Such mutations have been identified in approximately 5–15% of lung adenocarcinomas, often with common demographics: female, nonsmokers, and in certain studies, Asian descent. Such interesting epidemiology suggests an alternative pathogenetic mechanism to tumorigenesis, in contrast to common lung adenocarcinomas associated with tobacco use that commonly harbor KRAS mutations (discussed below). EGFR-mutated neoplasms are responsive to two small molecule inhibitor therapies to the EGFR, gefitinib (Iressa™, AstraZeneca, Wilmington, DE) and erlotinib (Tarceva®, Genentech, South San Francisco, CA), and harbor a better prognosis [12, 15, 16]. Thus, molecular analysis for EGFR mutations in NSCLC, especially in nonsmokers, is now performed in clinical diagnostic laboratories, often by dideoxy sequencing or real-time PCR analysis, to identify a group of patients who will be responsive to therapy. The use of patient demographic information, algorithmic testing for EGFR mutations, and additional molecular alterations discussed below (KRAS mutations and EML4-ALK translocation) is commonly employed to better select targeted therapy. Resistance to EGFR inhibitor therapy has been identified due to point mutations identified

64

A.M. Cushman-Vokoun

within the kinase domain, most commonly p.T790M [17, 18]. Many of these resistance mutations are also commonly evaluated clinically. Additional generations of EGFR inhibitor therapies will be necessary to overcome such resistance.

EGFR Variant III EGFR amplification and overexpression is commonly identified in the CNS neoplasm glioblastoma multiforme (GBM). Many of these GBMs that overexpress EGFR also express a variant of the EGFR (EGFRvarIII) [19, 20]. EGFRvarIII has been identified in approximately 25–60% of GBMs, the most severe form of brain cancer, 5% of lung squamous cell carcinomas (SCC) and other cancers of the colon, breast, ovary, prostate, and head and neck [21, 22]. This variant results from an inframe deletion of exons 2 to 7, fusion of exons 1 and 8, and a shortened extracellular domain, which is ligand independent, resulting in constitutive activation. Activation results in downstream stimulation of pathways such as the PI3K pathway [23]. EGFRvarIII may have a prognostic significance, but the results have been conflicting [21]. Studies have suggested that the presence of this variant may have a negative effect on long-term survival, at least in the presence of EGFR amplification [20, 24]. Responsiveness to EGFR inhibitors has been demonstrated in GBMs coexpressing the EGFRvarIII with intact PTEN, an inhibitor of PI3K [25]. A targeted therapeutic approach has been undertaken with regard to the EGFRvarIII unlike other approaches discussed in this chapter. The deletion and alternate fusion of exons 1–8 in this variant results in an epitope containing a novel glycine residue. This unique epitope has been utilized to create a peptide, CDX-110, that is given as a vaccination, eliciting an immunogenetic response to tumors containing the EGFRvarIII [26]. Thus, only tumors with this variant will respond to the vaccine, making molecular diagnostic testing critical in identifying those patients who may respond. Early studies with this peptide in GBM have been promising, and late phase clinical trials are currently under way [21]. Companion diagnostic studies are also under way to create a specific molecular test for evaluating GBMs for this variant. Additional studies are also necessary to evaluate the CNS immune response and how it may be further modulated to make the vaccine more effective [21]. If such vaccination therapy is found to be efficacious, this therapy could be extended to other tumors that harbor the EGFRvarIII. In a broader sense, the success of such a peptide-based therapy will ultimately lead to a broad array of peptide vaccines based upon multiple unique genetic signatures in various neoplasms.

HER2 (ERBB2) The HER2 narrative is an interesting and important development when one is discussing the topic of targeted therapies. ERBB2 (chromosome 17q11.2-q12) and its protein product, HER2, can be considered one of the first molecular targets for

4


65

therapy in a malignancy. HER2 is overexpressed in 20–30% of breast cancers and is assessed in all invasive breast carcinomas by IHC, along with the estrogen and progesterone receptors. Alternatively, ERBB2 gene amplification may be assessed by FISH analysis. Interestingly, HER2 does not have a known ligand; instead, HER2 monomers heterodimerize with other EGFR monomers in the presence of their ligands (Fig. 4.2a) [27, 28]. Similar to other EGFR receptor family members, HER2 activates many downstream signaling pathways involved in cell proliferation. Breast cancers with overexpression of HER2 exhibit an overall worse prognosis and behave more aggressively [29]. HER2 expression has also been correlated to a specific molecular subtype of breast cancer called the HER2- or ERBB2-overexpressing cell type [30, 31]. This molecular class of tumors has an overall worse prognosis and is most often estrogen and progesterone receptor negative. However, HER2 expression and molecular subtype do not always correlate, making molecular classification somewhat difficult for therapeutic decision making [31]. Two clinical molecular gene expression profiling assays using either a microarray platform (MammaPrint, Agendia®, Irvine, CA) or real-time RT-PCR (Oncotype Dx, Genomic Health Inc., Redwood City, CA) are currently available to further identify recurrence risk based on the molecular subtype of breast cancer. However, they are mainly used to aid clinicians in deciding which patients should receive adjuvant chemotherapy. Two drugs targeting HER2 have been approved by the FDA for HER2-positive breast cancer: the monoclonal antibody, trastuzumab (Herceptin, Genentech, USA), and the small molecule inhibitor of HER2, lapatinib (Tykerb, GlaxoSmithKline, London, United Kingdom). These drugs are approved in combination with other agents for metastatic breast cancer [32–35]. Thus, testing for expression or amplification of HER2 in invasive cancer is vital for properly targeted therapy. Testing invasive breast adenocarcinomas for HER2 expression is not easily consistent, and the handling of the breast tissue requires detailed and often tedious steps considering the busy workflow in anatomic pathology laboratories. Due to the subjectivity of interpretation of HER2 IHC and the sensitivity of the HER2 antigen to tissue fixation in formalin, stringent standardized criteria have been set forth by the American Society of Clinical Oncology in order to more objectively assess the presence of the antigen by IHC [36]. Two systems have been introduced to help with standardization of analysis of IHC staining (Pathway, Ventana, Tucson, AZ; HercepTest, Dako, Carpinteria, CA). By IHC, HER2 expression is graded as 0–3+, with 0 and 1+ considered negative and 2+ as indeterminate. A score of 3+ in ³30% of invasive tumor cells is positive and correlates with gene amplification at 17q12 (Fig. 4.2b, c). Many pathology laboratories initially test tumors using IHC and reflex to FISH in cases that are indeterminate (2+ staining). However, some advocate using FISH as a primary testing assay to identify gene amplification on chromosome 17 (defined as a HER2/CEP17 ratio >2.2) due to the sensitivity of the HER2 protein to tissue processing [37]. Overall, there is good correlation between IHC and FISH; however, 5–22% of IHC 3+ positive breast cancers are negative by FISH amplification and 2–8% of FISH-amplified ERBB2 breast cancers are IHC negative [37]. The goals of such standardized testing is to provide the most appropriate therapy to the appropriate patient while preventing side effects in patients who are unlikely to

66

A.M. Cushman-Vokoun

respond. The HER2 experience has established what may ultimately be more global expectations when evaluating molecular targets for certain therapies with regard to tissue handling and standardization of testing.

KIT KIT is a type III RTK that, when activated by its ligand, stem cell factor, activates proliferative pathways such as RAS/MAPK, PI3K/AKT, and JAK/STAT. Activating mutations in KIT, mainly present in, but not limited to, the kinase domains, are identified in various neoplasms, including melanomas, gastrointestinal stromal tumors (GISTs), and a range of hematologic malignancies. Imatinib has been shown to be effective in some tumors harboring specific KIT mutations. As a result, many clinical laboratories analyze hot-spot exons where the most common mutations are identified, often by mutation scanning techniques and subsequent dideoxy sequencing. GISTs are mesodermal spindle (sometimes epithelioid) cell tumors that can occur anywhere along the gastrointestinal tract, most commonly in the stomach but also in the small intestine, esophagus, colon, and rectum. Putatively derived from the interstitial cells of Cajal, GISTs may be benign or malignant depending on factors such as size, location, and mitotic rate. Most notably, GISTS are almost always positive for KIT (CD117) expression. Although unrelated to KIT expression [38, 39], activating mutations in KIT have been found in approximately 60–80% of GISTs [40–42]. In GISTs lacking KIT mutations, mutations in the gene PDGFRA have been identified [40–42]. Both the KIT and PDGFRA genes are present on chromosome 4q12 and encode the same class of RTK proteins (class III). A comprehensive review of the clinical parameters and molecular biology of GISTs can be found elsewhere [43]. Schematic representation of the two receptors and commonly mutated exons can be found in Fig. 4.3a. The mutations most commonly found in KIT are in-frame deletions in exon 11, which codes for the juxtamembrane region (Fig. 4.3b) [38, 39, 44]. Importantly, these deletions are in frame, still allowing for protein translation, and disrupt juxtamembrane inhibition of the tyrosine kinase region [44]. Missense mutations also occur, mainly in four codons within exon 11 (Trp557, Val559, Val560, and Leu576) [39, 43, 45]. Exon 13 (coding for the TKD 1) and exon 17 (coding for the TKD 2) less commonly harbor activating missense mutations (p.Lys642Glu and p. Asn822Lys, respectively) [43, 46]. Duplications, insertions, and complex combinations of the above may also occur [43, 47]. Specific mutations have pathologic and prognostic associations. For instance, mutations in exon 17 are more often identified in small intestinal GISTs [46]. Prognostically, gastric GISTs with deletions in exon 11 [39] may demonstrate a more aggressive behavior compared to those with missense mutations, and gastric GISTs with mutations in exon 13 tend to be more aggressive [46]. KIT exon 11 mutations appear to be most responsive to imatinib [40, 48]. Tumors with wild-type KIT have also shown partial response to TKIs [40]. Development of resistance to

4


67

imatinib can occur, primarily through secondary mutations in the tyrosine kinase domains of KIT on the same allele [49]. Thus, other TKI inhibitors and inhibitors of other downstream effectors of growth factor pathways are currently in development (reviewed in [43]). KIT mutations and/or copy number increases have also been identified in certain subtypes of melanoma: mucosal (39%), acral (36%), and cutaneous melanomas related to sun damage (28%) [50]. They are not usually identified in melanomas related to non-sun-damaged skin. KIT mutations (approximately 15–20% of acral and mucosal melanomas) are usually mutually exclusive of both BRAF and NRAS mutations [50, 51]. Most KIT mutations in melanomas are missense mutations, commonly affecting the juxtamembrane region; they do not occur in the extracellular domain [51, 52]. KIT gene amplification has been identified in 25–30% of melanomas with KIT mutations [52]. Prior to detection of KIT mutations, imatinib had been used in clinical trials to treat melanoma with little success [53, 54]. However, after refinement of drug administration to those patients whose tumors have a mutated KIT gene, case reports have indicated some efficacy of imatinib [52, 55]. As a result, large clinical studies have been instigated to further evaluate the effectiveness of TKIs, such as imatinib, in KIT-mutated melanomas [52, 55]. Finally, the KIT gene is evaluated in clinical molecular diagnostics laboratories for a recurrent missense mutation in the rare disease, adult systemic mastocytosis, a myeloproliferative disorder of abnormal mast cell proliferation causing severe allergic and hematologic manifestations (pruritus, anaphylaxis, hepatomegaly, splenomegaly) [56, 57]. KIT analysis is mainly used to confirm a diagnosis as the specific mutation, p.Asp816Val, is resistant to imatinib therapy, and thus, supportive treatment is still currently the mainstay for disease control [58]. Further studies are necessary and under way to identify a targeted therapy for which resistance is not a problem.

Platelet-Derived Growth Factor PDGF and its receptor, PDGFR, have been implicated in many cancers including those of CNS, hematologic, and mesodermal origin. There are two isoforms of the PDGF ligand, A and B (PDGFA, chromosome 7p22; PDGFB, chromosome 22q13.1), and two receptors, PDGFR-a and PDGFR-b (PDGFRA, chromosome 4q12; PDGFRB, chromosome 5q33.1). Similar to KIT, PDGFR is a type III RTK. Both the ligand and receptor form dimers in the process of receptor activation of downstream proliferative pathways. Various combinations of the ligand isoforms can activate combinations of dimerized PDGFR isoforms. PDGF activation is an important mediator in cell proliferation, cell migration, and angiogenesis. Mutations in the PDGFR-a are present in approximately 5–10% of GISTs. They are identified mainly in gastric GISTs, which tend to have a more epithelioid morphology, and overall, demonstrate less aggressive behavior [43, 59]. While most GISTs demonstrate CD117 positivity regardless of the presence of a

68

A.M. Cushman-Vokoun

KIT or PDGFRA mutation, rare CD117-negative GISTs most often demonstrate PDGFR-a mutations [59]. Mutations in the PDGFR-a consist mainly of missense mutations in the second tyrosine kinase domain (exon 18) and less often in the juxtamembrane domain (JMD; exon 12). These mutations most commonly involve codon 842 but may also occur in codons 846 and 849 [59–61]. Activating, in-frame deletions have also been identified in exons 18 and 12 [43, 59]. Rarely, double point mutations and insertions can occur [59–61]. With regard to imatinib treatment in PDGFR-a-mutated GISTs, mutations in exons 12, 14, and some in exon 18 demonstrate sensitivity [40, 48, 61]. Tumors wild-type for PDGFRA (and KIT) have also shown partial response to TKIs, indicating that other mechanisms involving tyrosine kinase activation also exist in GISTs [40, 43]. A primary mutation in the PDGFRA gene has shown resistance to imatinib, involving codon 842 (p.Asp842Val) (Fig. 4.3c) [40, 48, 49, 61]. Because of the possibility of effective therapeutic intervention for GISTs and potential mechanisms of resistance, clinical molecular diagnostic testing for PDGFRA (and KIT) gene mutations is offered and recommended for GISTs that are malignant with metastatic risk or that have shown the development of resistance to imatinib. The PDGFRA gene is also involved in a gene fusion with the gene FIP1L1 on the same chromosome (4q11–12), which is identified in approximately 60% of cases of hypereosinophilic syndrome (HES) [62]. This fusion results in constitutive activation of the PDGFR-a kinase, and patients with this fusion respond to imatinib treatment [62]. Interestingly, fusion of these two genes results in a deletion of chromosomal material between the two genes, including the CHIC2 locus [62]. This knowledge has been translated into a clinical FISH assay in which deletion of the CHIC2 locus is evaluated to aid in the diagnosis of HES and to identify patients who will respond to imatinib therapy. In addition to the involvement of PDGFR alterations in the development of various clonal processes such as GISTs and HES, an alteration of the gene encoding the ligand of the PDGFR-b receptor has also been implicated in a clonal process. PDGFB on chromosome 22q13 was identified as a fusion partner with the gene encoding collagen type I (COL1A1) on chromosome 17q21–22 in an infiltrative sarcoma of the dermis called dermatofibrosarcoma protuberans (DFSP) (Fig. 4.4a, b) [63]. DFSP is a disease that has a low rate of metastasis (1–5%) but a high rate of recurrence and thus can cause significant morbidity [64]. This COL1A1-PDGFB fusion can occur on either a supernumerary ring chromosome with material from both genes or, less commonly, by a reciprocal translocation (Fig. 4.4c-inset). Either mechanism results in the placement of the truncated PDGFB gene under control of the COL1A1 promoter, increased improper transcription and translation of PDGF-B ligand, and increased autocrine activation of the PDGFR (Fig. 4.4a) [65]. Thus, the development and growth of DFSP is promoted by PDGF activation of its receptor and downstream signaling pathways that stimulate proliferation. Due to the underlying molecular signature and resultant growth mechanisms in DFSP that are reliant upon PDGFR tyrosine kinase signaling, it was surmised that imatinib may be efficacious in controlling the disease, similar to its effects in GISTs and CML (see below). Studies have shown that imatinib therapy can be effective in

4


69

patients with locally advanced or metastatic DFSP (with a proven 17;22 COL1A1-PDGFB fusion) as both a treatment to halt progression and as a neoadjuvant therapy for nonresectable or difficult to resect tumors [66, 67]. Thus, molecular analysis for this fusion is important not only for diagnosis but also for TKI therapy allowing for disease control and better surgical outcomes. Due to the large number of breakpoints, a set of custom-designed FISH probes, such as those shown in Fig. 4.4c, has been employed to detect this translocation. Detection of the COL1A1PDGFB fusion is an example of how a fusion protein with constitutive, oncogenic activity, derived from chromosomal fusion, can also provide an important target for therapy. Additional examples of fusion protein targets to which therapies have been designed will be discussed below.

FMS-Like Tyrosine Kinase 3 FMS-like tyrosine kinase 3 (FLT3) is a RTK (class III) expressed in myeloid and lymphoid hematopoietic cells that is involved in many of the important processes attributed to RTKs, including normal hematopoietic development [68, 69]. Two specific types of important mutations have been identified in the FLT3 gene, located on chromosome 13q12, which have clinical implications in acute myelogenous leukemia (AML). The first consists of internal tandem duplication (ITD) within the juxtamembrane region. The second involves a missense mutation within the TKD. FLT3-ITD mutations were first discovered in AML in 1996 [69]. These mutations consist of a series of duplications within the juxtamembrane region (exons 14 and 15), resulting in a release of autoinhibition on the activation loop and constitutive activation of the FLT3 receptor [70, 71]. Importantly, these duplications can be of varying lengths but are always in frame, resulting in continuous read-through by the translational machinery. Multiple different lengths can be identified at various stages of AML, with novel-appearing duplications in relapsing AML likely indicating clonal progression. Various downstream pathways are plausibly activated by the presence of these ITD mutations including the RAS-RAF-MAPK pathway, the JAK-STAT pathway, and the PI3-AKT pathway [71]. ITD mutations are assessed in clinical molecular diagnostics laboratories, often through the use of fluorescent PCR and fragment analysis, which identifies both the normal allele length and the increased length or lengths as a result of the duplication. Whereas this assessment is useful for prognostic and therapeutic indications, the sensitive nature of these assays also allows for minimal residual disease monitoring in treated patients with a known FLT3-ITD mutation. One caveat is that FLT3 mutations may be absent in primary testing of an AML but may arise in a relapsed AML that has clonally progressed [72]. Clinically, FLT3-ITD mutations are found in approximately 25–30% of AMLs, often cytogenetically normal AML, and the incidence of these mutations increases with age, being very rare in infant AML and less common in pediatric and young adult AML [71, 73]. The presence of a FLT3-ITD is an independent prognostic risk factor in AML in that AMLs with an ITD have a higher rate of recurrence and

70

A.M. Cushman-Vokoun

poorer overall survival [74–76]. Additionally, studies have shown that the relative presence of the FLT3-ITD allele compared to the normal allele (allelic ratio) can also be used as a prognostic factor (higher ratio = worse prognosis) [73, 75]. Identification of a FLT3-ITD is not only prognostically useful but also has therapeutic implications. Studies have shown that FLT3-ITD+AML patients who are treated with matched allogeneic bone marrow transplantation in their first complete remission have better overall survival than those just treated with standard chemotherapy alone [73, 77]. This is in contrast to AMLs that have nucleophosmin (NPM1) mutations, which are discussed in a later section. FLT3-ITD mutations are an obvious target for inhibitor therapy due to their high incidence in AML and constitutive activation of important pathways that drive abnormal hematopoiesis. Multiple small molecule inhibitors have been developed to target the activated receptor (reviewed in [78]), some of which have shown clinical potential. Sorafenib, a nonspecific kinase inhibitor, has shown some promise for complete remission in patients with refractory AML [79]. Ongoing clinical trials are under way for certain inhibitors, while others require larger prospective studies. Some of these inhibitors may be of greater benefit if used in conjunction with conventional chemotherapy. The second class of FLT3 mutations is missense mutations in the activation loop, also known as TKD mutations [75, 80]. These occur in approximately 5–10% of AMLs, the most common affecting amino acid D835 (p.D835Y). These mutations have a different effect on downstream signaling [81] and may not portend the same poorer prognosis as the ITD mutations [73, 82], although a poorer prognosis has been shown in younger adults with cytogenetically normal de novo AML with a FLT3-TKD mutation [83]. Larger studies analyzing various AML subsets are required to further assess the significance of these mutations. Some clinical molecular diagnostics labs do test for this mutation in addition to the ITD.

Anaplastic Lymphoma Kinase Anaplastic lymphoma kinase (ALK) is a single transmembrane receptor tyrosine kinase with unknown or at least controversial ligands and is currently known as an orphan receptor (reviewed in [84, 85]). In normal physiology, ALK mRNA is predominantly expressed in the central and peripheral nervous system where it is thought to be involved in its development [86]. While the function of ALK in normal physiology requires further understanding, the gene is a common partner in various translocations identified in tumors of hematopoietic, mesodermal, and epithelial origin. More recently, mutations in the ALK gene, located on chromosome 2p23, have also been identified in hereditary and sporadic neuroblastomas [87, 88]. Translocations and mutations involving ALK result in constitutive activation of the ALK protein and abnormal regulation of cellular proliferation, survival, and migration, making tumors with these abnormalities optimal targets for therapy.

4


71

The involvement of ALK in a translocation associated with a malignancy was first identified in anaplastic large cell lymphoma (ALCL), which led not only to the discovery of ALK but also its appellation. ALCL is a T cell lymphoma characterized by CD30 IHC protein expression. ALK expression is identified in 50–80% of ALCLs, predominantly in pediatric and young adult ALCL, but also in adult ALCL [89]. It serves as a favorable prognostic indicator in this disease, regardless of age, but especially in younger individuals. Approximately 80% of ALK+ALCL have an NPM-ALK [t(2;5)(p23;q35)] translocation, first identified in 1994 [85, 90]. This translocation results in the fusion of the kinase domain of ALK to NPM1, an important multifunctional shuttling protein within the cell. The chimeric protein is composed of a dimerization motif from NPM1 and a TKD from ALK resulting in dimerization of two fusion proteins and autophosphorylation of the kinase domains, mimicking what happens with physiologically activated RTKs. Staining for ALK can be identified in the cell cytoplasm, nucleolus, and nucleus, likely due to its gene partner functioning as a shuttle between the nucleus and cytoplasm [85]. Constitutive activation of ALK results in stimulation of multiple downstream pathways involved in cellular proliferation and survival (RAS-MAPK, PI3K-AKT, JAK-STAT, phospholipase C-protein kinase C) [85]. The other 20% of ALK+ALCLs result from various alternative gene partners fusing with the ALK gene. Some of these include tyrosine receptor kinase–fused gene-TFG (3q12.2), tropomyosin 3-TPM3 (1q21.2), tropomyosin 4-TPM4 (19p13.1), and clathrin heavy chain–like gene-CLTC (17q11ter). Common features of these partners include their ability to both promote transcription of the fusion protein and induce oligomerization of the fusion protein, placing the ALK kinase domains in close proximity and fostering phosphorylation and activation [84]. The ALK gene is also involved in various translocations identified in inflammatory myofibroblastic tumor (IMT), a malignant soft tissue tumor often found in the lung, retroperitoneum, and abdomen with predilection for the pediatric population (reviewed in [91]). Approximately 50% of IMTs have ALK gene rearrangements, involving multiple gene partners, and demonstrate ALK positivity by IHC [91–93]. Many of these partners are similar to those found in ALCL, with the exception of NPM1, predicting a similarly unregulated mechanism of ALK activation (dimerization and autophosphorylation). Until more recently, recurrent translocations in epithelial-derived carcinomas were thought to be rare, unlike those found in so many tumors of mesodermal and hematopoietic origins. This has since been challenged by more recent identification of recurrent translocations in tumors such as prostate adenocarcinoma (TMPRSSETS) [94]. In 2007, a fusion gene was reported in lung adenocarcinoma, resulting from a translocation placing the echinoderm microtubule–associated protein-like 4 (EML4) gene adjacent to the ALK gene [95]. Both of these genes reside on chromosome 2p within close proximity, but they are transcribed in opposite directions. A small inversion within chromosome 2p [inv(2)(p21;p23)] results in a fusion of EML4 intron 13 (variant 1) or intron 20 (variant 2) to the ALK intron 20. Subsequently, several other breakpoints within the EML4 gene have been identified [96, 97]. The fusion protein produced by this translocation dimerizes by an EML4-derived

72

A.M. Cushman-Vokoun

coiled-coiled domain that results in autophosphorylation of ALK [95, 97]. Although still being elucidated, it is likely that downstream mitogenic pathways become constitutively activated resulting in transformation [97]. The shared location of each gene on chromosome 2p and the multiple breakpoints involved in various translocations make this translocation somewhat complicated to assess through FISH and RT-PCR, respectively, although multiple techniques have been developed. The EML4-ALK translocation has been identified in approximately 5% of lung adenocarcinomas in a population somewhat similar to that in which EGFR mutations are identified: nonsmokers or light smokers [98–101]. EML4-ALK translocations have also been associated with a younger age of onset, unlike EGFR mutations [98, 100]. EGFR mutations, KRAS mutations, and EML4-ALK fusions are mutually exclusive [98, 99]. While some specific adenocarcinoma subtypes have been associated with EML4-ALK positivity (acinar, papillary, signet ring), these translocations are specific mainly for adenocarcinomas [98, 100]. The recent discovery of this fusion has allowed molecular diagnostics laboratories to provide yet another molecular tool to clinicians as an aid in molecular stratification of lung cancer and, as discussed below, a potential target for TKIs. Finally, ALK mutations have recently been identified in both hereditary and sporadic neuroblastomas, neural-crest-derived tumors common in the pediatric population. Although neuroblastomas have been well characterized by other genetic abnormalities (for example, MYCN amplification), activating mutations in the ALK gene have been described within the kinase domain [87, 88, 102, 103]. Neuroblastomas harboring these mutations may have a poorer clinical outcome [102]. However, these activating mutations may also serve as prime targets for specifically developed small molecule inhibitors. Recently, a small molecule inhibitor of ALK, PF-2341066 (crizotinib, Pfizer, New York, NY), has been shown to have promising therapeutic effect in various tumors with ALK-involved translocations. In a multi-institutional study of 82 patients with advanced ALK rearrangement–positive NSCLC, there was a 57% complete or partial response rate, with an additional 33% disease stabilization rate [101]. Similarly, PF-2341066 effectiveness was identified in a case report of an ALK translocation–positive IMT, whereas no response was identified in an ALK translocation–negative IMT [104]. Finally, two patients with relapsed ALCL have shown early remarkable responses to PF-2341066 after failure with cytotoxic chemotherapy [105]. This drug has recently been approved by the FDA for locallyadvanced or metastatic NSCLC that is positive for an ALK rearrangement. As with many targeted therapies, however, resistance may become a problem. For example, within an EML4-ALK-positive adenocarcinoma, secondary mutations in the ALK gene involved in the translocation have been identified, putatively causing resistance to PF-2341066 [106]. The availability of a therapy targeted to patients with these specific activating ALK translocations and the risk of developing secondary mutations make clinical molecular diagnostics capability even more vital in identifying tumors with these translocations and subsequent resistance mutations. Additionally, based on these studies, it is possible that such targeted therapy will also be of benefit to those patients harboring ALK mutation–positive neuroblastomas.

4


73

Vascular Endothelial Growth Factor New blood vessel growth, known as angiogenesis, is necessary for tumor growth and proliferation, first recognized by a prominent surgeon, Dr. Judah Folkman, in 1971 [107]. When tumors reach a certain size, they can induce formation of their own vasculature through endothelial cell activation by various angiogenic factors such as the vascular endothelial growth factor (VEGF, gene VEGFA on chromosome 6p12) [108]. VEGF is expressed within tumors, and its expression is regulated by hypoxia-inducible factor (HIF). In hypoxic conditions, HIF is stabilized and activated allowing for increased transcription of VEGF and angiogenesis [109]. VEGF is a ligand for the vascular endothelial growth factor receptor (VEGFR, gene KDR on chromosome 4q11–12), a member of the RTK superfamily of receptors. More than one VEGF isoform exist. VEGF-A is the major isoform involved in angiogenic signaling that activates the VEGFR-2 [110]. Binding of VEGFR-2 by VEGF results in activation of many of the common intracellular signaling pathways (RAS-MAPK; PI3K-AKT) involved in cellular proliferation, growth, and migration. Increased VEGF expression has been implicated in the pathogenesis of many common cancers including clear cell renal cell carcinoma (CCRCC), hepatocellular carcinoma (HCC), and head and neck cancers; thus, it has been an important target for therapy. The mechanism by which VEGF is upregulated in CCRCC has been well characterized (reviewed in [111]). Under normal oxygen conditions, hydroxylated HIF is ubiquitin-tagged for proteasomal degradation, mediated by an E3 ligase complex, containing the von Hippel Lindau (VHL) protein. In hypoxic conditions, HIF is not hydroxylated and the E3 ligase complex cannot bind to HIF. HIF levels rise and allow for transcription of various genes such as VEGFA. The VHL gene (3p25) is mutated, silenced, or lost in a majority of CCRCC and is therefore absent or nonfunctional. As a result, HIF cannot bind VHL, is not tagged for degradation, and continues to act as a transcriptional activator of genes such as VEGFA. As expected, CCRCCs are often very vascular tumors. Like CCRCC, HCC neoplasms are also very vascular tumors. VEGF is expressed in HCC, and its expression has been correlated to poorer prognostic features such as capsular infiltration, metastasis, and shortened survival (reviewed in [112]). Similarly, in head and neck squamous cell carcinomas (SCC), expression of VEGF has been linked to increased tumor aggressiveness [113]. In both HCC and head and neck SCC, the presence of serum VEGF has also been linked in increased aggressiveness [112, 113]. VEGF appears to be clinically relevant in many tumors, which are thus often treated with VEGF inhibitors at advanced stages. Currently, molecular diagnostics are not utilized to assess VEGF status but can be used to analyze VHL gene mutations in certain CCRCC. Inhibiting angiogenesis by therapeutically targeting VEGF or the VEGFR has led to the development of various antiangiogenic drugs. Sorafenib (Nexavar®, Bayer Corporation, Pittsburgh, PA), a nonspecific RAF inhibitor that inhibits multiple tyrosine kinases including the VEGFR, has an antiangiogenic effect [114]. It was approved by the FDA in 2005 for treatment of advanced CCRCC and in 2007 for treatment of unresectable HCC [110]. In 2004, bevacizumab (Avastin®,

74

A.M. Cushman-Vokoun

Genentech, South San Francisco, CA) a monoclonal antibody against VEGF-A, was first approved by the FDA for treatment of metastatic colorectal cancer in combination with IFL (irinotecan, fluorouracil, and leucovorin) therapy due to increased survival [115]. Subsequently, it has been approved for treatment of nonresectable, locally advanced, recurrent, or metastatic non–squamous non-small cell lung cancer in combination with carboplatin or paclitaxel as an increase in overall survival was shown [116, 117]. It has also been approved in metastatic renal cell carcinoma in combination with interferon-alfa due to an increase in progression-free survival and as a single agent in glioblastoma multiforme as a second-line therapy for recurrent disease [118, 119]. Sunitinib (Sutent®, Pfizer, New York, NY), a small molecular inhibitor of VEGFR, is also used to treat metastatic RCC [120]. Targeted therapies, such as these, are often used as an adjunct therapy, given in combination with other drugs, such as cytotoxic chemotherapy. Although therapies directed at specific targets are less indiscriminate than generalized therapies, they can still result in side effects. As more targets and gene mutations are discovered through ongoing research, combination targeted therapies will be utilized, replacing toxic generalized chemotherapy and enhancing personalized medicine.

RET The RET proto-oncogene, located on 10q11.2, is an RTK expressed in cells of neuronal, neuroendocrine, urogenital, and testicular origin [121]. RET signaling is somewhat complicated as the RET protein functions in a complex with other proteins including members of the glial-derived neurotrophic factor (GDNF) ligand family and the GDNF-family a receptors. Interaction of these molecules allows for RET dimerization and autophosphorylation and activation of downstream proliferative and migratory signaling pathways such as those involving JAK-STAT, RAS-MAPK, Src kinase, and Rac1-JNK [121]. Genetic alterations in RET have been identified in two types of thyroid carcinomas. In approximately 30% of papillary thyroid carcinomas (PTC), RET has been identified as the common member in chromosomal translocations (inversions or reciprocal translocations) involving multiple genes, including most commonly, CCDC6 (PTC1; 10q21.2) and NCOA4 (PTC3; 10q11.2) (reviewed in [122]). Common to these translocations (generically called RET-PTC) is juxtaposition of the RET kinase domain to an alternate gene sequence containing dimerization motifs, resulting in dimerization and aberrant kinase activation. Such translocations are present mainly in classical PTC (predominantly RET-PTC1), variants such as the cribriforming variant, Hurthle cell variant, and solid variant (predominantly RET-PTC3), and the rare hyalinizing trabecular adenoma [122]. Interestingly, these translocations have been associated with ionizing radiation and induction of doublestranded DNA breakage as a high incidence has been found in victims (especially children) of the Chernobyl nuclear accident in 1986. Such rearrangements likely allow for abnormal chronologic expression of RET in thyroid tissues [122].

4


75

The translocations are mutually exclusive of BRAF mutations (discussed below), and both activated proteins stimulate the MAPK kinase pathway. However, BRAFmutated PTCs (45% of PTCs) have different clinical characteristics (older, more aggressive, advanced stage at presentation, classical and tall cell variant) than those of RET-PTC-translocated tumors (younger, earlier stage at presentation, classical variant) indicating variations in cellular signaling mechanisms [122–124]. Clinical cytogenetic and molecular diagnostics laboratories can identify RET-PTC translocations within PTC by RT-PCR or FISH analysis. However, until targeted therapies become clinically available for these specifically mutated PTCs, the clinical utility is primarily diagnostic in cases where a diagnosis of PTC is not easily accomplished. RET alterations, specifically missense mutations, have also been identified in medullary thyroid carcinomas, which are derived from neuroendocrine C cells that produce calcitonin in the thyroid. Such mutations were first discovered as germ-line mutations in the familial cancer syndromes, multiple endocrine neoplasia types 2A and 2B (MEN 2A and 2B), and in familial medullary thyroid carcinoma (FMTC) syndrome. While MEN2A and 2B vary somewhat in their phenotypes, MTC is a feature common to both. MEN2A and 2B result from different germ-line mutations in the RET gene [121]. RET mutations in MEN2A involve multiple cysteine codons in the extracellular domain, resulting in ligand-independent dimerization [125–127]. In MEN2B, mutations are found most commonly at codon 918 (threonine replacing a methionine at position 918) within the intracellular domain, resulting in altered catalytic properties [127, 128]. RET mutations have been identified in 40–50% of sporadic MTCs, most commonly in codon 918 (p.M918T), and are correlated with poorer prognostic features such as advanced stage and decreased survival [129]. Currently, larger molecular diagnostics laboratories can sequence the RET gene in order to identify a hereditary cancer syndrome. Such analysis will also eventually identify candidates who are eligible for approved targeted therapies that can inhibit activated RET. RET inhibitors have been studied in clinical trials in MEN patients with MTC with promising results. Vandetanib (Zactima™, AstraZeneca, Wilmington, DE), a chemical inhibitor of activated RET, has been shown to induce partial response or disease stabilization in a majority of patients (adults and children) with advanced hereditary MTC [130, 131]. The nonspecific kinase inhibitor, sorafenib, has also shown some promise in inducing a partial response or, more commonly, stabilization of disease in patients with sporadic MTC, although with some toxic side effects [132]. These studies have been promising, and further studies are ongoing in order to substantiate clinical benefit. Additionally, studies in the use of other targeted therapies are also under way [124].

MET The MET gene, located on chromosome 7q31, encodes another member of the RTK family that differs in that it has a smaller extracellular subunit (a) and a separate transmembrane subunit (b) (reviewed in [133]). Its single known ligand is hepatocyte

76

A.M. Cushman-Vokoun

growth factor (HGF). MET signaling is important in numerous cellular functions including those involved in embryogenesis (limb muscle and neuronal and placental development) and in normal mechanisms involved in tissue repair and organ (liver and kidney) regeneration. Such functions occur through important signaling pathways including RAS-MAPK, PI3K-AKT, STAT, and NFkappaB activation, the activation of which results in cell proliferation, growth, and survival. If MET signaling is inappropriately deregulated, cellular functions such as proliferation, growth, and migration become uncontrolled. Not surprisingly, abnormal MET activation has been implicated in various types of malignancy. It is often activated in response to hypoxia, resulting in establishment of new mechanisms (angiogenesis, invasion) to overcome limitations to tumor growth [134]. The MET gene is overexpressed or mutated in multiple tumors including those of epithelial (colorectal, lung, gastric, head and neck, cervical, ovarian, pancreatic, prostate, thyroid), mesodermal (sarcomas), and hematopoietic origins and in melanomas and brain tumors [134, 135]. Specific MET activating missense mutations were first identified in hereditary papillary renal cell carcinoma (PRCC) syndrome, in a small percentage of sporadic PRCC, and later in multiple epithelial neoplasms (lung, ovarian, liver, thyroid, gastric) [135, 136]. Trisomy of chromosome 7, the chromosome on which both HGF and MET reside, occurs in PRCC [135]. In many tumors, overexpression or gene amplification of either MET or its ligand (HGF) correlates with increased aggressiveness [135, 137]. Recently, MET amplification has been shown to be a marker of resistance in non-small cell lung cancers treated with EGFR inhibitor therapy [138, 139]. Given the involvement of MET activation in so many neoplasms and in various cellular functions, it has become an obvious target for the development of multiple TKI therapies, both small molecular inhibitors and monoclonal antibodies (against both MET and HGF) (reviewed in [134, 135]). Many of these therapies (both selective and nonselective for MET) are in phase I and II clinical trials, alone or in combination with other therapies targeting activated pathways (e.g., EGFR activation in lung cancer). Many of these trials are being conducted in various tumors including PPRC, NSCLC, HCC, pancreatic carcinoma, GBM, gastrointestinal, prostate, and head and neck tumors [135]. Questions still need to be answered with regard to MET inhibition including possible toxicities, possible primary and secondary resistance mutations in MET, the role of amplification versus mutation, and sensitivity of various tumor types and subtypes to MET inhibition [135].

Intracellular Tyrosine Kinases ABL1 [BCR-ABL1; t(9;22)] Chronic myelogenous leukemia (CML) is a myeloproliferative disorder in which there is an abnormal clonal proliferation of the mature and immature forms of the myeloid lineages, especially granulocytes, within the bone marrow and peripheral blood.

4


77

The disease eventually progresses through an accelerated phase and subsequently into a blast phase in which the clone transforms into a blastic cell population. The seminal discovery of a recurrent reciprocal translocation between the long arms of chromosome 22 and chromosome 9 was the important initial step in defining the disease. This translocation results in a fusion of the ABL1 gene (ABL), located on chromosome 9q34.1, and the BCR gene, located on chromosome 22q11 [140, 141]. Today, the presence of this translocation, usually identified by cytogenetic and FISH analysis at presentation (Fig. 4.5a, b), is used to definitively diagnose CML. The transposition of the ABL gene to a portion of the BCR gene on chromosome 22 results in the production of a fusion protein with constitutive activation of ABL kinase activity [142, 143]. Such kinase activation within the cell cytoplasm abnormally stimulates various pathways involved in important cellular processes such as cell proliferation and cell survival (reviewed in [144]). Additional cytogenetic and molecular abnormalities (trisomy 8, isochromosome 17q, and TP53 mutations) accumulate throughout disease progression, resulting in the development of an accelerated phase or blast crisis [145]. Thus, inhibition of the initial activating event (i.e., BCR-ABL fusion protein kinase constitutive activity) is vital to inhibiting progression of the disease. Further characterization of the resultant fusion transcript and translated protein as a constitutively activated kinase has allowed for the development of the TKI, imatinib (STI571, Gleevec®, Novartis, East Hanover, NJ). This development was not only exceedingly important for the treatment of CML, a disease with historically poor survival, but also, more globally speaking, served as the formative introduction of targeted therapy and, ultimately, personalized medicine. Four clinically relevant breakpoints within the BCR gene have been identified that fuse with the second exon of ABL (see Fig. 4.5c for detailed breakpoint exons) (reviewed in [146]). Two of these breakpoints are most commonly identified in CML, b2a2, and b3a2 and result in a fusion mRNA transcript producing a protein of 210 kiloDaltons (kDa). A third common breakpoint, e1a2, results in a fusion mRNA transcript producing a 190 kDa protein. This variant can be identified in CML but is more commonly identified in adult precursor B cell acute lymphoblastic leukemia (ALL), rarely in childhood ALL, and has been correlated with higher blast counts and a worse prognosis than BCR-ABL-negative ALL patients [145, 147]. The rare variant breakpoint, e19a2, creates a fusion mRNA transcript translated to a 230 kDa protein, which is associated with prominent peripheral blood neutrophilia in CML ([145, 148]). These recurrent shared breakpoints make this translocation amenable to clinical laboratory testing using reverse-transcriptase polymerase chain reaction (RT-PCR) with an analytical sensitivity as high as 1 malignant cell in 105–106 normal cells (Fig. 4.5d). Analysis of these breakpoints is usually done in a qualitative manner at diagnosis, as opposed to quantitative analysis, which is done to monitor response to therapy (see below). Whereas conventional cytogenetic karyotyping and FISH are used to identify this translocation in bone marrow and peripheral blood, respectively, at the initial diagnosis of CML, neither of these methods is able to distinguish between the various breakpoints.

78

A.M. Cushman-Vokoun

Fig. 4.5 Various laboratory methods used in the detection of the t(9;22) BCR-ABL for diagnosis of CML and treatment with TKIs. (a) Conventional karyotype showing the reciprocal translocation between the long arm of chromosome 9 and the long arm of chromosome 22(t(9;22)(q34;q11)). (b) FISH analysis of a BCR-ABL fusion–positive case of CML using a dual fusion translocation probe (white arrows indicate fusion) (Vysis Probe, Abbott Molecular, Abbot Park, IL). (c) Schematic representation of the BCR and ABL1 exonic structures and various breakpoints creating the three most common BCR-ABL fusions transcripts (b2a2, b3a2 and e1a2). The e1a2 fusion is more commonly identified in precursor B cell acute lymphoblastic leukemia (ALL). The rare e19a2 fusion is not shown as it is not commonly evaluated in clinical molecular laboratories. (d) Qualitative RT-PCR assay for detection of the three common fusion transcripts. Left panel, a CML patient with both b2a2 and b3a2 fusions that yield p210 kDa proteins (lanes 1 and 2). Right panel, an ALL patient with an e1a2 fusion that yields a p190 kDa protein (lanes 1 and 2). (e) Quantitative real-time RT-PCR assay showing the low level presence of the BCR-ABL p210 fusion transcript (blue curve). This particular assay can identify b2a2 and b3a2 fusion transcripts but not the e1a2 transcript. The green curve represents the internal control assay for the ABL transcript that is used to verify RNA integrity and to provide for a ratio comparison. The quantitative assay is used for TKI treatment monitoring and not for diagnosis of CML (Karyotype and FISH images courtesy of Dr. Warren Sanger, Human Genetics Laboratory, University of Nebraska Medical Center, Omaha, NE)

4


c

79

BCR

Exon 12 Exon 13 Exon 2 (b1) (b2) (a2)

ABL

Exon 12 Exon 13 Exon 14 Exon 2 (b1) (b2) (b3) (a2)

Exon 1 Exon 2 (e1) (a2)

CML b3a2 (p210 kDa)

CML b2a2 (p210 kDa)

ALL e1a2 (p190kDa)

e 800

Abl=Green Bcr-Abl=Blue

Fluorescence

600

400

200

0 10


20 Cycles

30

80

A.M. Cushman-Vokoun

The identification of a single molecular target in CML made the disease an ideal candidate for the development of a targeted therapy. Through the innovative and collaborative work of multiple groups (reviewed in [149]), the highly selective chemical TKI of the BCR-ABL fusion protein, imatinib, was developed and rapidly approved by the FDA in 2001. Imatinib inhibits binding of adenosine triphosphate (ATP) to the BCR-ABL kinase, thereby blocking the kinase’s ability to transfer a phosphate group to tyrosines on other signaling proteins in downstream signaling pathways. This drug has been highly effective in increasing overall survival in CML chronic phase patients with 5-year survival approximating 90% [150]. Furthermore, compared to more traditional forms of chemotherapy, which have numerous toxicities, imatinib is relatively well tolerated with minimal side effects. Minimizing toxicity is an added benefit of selectively targeting a specific protein, providing a major impetus to identifying drug targets and developing corresponding therapies. Since the introduction of imatinib into clinical management of CML, additional, secondgeneration TKIs have been developed to counter both side effects and problems with resistance. As a result of the introduction of a targeted inhibitor to BCR-ABL, quantitation of the BCR-ABL mRNA transcript has become necessary in order to monitor response to therapy. Through the introduction of real-time PCR (qPCR) combined with RT-PCR, sensitive quantitation of the BCR-ABL mRNA transcript is now the standard of care for monitoring treatment (Fig. 4.5e). mRNA is extracted from a previously diagnosed patient’s bone marrow or peripheral blood for testing. RT-qPCR allows the amount of circulating patient BCR-ABL mRNA transcripts to be compared to a standardized control set of transcripts to determine the patient’s transcript load. Since mRNA can be easily degraded if not properly handled, it is important that an internal control mRNA, such as the normal BCR or ABL mRNA transcript, simultaneously be tested in order to evaluate the integrity of the RNA. These normal transcripts are also used to calculate a ratio of the BCR-ABL transcript. Molecular testing by RT-qPCR can detect a translocation in as little as 1 in 10,000 to 100,000 cells. Definitions for response have been developed in order to determine whether the therapy is working or whether an alternative therapy should be employed. Molecular detection of the t(9:22) translocation is initially performed by FISH and qualitative RT-PCR in order to provide a diagnosis of CML, in addition to conventional karyotyping. Qualitative RT-PCR is helpful in determining the exact breakpoint for future monitoring. Since not all quantitative assays detect all possible breakpoints (e.g., p190), qualitative RT-PCR is also helpful in detecting minimal residual disease in certain cases (p190 in CML or ALL). If patients are positive for a common breakpoint (b2a2 or b3a2), a quantitative RT-qPCR analysis may be done to provide a pretreatment baseline value. Patients are then monitored by FISH/cytogenetics and quantitative RT-qPCR in order to monitor for a complete cytogenetic response (CCR) or a major molecular response (MMR), respectively (reviewed in [151]). A CCR is defined as the lack of a BCR-ABL-positive cell in at least 20 bone marrow metaphases by conventional karyotyping. Even with a CCR, FISH can be positive at low levels in a fraction of patients. A MMR is defined as a 3 log reduction in

4


81

BCR-ABL transcripts as compared to the initial diagnostic levels or compared to a standardized baseline from a population of untreated patients [151]. A complete molecular response (CMR) occurs when the transcript is no longer detected by RT-qPCR. Because of the increased analytical sensitivity of RT-qPCR over FISH, a patient may be negative for the BCR-ABL fusion by FISH but positive by RT-qPCR. Thus, RT-qPCR is utilized either in lieu of FISH analysis or after FISH analysis becomes negative, in order to monitor for a low level of fusion transcripts. However, proper assay calibration and intact, high-quality mRNA are necessary for accurate results. In the future, international standardized controls incorporated into various commercial assays will allow for better control of interlaboratory variability. In the IRIS study, a lack of CCR to imatinib conferred a poorer prognosis, whereas patients with a CCR and MMR in 12 months did not progress to blast crisis in 60 months [150]. Therefore, monitoring of these parameters is important in identifying primary resistance to a TKI. If a primary response is not identified, there may be primary resistance mutations or other complex cytogenetic/molecular factors that may inhibit a tumor’s responsiveness. Alternatively, a patient may not adhere to the given drug regimen, resulting in a lack of response. Patients who have achieved responses are also monitored (often every 3 months) for an increase in the percent BCR-ABL transcript. A greater than 0.5–1 log increase or doubling in BCRABL mRNA transcripts usually elicits concern on the part of the treating physician for the development of resistance. One of the major mechanisms of resistance is the presence of secondary missense mutations in the ABL kinase that affect the ability of imatinib to bind properly and inhibit the activated kinase. These most commonly occur in the ATP-binding domain (amino acids 244–255); at amino acid T315 (often p.T315I), involved in hydrogen bonding between the drug and kinase; at amino acid M351, involved in autoregulation of the kinase; and in the A loop, which regulates kinase activity (amino acids 381–402) [152]. Such missense mutations can be identified in clinical laboratories offering sequencing of the transcript cDNA. If a resistance mutation is identified, a second-generation TKI may be utilized. However, many of the current second-generation TKIs remain ineffective against the common resistance mutation T315I [153, 154]. Thus, drug companies are currently evaluating new compounds.

JAK2 Janus kinase 2 (JAK2) is a tyrosine kinase that serves as a signaling mediator in the JAK-STAT cell signaling pathway (reviewed in [155]). This pathway is involved in various hematopoietic signal transduction cascades and is activated by multiple receptors within hematopoietic cells including the thrombopoietin receptor (TPOR), the erythropoietin receptor (EPOR), and other cytokine receptors. Upon ligand stimulation of its receptor, a complex series of phosphorylation events occurs between bound JAK2, the intracytoplasmic tail of the receptor and other signaling molecules, and the signal transducers and activators of transcription (STATs).

82

A.M. Cushman-Vokoun

The phosphorylated, activated STAT molecules dimerize and travel to the cell nucleus, regulating gene transcription. There are seven JAK homology (JH) domains within JAK2. JH1 is the catalytic domain important for JAK2 activity. JH2, homologous to JH1, is catalytically inactive, but is thought to have an inhibitory effect on JH1 activity. In 2005, multiple groups identified a specific activating mutation, c.1849G>T (p.V617F), in the JAK2 gene, located on 9p24, in BCR-ABL-negative myeloproliferative disorders (MPD) [156–159]. The JAK2 mutation is identified in over 90% polycythemia vera (PV), 50% of essential thrombocythemia (ET), and 30–50% of primary myelofibrosis (PMF). The JAK2 mutation can be present in the homozygous state, due to mitotic recombination within 9p, and often clinically manifests as splenomegaly and increased symptoms of an MPD [156, 158–160]. It can also be associated with other cytogenetic abnormalities and rarely is identified in myelodysplastic syndromes and AMLs [161–163]. The JAK2 p.V617F mutation is specific for myeloid proliferations and is not seen in lymphoid malignancies [164]. Occurring in exon 14, the JAK2 p.V617F is thought to affect the ability of the JH2 domain to autoinhibit the JH1 domain’s activity [159]. This results in constitutive ligand-independent activation of JAK2 and downstream activation of the JAK-STAT pathway. Other mutations have also been in found in JAK2, occurring mainly in exon 12, which are predominantly substitutions, deletions, and duplications [165, 166]. Such mutations are activating and are often identified in p.V617F-negative PV. Clinical testing for the p.V617F JAK2 mutation has become quite common and useful in the molecular diagnostics laboratory to aid in differentiating a BCR-ABLnegative MPD from a reactive leukemoid reaction, an elevation in platelets as an acute phase reactant, or a secondary erythrocytosis. Several methods have been employed including direct sequencing, allele-specific PCR with fragment analysis, pyrosequencing, and qPCR, although due to patent issues, most laboratories now employ qPCR technology. Currently, most laboratories are performing only qualitative analysis as a diagnostic tool. However, multiple JAK2 inhibitors are currently being studied in late phase clinical trials with the goal of treating and controlling symptoms associated with myeloproliferative disorders, similar to imatinib in CML [167, 168]. As these therapies are introduced into the clinical mainstream, quantitative analysis, through the use of standards and qPCR, will be further employed to monitor response to treatment.

Serine-Threonine Kinases BRAF BRAF is a serine-threonine kinase which acts downstream of KRAS upon stimulation of various receptors, including growth factor receptors, such as EGFR. BRAF interacts with KRAS and, upon activation by phosphorylation, stimulates the MEK/ MAPK pathway and subsequent gene transcription. Mutations in the BRAF gene (located on 7q34) have been identified in multiple cancers. Interestingly, up to

4


83

80–90% of mutations in BRAF-mutated cancers occur at position c.1799T>A, resulting in a missense substitution of glutamic acid for valine (p.V600E, previously known as p.V599E). Such a high incidence indicates that this specific nucleotide is a highly mutagenic site [169]. It has been surmised that the substitution of a polar glutamic acid for a hydrophobic valine mimics a phosphorylation site necessary for BRAF activation [169]. Currently, this mutation is clinically evaluated predominantly in CRCs, melanoma, and PTC. Non-p.V600E BRAF mutations can also be identified in cancers including CRCs, melanomas, and lung adenocarcinomas, but are not always routinely evaluated in these tumors, due to their rare presence [169, 170]. The BRAF p.V600E mutation is found in approximately 40–70% of cutaneous melanomas [171, 172]. Similar to KRAS mutations in the adenoma-CRC sequence, the BRAF p.V600E mutation occurs as an early mutation in the melanocytic nevimelanoma spectrum [173]. There is debate as to the prognostic significance of the BRAF p.V600E mutation although one study has shown BRAF p.V600E–mutated melanomas to have more favorable pathologic features, such as reduced mitotic index and decreased depth of invasion [172]. BRAF p.V600E mutations are more commonly associated with superficial spreading and nodular phenotypes and found less commonly in lentigo maligna melanoma [172]. Because the BRAF single mutation is so amenable to targeted therapy, many BRAF inhibitors have been developed. Early studies with nonspecific kinase inhibitors, such as the TKI sorafenib, were discouraging [55]. However, one such therapy highly selective for BRAF inhibition, PLX4032, has shown tremendous promise in a phase I clinical trial for significant reduction of metastatic disease in patients whose melanomas tested positive for the p.V600E BRAF mutation [174]. This drug has recently been rapidly approved by the FDA for advanced melanomas with BRAF p.V600E mutations due to its efficacy (Vemurafenib, Genentech, South San Francisco, California). Approximately 45% of papillary thyroid carcinomas (PTCs) demonstrate a p.V600E BRAF mutation [175, 176]. This mutation is exclusive to PTC, mainly conventional and tall cell subtypes, and is not identified in other thyroidal processes such as follicular carcinoma, medullary carcinoma, or benign diseases. The putative clinical utility of BRAF testing in thyroid neoplasms encompasses diagnostic, prognostic, and therapeutic value. Diagnostically, BRAF p.V600E positivity can confirm PTC within the differential diagnosis of follicular neoplasms of undetermined significance (FLUS) in fine-needle aspirate cytology [177, 178]. Prognostically, multiple studies have associated this mutation with a more aggressive tumor phenotype as demonstrated by extracapsular invasion, recurrence, and lymph node metastasis [179–181]. Evidence also suggests a relationship between hematogenous spread of the tumor and the presence of the p.V600E BRAF mutation [179, 182]. Such information may become clinically useful in treating these mutated tumors more aggressively, although clinical prospective studies are still necessary. Finally, the presence of this mutation may be used therapeutically in PTC. In the same study utilizing PLX4032 in metastatic melanoma, three patients with BRAF p.V600E–mutated PTC showed a partial or complete response when treated with this therapy [174]. Further evaluation of targeted therapy toward the p.V600E mutation in these tumors is currently required; however, preliminary results are promising.

84

A.M. Cushman-Vokoun

Finally, the BRAF p.V600E mutation is identified in approximately 10% of CRCs [183, 184]. Molecular analysis of CRCs for BRAF p.V600E is currently performed for two purposes. First, similar to KRAS mutations (discussed below), the presence of the p.V600E mutation has been shown to render a CRC unresponsive to EGFR inhibitor antibody therapy [184, 185]. This is not surprising since BRAF is a downstream mediator of the EGFR receptor pathway, and therefore, an activated BRAF would bypass EGFR inhibition (see Fig. 4.1a). Thus, patients whose tumors are positive for BRAF p.V600E are not likely to be treated with the EGFR inhibitors cetuximab or panitumumab. CRCs harboring the BRAF p.V600E have also been shown to have a poorer clinical outcome with regard to survival, especially in microsatellite-stable tumors [186–188]. Secondly, the presence of the BRAF p.V600E mutation in microsatellite-unstable tumors reliably indicates that the tumor is of a sporadic, and not hereditary, nature (discussed below). The limited mutation spectrum makes BRAF an ideal candidate for molecular diagnostics analysis and targeted therapy (reviewed in [189]). Thus, multiple studies are under way to evaluate kinase inhibitors in tumors with abnormally activated BRAF. Molecular testing for the p.V600E (c.1799T>A) mutation is done commonly in clinical laboratories, an example of such testing is shown in Fig. 4.1c. Importantly, recent studies have shown that administration of BRAF kinase inhibitors to wildtype BRAF tumor cells actually activates downstream MAPK signaling through RAF1 (a kinase related to BRAF), enhancing cell growth [190]. Such findings highlight the importance of performing accurate molecular analysis of tumors for mutations in BRAF before administrating inhibitor therapy. Furthermore, they illustrate that therapeutic targeting of signaling molecules can be difficult due to the extreme complexity of signaling networks within both normal and malignant cells.

Other Clinically Relevant Molecular Markers KRAS Whereas most targets discussed thus far are mutated variants of cellular signaling molecules against which agents are developed, KRAS mutational analysis has become a preeminent clinical molecular marker for determining whether a tumor will respond to EGFR inhibitor therapy. RAS proteins (KRAS, NRAS, and HRAS) are 21 kDa GTPases that cycle through a growth factor–stimulated, active, guanosine triphosphate (GTP)–bound state to an inactive guanosine diphosphate (GDP)–bound state. RAS activation is an important step in many signaling pathways, including the MAPK pathway. In all three RAS isoforms, there are conserved codons important for GTP hydrolysis and inactivation, which, when mutated, result in constitutive protein activation. Codons 12 and 13 are the most clinically significant codons; however, mutations in other codons such as 61 and 146 have also shown constitutive activity and are included in some laboratory panels. KRAS mutations are present in

4


85

approximately 20% of lung adenocarcinomas, predominantly in older smokers, and in approximately 30–50% of colorectal adenocarcinomas [16, 191–193]. Interestingly, certain mutations are more common in the lung (p.G12C) while others are more common in the colon (p.G12D, p.G13D) [16, 191–193]. In multiple studies, it has been shown that somatic mutations in KRAS, within both lung and colorectal adenocarcinomas, render the tumors unresponsive to EGFR inhibitor therapy [16, 194–197]. Similar to the BRAF p.V600E mutation, a mutated KRAS bypasses inhibition of the EGFR (Fig. 4.1a). Other studies have suggested that KRAS mutations may serve as a prognostic indicator since they have been found at higher frequency in later stage tumors and in recurrent disease and have been associated with poorer survival, although other studies have not substantiated this [196, 198–200]. There has been no definitive conclusion that certain KRAS mutations confer a better or worse prognosis or response to therapy; however, a recent study suggests that the p.G13D mutation may be more responsive to EGFR inhibitor therapy than other mutations [201]. Whereas molecular testing had previously been done to identify targets for therapeutic response (e.g., CML and imatinib), KRAS mutational analysis was the first molecular assay to enter the clinical mainstream as a marker of therapy ineffectiveness. The data correlating EGFR inhibitor ineffectiveness in CRC with KRAS mutations is so strong that the American Society for Clinical Oncology has recommended that all metastatic CRCs eligible for EGFR inhibitor therapy be tested for KRAS mutations prior to therapy [202]. Lung adenocarcinomas are rarely tested for therapeutic response but instead often to exclude an EGFR mutation, as KRAS and EGFR mutations are mutually exclusive. Either the primary resection specimen or a metastatic lesion may be tested provided that there is enough tumor tissue to fulfill an assay’s sensitivity limit of detection. Some clinicians advocate testing the metastasis as this is theoretically the most clonally advanced lesion; however, in CRC, KRAS mutations are thought to occur as an early mutation in the adenoma-carcinoma continuum [203]. Most molecular laboratories are now performing KRAS testing using highly sensitive techniques such as allele-specific PCR, qPCR, microarray analysis, or pyrosequencing (Fig. 4.1b). Anywhere from 7 to 12 mutations are evaluated in codons 12 and 13, and occasionally, mutations in codon 61 and rarely codon 146 are included.

NPM1 Nucleophosmin 1 (NPM1) is a ubiquitously expressed 37 kDa protein involved in multiple functions including cell growth and proliferation, tumor suppression, DNA repair, transportation of ribosomal particles and ribosomal biogenesis, cellular stress response, chromatin alteration and transcription, and maintenance of genomic stability (reviewed in [204]). The NPM1 gene is located on chromosome 5q35. NPM1 functions as a shuttling protein or chaperone between the nucleus and cytoplasm for multiple proteins. Not surprisingly, it has multiple functional domains including

86

A.M. Cushman-Vokoun

nucleolar and nuclear localization signals and oligomerization regions to aid in molecular chaperoning. NPM1 binds a wide array of proteins including transcription factors, mitotic proteins, ribosomes, and histones. Importantly, it has been shown to regulate and stabilize the ARF-p53 tumor suppressor pathway [204, 205]. As a result, NPM1 plays an important basic role in cellular regulation and function. Due to its multiple functions within the cell, it is not surprising that inappropriate alterations in NPM1 are present in various malignancies. Increased expression of NPM1 has been identified in epithelial malignancies including gastric, colon, ovarian, prostate, and bladder carcinomas [204, 206–210]. The NPM1 gene is also involved in various translocations for specific hematologic malignancies. NPM1 serves as a balanced translocation partner with the ALK gene on chromosome 2p23 in anaplastic large cell lymphoma [90] and as a rare partner with the gene RARA (17q12) in acute promyelocytic leukemia [211]. In myelodysplastic syndrome (MDS), NPM1 may be involved with a translocation, partnered to MLF1 on chromosome 3q25 [212], or may be lost due to partial deletion of the long arm of chromosome 5. These NPM1 alterations are usually assessed for diagnostic and possibly prognostic purposes by a cytogenetics/FISH laboratory. How these alterations result in tumorigenesis is still under investigation. More recently, a 4-base-pair insertion in exon 12 (TCTG, CATG, or CCTG) has been described in the nucleolar localization signal of the NPM1 gene [213]. This results in a disruption of the localization signal through addition and alteration of amino acids and abnormal cytoplasmic localization of the NPM1 protein [205, 213]. More rarely, examples of concomitant insertion-deletions resulting in a net 4-basepair insertion and also leading to a similar amino acid modification have been identified. These insertions have been found in approximately 30–40% of newly diagnosed adult AML (especially FAB subtypes M1, M2, M4, and M5), are found in 50–60% of normal karyotype AML, and may indicate a better responsiveness to induction chemotherapy [205, 213]. Unlike FLT3-ITD-mutated AML patients, patients with mutated NPM1 have a higher complete remission rate and better prognosis, especially in the absence of a FLT3-ITD, but importantly, do not seem to benefit from allogeneic stem cell transplantation [205, 213, 214]. NPM1 mutations are more rarely identified in pediatric AML [205]. Currently, no specific targeted therapies are available for NPM1-mutated AMLs. NPM1 insertions are routinely analyzed in molecular diagnostics laboratories by PCR and subsequent fragment analysis, along with other molecular markers such as FLT3, in order to stratify patients with AML into high- and low-risk categories and evaluate for prognosis and therapeutic decision making. However, knowledge of the mechanism of NPM1’s role in AML will likely allow for the development of targeted therapies in the future.

Microsatellite Instability (MSI) Microsatellites are DNA tracts containing multiple repeats of short (1–6 bp) elements that are present throughout the genome. DNA polymerases often have

4


87

difficulty accurately replicating these elements during synthesis, and therefore, errors occur (either addition or deletion of the repetitive element). Such errors are repaired by mismatch repair (MMR) enzymes, which are involved in the maintenance of genomic integrity [215]. If MMR is compromised, either by genetic mutation or by gene transcription silencing mechanisms, these microsatellites are not properly repaired and microsatellite instability (MSI) results. Microsatellite instability is a marker for a “mutator” phenotype in which mutations accrue throughout the genome [215, 216]. This phenotype is found in approximately 15% of CRCs. The other 85% of CRCs are thought to follow the chromosomal instability (CIN) pathway, defined by mutations in various oncogenes and tumor suppressor genes (APC, DCC, KRAS), loss of heterozygosity, and overall CIN [203]. There are four clinically important MMR genes: MLH1 (3p22.3), MSH2 (2p21), MSH6 (2p16), and PMS2 (7p22.1) [217–222]. The proteins encoded by these genes interact as stabilized dimers (e.g., MLH1-PMS2 and MSH2-MSH6) [223]. People with inherited germ-line defects in these genes have an autosomal dominantly inherited hereditary cancer predisposition syndrome called hereditary nonpolyposis colorectal cancer (HNPCC), or Lynch, syndrome. Approximately 1–6% of colon cancers are associated with HNPCC [222]. Carriers of an HNPCC mutation are at risk for multiple types of cancer (colon, endometrial, ovarian, upper urothelial, brain, upper gastrointestinal, skin, and hepatobiliary) [222]. HNPCC-related CRCs, which frequently occur before the age of 50 years, often have different pathologic characteristics than more common CRCs including right-sided location, mucinous or poor differentiation (Fig. 4.6a), and/or tumor-infiltrating lymphocytes [224, 225]. Guidelines have been developed to aid in identification of these families, initially the Amsterdam criteria, later the revised Bethesda criteria [226]. Approximately 15% of sporadic colon cancers are microsatellite unstable, mainly due to promoter hypermethylation of the MLH1 gene and silencing of its gene expression [227]. Patients with sporadic MSI CRCs have an overall better prognosis [228, 229]. Data also suggests that MSI sporadic CRCs do not respond to adjuvant 5-fluorouracil (5-FU)-based therapy; thus, it has been proposed that testing for MSI may be beneficial, especially in patients with stage II disease or higher. Distinguishing a sporadic MSI-positive tumor from a CIN tumor appears to be important in determining prognosis, the need for adjuvant therapy, and therapeutic regimens such as those containing 5-FU. Clinical evaluation of CRCs for MSI has become quite common in clinical laboratories as an indicator of response to therapy, allowing for better patient selection for 5-FU-based regimens and enhanced therapeutic targeting. There are multiple ways to assess for MSI CRC in the laboratory. Many labs utilize fluorescent-based multiplex PCR and capillary electrophoresis (Fig. 4.6b). This method requires both normal and tumor DNA for comparison and can be done on formalin-fixed paraffin-embedded (FFPE) tissue. This test is a screen for MSI within the tumor but does not indicate which MMR protein is affected. In both settings (HNPCC and sporadic MLH1 methylation), the tumor will usually demonstrate MSI. IHC can also be performed to evaluate the four MMR proteins within the tumor cell nuclei; the absence of one or more of the proteins is consistent with MSI. Loss of MLH1 by IHC can indicate a gene defect related to HNPCC or

88

A.M. Cushman-Vokoun

Fig. 4.6 Analysis for microsatellite instability in CRC. (a) Characteristic morphologic findings in MSI-H CRC that would indicate a need for MSI analysis. Left panel, mucinous histology; right panel, solid, poorly differentiated, medullary histology. Other factors would include proximal location, age F mutation in patients with polycythemia vera or essential thrombocythemia. Blood. 2007;110(3):840–6. 161. Vizmanos JL, Ormazabal C, Larrayoz MJ, Cross NC, Calasanz MJ. JAK2 V617F mutation in classic chronic myeloproliferative diseases: a report on a series of 349 patients. Leukemia. 2006;20(3):534–5. 162. Steensma DP, McClure RF, Karp JE, et al. JAK2 V617F is a rare finding in de novo acute myeloid leukemia, but STAT3 activation is common and remains unexplained. Leukemia. 2006;20(6):971–8.

98

A.M. Cushman-Vokoun

163. Steensma DP, Dewald GW, Lasho TL, et al. The JAK2 V617F activating tyrosine kinase mutation is an infrequent event in both “atypical” myeloproliferative disorders and myelodysplastic syndromes. Blood. 2005;106(4):1207–9. 164. Lee JW, Soung YH, Kim SY, et al. JAK2 V617F mutation is uncommon in non-Hodgkin lymphomas. Leuk Lymphoma. 2006;47(2):313–4. 165. Scott LM, Tong W, Levine RL, et al. JAK2 exon 12 mutations in polycythemia vera and idiopathic erythrocytosis. N Engl J Med. 2007;356(5):459–68. 166. Pietra D, Li S, Brisci A, et al. Somatic mutations of JAK2 exon 12 in patients with JAK2 (V617F)-negative myeloproliferative disorders. Blood. 2008;111(3):1686–9. 167. Sayyah J, Sayeski PP. Jak2 inhibitors: rationale and role as therapeutic agents in hematologic malignancies. Curr Oncol Rep. 2009;11(2):117–24. 168. Verstovsek S. Janus-activated kinase 2 inhibitors: a new era of targeted therapies providing significant clinical benefit for Philadelphia chromosome-negative myeloproliferative neoplasms. J Clin Oncol. 2011;29(7):781–3. 169. Mercer KE, Pritchard CA. Raf proteins and cancer: B-Raf is identified as a mutational target. Biochim Biophys Acta. 2003;1653(1):25–40. 170. Schmid K, Oehl N, Wrba F, Pirker R, Pirker C, Filipits M. EGFR/KRAS/BRAF mutations in primary lung adenocarcinomas and corresponding locoregional lymph node metastases. Clin Cancer Res. 2009;15(14):4554–60. 171. Downward J. Targeting RAS signalling pathways in cancer therapy. Nat Rev Cancer. 2003;3(1):11–22. 172. Liu W, Kelly JW, Trivett M, et al. Distinct clinical and pathological features are associated with the BRAF(T1799A(V600E)) mutation in primary melanoma. J Invest Dermatol. 2007;127(4):900–5. 173. Miller AJ, Mihm Jr MC. Melanoma. N Engl J Med. 2006;355(1):51–65. 174. Flaherty KT, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med. 2010;363(9):809–19. 175. Nikiforov YE. Thyroid carcinoma: molecular pathways and therapeutic targets. Mod Pathol. 2008;21 Suppl 2:S37–43. 176. Fukushima T, Suzuki S, Mashiko M, et al. BRAF mutations in papillary carcinomas of the thyroid. Oncogene. 2003;22(41):6455–7. 177. Salvatore G, Giannini R, Faviana P, et al. Analysis of BRAF point mutation and RET/PTC rearrangement refines the fine-needle aspiration diagnosis of papillary thyroid carcinoma. J Clin Endocrinol Metab. 2004;89(10):5175–80. 178. Nikiforov YE, Steward DL, Robinson-Smith TM, et al. Molecular testing for mutations in improving the fine-needle aspiration diagnosis of thyroid nodules. J Clin Endocrinol Metab. 2009;94(6):2092–8. 179. Kebebew E, Weng J, Bauer J, et al. The prevalence and prognostic value of BRAF mutation in thyroid cancer. Ann Surg. 2007;246(3):466–70. discussion 470–461. 180. Xing M, Westra WH, Tufano RP, et al. BRAF mutation predicts a poorer clinical prognosis for papillary thyroid cancer. J Clin Endocrinol Metab. 2005;90(12):6373–9. 181. Lupi C, Giannini R, Ugolini C, et al. Association of BRAF V600E mutation with poor clinicopathological outcomes in 500 consecutive cases of papillary thyroid carcinoma. J Clin Endocrinol Metab. 2007;92(11):4085–90. 182. Namba H, Nakashima M, Hayashi T, et al. Clinical implication of hot spot BRAF mutation, V599E, in papillary thyroid cancers. J Clin Endocrinol Metab. 2003;88(9):4393–7. 183. Oliveira C, Velho S, Moutinho C, et al. KRAS and BRAF oncogenic mutations in MSS colorectal carcinoma progression. Oncogene. 2007;26(1):158–63. 184. Di Nicolantonio F, Martini M, Molinari F, et al. Wild-type BRAF is required for response to panitumumab or cetuximab in metastatic colorectal cancer. J Clin Oncol. 2008;26(35): 5705–12. 185. Loupakis F, Ruzzo A, Cremolini C, et al. KRAS codon 61, 146 and BRAF mutations predict resistance to cetuximab plus irinotecan in KRAS codon 12 and 13 wild-type metastatic colorectal cancer. Br J Cancer. 2009;101(4):715–21.

4


99

186. Samowitz WS, Sweeney C, Herrick J, et al. Poor survival associated with the BRAF V600E mutation in microsatellite-stable colon cancers. Cancer Res. 2005;65(14):6063–9. 187. Ogino S, Nosho K, Kirkner GJ, et al. CpG island methylator phenotype, microsatellite instability, BRAF mutation and clinical outcome in colon cancer. Gut. 2009;58(1):90–6. 188. Roth AD, Tejpar S, Delorenzi M, et al. Prognostic role of KRAS and BRAF in stage II and III resected colon cancer: results of the translational study on the PETACC-3, EORTC 40993, SAKK 60–00 trial. J Clin Oncol. 2010;28(3):466–74. 189. Halilovic E, Solit DB. Therapeutic strategies for inhibiting oncogenic BRAF signaling. Curr Opin Pharmacol. 2008;8(4):419–26. 190. Hatzivassiliou G, Song K, Yen I, et al. RAF inhibitors prime wild-type RAF to activate the MAPK pathway and enhance growth. Nature. 2010;464(7287):431–5. 191. Finkelstein SD, Sayegh R, Christensen S, Swalsky PA. Genotypic classification of colorectal adenocarcinoma. Biologic behavior correlates with K-ras-2 mutation type. Cancer. 1993;71(12):3827–38. 192. Andreyev HJ, Norman AR, Cunningham D, Oates JR, Clarke PA. Kirsten ras mutations in patients with colorectal cancer: the multicenter “RASCAL” study. J Natl Cancer Inst. 1998;90(9):675–84. 193. Bazan V, Migliavacca M, Zanna I, et al. Specific codon 13 K-ras mutations are predictive of clinical outcome in colorectal cancer patients, whereas codon 12 K-ras mutations are associated with mucinous histotype. Ann Oncol. 2002;13(9):1438–46. 194. Di Fiore F, Blanchard F, Charbonnier F, et al. Clinical relevance of KRAS mutation detection in metastatic colorectal cancer treated by Cetuximab plus chemotherapy. Br J Cancer. 2007;96(8):1166–9. 195. Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med. 2008;359(17):1757–65. 196. Amado RG, Wolf M, Peeters M, et al. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(10):1626–34. 197. Van Cutsem E, Kohne CH, Hitre E, et al. Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. N Engl J Med. 2009;360(14):1408–17. 198. Benhattar J, Losi L, Chaubert P, Givel JC, Costa J. Prognostic significance of K-ras mutations in colorectal carcinoma. Gastroenterology. 1993;104(4):1044–8. 199. Span M, Moerkerk PT, De Goeij AF, Arends JW. A detailed analysis of K-ras point mutations in relation to tumor progression and survival in colorectal cancer patients. Int J Cancer. 1996;69(3):241–5. 200. Cerottini JP, Caplin S, Saraga E, Givel JC, Benhattar J. The type of K-ras mutation determines prognosis in colorectal cancer. Am J Surg. 1998;175(3):198–202. 201. De Roock W, Jonker DJ, Di Nicolantonio F, et al. Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. JAMA. 2010;304(16):1812–20. 202. Allegra CJ, Jessup JM, Somerfield MR, et al. American Society of Clinical Oncology provisional clinical opinion: testing for KRAS gene mutations in patients with metastatic colorectal carcinoma to predict response to anti-epidermal growth factor receptor monoclonal antibody therapy. J Clin Oncol. 2009;27(12):2091–6. 203. Fearon ER, Vogelstein B. A genetic model for colorectal tumorigenesis. Cell. 1990;61(5): 759–67. 204. Grisendi S, Mecucci C, Falini B, Pandolfi PP. Nucleophosmin and cancer. Nat Rev Cancer. 2006;6(7):493–505. 205. Falini B, Nicoletti I, Martelli MF, Mecucci C. Acute myeloid leukemia carrying cytoplasmic/ mutated nucleophosmin (NPMc+ AML): biologic and clinical features. Blood. 2007;109(3): 874–85. 206. Tanaka M, Sasaki H, Kino I, Sugimura T, Terada M. Genes preferentially expressed in embryo stomach are predominantly expressed in gastric cancer. Cancer Res. 1992;52(12):3372–7. 207. Shields LB, Gercel-Taylor C, Yashar CM, et al. Induction of immune responses to ovarian tumor antigens by multiparity. J Soc Gynecol Investig. 1997;4(6):298–304.

100

A.M. Cushman-Vokoun

208. Subong EN, Shue MJ, Epstein JI, Briggman JV, Chan PK, Partin AW. Monoclonal antibody to prostate cancer nuclear matrix protein (PRO:4–216) recognizes nucleophosmin/B23. Prostate. 1999;39(4):298–304. 209. Tsui KH, Cheng AJ, Chang PL, Pan TL, Yung BY. Association of nucleophosmin/B23 mRNA expression with clinical outcome in patients with bladder carcinoma. Urology. 2004;64(4):839–44. 210. Nozawa Y, Van Belzen N, Van der Made AC, Dinjens WN, Bosman FT. Expression of nucleophosmin/B23 in normal and neoplastic colorectal mucosa. J Pathol. 1996;178(1): 48–52. 211. Redner RL, Rush EA, Faas S, Rudert WA, Corey SJ. The t(5;17) variant of acute promyelocytic leukemia expresses a nucleophosmin-retinoic acid receptor fusion. Blood. 1996;87(3): 882–6. 212. Yoneda-Kato N, Look AT, Kirstein MN, et al. The t(3;5)(q25.1;q34) of myelodysplastic syndrome and acute myeloid leukemia produces a novel fusion gene, NPM-MLF1. Oncogene. 1996;12(2):265–75. 213. Falini B, Mecucci C, Tiacci E, et al. Cytoplasmic nucleophosmin in acute myelogenous leukemia with a normal karyotype. N Engl J Med. 2005;352(3):254–66. 214. Dohner K, Schlenk RF, Habdank M, et al. Mutant nucleophosmin (NPM1) predicts favorable prognosis in younger adults with acute myeloid leukemia and normal cytogenetics: interaction with other gene mutations. Blood. 2005;106(12):3740–6. 215. Kolodner R. Biochemistry and genetics of eukaryotic mismatch repair. Genes Dev. 1996; 10(12):1433–42. 216. Parsons R, Li GM, Longley MJ, et al. Hypermutability and mismatch repair deficiency in RER+ tumor cells. Cell. 1993;75(6):1227–36. 217. Fishel R, Lescoe MK, Rao MR, et al. The human mutator gene homolog MSH2 and its association with hereditary nonpolyposis colon cancer. Cell. 1993;75(5):1027–38. 218. Bronner CE, Baker SM, Morrison PT, et al. Mutation in the DNA mismatch repair gene homologue hMLH1 is associated with hereditary non-polyposis colon cancer. Nature. 1994;368(6468):258–61. 219. Nicolaides NC, Papadopoulos N, Liu B, et al. Mutations of two PMS homologues in hereditary nonpolyposis colon cancer. Nature. 1994;371(6492):75–80. 220. Kolodner RD, Tytell JD, Schmeits JL, et al. Germ-line msh6 mutations in colorectal cancer families. Cancer Res. 1999;59(20):5068–74. 221. Modrich P. Mechanisms in eukaryotic mismatch repair. J Biol Chem. 2006;281(41):30305–9. 222. Lynch HT, de la Chapelle A. Hereditary colorectal cancer. N Engl J Med. 2003;348(10): 919–32. 223. de la Chapelle A, Hampel H. Clinical relevance of microsatellite instability in colorectal cancer. J Clin Oncol. 2010;28(20):3380–7. 224. Pinol V, Castells A, Andreu M, et al. Accuracy of revised Bethesda guidelines, microsatellite instability, and immunohistochemistry for the identification of patients with hereditary nonpolyposis colorectal cancer. JAMA. 2005;293(16):1986–94. 225. Greenson JK, Huang SC, Herron C, et al. Pathologic predictors of microsatellite instability in colorectal cancer. Am J Surg Pathol. 2009;33(1):126–33. 226. Umar A, Boland CR, Terdiman JP, et al. Revised Bethesda Guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J Natl Cancer Inst. 2004;96(4):261–8. 227. Kuismanen SA, Holmberg MT, Salovaara R, de la Chapelle A, Peltomaki P. Genetic and epigenetic modification of MLH1 accounts for a major share of microsatellite-unstable colorectal cancers. Am J Pathol. 2000;156(5):1773–9. 228. Sinicrope FA, Sargent DJ. Clinical implications of microsatellite instability in sporadic colon cancers. Curr Opin Oncol. 2009;21(4):369–73. 229. Guastadisegni C, Colafranceschi M, Ottini L, Dogliotti E. Microsatellite instability as a marker of prognosis and response to therapy: a meta-analysis of colorectal cancer survival data. Eur J Cancer. 2010;46(15):2788–98.

4


101

230. Hartmann C, Mueller W, von Deimling A. Pathology and molecular genetics of oligodendroglial tumors. J Mol Med. 2004;82(10):638–55. 231. Cairncross JG, Ueki K, Zlatescu MC, et al. Specific genetic predictors of chemotherapeutic response and survival in patients with anaplastic oligodendrogliomas. J Natl Cancer Inst. 1998;90(19):1473–9. 232. Hegi ME, Diserens AC, Gorlia T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997–1003. 233. Yan H, Parsons DW, Jin G, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73.

Chapter 5

Genome-Wide Association Studies in Disease Risk Calculation: The Role of Bioinformatics in Patient Care Todd L. Edwards, Digna R. Velez Edwards, and Marylyn DeRiggi Ritchie

The GWAS Era, Recent Discoveries and Advances, and Overview of the Haplotype Tag Strategy GWAS – What Is It? How Do Researchers Use GWAS to Make Discoveries? The study of epidemics of heritable diseases and knowledge about the genetic architecture of complex human traits have developed rapidly in the last two decades. These advances have been primarily due to improvements in genotyping technology and a commensurate increase in the amount and availability of data with which to describe and understand the nature of genetic variation in human populations. During this period, genetic studies of human traits have moved away from a focus on assaying a relatively small number of loci to identify regions of linkage to traits in family studies to samples of hundreds of thousands of study subjects assaying millions of single nucleotide polymorphisms (SNPs) for statistical association with traits. There is perhaps no better example of this than the genome-wide association study (GWAS).

T.L. Edwards, Ph.D. (*) Center for Human Genetics Research, Vanderbilt Medical Center, Vanderbilt University, Nashville, TN, USA e-mail: [email protected] D.R.V. Edwards Division of Epidemiology, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA M.D. Ritchie, Ph.D., M.S. Molecular Physiology & Biophysics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_5, © Springer Science+Business Media, LLC 2012

103

104

T.L. Edwards et al.

GWAS is an approach for studying variation throughout the genome for association with traits using recently developed technology for measuring germ-line genotypes. These study designs rely on genotyping platforms which are designed a priori by assay manufacturers and genotyping in cases and controls, families that contain multiple affected individuals, or random subjects from the population if a quantitative trait is the focus of the investigation. GWAS platforms are array-based assays of hundreds of thousands to millions of SNPs distributed throughout the autosomes, sex chromosomes, and mitochondrial genome. These platforms come primarily from two manufacturers, Affymetrix (http://www.affymetrix.com/index.affx) and Illumina (http://www.illumina.com/), and the composition of and rationale for the SNPs assayed differs between these platforms. The Illumina platform employs haplotype tagging to select SNPs based on local correlation with other nearby SNPs, such that redundant genetic variation containing very similar statistical information is not assayed. The Affymetrix platforms employ a different design, where the genome is saturated with somewhat evenly spaced SNPs that are selected based on their location between two restriction enzyme sites. Regardless of platform, the goal of GWAS is to capture the majority of common alleles through pairwise correlation with assayed SNPs and relate that variation to trait risk or average trait value. For estimates of genomic coverage for various platforms, see Barrett and Cardon [1]. and Li et al. [2]. While the cost of conducting a well-powered GWAS is substantial, on a per-genotype basis it is relatively economical. This approach stands in contrast with previous study designs for assessing association between phenotypes and genotypes that relied on technology, which produced many fewer genotypes per unit of economic resources expended. This limitation imposed the restriction that only a few genomic regions could be investigated at a time, and so biological hypotheses or linkage evidence are typically the motivation behind the selection of a few candidate gene regions of interest. The candidate gene approach to genetic association studies resulted in some issues with multiple testing discipline and failure to replicate association signals [3], although some notable successes were achieved.

Common Disease, Common Variant, and Haplotype Tagging GWAS were motivated by new thinking about approaches for mapping traits to genomic regions and several developments in large scientific projects, such as the completion of the Homo sapiens reference sequence by the Human Genome Project [4] and the cataloging of common genetic variants and pairwise correlations by the International HapMap Project [5, 6]. GWAS are based on the premise that densely genotyped common, or high frequency, alleles will have statistical power to detect causal associations with traits at nearby, ungenotyped common polymorphisms through short-range linkage disequilibrium (LD). LD is the nonrandom association (e.g., correlation) of alleles at nearby loci. The basis for this strategy is the common disease common variant (CDCV) hypothesis [7], in which it is proposed that

5

Genome-Wide Association Studies in Disease Risk Calculation…

105

high-prevalence traits are most likely determined by high-frequency genetic variants. This approach has been proven effective in many scenarios for mapping small genomic regions to traits (see the National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies) [1, 8]. Additionally, many of these newly associated regions would not have been considered good candidates for targeted genotyping studies based on biological knowledge or previous linkage evidence, illustrating the difficulty of improvising a hypothesis based on the molecular biology of a gene and its products. Commonly when associations are observed at a marker, rather than indicating a direct casual relationship, the association is observed because the marker is in LD with the locus that directly cause the disease. The human genome contains many regions of strong LD, which vary by ethnicity and local recombination rates, and these patterns should be considered when evaluating association results. For a large majority of studies, the most common approach is to select markers that are in strong LD with several other markers in a gene. These markers are generally termed tag SNPs. A tag SNP approach allows for a few SNPs to provide coverage for a larger genetic region through strong correlations between the genotyped markers and ungenotyped SNPs. Causal relationships can be complicated and may involve interactions with other genes or environmental variables. Because most of the associations observed are indirect, it is important to be cautious in interpretations of GWAS results. It is also common practice to require that the same association be observed in an independent set of study subjects.

GWAS Versus Candidate Gene Studies GWAS have advantages over the candidate gene-based approaches. The cost of GWAS genotyping platforms is decreasing, and the prices for performing a GWAS over a candidate gene study may be comparable, depending on the number of SNPs in the candidate gene study. The advantages in genomic coverage, however, may not necessarily outweigh the limitations of a GWAS today. Principally, due to stringent significance thresholds due to multiple testing corrections, GWAS have less power than a more confined study to detect genes with small effects that may localize disease genes more tightly. It is therefore very important to consider the sample size available to the study when deciding whether to proceed with a GWAS or a smaller genotyping platform. For example, if a study featured a small sample, and the candidate gene and GWAS genotyping were the same cost, then the candidate gene study would be more likely to succeed due to power after multiple testing corrections were applied. However, for a sufficiently large sample, the GWAS methodology might be more likely to succeed due to increased genomic coverage. These questions can be answered using the power software package described later in this chapter. Due to current analytical methods and computational limitations, data sample structures can be prohibitive, e.g., studies may be limited to unrelated cases and controls or family trios rather than extended pedigrees or more complicated family structures.

106

T.L. Edwards et al.

Type I error (the false positive rate) is also a considerable problem in GWAS studies, as analyzing hundreds of thousands of markers across the human genome can limit the interpretability of the results. This is compounded by the fact that the markers being tested are not independent due to LD, which makes a straightforward analytical correction approach (such as Bonferroni correction) conservative. Analytic methods to deal with the large multiple testing corrections associated with testing hundreds of thousands of markers across the genome have been under development [8]. Finally, the volume of genotypic data that is generated creates large computational demands, both in terms of data storage and analysis. This means that, although financially available, GWAS are limited to those with access to high-performance computing.

GWAS Genotyping Platforms Genotyping for GWAS are currently performed under two genotyping platforms, Affymetrix and Illumina.

Affymetrix Affymetrix is currently being used to analyze over 1.8 million SNPs on one chip of the two platforms. Affymetrix is the more cost-effective of the genotyping platforms using ~250 nanograms of DNA per genotype (http://affymetrix.com/index. affx). Affymetrix uses DNA microarrays for high-throughput SNP detection using photolithography to create site-specific primers that are attached to a silicon chip. Primers attach to specifically amplified DNA that is equivalent to a site-specific complement probe. For detection, a reporter (e.g., fluorescent molecule) attaches to either a photolithographic probe or an amplified DNA probe and is chemically released upon hybridization of the two oligo chains. The Affymetrix Genome-Wide Human SNP Array 6.0 allows for the selection of more than 906,600 SNPs from a combination of 482,000 unbiased “historical SNPs” selected by Affymetrix and a combination of 424,000 additional SNPs that consist of tags, mitochondrial SNPs, SNPs in recombination hotspots, SNPs on chromosomes X and Y, and new SNPs added to the dbSNP database. The remaining 946,000 markers consist of copy number variant (CNV) probes.

Illumina The Illumina genotyping platform is currently able to analyze 1.1 million SNPs on an Illumina Human1M-Duo BeadChip array (http://www.illumina.com). The Illumina system consists of a complex bead array that allows all SNPs to be detected at once. Much like Affymetrix, this system also relies on a fluorescence reporting

5


107

mechanism, but with a locus specification step at the beginning of the process that creates a specifically addressed oligonucleotide chain that is then amplified by a process that is similar to whole genome amplification.(http://www.illumina.com) SNPs for the Illumina platform are selected based on being tag SNPs in the populations from the International HapMap Project dataset [7]. Additional SNPs are selected based on SNP coverage reference sequence (RefSeq) genes (within 10 kilobases) (http://www.ncbi.nlm.nih.gov/RefSeq/), nonsynonymous SNPs, ADME SNPs, and SNPs found in the major histocompatibility complex (MHC) region. With regard to genomic coverage, Affymetrix and Illumina are comparable [1]. GWAS data analysis, following implementation of genotype-calling algorithms (genotype-calling algorithms are available in Bead Studio software for Illumina and Birdsuite software for Affymetrix) and laboratory quality control (QC), is comprised of four distinct parts: QC analysis, data summary statistics, association analysis, and detailed follow-up analysis. Extensive reviews of GWAS QC and analysis are available for more information [9–11].

GWAS Study Designs and Analytical Considerations The design of a GWAS can involve one of several sampling strategies and stages. The sampling approach taken is most likely determined by properties of the trait and the availability of existing studies with a biological specimen from the study subjects. For example, to study a trait with an onset that is typically early in life such as autism, it may not be feasible to recruit healthy unrelated control children. As a result, a family-based design may be most efficient, although there are examples of both family and case–control studies of autism. Conversely, for a trait with an onset late in life, other relatives in the family may not be available, and so a case–control study may be most appropriate. For a quantitative trait, most often, the best strategy is to draw random unrelated subjects from the population of interest; however, this may be difficult to accomplish if the population is inbred or if the trait is measured early in life. If a case–control strategy is taken, then the accurate classification of cases is crucial, but equally important is the selection of controls. An ideal sample of controls will resemble the cases with regard to potential for exposure to risk factors, and who if they had manifested the trait, would be selected as cases for the study. Violation of the equal potential for exposure to risk factors among controls principle occurs when there is population stratification (PS) or admixture in the study sample. PS is a major concern in genetic association studies. PS refers to the systematic difference in allele frequencies between cases and controls due to their ancestral origin rather than the association between genes and diseases [12]. Failure to adjust for PS effectively can lead to excess false positive results and failures to detect true associations [13]. The issue of PS for association is more serious in GWAS since its effect increases with sample size [13]. PS most often arises due to geographic isolation with low rates of migration for a human subpopulation for several generations, which results in nonrandom mating across the larger population

108

T.L. Edwards et al.

of humans. The effect of this isolation is divergent random genetic drift of allele frequencies among subpopulations, as well as the potential for novel mutations or differential selection of alleles among subpopulations in differing environments, such as has been observed in the lactase gene (LCT), which controls the ability of adult humans to digest milk [14]. As a result, various subpopulations will have distinct allele frequencies at sites throughout the genome. This phenomenon can also be observed among populations where migration rates were once low, but have recently increased, such as in studies of the European population [15]. A population can also be genetically isolated by cultural phenomena, such as African populations speaking Khoesan languages featuring click consonants [16], which are genetically distinguishable from persons arising from non-Khoesan subpopulations in the same geographical region, presumably due to nonrandom assortative mating within language groups. This difference in genetic backgrounds and environments can lead to differential prevalence rates of traits across populations, and the combination of these events, allele, and prevalence differences across genetically distinct subpopulations can lead to confounding of associations in genetic association studies, where these issues are not accounted for statistically or by study design. This confounding arises due to alleles associating with subpopulation membership, defined here as a genetically homogeneous group undergoing Hardy-Weinberg mating, rather than the trait of interest. Another issue related to PS that arises from genetically isolated subpopulations is that of admixture. Admixture occurs when members of two distinct subpopulations produce offspring. The genome of these offspring could be considered an example of PS, where one chromosome comes from the maternal subpopulation, and the other chromosome comes from the paternal subpopulation. In subsequent generations, crossing over will mix these chromosomes so that some offspring chromosomes carry portions of both original subpopulations. An example of this is African Americans, a subpopulation in which approximately 81.5% of the genome is derived from African ancestors, and 18.5% from European ancestors, with evidence for significantly more African-derived DNA in the X chromosomes due to sex-based gene flow consisting of European males and African females [17]. Appropriate methods for statistically adjusting for PS depend on the design of the association study. If the study is a family-based study, and the transmission disequilibrium test or one of the many extensions of this test are used to assess association, then the study design inherently protects against validity issues for p-values [18]. This occurs because this family of association tests measures the deviation from Mendelian transmission of alleles from parents to affected offspring and does not rely on the estimation of population parameters such as allele frequency to conduct the test. If the study is a case–control study of unrelated subjects, then other methods such as the Bayesian Markov-chain Monte Carlo method STRUCTURE [19], principal components analysis implemented in the software package EIGENSTRAT [20], or multidimensional scaling implemented in PLINK [21, 22] can be used to estimate global ancestry, or summary variables that generally capture ancestral differences among study subjects, and can be further used to adjust regression models for confounding by ancestry.

5


109

Another important consideration for an effective GWAS study is statistical power. Substantial research on the appropriate pointwise threshold for statistical significance (a) has converged on approximately a of p-value £5 × 10−8 as a level that controls the probability of a type I error across all tests at around 5% [23–26]. Using this threshold for significance increases the sample size required for a high probability of study success by 4–5-fold for realistic effect sizes over an a of p-value £0.05. Some tools that are useful for calculating power are Quanto (http://hydra.usc. edu/gxe/) for gene-gene and gene-environment power, PS, (http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize) [27], and CaTS (http://www.sph.umich. edu/csg/abecasis/CaTS/index.html) [28] for two-stage association studies.

Notable Disease Gene Discoveries from GWAS Studies, Large Consortium GWAS Studies, and the Future of GWAS and Genetic Studies Notable Disease Gene Discoveries A comprehensive review of all of the notable discoveries from GWAS is out of the scope of this chapter. However, we will make note of a few of the interesting findings. The NHGRI GWAS catalog is an excellent database resource to explore a more comprehensive survey of the literature (http://www.genome.gov/gwastudies/).

Body Mass Index Globally, the prevalence of obese (body mass index [BMI] > 30 kg/m [2]) and overweight (BMI > 25) adults has risen to approximately 1.6 billion, with a corresponding increase in comorbid disease burdens [29]. The public health impact of obesity is substantial since the condition is associated with increased risks for type 2 diabetes mellitus (T2DM), cardiovascular disease, dyslipidemia, hypertension, sleep apnea, and potentially several forms of cancer [30, 31]. As a result, obesity has become a major global economic and public health burden. In the United States, obesity has been found to disproportionately affect ethnic minorities, including African-Americans [32]. The causes of this are poorly understood but may be related to a combination of behavioral, environmental, and genetic risk factors [32]. Although understanding the way in which genetic and environmental/behavioral risk factors interact may offer insights into modifiable factors, few studies have been performed to evaluate those phenomena [33]. Several lines of evidence indicate that a large proportion of obesity risk is mediated by heritable factors, with studies estimating that 40–90% of human BMI variation is the result of genetic factors [34–36]. Multiple GWAS have identified reproducible associations, and the majority of the genes associated are known to function in the central nervous system (CNS) or in energy homeostasis [37–44].

110

T.L. Edwards et al.

Notable among GWAS with BMI are variants identified in fat mass and obesity association gene (FTO) [38] and melanocortin 4 receptor (MC4R) [39] which have consistently replicated across studies of BMI among Caucasian populations. New consortia have been established to assess whether variants in these genes replicate across among Africa-American populations.

Parkinson’s Disease Parkinson’s disease [PD (OMIM 168600)] is a chronic neurodegenerative disorder with a cumulative prevalence of greater than one per thousand [45] with at least 1.5 million cases in the United States and 6 million worldwide [46]. Some genetic contributions to PD are well recognized. Mutations conferring high risk for PD were initially identified due to strong effects in relatively rare, early-onset, and Mendelian forms of PD [47–51]. These mutations explain fewer than 10% of PD cases [52]. Over the last several years, significant effort has been focused on investigating the contributions of common variants to idiopathic PD risk and age-at-onset. Previous research employed candidate gene approaches to identify genetic associations in regions identified by genome-wide linkage studies (reviewed by Lesage et al. [52]). This focused approach limits the number of association tests performed, but is limited to identifying loci detectable by linkage analysis [53]. Early GWAS conducted in PD did not yield results that reached genome-wide significance [54–56]. Associations with PD have been replicated in the candidate gene and GWAS contexts, including those described early in PD association studies, such as alpha-synuclein (SNCA) [57–63] and the microtubule-associated protein tau (MAPT) inversion region on chromosome 17 [64–77], as well as ubiquitin-specific protease 24 (USP24) [78–80], ELAV-like 4 (ELAVL4) [81–83], monoamine oxidase B (MAOB) [84], apolipoprotein E (APOE) [85], and the mitochondrial haplogroups [86–93]. The consistency of results, particularly for SNCA and MAPT, suggests that the failure to reach genome-wide significance in previous studies is due to the relatively small GWAS datasets. More recently, GWAS-based investigations into the genetic determinants of PD have been more fruitful, definitively identifying several associated regions in the genes MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2, ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R in both Caucasian and Asian patients [94–99]. These risk alleles in aggregate do not provide much predictive ability for who will develop PD, but the utility of these discoveries is more evident with regard to fundamental knowledge about the biology of PD and perhaps eventually as targets for pharmacological therapies.

Age-Related Macular Degeneration Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss among the elderly in the United States [100, 101]. Projections show that the rate

5


111

of vision loss among those with AMD will double by 2020 due to the fast growing population of those over 65 years of age in the United States [102]. AMD is a multifactorial disease consisting of both environmental and genetic risk factors affecting the retina, particularly the macula, and that often causes varying degrees of central visual loss [103–106]. If AMD is not treated, severe and irreversible vision loss can occur, highlighting the importance of identifying both risk factors and those at risk for AMD. Known risk factors for AMD include age, cigarette smoking, and variants in multiple complement genes (complement factor H (CFH), complement factor B (CFB)) and genes on chromosome 10q26 (age-related macular degeneration 2 [ARMS2]) [107–112]. Variants in these genes and gene regions are thought to explain one half of the heritability of AMD; however, with half of the heritability still unexplained, several GWAS and consortia are under way to further identify novel variants that associate with risk [113–118].

Large Consortia Conducting Genetic Studies from GWAS Data One of the many lessons learned in the era of GWAS is the critical importance of sample size and its relation to statistical power. As seen in the NHGRI GWAS catalog, [119] many of the notable GWAS discoveries were made through large-scale collaborations which evolved into consortia. While any single study may be well powered on its own, one of the critical components of GWAS is replication of the findings in one or more independent datasets. Subsequently, it was seen that through meta-analysis of two or more datasets, not only was replication possible, but also even greater statistical power. Thus, GWAS consortia emerged. Most GWAS consortia have formed around a particular theme. For example, the GIANT (Genome-wide Investigation of ANThropometric measures) consortium is an international collaboration developed to pool GWAS results on anthropometric measures such as body mass index and height [37, 120, 121]. CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) is another international consortium established initially as a collaboration between five prospective cohort studies: the Age, Gene/Environment Susceptibility—Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study [122]. Their preliminary analyses included up to 38,000 individuals who had multiple health-related phenotypes measured in similar ways. Each trait that was harmonized across studies was selected for GWAS analysis within each independent study and then meta-analysis was performed to combine studies. While the initial CHARGE consortium included ~38,000 individuals, more cohorts have contributed to various CHARGE publications including ~80,000 individuals for C-reactive protein levels [123], over 28,000 individuals for the electrocardiographic PR interval [124], and ~9,000 individuals for MRIdefined brain infarcts [125] to name a few. More details and a comprehensive list of publications for the CHARGE consortium can be found at http://web.chargeconsortium.com/.

112

T.L. Edwards et al.

The eMERGE Network (electronic MEdical Records and GEnomics) is an NHGRI-funded consortium to explore the utility of DNA biobanks linked to electronic medical records (EMR) for performing large-scale genomic analyses [126]. eMERGE is comprised of five institutions and has collectively performed GWAS genotyping on approximately 18,000 individuals, all of whom have extensive EMR data available for phenotype mining. Thus far, eMERGE has performed GWAS in peripheral arterial disease [127], red blood cell traits [128], HDL cholesterol [129], and electrocardiographic PR interval [130] among 14 total traits. More details about the electronic phenotype algorithms and the other traits studied by eMERGE can be found at http://www.gwas.org. In addition to thematic consortia, many large-scale collaborations have developed around one particular phenotype such as the International Multiple Sclerosis Genomics Consortium (IMSGC) [131], Psychiatric GWAS Consortium which studies five psychiatric traits [132], Colorectal Cancer GWAS Consortium (GECCO), and International Stroke Genetics Consortium (ISGC) [133]. In addition to large scale meta-analysis conducted by GWAS consortia, other groups are interested in characterizing GWAS-identified genetic variants in ancestral populations other than those of European descent. Most GWAS to date have been conducted in Europeans or European-Americans. Part of the challenge in a global understanding of the importance of GWAS-identified variants for advancing drug discovery and improving public health is a better understanding of whether these variants are important in multiple populations, or only in the one in which they were identified. The PAGE network (Population Architecture using Genomics and Epidemiology) is an NHGRI-funded initiative designed to characterize GWASidentified variants in cohorts including individuals of ancestral groups other than European-decent to determine if the variants identified are globally associated with various complex traits [134]. Investigators in PAGE are exploring traits that have undergone extensive evaluation in GWAS including lipids, obesity, type II diabetes, stroke, and various cancers. More information about the PAGE network can be found at http://www.pagestudy.org.

Bioinformatics in the GWAS Era for Consumers and Practitioners The process of examining a genetic trait most often proceeds through a series of stages that include but are not limited to defining the phenotype, assessing the heritability of the disease trait, gene and SNPs discovery and mapping, and evaluating the role of identified genes in biological processes and/or mechanisms that lead to disease. To proceed through these steps involves a team of researchers with expertise in clinical, genetic, epidemiologic, and bioinformatics interwoven in each step. Bioinformatics tools and resources are arguably the most critical component to a thorough examination of genetic/genomic data, particularly in the age of GWAS where a researcher is searching through as many as a million SNPs in one experiment.

5


Table 5.1 Bioinformatics GWAS database summary Utility Database Literature searches Genome.gov OMIM Disease trait information/ WebMD comorbidities Genome reference panels HapMap and SNP/gene 1000 Genomes information UCSC Genome Browser Pathway information KEGG Gene Ontology PANTHER WebGestalt

113

Web site URL http://genome.gov/gwastudies/ http://www.ncbi.nlm.nih.gov/omim http://www.webmd.com http://www.hapmap.org http://www.1000genomes.org http://genome.ucsc.edu http://www.genome.jp/kegg/ http://www.geneontology.org http://www.pantherdb.org/pathway/ http://bioinfo.vanderbilt.edu/ webgestalt/

Establishing the right expertise in the latest bioinformatics tools has become essential for every scientist in order to gain the competence in handling and synthesizing the information obtained from a GWAS analysis. In the following section, we will discuss publically available bioinformatics tools and resources available to assist in conducting and interpreting a GWAS analysis and findings. Broadly, we will discuss how bioinformatics can be used to gather information for (Table 5.1) (1) literature searches, (2) disease trait information and comorbidities, (3) genome reference panels and SNP/gene information, and (4) pathway information.

Literature Searches Understanding the current state of the literature, regarding either a disease trait prior to conducting a study or a gene or pathway identified in GWAS, is important for a researcher in order to (1) establish a focused approach for analyzing a GWAS dataset, (2) indentify key candidate confounders to be used in statistical analysis, (3) compare study findings to the findings of other GWAS, and (4) identify functional and animal model studies that support the results of your study. There are currently several databases and search engines that, in the field of human genetics, have become the primary sources for literature regarding disease, gene, and pathway information. We will discuss a few commonly used literature search engines and databases in the following section.

Genome.gov The National Human Genome Research Institute’s Office of Population Genomics (NHGRI) established a catalog of GWAS studies in order to provide a single source for cataloging published GWAS studies (http://www.genome.gov/gwastudies/).

114

T.L. Edwards et al.

The goals of the NHGRI and this catalog are to provide a focused set of resources to identify genes related to complex diseases and their environmental modifiers, improve analysis strategies for relating one’s genotypic and phenotypic data, build collaborations among NIH researchers, and to support a cross disciplinary approach to training geneticists, epidemiologists, clinical researchers, and clinicians. The publications listed in this database include all studies with over 100,000 SNPs genotyped in the initial phase of genotyping. Weekly review of the literature is performed through literature searches in order to identify new papers to include in the catalog. In addition to literature searchers, daily NIH-distributed compilations of news and media resources and comparisons with existing GWAS databases are also performed. Excluded from the catalog are those studies focused exclusively on candidate genes. Literature can be queried using journal, first author, disease/trait, chromosome region, gene, SNP, or greater than a particular threshold and p-value threshold. Data references are provided including a summary report of the existing publications. These summaries include initial sample size, replication sample size, region associated, reported genes, risk allele frequencies, effect sizes, and platform used. Literature pulled into Genome.gov is commonly extracted from PubMed.gov (http://www. ncbi.nlm.nih.gov/pubmed/) where there are currently over 20 million citations for biomedical literature from MEDLINE, life science journals, and online tools. Although PubMed.gov is a very useful resource, however, its focus is not organizing GWAS information or gene-centric literature. Additional resources also available through the NHGRI and the Genome.gov web site include a list of existing research programs and research networks, as well a list of existing meetings and workshops.

OMIM In order to facilitate quick evaluation of the putative relationships between a candidate gene and/or target pathway and a disease trait, web-based interfaces have been developed to capture gene/focused literature. Literature is the most powerful tool that may be used to assess the current understanding of a particular disease trait, gene, and/or biological mechanism. A database that has been developed to expedite this process is the Online Mendelian Inheritance in Man (OMIM) database (http:// www.ncbi.nlm.nih.gov/omim). OMIM provides a centralized database that provides a quick reference overview of genes and pathways. This database was initially developed in the 1960s by Dr. Victor A. McKusick as a catalog of Mendelian disease traits and was titled Mendelian Inheritance in Man (MIM). It subsequently expanded in 1985 upon collaboration with the National Library of Medicine and the Williams H. Welch Medical Library at Hopkins University to its current manifestation at OMIM. It was made widely available to the public in 1987 on the internet and for the World Wide Web with the assistance of the National Center for Biotechnology Information. OMIM is most useful as a tool post GWAS to assess the state of the literature for a candidate gene or pathway identified through GWAS analyses as well as to identify any other putative relationships between the genes/ pathways and other disease traits.

5


115

Disease Trait Information/Comorbidities Although literature search engines provide much needed information regarding the state of the literature and what is known through published research and studies, it may be more prudent to utilize clinically driven databases. These databases provide detailed information regarding disease etiology, symptoms, and comorbidities that may help a researcher in identifying how a gene associated with disease risk functions mechanistically. For example, if a novel gene was associated with disease risk, you may be able to utilize a clinical database to identify other comorbid conditions that provide a researcher with a clearer picture of the relationship between a gene and other comorbid conditions. One such resource is WebMD (http://www.webmd. com/). WebMD provides detailed information regarding health news, journalism, disease etiology, disease symptoms, as well as links to the medical community and resources related to a particularly medical complication or disease. Information regarding human physiology is also available on WebMD. The site contains an independent medical review board that continuously updates and reviews the content of the web site. In addition to this, WebMD also provides detailed drug interaction information, which may be useful to a researcher conducting a study with pharmacogenetic end points.

Genome Reference Panels and SNP/Gene Information Human genome reference panels are required in order to conduct a GWAS study. They are necessary both for assessing patterns of population stratification as well as providing data for imputing nongenotyped SNPs in a GWAS.

HapMap Project The HapMap Project was initiated as part of the International HapMap Consortium and made publically available in 2005 [135]. The HapMap Project contains approximately 10 million SNPs from across the human genome, initially genotyped in a reference panel of three populations, YRI, CEU, and CHB + JPT, and subsequently expanded to include populations from African ancestry in the Southwest USA (ASW); Chinese in Metropolitan Denver, Colorado (CHD); Gujarati Indians in Houston, Texas (GIH); Luhya in Webuye, Kenya (LWK); Mexican ancestry individuals from Los Angeles, California (MEX); Maasai in Kinyawa, Kenya (MKK); and Italians from Tuscany, Italy (TSI) [136]. A detailed list of the populations included in the HapMap is provided in Table 5.2. The HapMap Project has genotype data, allele and genotype frequencies, LD data, phase information, SNP assay details, and protocol and sample documentation all available for downloading from the primary web site (http://www.hapmap.org). All HapMap data can be downloaded from the web site with refSNP identifiers.

1000 Genomes (N = 2,500)

Iberian populations from Spain (IBS)

Utah CEPH with Northern and Western European ancestry (CEU) British from England and Scotland (GBR) Finish form Finland (FIN)

Chinese Dai in Xishuangbanna (CDX) Kinh in Ho Chi Minh City, Vietnam (KHV) Chinese in Denver, Colorado (CHD) (pilot 3 only)

Japanese in Toyko, Japan (JPT) Han Chinese South (CHS)

Han Chinese in Beijing, China (CHB)

Table 5.2 Comparison of HapMap and 1000 Genomes human reference panels Reference ancestral population Data source European East Asian Han Chinese in Beijing, HapMap Utah residents with Northern China (CHB) (N = 270) and Western European ancestry from the CEPH collection (CEU) Tuscan in Italy (TSI) Chinese in Metropolitan Denver, Colorado (CHD) Japanese in Tokyo, Japan (JPT)

Luhya in Webuye, Kenya (LWK) Gambian in Western Division, The Gambia (GWD) Malawian in Blantyre, Malawi (MAB) West African Population (TBD)

Maasai in Kinyawa, Kenya (MKK) Yoruban in Ibadan, Nigeria.(YRI) Yoruba in Ibadan, Nigeria (YRI)

Luhya in Webuye, Kenya (LWK)

African African ancestry in Southwest USA (ASW)

Peruvian in Lima, Peru (PEL)

Mexican Ancestry in Los Angeles, CA (MXL) Puerto Rican in Puerto Rico (PUR) Colombian in Medellin, Colombia (CLM)

African Ancestry in Southwest USA (ASW) African American in Jackson, MS (AJM) African Caribbean in Barbados (ACB)

Americas Mexican ancestry in Los Angeles, California (MEX)

Kayastha in Calcutta, India Reddy in Hyderabad, India Maratha in Bombay, India Punjabi in Lahore, Pakistan

Ahom in the State of Assam, India

South Asian Gujarati Indians in Houston, Texas (GIH)

116 T.L. Edwards et al.

5


117

HapMap data can be used for bulk download of genetic data from the available referent populations. This is particularly helpful for serving as referent datasets to impute SNPs in a GWAS to examine ungenotyped SNPs or SNPs that may have been removed during the QC process for a GWAS. HapMap is also a helpful tool when examining genetic ancestry in a GWAS dataset. The HapMap populations can be compared to genotyped study participants to assess admixture and how genetically similar a study population is to their referent ancestral population. This can assist in identifying outliers to remove from a dataset if they do not cluster with the expected racial group. The HapMap has the Generic Genome Browser that allows searching the genome by chromosomal position, gene, or SNP in order to select a region of the chromosome either for download or for assessing LD and or SNP information [137]. The downloaded genotype data will be output in the same format as bulk download data and can be viewed directly using HaploView software [138]. Finally, the HapMap can be used to inform follow-up and fine-mapping experiments post-GWAS particularly in the selection of tag SNPs and HapMap utility for SNP selection for association studies. In fine-mapping studies, the HapMap can be used to determine the size and composition of the relevant chromosomal region where and initial GWAS association was observed to aide in identifying the “causal” variant. The LD observed through examination of r [2] between the associated marker and other nearby SNPs will allow one to narrow down the region to follow-up for resequencing experiments.

1000 Genomes 1000 Genomes is an online database that was developed to provide comprehensive data regarding human genetic variation obtained from sequencing the genomes of large pools of people (http://www.1000genomes.org). The goal of 1000 Genomes is to identify rare genetic variants that have frequencies of at least 1% in the populations examined. This is in contrast to the goals of the HapMap which were to identify common variants from across the human genome. 1000 Genomes currently plans to sequence with 4× coverage allowing for detection of variants as low as 1%. Currently, 2,500 samples are being used to generate data for 1000 Genomes allowing for accurate imputation estimates. The populations currently in 1000 Genomes include 500 samples from five populations with European ancestry (Utah residents (CEPH) with Northern and Western European ancestry (CEU), British (GBR), Finish (FIN), and Iberian (IBS)); 500 samples from six populations of Asian ancestry (Han Chinese (CHB, CHS, CHD), Japanese (JPT), Chinese Dai in Xishuangbanna (CDX), Vietnamese (KHV)); 500 samples from seven populations with Western African ancestry (Yoruban (YRI), Kenyan (LWK), Gambian (GWD), Malawian (MAB), West African (TBD)); 500 from the Americas (African ancestry USA (ASW, AJM), African Caribbean ancestry (ACB), Mexican ancestry (MXL), Puerto Rican (PUR), Colombian (CLM), Peruvian (PEL)); and 500 samples from 500 Southeastern Asians (available in 2011) that will include Indian and Pakistani

118

T.L. Edwards et al.

samples. Sequencing in these populations is still ongoing and expected to be complete by late 2011. A detailed comparison of reference populations available in the 1000 Genomes and the HapMap are provided on Table 5.2.

UCSC Genome Browser The University of California at Santa Cruz (UCSC) Genome Browser provides annotations of NCBI assemblies and provides information regarding confirmed genes from Ensembl (http://genome.ucsc.edu/). The database is maintained by the Genome Bioinformatics Group, a cross-departmental team within the Center for Bimolecular Science and Engineering (CBSE) at UCSC. The database contains the reference sequence for multiple genomes including from multiple animal models. Among the browser tools available, the Genome Browser allows for one to zoom and scroll across chromosomes with detailed annotation, the Gene Sorter tool shows gene expression, homology, and other information related to groups of genes, and the Blat tool will allow one to map a sequence directly to the genome [139]. UCSC currently contains information regarding regions of significant homology across multiple vertebrate species and allows for one to regions of overall conservation across multiple genomes. In addition to this, the UCSC Genome Browser has detailed graphical representations of genomic sequence assembly as well as gene expression information that has been curated from the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and ArrayExpress at the EBI (http:// ebi.ac.uk/arrayexpress/). Bulk downloads of sequence data and annotation are also available through the UCSC Genome Browser. Figure 5.1 provides a graphical representation of a search for interleukin 6 (IL-6) performed with the UCSC Genome Browser.

Pathway Information As more and more GWAS data are generated and we begin to move beyond single SNP approaches to understanding disease risk, GWAS are moving toward pathwaybased approach to examining disease risk. There are currently several approaches and software available to perform pathway-based tests of association utilizing GWAS data. Pathway tests of association are most commonly followed after primary single marker analyses; they take advantage of the large quantity of genetic data available from a GWAS including data from several markers that directly map to genes. The difficulty in doing pathway analysis is mapping genes back to a particular pathway. There are currently publically available gene databases that are used to map genes back to a particular pathway of interest. They vary based on the way in which the data are cultivated as well as how the pathway data are output. The following are a few of the most commonly used pathway databases that allow for mapping genes to pathways with a large quantity of genes.


Fig. 5.1 Interleukin 6 (IL6) genomic region search results using the UCSC Genome Browser

5 119

120

T.L. Edwards et al.

KEGG The Kyoto Encyclopedia of Genes and Genomes (KEGG) allows for pathway maps to be created for metabolism and other cellular processes (http://www.genome.jp/ kegg). The KEGG database was initially developed in 1995 with the goal of developing knowledge-based methods for understanding higher-order biological system behavior [140]. The KEGG database is made up of 16 secondary databases broadly focused on systems information such as gene pathways and disease etiology, genomic information including gene annotation, and chemical information such as enzyme nomenclature and chemical structure information. KEGG integrates information regarding biological relationships and systems to create networks of genes. In addition to cellular processes and metabolism pathways, KEGG also allows for networks to be created for drug pathways and human diseases. Several tools are available on the KEGG web site to query data individually or in batches. Additional Java applications are available to download for microarray data analysis, browsing KEGG, and drawing compound structures. Figure 5.2 shows an example of a pathway generated when searching for the IL6 network.

Gene Ontology The Gene Ontology (GO) project was developed as a major bioinformatics initiative to provide an easily accessible online catalog of gene function, organized so that genes can be searched and categorized by their biological processes, molecular functions, and cellular component products organized using a standardized representation of gene and gene products across species and databases (http://www. geneontology.org/) [141]. The GO database is carefully annotated and maintained by trained biologists who curate the literature to update what is known regarding the annotated biological pathways. The web site has incorporated software that allows for quick extraction of GO information by gene [142]. Searches can be conducted by using several key terms including gene, proteins, or GO terms. Similar annotation used by GO has been adopted by other databases for numerous model organisms, including FlyBase (http://flybase.org/) [143], the Mouse Genome Database (http://www.informatics.jax.org/) [144], and the Saccharomyces Genome Database (http://genome-www.stanford.edu/Saccharomyces/) [145]. GO allows for subsets of genes involved in a larger pathway to be searched for as well.

Ethical Issues Around the Use of GWAS Data With the advances of GWAS technology for research, a number of issues in the ethical, legal, social, and policy arenas arise. For example, how do the informed consent models change in this era of genome-wide genotyping (and soon whole

Fig. 5.2 KEGG cytokine-cytokine receptor pathway network when searching for interleukin 6 (IL6)

5 Genome-Wide Association Studies in Disease Risk Calculation… 121

122

T.L. Edwards et al.

genome sequencing) and vast data sharing across the world, in databases such as dbGaP? Historically, research results were not reported back to individual participants – only study summary results were released. Now that we are learning more about how to interpret the genome, incidental findings occur with greater frequency. Should research results be returned at the individual level, and if so, when and how should this be done? The Consent and Community Consultation Working Group of the eMERGE Consortium has addressed many of these issues with the community in the surrounding areas for each of the eMERGE biobank institutions [146]. This group is addressing issues such as how to engage the community in research, privacy and identifiability, data sharing, informed consent, and return of research results. While there are significant ethical, legal, and social issues to be aware of in the research arena, GWAS have entered the public sector and allowed individuals to perform their own genome-wide genotyping. Direct-to-consumer genetic testing companies have emerged including 23andMe, deCODEme, Navigenics, and Knome who offer tests using the GWAS technology direct to consumers over the Internet [147]. These companies perform the GWAS genotyping and provide a report to the consumer describing their risk profile based on findings from the literature. This approach has brought about a number of additional regulatory and legal issues [147]. One of the primary differences in this arena is that it shifts control of the genetic testing from the clinical under medical practitioners to health-care consumers. This will undoubtedly force a reevaluation of the way genetic testing is carried out and may in the end lead to better services for consumers. However, currently, the direct-to-consumer test results are not returned with medical advice or any involvement of medical professional, and this may be problematic due to the need for interpretation of such results by medical professionals before action is taken [147].

Conclusions As GWAS methodologies are becoming easily available and commercially feasible, successful outcome of a study will depend on many of the factors described in this chapter starting with phenotype definition, appropriate study design, power, and analytical methods. Replication of data in multiple cohorts using similar strategies is one of the major and essential aspects of any genetic association study. Many of these issues will extend into the next phase of genomic technology, namely, whole genome sequencing. As these next-generation technologies emerge, new analytical and interpretation challenges will emerge. However, the lessons learned from GWAS will serve as an intellectual springboard for the next phase of understanding human genomic variation.

5


123

References 1. Barrett JC, Cardon LR. Evaluating coverage of genome-wide association studies. Nat Genet. 2006;38(6):659–62. 2. Li C, Li M, Long JR, Cai Q, Zheng W. Evaluating cost efficiency of SNP chips in genomewide association studies. Genet Epidemiol. 2008;32(5):387–95. 3. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med. 2002;4(2):45–61. 4. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921. 5. A haplotype map of the human genome. Nature. 2005;437(7063):1299–1320. 6. Eichler EE, Nickerson DA, Altshuler D, et al. Completing the map of human genetic variation. Nature. 2007;447(7141):161–5. 7. The International HapMap Project. Nature. 2003;426(6968):789–96. 8. Cardon LR, Bell JI. Association study designs for complex diseases. Nat Rev Genet. 2001;2(2):91–9. 9. Holmans P. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. Adv Genet. 2010;72:141–79. 10. Laurie CC, Doheny KF, Mirel DB, et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet Epidemiol. 2010;34(6):591–602. 11. Turner S, Armstrong LL, Bradford Y, et al. Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet. 2011;Chapter 1:Unit1. 12. Freedman ML, Reich D, Penney KL, et al. Assessing the impact of population stratification on genetic association studies. Nat Genet. 2004;36(4):388–93. 13. Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet. 2004;36(5):512–7. 14. Tishkoff SA, Reed FA, Ranciaro A, et al. Convergent adaptation of human lactase persistence in Africa and Europe. Nat Genet. 2007;39(1):31–40. 15. Novembre J, Johnson T, Bryc K, et al. Genes mirror geography within Europe. Nature. 2008;456(7218):98–101. 16. Tishkoff SA, Reed FA, Friedlaender FR, et al. The genetic structure and history of Africans and African Americans. Science. 2009;324(5930):1035–44. 17. Bryc K, Auton A, Nelson MR, et al. Genome-wide patterns of population structure and admixture in West Africans and African Americans. Proc Natl Acad Sci USA. 2010;107(2): 786–91. 18. Spielman RS, McGinnis RE, Ewens WJ. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet. 1993;52(3):506–16. 19. Pritchard JK, Donnelly P. Case–control studies of association in structured or admixed populations. Theor Popul Biol. 2001;60(3):227–37. 20. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9. 21. Li Q, Yu K. Improved correction for population stratification in genome-wide association studies by identifying hidden population structures LI2008. Genet Epidemiol. 2008;32(3):215. 22. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. 23. Dudbridge F, Koeleman BP. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am J Hum Genet. 2004;75(3):424–35. 24. Gao X, Starmer J, Martin ER. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol. 2008;32(4): 361–9.

124

T.L. Edwards et al.

25. Gao X, Becker LC, Becker DM, Starmer JD, Province MA. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet Epidemiol. 2010;34(1):100–5. 26. Gao X. Multiple testing corrections for imputed SNPs. Genet Epidemiol. 2011;35(3):154–8. 27. Dupont WD, Plummer Jr WD. Power and sample size calculations. A review and computer program. Control Clin Trials. 1990;11(2):116–28. 28. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006;38(2): 209–13. 29. WHO Global Database on Body Mass Index (BMI). Geneva: World Health Organization; 2009. Available at http://www.who.int/mediacentre/factsheets/fs311/en/index.html. 30. Field AE, Coakley EH, Must A, et al. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med. 2001;161(13):1581–6. 31. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282(16):1523–9. 32. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295(13):1549–55. 33. Hetherington MM, Cecil JE. Gene-environment interactions in obesity. Forum Nutr. 2010; 63:195–203. 34. Hjelmborg JB, Fagnani C, Silventoinen K, et al. Genetic influences on growth traits of BMI: a longitudinal study of adult twins. Obesity (Silver Spring). 2008;16(4):847–52. 35. Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27(4):325–51. 36. Wardle J, Carnell S, Haworth CM, Plomin R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am J Clin Nutr. 2008; 87(2):398–404. 37. Lindgren CM, Heid IM, Randall JC, et al. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet. 2009;5(6): e1000508. 38. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94. 39. Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40(6):768–75. 40. Scherag A, Dina C, Hinney A, et al. Two new Loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and German study groups. PLoS Genet. 2010;6(4):e1000916. 41. Willer CJ, Speliotes EK, Loos RJ, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41(1):25–34. 42. Thorleifsson G, Walters GB, Gudbjartsson DF, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41(1):18–24. 43. Meyre D, Delplanque J, Chevre JC, et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009;41(2):157–9. 44. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48. 45. Kuopio AM, Marttila RJ, Helenius H, Rinne UK. Changing epidemiology of Parkinson’s disease in southwestern Finland. Neurology. 1999;52(2):302–8. 46. Thomas B, Beal MF. Parkinson’s disease. Hum Mol Genet. 2007;16(Spec No. 2):R183–94. 47. Bonifati V, Rizzu P, van Baren MJ, et al. Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science. 2003;299(5604):256–9. 48. Kitada T, Asakawa S, Hattori N, et al. Mutations in the parkin gene cause autosomal recessive juvenile parkinsonism. Nature. 1998;392(6676):605–8.

5


125

49. Paisan-Ruiz C, Jain S, Evans EW, et al. Cloning of the gene containing mutations that cause PARK8-linked Parkinson’s disease. Neuron. 2004;44(4):595–600. 50. Polymeropoulos MH, Lavedan C, Leroy E, et al. Mutation in the alpha-synuclein gene identified in families with Parkinson’s disease. Science. 1997;276(5321):2045–7. 51. Zimprich A, Biskup S, Leitner P, et al. Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron. 2004;44(4):601–7. 52. Lesage S, Brice A. Parkinson’s disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet. 2009;18(R1):R48–59. 53. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273(5281):1516–7. 54. Fung HC, Scholz S, Matarin M, et al. Genome-wide genotyping in Parkinson’s disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol. 2006;5(11):911–6. 55. Maraganore DM, de Andrade M, Lesnick TG, et al. High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet. 2005;77(5):685–93. 56. Pankratz N, Wilk JB, Latourelle JC, et al. Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum Genet. 2009;124(6):593–605. 57. Farrer M, Maraganore DM, Lockhart P, et al. alpha-Synuclein gene haplotypes are associated with Parkinson’s disease. Hum Mol Genet. 2001;10(17):1847–51. 58. Kruger R, Vieira-Saecker AM, Kuhn W, et al. Increased susceptibility to sporadic Parkinson’s disease by a certain combined alpha-synuclein/apolipoprotein E genotype. Ann Neurol. 1999;45(5):611–7. 59. Maraganore DM, de Andrade M, Elbaz A, et al. Collaborative analysis of alpha-synuclein gene promoter variability and Parkinson disease. JAMA. 2006;296(6):661–70. 60. McCulloch CC, Kay DM, Factor SA, et al. Exploring gene-environment interactions in Parkinson’s disease. Hum Genet. 2008;123(3):257–65. 61. Mueller JC, Fuchs J, Hofer A, et al. Multiple regions of alpha-synuclein are associated with Parkinson’s disease. Ann Neurol. 2005;57(4):535–41. 62. Myhre R, Toft M, Kachergus J, et al. Multiple alpha-synuclein gene polymorphisms are associated with Parkinson’s disease in a Norwegian population. Acta Neurol Scand. 2008;118(5): 320–7. 63. Sutherland GT, Halliday GM, Silburn PA, et al. Do polymorphisms in the familial Parkinsonism genes contribute to risk for sporadic Parkinson’s disease? Mov Disord. 2009;24(6):833–8. 64. Fidani L, Kalinderi K, Bostantjopoulou S, et al. Association of the Tau haplotype with Parkinson’s disease in the Greek population. Mov Disord. 2006;21(7):1036–9. 65. Fung HC, Xiromerisiou G, Gibbs JR, et al. Association of tau haplotype-tagging polymorphisms with Parkinson’s disease in diverse ethnic Parkinson’s disease cohorts. Neurodegener Dis. 2006;3(6):327–33. 66. Goris A, Williams-Gray CH, Clark GR, et al. Tau and alpha-synuclein in susceptibility to, and dementia in, Parkinson’s disease. Ann Neurol. 2007;62(2):145–53. 67. Healy DG, bou-Sleiman PM, Lees AJ, et al. Tau gene and Parkinson’s disease: a case–control study and meta-analysis. J Neurol Neurosurg Psychiatry. 2004;75(7):962–5. 68. Kwok JB, Teber ET, Loy C, et al. Tau haplotypes regulate transcription and are associated with Parkinson’s disease. Ann Neurol. 2004;55(3):329–34. 69. Levecque C, Elbaz A, Clavel J, et al. Association of polymorphisms in the Tau and Saitohin genes with Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2004;75(3):478–80. 70. Mamah CE, Lesnick TG, Lincoln SJ, et al. Interaction of alpha-synuclein and tau genotypes in Parkinson’s disease. Ann Neurol. 2005;57(3):439–43. 71. Martin ER, Scott WK, Nance MA, et al. Association of single-nucleotide polymorphisms of the tau gene with late-onset Parkinson disease. JAMA. 2001;286(18):2245–50. 72. Scott WK, Nance MA, Watts RL, et al. Complete genomic screen in Parkinson disease: evidence for multiple genes. JAMA. 2001;286(18):2239–44.

126

T.L. Edwards et al.

73. Skipper L, Wilkes K, Toft M, et al. Linkage disequilibrium and association of MAPT H1 in Parkinson disease. Am J Hum Genet. 2004;75(4):669–77. 74. Vandrovcova J, Pittman AM, Malzer E, et al. Association of MAPT haplotype-tagging SNPs with sporadic Parkinson’s disease. Neurobiol Aging. 2007;30(9):1477–82. 75. Winkler S, Konig IR, Lohmann-Hedrich K, Vieregge P, Kostic V, Klein C. Role of ethnicity on the association of MAPT H1 haplotypes and subhaplotypes in Parkinson’s disease. Eur J Hum Genet. 2007;15(11):1163–8. 76. Zabetian CP, Hutter CM, Factor SA, et al. Association analysis of MAPT H1 haplotype and subhaplotypes in Parkinson’s disease. Ann Neurol. 2007;62(2):137–44. 77. Zappia M, Annesi G, Nicoletti G, et al. Association of tau gene polymorphism with Parkinson’s disease. Neurol Sci. 2003;24(3):223–4. 78. Haugarvoll K, Toft M, Skipper L, et al. Fine-mapping and candidate gene investigation within the PARK10 locus. Eur J Hum Genet. 2009;17(3):336–43. 79. Li Y, Schrodi S, Rowland C, Tacey K, Catanese J, Grupe A. Genetic evidence for ubiquitinspecific proteases USP24 and USP40 as candidate genes for late-onset Parkinson disease. Hum Mutat. 2006;27(10):1017–23. 80. Oliveira SA, Li YJ, Noureddine MA, et al. Identification of risk and age-at-onset genes on chromosome 1p in Parkinson disease. Am J Hum Genet. 2005;77(2):252–64. 81. DeStefano AL, Latourelle J, Lew MF, et al. Replication of association between ELAVL4 and Parkinson disease: the GenePD study. Hum Genet. 2008;124(1):95–9. 82. Haugarvoll K, Toft M, Ross OA, et al. ELAVL4, PARK10, and the Celts. Mov Disord. 2007;22(4):585–7. 83. Noureddine MA, Qin XJ, Oliveira SA, et al. Association between the neuron-specific RNAbinding protein ELAVL4 and Parkinson disease. Hum Genet. 2005;117(1):27–33. 84. Kurth JH, Kurth MC, Poduslo SE, Schwankhaus JD. Association of a monoamine oxidase B allele with Parkinson’s disease. Ann Neurol. 1993;33(4):368–72. 85. Rubinsztein DC, Hanlon CS, Irving RM, et al. Apo E genotypes in multiple sclerosis, Parkinson’s disease, schwannomas and late-onset Alzheimer’s disease. Mol Cell Probes. 1994;8(6):519–25. 86. Autere J, Moilanen JS, Finnila S, et al. Mitochondrial DNA polymorphisms as risk factors for Parkinson’s disease and Parkinson’s disease dementia. Hum Genet. 2004;115(1):29–35. 87. Gaweda-Walerych K, Maruszak A, Safranow K, et al. Mitochondrial DNA haplogroups and subhaplogroups are associated with Parkinson’s disease risk in a Polish PD cohort. J Neural Transm. 2008;115(11):1521–6. 88. Ghezzi D, Marelli C, Achilli A, et al. Mitochondrial DNA haplogroup K is associated with a lower risk of Parkinson’s disease in Italians. Eur J Hum Genet. 2005;13(6):748–52. 89. Huerta C, Castro MG, Coto E, et al. Mitochondrial DNA polymorphisms and risk of Parkinson’s disease in Spanish population. J Neurol Sci. 2005;236(1–2):49–54. 90. Kosel S, Grasbon-Frodl EM, Mautsch U, et al. Novel mutations of mitochondrial complex I in pathologically proven Parkinson disease. Neurogenetics. 1998;1(3):197–204. 91. Pyle A, Foltynie T, Tiangyou W, et al. Mitochondrial DNA haplogroup cluster UKJT reduces the risk of PD. Ann Neurol. 2005;57(4):564–7. 92. Ross OA, McCormack R, Maxwell LD, et al. mt4216C variant in linkage with the mtDNA TJ cluster may confer a susceptibility to mitochondrial dysfunction resulting in an increased risk of Parkinson’s disease in the Irish. Exp Gerontol. 2003;38(4):397–405. 93. van der Walt JM, Nicodemus KK, Martin ER, et al. Mitochondrial polymorphisms significantly reduce the risk of Parkinson disease. Am J Hum Genet. 2003;72(4):804–11. 94. Edwards TL, Scott WK, Almonte C, et al. Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann Hum Genet. 2010;74(2):97–109. 95. Saad M, Lesage S, Saint-Pierre A, et al. Genome-wide association study confirms BST1 and suggests a locus on 12q24 as the risk loci for Parkinson’s disease in the European population. Hum Mol Genet. 2011;20(3):615–27.

5


127

96. Satake W, Nakabayashi Y, Mizuta I, et al. Genome-wide association study identifies common variants at four loci as genetic risk factors for Parkinson’s disease. Nat Genet. 2009;41(12): 1303–7. 97. Simon-Sanchez J, Schulte C, Bras JM, et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat Genet. 2009;41(12):1308–12. 98. Simon-Sanchez J, van Hilten JJ, van de Warrenburg B, et al. Genome-wide association study confirms extant PD risk loci among the Dutch. Eur J Hum Genet. 2011;19(6):655–61. 99. Spencer CC, Plagnol V, Strange A, et al. Dissection of the genetics of Parkinson’s disease identifies an additional association 5 of SNCA and multiple associated haplotypes at 17q21. Hum Mol Genet. 2011;20(2):345–53. 100. Hyman L. Epidemiology of AMD. In: Hampton R, Neslon P, editors. Age-related macular degeneration: principles and practice. New York: Raven; 1992. p. 1–35. 101. Munoz B, West SK, Rubin GS, et al. Causes of blindness and visual impairment in a population of older Americans: The Salisbury Eye Evaluation Study. Arch Ophthalmol. 2000;118(6):819–25. 102. Spencer G. Projections of the population of the United States by age, sex, and race: 1988– 1989. Report No.: 1018. Washington, DC: US Bureau of the Census; 1989. 103. Attebo K, Mitchell P, Smith W. Visual acuity and the causes of visual loss in Australia. The Blue Mountains Eye Study. Ophthalmology. 1996;103(3):357–64. 104. Klaver CC, Wolfs RC, Assink JJ, van Duijn CM, Hofman A, de Jong PT. Genetic risk of agerelated maculopathy. Population-based familial aggregation study. Arch Ophthalmol. 1998;116(12):1646–51. 105. Klaver CC, Wolfs RC, Vingerling JR, Hofman A, de Jong PT. Age-specific prevalence and causes of blindness and visual impairment in an older population: the Rotterdam Study. Arch Ophthalmol. 1998;116(5):653–8. 106. Tielsch JM, Javitt JC, Coleman A, Katz J, Sommer A. The prevalence of blindness and visual impairment among nursing home residents in Baltimore. N Engl J Med. 1995;332(18): 1205–9. 107. Ambati J, Ambati BK, Yoo SH, Ianchulev S, Adamis AP. Age-related macular degeneration: etiology, pathogenesis, and therapeutic strategies. Surv Ophthalmol. 2003;48(3): 257–93. 108. Sepp T, Khan JC, Thurlby DA, et al. Complement factor H variant Y402H is a major risk determinant for geographic atrophy and choroidal neovascularization in smokers and nonsmokers. Invest Ophthalmol Vis Sci. 2006;47(2):536–40. 109. Smith W, Assink J, Klein R, et al. Risk factors for age-related macular degeneration: pooled findings from three continents. Ophthalmology. 2001;108(4):697–704. 110. Spencer KL, Hauser MA, Olson LM, et al. Protective effect of complement factor B and complement component 2 variants in age-related macular degeneration. Hum Mol Genet. 2007;16(16):1986–92. 111. Jakobsdottir J, Conley YP, Weeks DE, Mah TS, Ferrell RE, Gorin MB. Susceptibility genes for age-related maculopathy on chromosome 10q26. Am J Hum Genet. 2005;77(3): 389–407. 112. Rivera A, Fisher SA, Fritsche LG, et al. Hypothetical LOC387715 is a second major susceptibility gene for age-related macular degeneration, contributing independently of complement factor H to disease risk. Hum Mol Genet. 2005;14(21):3227–36. 113. Kopplin LJ, Igo Jr RP, Wang Y, et al. Genome-wide association identifies SKIV2L and MYRIP as protective factors for age-related macular degeneration. Genes Immun. 2010;11(8):609–21. 114. Meyer KJ, Davis LK, Schindler EI, et al. Genome-wide analysis of copy number variants in age-related macular degeneration. Hum Genet. 2011;129(1):91–100. 115. Neale BM, Fagerness J, Reynolds R, et al. Genome-wide association study of advanced agerelated macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci USA. 2010;107(16):7395–400.

128

T.L. Edwards et al.

116. Nussenblatt RB, Liu B, Li Z. Age-related macular degeneration: an immunologically driven disease. Curr Opin Investig Drugs. 2009;10(5):434–42. 117. Ryu E, Fridley BL, Tosakulwong N, Bailey KR, Edwards AO. Genome-wide association analyses of genetic, phenotypic, and environmental risks in the age-related eye disease study. Mol Vis. 2010;16:2811–21. 118. Tam PO, Ng TK, Liu DT, et al. HTRA1 variants in exudative age-related macular degeneration and interactions with smoking and CFH. Invest Ophthalmol Vis Sci. 2008;49(6): 2357–65. 119. Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009;106(23):9362–7. 120. Hebebrand J, Volckmar AL, Knoll N, Hinney A. Chipping away the “missing heritability”: GIANT steps forward in the molecular elucidation of obesity — but still lots to go. Obes Facts. 2010;3(5):294–303. 121. Renstrom F, Payne F, Nordstrom A, et al. Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet. 2009;18(8):1489–96. 122. Psaty BM, O’Donnell CJ, Gudnason V, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2(1):73–80. 123. Dehghan A, Dupuis J, Barbalic M, et al. Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation. 2011;123(7):731–8. 124. Pfeufer A, van Noord C, Marciante KD, et al. Genome-wide association study of PR interval. Nat Genet. 2010;42(2):153–9. 125. Debette S, Bis JC, Fornage M, et al. Genome-wide association studies of MRI-defined brain infarcts: meta-analysis from the CHARGE Consortium. Stroke. 2010;41(2):210–7. 126. McCarty CA, Chisholm RL, Chute CG, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011;4:13. 127. Kullo IJ, Fan J, Pathak J, Savova GK, Ali Z, Chute CG. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc. 2010;17(5):568–74. 128. Kullo IJ, Ding K, Jouni H, Smith CY, Chute CG. A genome-wide association study of red blood cell traits using the electronic medical record. PLoS One. 2010;5(9):e13011. 129. Turner SD, Berg RL, Linneman JG, et al. Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One. 2011;6(5):e19586. 130. Denny JC, Ritchie MD, Crawford DC, et al. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation. 2010;122(20):2016–21. 131. Bush WS, Sawcer SJ, de Jager PL, et al. Evidence for polygenic susceptibility to multiple sclerosis–the shape of things to come. Am J Hum Genet. 2010;86(4):621–5. 132. Sullivan PF. The psychiatric GWAS consortium: big science comes to psychiatry. Neuron. 2010;68(2):182–6. 133. Arsava EM, Ballabio E, Benner T, et al. The Causative Classification of Stroke system: an international reliability and optimization study. Neurology. 2010;75(14):1277–84. 134. Pendergrass SA, Brown-Gentry K, Dudek SM, et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet Epidemiol. 2011;35(5):410–22. 135. Thorisson GA, Smith AV, Krishnan L, Stein LD. The International HapMap Project Web site. Genome Res. 2005;15(11):1592–3. 136. Altshuler DM, Gibbs RA, Peltonen L, et al. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467(7311):52–8.

5


129

137. Stein LD, Mungall C, Shu S, et al. The generic genome browser: a building block for a model organism system database. Genome Res. 2002;12(10):1599–610. 138. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–5. 139. Kent WJ. BLAT – the BLAST-like alignment tool. Genome Res. 2002;12(4):656–64. 140. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. 141. Creating the gene ontology resource: design and implementation. Genome Res. 2001; 11(8):1425–33. 142. Blaschke C, Leon EA, Krallinger M, Valencia A. Evaluation of BioCreAtIvE assessment of task 2. BMC Bioinformatics. 2005;6 Suppl 1:S16. 143. The FlyBase database of the Drosophila genome projects and community literature. Nucleic Acids Res. 2002;30(1):106–8. 144. Blake JA, Richardson JE, Bult CJ, Kadin JA, Eppig JT. The Mouse Genome Database (MGD): the model organism database for the laboratory mouse. Nucleic Acids Res. 2002;30(1):113–5. 145. Dwight SS, Harris MA, Dolinski K, et al. Saccharomyces Genome Database (SGD) provides secondary gene annotation using the Gene Ontology (GO). Nucleic Acids Res. 2002;30(1): 69–72. 146. Clayton EW, Smith M, Fullerton SM, et al. Confronting real time ethical, legal, and social issues in the Electronic Medical Records and Genomics (eMERGE) Consortium. Genet Med. 2010;12(10):616–20. 147. Kaye J. The regulation of direct-to-consumer genetic tests. Hum Mol Genet. 2008;17(R2): R180–3.

Chapter 6

Presymptomatic Genetic Testing: Shifting the Emphasis from Reaction to Prevention Irene H. Hung and John C. Carey

Presymptomatic genetic testing refers to genetic analysis of healthy individuals who are at increased risk for a specific heritable disorder, with the aim of predicting their likelihood to develop disease symptoms. At-risk individuals are usually identified because of a positive family history for a particular genetic condition. Presymptomatic testing differs from diagnostic genetic testing in that the information obtained is used to determine future health status rather than to confirm a diagnosis based on current clinical manifestations. This type of genetic testing is readily available for many disorders (Table 6.1), including adult-onset conditions and cancer predisposition syndromes. Knowledge obtained through presymptomatic genetic testing can be beneficial for an individual. For example, prevention and early detection of certain cancers to reduce morbidity and mortality clearly improves clinical outcome for those found to carry a highly penetrant predisposing genetic mutation, especially for malignancies where well-defined surveillance and preventive measures exist. If testing demonstrates that a person does not carry the familial cancer-causing mutation, their risk is reduced to background levels, and their need to undergo rigorous cancer screening protocols is obviated. For many genetic conditions, proven medical interventions are not available, yet there are valuable psychological gains from testing, such as reassurance if the result is negative for the familial disease-causing mutation, or relief from uncertainty regarding disease status. A person may use their genetic risk information to help with other life issues such as deciding whether or not to have children, what kind of employment to seek, and how to plan their finances, insurance coverage, and long-term care. Besides personal benefits, presymptomatic genetic information is also sought by individuals because of concerns regarding whether children or other family members are at risk.

I.H. Hung, M.D. (*) • J.C. Carey, M.D., M.P.H. Division of Medical Genetics, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT 84132, USA e-mail: [email protected]; [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_6, © Springer Science+Business Media, LLC 2012

131

VHL

TP53

Li-Fraumeni syndrome

APC

BRCA1, BRCA2

KCNQ1, KCNH2, SCN5A, KCNE1, ANK2, others

MYH7, MYBPC3, TNNT2, TNNI3, TPM1, MYL2, MYL3, ACTC1, TNNC1, others LMNA, MYH7, others

Genes

von Hippel-Lindau syndrome

Oncologic Hereditary breast and ovarian cancer APC-associated polyposis conditions

Condition Cardiovascular Familial hypertrophic cardiomyopathy Familial dilated cardiomyopathy Long QT syndrome

Table 6.1 Presymptomatic genetic testing in selected disorders

Early breast, ovarian, prostate cancer screening, consideration of prophylactic surgical and chemopreventive measures Early surveillance for hepatoblastoma, gastrointestinal polyps, colectomy for individuals with familial adenomatous polyposis Childhood screening with ophthalmologic evaluations, blood pressure monitoring, urinary catecholamine metabolites, abdominal ultrasound, audiologic evaluations, others Annual physical examinations beginning in childhood with special attention to dermatologic and neurologic systems, early breast cancer and colorectal screening, consideration of prophylactic surgical measures, avoidance of radiation therapy

Cardiovascular screening (physical examination, echocardiogram, ECG), pharmacologic therapy, lifestyle changes Cardiovascular screening (physical examination, echocardiogram, ECG), pharmacologic therapy, lifestyle changes QTc analysis on resting and exercise ECGs, cardiovascular screening, pharmacologic therapy, anesthesia precautions, lifestyle changes

Clinical management recommendations for presymptomatic mutation carriers

132 I.H. Hung and J.C. Carey

GLA (biochemical testing is done to identify at-risk males) ENG, ACVRL1, SMAD4

Adapted from GeneReviews [http://www.genetests.org]

Hereditary hemorrhagic telangiectasia

Fabry disease

Cardiovascular screening (physical examination, ECG, Holter), ophthalmologic evaluations, diabetes mellitus screening, attention to nutritional status Renal, cardiovascular, audiologic surveillance, enzyme replacement therapy to prevent primary manifestations Annual physical examinations, surveillance for anemia, pulmonary arteriovenous malformations (AVMs) beginning in childhood, MRI surveillance for cerebral AVMs beginning in infancy

DMPK

PKHD1

Renal, cardiovascular, neurologic surveillance, blood pressure and lipid concentration monitoring, avoidance of nephrotoxic agents, caffeine, estrogens, smoking Surveillance of renal and hepatic function, blood pressure monitoring, nutritional status, avoidance of sympathomimetic and nephrotoxic agents, others

Surveillance to avoid secondary complications, management remains supportive as there is no currently known therapy to delay or halt progression of disease Surveillance to avoid secondary complications, management remains supportive as there is no currently known therapy to delay or halt progression of disease Surveillance including BAER testing, neurologic MRI, ophthalmologic evaluations

PKD1, PKD2

NF2

Neurofibromatosis 2

Renal Autosomal dominant polycystic kidney disease Autosomal recessive polycystic kidney disease Multisystem Myotonic dystrophy type 1

PSEN1, PSEN2, APP

ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, others

Early-onset familial Alzheimer’s disease

Neurologic Autosomal dominant cerebellar ataxias

6 Presymptomatic Genetic Testing… 133

134

I.H. Hung and J.C. Carey

However, presymptomatic testing can negatively impact an individual, having complex psychological and social effects, as well as financial consequences. For example, a positive test result foretelling a serious and possibly fatal genetic condition in an otherwise healthy person can cause severe mental distress and catastrophic results, such as precipitating a major depressive episode or suicide [1]. Other consequences may include loss of self-worth and heightened worry about one’s well-being. Even if results are negative, individuals may experience survivor guilt and difficulties coping with their new health status [2]. An individual who receives a positive test result may feel ashamed and fear rejection from significant others and family members, and family dynamics may be altered whether a test result is positive or negative. Besides these adverse psychosocial outcomes, presymptomatic testing results are sometimes misinterpreted as diagnostic, conferring active disease status to an individual who is still healthy. The possibility of genetic discrimination in the workplace or with health insurance coverage is of concern to at-risk persons. In the United States, the Genetic Information Nondiscrimination Act (GINA) of 2008 was passed to protect individuals by preventing employers from taking adverse employment actions and health insurance companies from denying coverage or issuing higher premiums based exclusively on genetic information. Federal legislation also mandates that genetic test results are subject to the same privacy protection as other sensitive health information. An important limitation of genetic testing is that not all patients with heritable disorders have detectable mutations due to technical difficulties of the testing methodologies or because causative genes have not yet been identified for specific disorders. In these cases, testing of at-risk family members should not be pursued since negative test results would not be informative. Accordingly, an optimal presymptomatic testing strategy is to first identify the pathogenic mutation in an affected individual using a peripheral blood sample or possibly tumor tissue. Subsequent testing of at-risk family members should only be offered if the familial pathogenic mutation is identified. This ensures that a negative test result lowers the individual’s risk of developing the disorder in question to that of the general population. In addition, the costs for genetic testing of a specific (known) mutation are usually substantially lower than for the index case. In this chapter, we will present the main genetic and clinical principles involved in presymptomatic genetic testing by illustrating its application in four common prototypic disorders: Huntington disease, hereditary hemochromatosis, hereditary nonpolyposis colon cancer, and multiple endocrine neoplasia type 2.

Huntington Disease: A Paradigm for Presymptomatic Genetic Testing Historically, Huntington disease (HD) serves as the prototypic disorder for discussion of presymptomatic genetic testing. HD is a heritable neuropsychiatric disorder characterized by movement abnormalities (e.g., chorea, dystonia, bradykinesia),

6

Presymptomatic Genetic Testing…

135

psychiatric disturbances (e.g., depression and anxiety), and cognitive impairment (e.g., inattention and executive dysfunction) that was first described in detail as hereditary chorea by George Huntington in 1872 [3]. Prevalence estimates for HD vary among different populations and are highest in those of western European descent at about 5–10 per 100,000 [4]. Affected individuals typically become symptomatic in the fourth decade of life, although the age of presentation can vary from early childhood to late adulthood. Disease symptoms are progressive and result in functional decline, with death usually occurring in the sixth decade. Although research is ongoing, there is currently no known medical cure for HD, nor are preventive measures available. HD is a single-gene disorder that is transmitted in an autosomal dominant manner; each child of an affected parent is at 50% risk to inherit the disease since only one mutated copy of the gene is necessary for an individual to be affected. In 1983, the HD locus was mapped to chromosome 4 [5], and one decade later, the causative gene (HTT) was identified [6]. The pathogenic mutation in the HTT gene is an unstable, expanded CAG trinucleotide repeat in the first exon which encodes an aberrant polyglutamine tract in the mutant huntingtin protein that causes toxic effects, including neuronal cell loss. HD alleles have been categorized as follows: (1) Normal: 26 or fewer CAG repeats. (2) Intermediate: 27–35 CAG repeats. These individuals will not develop HD, but may be at risk of having a child with an HD-causing allele. (3) Reduced penetrance: 36–39 CAG repeats. These individuals are at risk for, but may not develop, HD, and they are more likely to have later onset disease. (4) Full penetrance: 40 or more CAG repeats. These individuals will develop HD. There is an inverse correlation between the number of CAG repeats and the age at which symptoms present; that is, individuals with higher numbers of CAG repeats present with symptoms at earlier ages than those with fewer repeats [7, 8]. The localization and subsequent identification of the HTT gene and the causative mutation permitted diagnostic testing for affected individuals and presymptomatic testing for those with a family history of HD. HD is generally a late-onset disorder with no curative or preventive measures, so inherent risks, including precipitation of severe psychological distress, were recognized early as potential outcomes of presymptomatic HD genetic testing. International testing protocols were, therefore, established to protect at-risk individuals and to provide guidance for clinical and laboratory personnel [9]. The HD testing protocol is conducted using a multidisciplinary approach, and a list of HD testing centers is available at the Huntington’s Disease Society of America’s web site, http://www.hdsa.org. Prior to offering HD testing, genetic counseling is recommended for discussion of HD genetics and for provision of information regarding risks, benefits, and limitations of testing. Psychological evaluation is also important to assess the individual’s mental state, coping mechanisms, and motivation to undergo testing. In addition, a detailed neurological examination is undertaken to identify early signs and symptoms of HD. After these initial sessions, there is a waiting period of about 1 month in which the individual is asked to consider the information presented, to reflect on how the positive, negative, or indeterminate test result may impact their lives, and to make a free and informed decision as to whether to proceed with testing. If the at-risk person decides to pursue testing, a blood sample is drawn for HD molecular genetic analysis.

136


A face-to-face results disclosure session is scheduled after an additional waiting period. Subsequent follow-up is important to reassess psychological needs and coping ability as well as to provide additional counseling and support. Fundamental features of the HD presymptomatic testing protocol include pretest counseling and evaluation and required waiting periods before obtaining a test sample and prior to disclosure of results. A multidisciplinary team knowledgeable about presymptomatic testing and familiar with the potential implications of the genetic information provide the required services. The aim of the pretest sessions is to ensure the individual requesting presymptomatic testing is fully informed regarding the implications of the genetic test results and is adequately prepared to receive the information. The individual may opt out of testing at any time, and multiple studies have shown that a significant number (between 12% and 50%) [10] choose not to proceed after the initial counseling and evaluation sessions, highlighting the importance of the waiting periods. The HD testing protocol has served as a model for presymptomatic genetic testing to safeguard at-risk persons, and its principles have been effectively implemented for other heritable disorders.

Hereditary Hemochromatosis Hereditary hemochromatosis (HH) is a common genetic disorder in people of Northern European origin that is caused by excessive absorption of iron from the gastrointestinal tract, leading to abnormal iron deposition and damage in multiple organs. Initially, biochemical abnormalities are detectable around 20 years of age and include elevated serum transferrin-iron saturation and serum ferritin concentration [11]. These laboratory studies are used as screening measures for evidence of iron overload; confirmatory diagnostic methods include liver biopsy to measure hepatic iron stores, quantitative phlebotomy, and molecular genetic testing (described in more detail below). Clinical symptoms present at approximately 40 years of age in males and 50 years of age in females. Affected patients may present earlier with nonspecific findings such as fatigue and joint pain. At later stages, individuals may experience hyperpigmentation of the skin, diabetes, gonadal dysfunction, hepatomegaly, cirrhosis, and hepatocellular carcinoma. Life expectancy is severely protracted without medical intervention. The treatment of choice for this disorder is serial phlebotomy, which is a simple and effective means of preventing iron accumulation. When this procedure is started early, organ damage can be avoided and a normal life span can be achieved [12]. The major form of HH is associated with the HFE gene, discovered in 1996, and located on chromosome 6. This gene encodes the HFE protein, which is thought to play a role in regulation of iron uptake [13, 14]. The inheritance pattern of HFE-associated HH is autosomal recessive, and there are two common clinically relevant mutations and multiple rare mutations. Molecular genetic testing of the most common mutations is widely available on a clinical basis. The most severe frequent mutation is a G to A change at nucleotide 845, resulting in

6


137

the substitution of tyrosine for cysteine at codon 282 (p.C282Y). Studies have demonstrated that 65–100% of individuals diagnosed with HH are homozygous for this mutation. The other common mutation is a C to G change at nucleotide 187, resulting in the substitution of aspartic acid for histidine at codon 63 (p. 63D). Individuals who are compound heterozygous (p.C282Y/p.H63D) are at increased risk to develop HH, though the risk is lower than for p.C282Y homozygotes [15, 16]. Those who are homozygous for the p.H63D mutation are not at increased risk for HH [17]. Although the majority of clinically diagnosed HH patients are homozygotes (p.C282Y/p.C282Y) or compound heterozygotes (p.C282Y/p.H63D) for the common mutations, there are many individuals with these genotypes that are phenotypically normal. This is an example of a genetic disorder with reduced penetrance, meaning a low proportion of people with mutant genotypes actually exhibit signs and symptoms of HH. The penetrance for mutant HFE genotypes is estimated to be 1% or less [18]. This likely results from a combination of genetic and environmental factors as well as lifestyle. For instance, factors known to contribute to iron overload include male sex, diet, alcohol use, and other genetic modifiers [19–21]. Although it is possible to accurately identify whether an individual has a genotype associated with HH, it is not possible to predict whether or at what age a person will develop clinical manifestations of HH. There is no test available to predict whether an individual will become symptomatic. Presymptomatic HFE genotyping is useful to identify individuals at risk for iron overload who can then be monitored using biochemical surveillance methods and treated prior to development of complications. Prevention and early detection in HH is clearly beneficial in improving quantity and quality of life for these patients [12].

Hereditary Nonpolyposis Colon Cancer (HNPCC) Individuals with hereditary nonpolyposis colon cancer (HNPCC), an autosomal dominant cancer predisposition syndrome, have a significantly increased risk for colorectal cancer (CRC) and several other malignancies. The lifetime cancer risk for men carrying a HNPCC mutation approaches 70–80%, while the risk for women is lower but may approach 50% [22–25]. However, women with HNPCC have a lifetime risk of 15–70% for endometrial cancer and also have a significantly increased cumulative risk for ovarian cancer. On average, diagnosis of CRC in HNPCC patients occurs at 61 years of age. Endometrial cancer is usually diagnosed between 46 and 62 years of age, while ovarian cancer is diagnosed at an average of 42 years of age [26–29]. Individuals with HNPCC are also at increased risk for malignancies of the stomach, small intestine, hepatobiliary tract, pancreas, renal pelvis, and urinary tract [30, 31]. Accurate identification of HNPCC patients and families allows for tailoring of clinical services, such as enhanced tumor surveillance programs and prophylactic measures, that may reduce morbidity and mortality through prevention and early detection of hereditary cancers.

138


Diagnosis on a clinical basis is possible using Amsterdam criteria with modifications [32, 33], which includes (1) having a family history of three affected individuals with HNPCC-related malignancies over two generations, (2) one of the individuals is a first-degree relative of the other two, and (3) one of the affected individuals had an HNPCC-related malignancy diagnosed when younger than 50 years of age. Because of concerns with the highly stringent characteristics of the Amsterdam criteria, alternative guidelines were established with broader criteria to include more at-risk individuals. Under revised Bethesda guidelines, designed to identify CRCaffected individuals that are most likely to benefit from tumor MSI testing, a patient needs to meet only one of the following criteria: (1) CRC diagnosed when younger than 50 years of age, (2) presence of synchronous, metachronous HNPCC-associated tumors, (3) MSI-high CRC at less than 60 years of age, (4) one or more first-degree relatives with HNPCC-associated tumor, with one tumor diagnosed at less than 50 years of age, or (5) CRC diagnosed in two or more first- or second-degree relatives with an HNPCC-associated tumor [34, 35]. However, basing HNPCC diagnoses upon these clinical criteria may exclude a significant proportion of individuals and families with this condition [36, 37]. Molecular genetic testing is used for diagnostic purposes as well as for identification of presymptomatic HNPCC individuals who may have the most to gain from preventive and surveillance measures. HNPCC is associated with germline mutations in mismatch repair genes (MMR) on chromosomes 2, 3, and 7. Although multiple MMR genes have been identified, mutations in four MMR genes, MLH1, MSH2, MSH6, and PMS2, are associated with the majority of HNPCC cases [38]. MMR gene products function to recognize and correct DNA replication errors such as nucleotide base mispairings that, if not eliminated, can predispose to tumor formation and cancer. Germline mutations have been identified throughout these genes. Mutations in MLH1 and MSH2 together account for about 70% of HNPCC cases, while MSH6 and PMS2 each account for about 15% [38]. A hallmark of HNPCC-related tumors is microsatellite instability (MSI), a molecular abnormality in which repetitive DNA sequences (microsatellites) accumulate errors that result in modified and variable repeat numbers. Tumors are classified based on proportion of MSI instability: a tumor is classified as MSIhigh if over 30% of markers show unstable changes, MSI-low if less than 30% of markers show unstable changes, or MSI-stable if no abnormalities are detected. In HNPCC patients, approximately 90% of CRCs are characterized as MSI-high [39]. Individuals with MSI-low or MSI-stable tumors are unlikely to have a detectable germline mutation. MMR gene mutations generally result in loss of protein products, and immunohistochemical (IHC) methods can be used to analyze for the presence or absence of specific MMR proteins in tumor tissues, though some germline mutations do not result in loss of protein product. IHC analysis can facilitate molecular genetic testing by predicting which MMR gene may have the germline mutation. Tumor phenotyping with combined use of MSI and IHC testing provides the most comprehensive level of screening, though IHC is more widely available and may be used without MSI testing if access to MSI analysis is problematic. Current screening algorithms for HNPCC include a combination of clinical criteria and molecular assays for diagnostic evaluation of individuals affected by cancer

6


139

Fig. 6.1 Suggested algorithm for HNPCC diagnostic testing. See text for details

(Fig. 6.1) [40, 41]. If a patient diagnosed with CRC or other malignancy associated with HNPCC meets revised Bethesda guidelines, cancer genetic counseling is recommended and tumor phenotyping is performed, including IHC and MSI testing. If these studies show MSI-high results and/or abnormal IHC results (absence or low levels of staining for MMR proteins), germline mutation analysis of MMR genes can be carried out, ideally using IHC results to focus the subsequent analyses, including molecular genetic testing. For example, if IHC results are abnormal for MLH1, studies for somatic events associated with nonhereditary tumors should be carried out, including analysis for the p.V600E BRAF mutation (found in about 10% of sporadically occurring CRCs) [42, 43] and MLH1 promoter methylation studies (to detect hypermethylation associated with sporadic cancers) [44–46]. If a BRAF mutation is not identified, and/or methylation studies are normal, germline gene analysis for HNPCC-associated mutations is warranted. If the tumor tissue is BRAF mutation positive and hypermethylation is detected, the malignancy is most likely sporadic in origin, and further testing for HNPCC-associated mutations is not necessary. If MSI-high levels are detected, and IHC studies are abnormal for MSH2, MSH6, or PMS2, germline gene analysis for HNPCC-associated mutations is warranted. If pathogenic mutations are identified, this confirms a diagnosis of HNPCC, prompting enhanced cancer surveillance for the individual tested and counseling referrals for at-risk family members. Negative studies do not completely exclude a diagnosis of HNPCC. If clinical suspicion for HNPCC or other hereditary cancer

140


predisposition syndromes remains in the face of negative or inconclusive results, heightened tumor surveillance is still appropriate. If a pathogenic HNPCC mutation is identified in an affected individual, presymptomatic genetic testing using targeted mutation analysis can then be offered to at-risk family members, after appropriate pretest counseling has occurred and informed consent has been obtained. Because HNPCC mutation-positive status confers increased risk for specific malignancies, the results of HNPCC genetic testing are highly significant since they will alter clinical management of CRC-affected and presymptomatic individuals. For those with a positive result on HNPCC mutation testing, consensus recommendations include colonoscopy screening every 1–2 years starting at age 20–25 years, or at age 30 years if an MSH6 mutation is identified. If CRC is diagnosed, subtotal colectomy with ileorectal anastomosis may be discussed as a treatment option but is dependent on individual circumstances. Women with HNPCC mutations are at increased risk for gynecologic cancers, and enhanced surveillance for malignancies includes transvaginal ultrasound combined with endometrial biopsy every 1–2 years beginning at age 30–35 years. Riskreducing hysterectomy and bilateral oophorectomy may be considered when childbearing is completed. In families with histories of urinary tract or other gastrointestinal tract cancers, suggested screening protocols include annual ultrasound and urinalysis with cytology and periodic upper endoscopies. In contrast, at-risk individuals who are tested and do not have the familial mutation are spared from undergoing the intensive screening programs and instead can follow cancer surveillance protocols that have been recommended for the general population. Although efficacy and/or evidence that an intervention’s benefits outweigh potential harms has not yet been proven for guidelines involving most HNPCC-associated malignancies, the efficacy of enhanced surveillance protocols has been clearly demonstrated for HNPCCassociated CRC, resulting in reduced morbidity and mortality and improvements in quality of life and life expectancy. Thus, presymptomatic genetic testing for this cancer predisposition syndrome has the ability to definitively impact clinical outcome by identifying individuals at increased cancer risk who will benefit from enhanced surveillance or risk reduction interventions [40, 47, 48].

Multiple Endocrine Neoplasia Type 2 (MEN2) Multiple endocrine neoplasia type 2 (MEN2) consists of a group of heritable cancer predisposition disorders in which affected individuals are at very high risk for malignant transformation of parafollicular calcitonin–producing cells (C-cells) resulting in medullary thyroid carcinoma (MTC), a rare form of thyroid malignancy in the general population. MEN2 syndromes affect 1 in 30,000 individuals, and patients with MEN2 account for 25% of all cases of MTC. Three different subtypes of MEN2 have been characterized: (1) MEN2A (75% of MEN2 cases), (2) MEN2B (5% of MEN2 cases), and (3) familial MTC (FMTC). Other clinical features of MEN2A include increased risk for pheochromocytoma (50%) and hyperparathyroidism due

6


141

to parathyroid adenoma/hyperplasia (10–30%). Additional features of MEN2B include increased risk for pheochromocytoma (50%), marfanoid habitus, and intestinal and mucosal ganglioneuromatosis. Patients with FMTC are only at increased risk for MTC, and FMTC is currently considered a variant of MEN2A [49]. Additional rare MEN2A variants include MEN2A or FMTC with Hirschsprung disease and MEN2A with cutaneous lichen amyloidosis (CLA). MTC in MEN2 patients is more often bilateral or multicentric and presents earlier in life compared with sporadic MTC. MTC in MEN2B is characteristically very aggressive and presents early in life, while MTC in MEN2A frequently presents in the third and fourth decades, and MTC associated with FMTC often presents in the fourth and fifth decades [49–52]. MEN2A and MEN2B patients are at increased risk for pheochromocytoma, which can present in childhood but usually presents in the fourth and fifth decades. Among MEN2A patients, up to 27% may present first with pheochromocytoma instead of MTC [53]. MEN2 is associated with autosomal dominant, gain-of-function mutations in the RET proto-oncogene, which is located on chromosome 10 and encodes a transmembrane tyrosine kinase receptor that resides in the plasma membrane of thyroid C-cells, adrenal medullary cells, parathyroid cells, and others [54]. The extracellular domain of this receptor consists of four cadherin-like repeats, a calcium-binding site, and a cysteine-rich domain. There is a single-pass transmembrane domain, and two tyrosine kinase domains are located intracellularly. The cadherin-like domains are involved in cell-cell signaling, while the cysteine-rich domain functions in receptor dimerization which results in autophosphorylation of intracellular tyrosine residues and activation of downstream signaling. The RET protein is a subunit of a multimolecular complex that binds growth factors of the glial-derived neurotrophic factor family; normally RET activation and signaling are tightly regulated [55]. MEN2-associated RET germline mutations are limited in number and are located primarily in exons 10, 11, 13, 14, 15, and 16, corresponding to the cysteine-rich domain (exons 10 and 11) or the kinase domains (exons 13 to 16). These mutations result in constitutive activation of the RET receptor, promoting malignant transformation of cells [52, 56]. MEN2A-associated mutations are located primarily in exons encoding the extracellular cysteine-rich domain, while MEN2B-associated mutations are located in exons encoding in the intracellular tyrosine kinase domains. For example, the most common RET mutation in MEN2A occurs at codon 634 (p.C634R) and results in the substitution of an arginine for a cysteine in the extracellular cysteine-rich domain, causing ligand-independent dimerization and activation of the receptor. The RET mutation responsible for most cases of MEN2B (p.M918T) affects the intracellular kinase domain. FMTC-associated RET mutations are more widely distributed throughout the RET gene and have been identified in both the extracellular cysteine-rich domain and in the intracellular kinase domain as well as other regions. Because the mutations occur mainly in certain exons, RET mutation analysis algorithms recommend examining the gene in a stepwise fashion. Clinical testing is widely available, and laboratories offer sequencing analysis alone or in combination with hotspot mutation studies for the RET gene. The American Thyroid Association (ATA) guidelines recommend initial analysis of exons 10 and 11, and

142


exons 13 to 16 since these exons contain the majority of mutations that have been described. For MEN2B, molecular genetic testing should include detection of the p.M918T and p.A883F mutations. Sequencing of the entire gene is only recommended if clinical suspicion remains high for MEN2. Studies have demonstrated strong genotype-phenotype correlations between specific RET mutations and aggressiveness of MTC. For example, the p.M918T mutation in MEN2B is associated with the most aggressive MTC that has the earliest age of onset, while MEN2A mutations in codons 634 or 618 are associated with a less aggressive MTC that has a later age of onset. In addition, there is also a relationship between specific RET mutations and risk for other MEN2 phenotypes, including pheochromocytoma and hyperparathyroidism. Mutations in RET codons 634 and 918 are identified most often in MEN2 patients with pheochromocytoma, and hyperparathyroidism has not been reported in patients with MEN2B mutations [57]. RET genotyping is the standard of care and has been integrated into clinical practice to tailor timing of preventive surgery and surveillance recommendations for MEN2 patients. In 2009, the ATA published guidelines for clinical management of MEN2 patients based in part on these genotype-phenotype correlations [49]. According to the ATA Guidelines, MEN2-associated RET mutations are currently classified into four different levels (ATA-A to ATA-D) based on degree of risk for aggressive MTC: ATA-A denotes mutations with lowest risk for aggressive MTC and are associated with FMTC, while ATA-D mutations are associated with MEN2B and confer the highest risk for aggressive MTC. Under these guidelines, surgical intervention and surveillance protocols are guided by RET germline mutation results. For example, patients with ATA-D mutations (codons 918 and 883 in MEN2B) are recommended to undergo prophylactic total thyroidectomy as soon as they are diagnosed or within the first year of life, while patients with ATA-C mutations (including codon 634) are recommended to undergo this procedure before 5 years of age. Those with ATA-B mutations are recommended to consider preventive surgery before 5 years of age, but may delay this procedure depending on additional criteria, including biochemical (calcitonin levels) and radiological screening (cervical ultrasound) results, aggressiveness of MTC in the family history, and family preference. Depending on family preference, those with ATA-A mutations may delay surgery beyond 5 years of age if they have normal results for biochemical and radiological screening and a family history of less aggressive MTC. Surveillance recommendations for MTC, pheochromocytoma, and hyperparathyroidism by biochemical testing are also based on RET genotype. Those with MEN2-associated mutations should have calcitonin levels tested and cervical ultrasound imaging by 6 months of age if surgery has not been performed. Screening for pheochromocytoma should be performed in individuals with MEN2A mutations yearly beginning at 8 years of age by measuring plasma-free metanephrines and normetanephrines or 24-h urine collection for metanephrines and normetanephrines in patients without symptoms or signs of pheochromocytoma. Screening for hyperparathyroidism is recommended annually beginning at 8 years of age for those with mutations in codons 630 or 634 and by 20 years of age for those with

6


143

other MEN2A mutations. For those with mutations only associated with FMTC, periodic surveillance is recommended. Because MTC can occur in infancy and early childhood in MEN2 patients, it is recommended that RET molecular genetic testing be offered to children at risk for MEN2A by 5 years of age, while those at risk for MEN2B should be offered testing as early as is feasible. For those without a family history of MEN2B, testing should be offered when there is clinical suspicion for the diagnosis. Presymptomatic genetic testing for MEN2 is performed routinely in children since medical benefit can be clearly demonstrated with surveillance and potentially curative interventions for those found to harbor RET mutations.

Presymptomatic Genetic Testing in Children Although the question of whether or not to perform presymptomatic genetic testing in children (GTIC) at risk for MEN2 is straightforward, GTIC is a controversial topic, and guidelines are less clear regarding testing of presymptomatic minors atrisk for childhood-onset or adult-onset conditions, such as HD, for which preventive and surveillance measures are unavailable. In fact, the 1990 HD guidelines by the International Huntington Association and the World Federation of Neurology stated that HD presymptomatic testing should be offered to “those having reached the age of majority,” which set a precedent for policies that followed regarding this issue. Ethical concerns of GTIC include, but are not limited to, the following: possible infringement on the autonomy of the child, breaches of confidentiality and privacy, future employment and/or insurance discrimination, and psychological harm. Within the many guidelines that have been put forth regarding GTIC [58], there is agreement that presymptomatic testing is primarily justified for minors when medical management can be altered to reduce morbidity and mortality. For genetic conditions without therapeutic interventions of proven benefit, it is recommended that genetic testing be delayed until an individual is of legal age and able to provide informed consent for testing.

Summary In this chapter, we have provided an overview of presymptomatic genetic testing in both adult- and childhood-onset disorders. Both the positive and negative aspects of presymptomatic testing in persons at risk for a late-onset disorder with no known treatment (i.e., Huntington disease) are discussed. We also describe how presymptomatic genetic testing can be a powerful tool for the medical management of heritable conditions with established and effective preventive or treatment interventions. Because of the complex issues associated with genetic testing, consultation with a genetics professional regarding the benefits, risks, and limitations of these procedures

144


is recommended prior to making a decision to undergo testing. The ability to predict future illness in asymptomatic, healthy individuals by using presymptomatic genetic testing adds a novel dimension to traditional clinical practice since available surveillance, preventive, and treatment measures can now be tailored for at-risk individuals to provide comprehensive and enhanced clinical care.

References 1. Almqvist EW, Bloch M, Brinkman R, Craufurd D, Hayden MR. A worldwide assessment of the frequency of suicide, suicide attempts, or psychiatric hospitalization after predictive testing for Huntington disease. Am J Hum Genet. 1999;64(5):1293–304. 2. Wexler NS. Huntington’s disease and other late onset genetic disorders. In: Emery A, Pullen G, editors. Psychological aspects of genetic counseling. New York: Academic Press, Inc.; 1984. p. 125–46. 3. Huntington G. On chorea. J Neuropsychiatry Clin Neurosci. 2003;15(1):109–12. 4. Harper PS. The epidemiology of Huntington’s disease. In: Bates G, Harper PS, Johnes L, editors. Huntington’s disease. 3rd ed. Oxford: Oxford University Press; 2002. 5. Gusella JF, Wexler NS, Conneally PM, et al. A polymorphic DNA marker genetically linked to Huntington’s disease. Nature. 1983;306(5940):234–8. 6. Macdonald M, et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. The Huntington’s Disease Collaborative Research Group. Cell. 1993;72(6):971–83. 7. Warby SC, Graham RK, Hayden MR. 2010 Huntington disease. In: GeneReviews at GeneTests: Medical Genetics Information Resource (database online). Copyright, University of Washington, Seattle. 1997–2011. Available at http://www.genetests.org. Accessed May 13, 2011. 8. The American College of Medical Genetics/American Society of Human Genetics Huntington Disease Genetic Testing Working Group. ACMG/ASHG statement. Laboratory guidelines for Huntington disease genetic testing. Am J Hum Genet. 1998;62(5):1243–7. 9. International Huntington Association and the World Federation of Neurology Research Group on Huntington’s Chorea. Guidelines for the molecular genetics predictive test in Huntington’s disease. J Med Genet. 1994;31(7):555–9. 10. Dufrasne S, Roy M, Galvez M, Rosenblatt DS. Experience over fifteen years with a protocol for predictive testing for Huntington disease. Mol Genet Metab. 2011;102(4):494–504. 11. Powell LW, George DK, McDonnell SM, Kowdley KV. Diagnosis of hemochromatosis. Ann Intern Med. 1998;129(11):925–31. 12. Niederau C, Fischer R, Purschel A, Stremmel W, Haussinger D, Strohmeyer G. Long-term survival in patients with hereditary hemochromatosis. Gastroenterology. 1996;110(4):1107–19. 13. Feder JN, Gnirke A, Thomas W, et al. A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nat Genet. 1996;13(4):399–408. 14. Waheed A, Parkkila S, Zhou XY, et al. Hereditary hemochromatosis: effects of C282Y and H63D mutations on association with beta2-microglobulin, intracellular processing, and cell surface expression of the HFE protein in COS-7 cells. Proc Natl Acad Sci USA. 1997;94(23):12384–9. 15. Burke W, Thomson E, Khoury MJ, et al. Hereditary hemochromatosis: gene discovery and its implications for population-based screening. JAMA. 1998;280(2):172–8. 16. Mura C, Raguenes O, Scotet V, Jacolot S, Mercier AY, Ferec C. A 6-year survey of HFE gene test for hemochromatosis diagnosis. Genet Med. 2005;7(1):68–73. 17. Gochee PA, Powell LW, Cullen DJ, Du Sart D, Rossi E, Olynyk JK. A population-based study of the biochemical and clinical expression of the H63D hemochromatosis mutation. Gastroenterology. 2002;122(3):646–51.

6


145

18. Beutler E, Felitti VJ, Koziol JA, Ho NJ, Gelbart T. Penetrance of 845G–>A (C282Y) HFE hereditary haemochromatosis mutation in the USA. Lancet. 2002;359(9302):211–8. 19. Loreal O, Deugnier Y, Moirand R, et al. Liver fibrosis in genetic hemochromatosis. Respective roles of iron and non-iron-related factors in 127 homozygous patients. J Hepatol. 1992; 16(1–2):122–7. 20. Piperno A, Fargion S, D’Alba R, et al. Liver damage in Italian patients with hereditary hemochromatosis is highly influenced by hepatitis B and C virus infection. J Hepatol. 1992; 16(3):364–8. 21. Rochette J, Le Gac G, Lassoued K, Ferec C, Robson KJ. Factors influencing disease phenotype and penetrance in HFE haemochromatosis. Hum Genet. 2010;128(3):233–48. 22. Carayol J, Khlat M, Maccario J, Bonaiti-Pellie C. Hereditary non-polyposis colorectal cancer: current risks of colorectal cancer largely overestimated. J Med Genet. 2002;39(5):335–9. 23. Hampel H, Stephens JA, Pukkala E, et al. Cancer risk in hereditary nonpolyposis colorectal cancer syndrome: later age of onset. Gastroenterology. 2005;129(2):415–21. 24. Vasen HF, Wijnen JT, Menko FH, et al. Cancer risk in families with hereditary nonpolyposis colorectal cancer diagnosed by mutation analysis. Gastroenterology. 1996;110(4):1020–7. 25. Watson P, Lynch HT. Cancer risk in mismatch repair gene mutation carriers. Fam Cancer. 2001;1(1):57–60. 26. Baglietto L, Lindor NM, Dowty JG, et al. Risks of Lynch syndrome cancers for MSH6 mutation carriers. J Natl Cancer Inst. 2010;102(3):193–201. 27. Dunlop MG, Farrington SM, Carothers AD, et al. Cancer risk associated with germline DNA mismatch repair gene mutations. Hum Mol Genet. 1997;6(1):105–10. 28. Senter L, Clendenning M, Sotamaa K, et al. The clinical phenotype of Lynch syndrome due to germ-line PMS2 mutations. Gastroenterology. 2008;135(2):419–28. 29. Stoffel E, Mukherjee B, Raymond VM, et al. Calculation of risk of colorectal and endometrial cancer among patients with Lynch syndrome. Gastroenterology. 2009;137(5):1621–7. 30. Lynch HT, Krush AJ. Cancer family “G” revisited: 1895–1970. Cancer. 1971;27(6):1505–11. 31. Mecklin JP, Jarvinen HJ. Tumor spectrum in cancer family syndrome (hereditary nonpolyposis colorectal cancer). Cancer. 1991;68(5):1109–12. 32. Vasen HF, Mecklin JP, Khan PM, Lynch HT. The International Collaborative Group on Hereditary Non-Polyposis Colorectal Cancer (ICG-HNPCC). Dis Colon Rectum. 1991;34(5):424–5. 33. Vasen HF, Watson P, Mecklin JP, Lynch HT. New clinical criteria for hereditary nonpolyposis colorectal cancer (HNPCC, Lynch syndrome) proposed by the International Collaborative group on HNPCC. Gastroenterology. 1999;116(6):1453–6. 34. Rodriguez-Bigas MA, Boland CR, Hamilton SR, et al. A National Cancer Institute Workshop on Hereditary Ponpolyposis Polorectal Cancer Syndrome: meeting highlights and Bethesda guidelines. J Natl Cancer Inst. 1997;89(23):1758–62. 35. Umar A, Boland CR, Terdiman JP, et al. Revised Bethesda guidelines for hereditary nonpolyposis colorectal cancer (Lynch syndrome) and microsatellite instability. J Natl Cancer Inst. 2004;96(4):261–8. 36. Hampel H, Frankel WL, Martin E, et al. Screening for the Lynch syndrome (hereditary nonpolyposis colorectal cancer). N Engl J Med. 2005;352(18):1851–60. 37. Syngal S, Fox EA, Eng C, Kolodner RD, Garber JE. Sensitivity and specificity of clinical criteria for hereditary non-polyposis colorectal cancer associated mutations in MSH2 and MLH1. J Med Genet. 2000;37(9):641–5. 38. Palomaki GE, McClain MR, Melillo S, Hampel HL, Thibodeau SN. EGAPP supplementary evidence review: DNA testing strategies aimed at reducing morbidity and mortality from Lynch syndrome. Genet Med. 2009;11(1):42–65. 39. Loukola A, Eklin K, Laiho P, et al. Microsatellite marker analysis in screening for hereditary nonpolyposis colorectal cancer (HNPCC). Cancer Res. 2001;61(11):4545–9. 40. Lindor NM, Petersen GM, Hadley DW, et al. Recommendations for the care of individuals with an inherited predisposition to Lynch syndrome: a systematic review. JAMA. 2006;296(12): 1507–17.

146


41. Pino MS, Chung DC. Application of molecular diagnostics for the detection of Lynch syndrome. Expert Rev Mol Diagn. 2010;10(5):651–65. 42. Davies H, Bignell GR, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417(6892):949–54. 43. Rajagopalan H, Bardelli A, Lengauer C, Kinzler KW, Vogelstein B, Velculescu VE. Tumorigenesis: RAF/RAS oncogenes and mismatch-repair status. Nature. 2002;418(6901): 934. 44. Hitchins M, Williams R, Cheong K, et al. MLH1 germline epimutations as a factor in hereditary nonpolyposis colorectal cancer. Gastroenterology. 2005;129(5):1392–9. 45. Hitchins MP, Wong JJ, Suthers G, et al. Inheritance of a cancer-associated MLH1 germ-line epimutation. N Engl J Med. 2007;356(7):697–705. 46. Suter CM, Martin DI, Ward RL. Germline epimutation of MLH1 in individuals with multiple cancers. Nat Genet. 2004;36(5):497–501. 47. Jarvinen HJ, Aarnio M, Mustonen H, et al. Controlled 15-year trial on screening for colorectal cancer in families with hereditary nonpolyposis colorectal cancer. Gastroenterology. 2000;118(5):829–34. 48. Syngal S, Weeks JC, Schrag D, Garber JE, Kuntz KM. Benefits of colonoscopic surveillance and prophylactic colectomy in patients with hereditary nonpolyposis colorectal cancer mutations. Ann Intern Med. 1998;129(10):787–96. 49. Kloos RT, Eng C, Evans DB, et al. Medullary thyroid cancer: management guidelines of the American Thyroid Association. Thyroid. 2009;19(6):565–612. 50. Skinner MA, DeBenedetti MK, Moley JF, Norton JA, Wells Jr SA. Medullary thyroid carcinoma in children with multiple endocrine neoplasia types 2A and 2B. J Pediatr Surg. 1996; 31(1):177–81. discussion 181–172. 51. Erlic Z, Hoffmann MM, Sullivan M, et al. Pathogenicity of DNA variants and double mutations in multiple endocrine neoplasia type 2 and von Hippel-Lindau syndrome. J Clin Endocrinol Metab. 2009;95(1):308–13. 52. Margraf RL, Crockett DK, Krautscheid PM, et al. Multiple endocrine neoplasia type 2 RET protooncogene database: repository of MEN2-associated RET sequence variation and reference for genotype/phenotype correlations. Hum Mutat. 2009;30(4):548–56. 53. Rodriguez JM, Balsalobre M, Ponce JL, et al. Pheochromocytoma in MEN 2A syndrome. Study of 54 patients. World J Surg. 2008;32(11):2520–6. 54. Takahashi M, Buma Y, Iwamoto T, Inaguma Y, Ikeda H, Hiai H. Cloning and expression of the ret proto-oncogene encoding a tyrosine kinase with two potential transmembrane domains. Oncogene. 1988;3(5):571–8. 55. Treanor JJ, Goodman L, de Sauvage F, et al. Characterization of a multicomponent receptor for GDNF. Nature. 1996;382(6586):80–3. 56. Marx SJ. Molecular genetics of multiple endocrine neoplasia types 1 and 2. Nat Rev Cancer. 2005;5(5):367–75. 57. Brandi ML, Gagel RF, Angeli A, et al. Guidelines for diagnosis and therapy of MEN type 1 and type 2. J Clin Endocrinol Metab. 2001;86(12):5658–71. 58. Borry P, Stultiens L, Nys H, Cassiman JJ, Dierickx K. Presymptomatic and predictive genetic testing in minors: a systematic review of guidelines and position papers. Clin Genet. 2006; 70(5):374–81.

Chapter 7

Prenatal Testing: Screening, Diagnosis, and Preimplantation Genetic Diagnosis Eugene Pergament

Introduction The past half-century has been witness to a remarkable evolution in the screening and diagnostic testing for developmental disorders in the fetus. Reproductive genetics, encompassing prenatal genetic screening, prenatal genetic diagnosis, and preimplantation genetic diagnosis, has assumed a dominant role in the obstetrical care and management of prospective parents. This dominance has been achieved by concurrent developments in obstetrical techniques for safely securing cells representative of the conceptus and in laboratory techniques for accurately genotyping embryonic and fetal cells at the chromosome and molecular levels. Prenatal testing for significant genetic disorders is currently divided into screening and diagnosis. A screening test either determines whether prospective parents are carriers of mutations, e.g., cystic fibrosis, or whether a pregnancy is at increased risk for a specific disorder, e.g., Down syndrome. This information is used to aid in a decision whether to proceed to additional procedures such as diagnostic testing and/or ultrasound evaluation. A diagnostic test is invasive and therefore engenders an obstetrical risk. Diagnostic testing involves either first trimester chorionic villus sampling (CVS) or midtrimester amniocentesis, although under certain circumstances, biopsy of the fetal skin or percutaneous umbilical blood sampling (PUBS) may be the procedure of choice. For patients with adverse pregnancies in the past, i.e., at significant reproductive risk because one or both parents are carriers of chromosome or gene mutations, preimplantation genetic screening and diagnosis may be possible as a means to distinguish genetically normal embryos from abnormal embryos. Historically, the field of reproductive genetics evolved from risk assessment based on previously

E. Pergament, M.D., Ph.D., FACMG (*) Northwestern Reproductive Genetics, Inc., and Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, 680 North Lake Shore Drive, Suite 1230, Chicago, IL 60611, USA e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_7, © Springer Science+Business Media, LLC 2012

147

148

E. Pergament

determined odds, to diagnostic testing first by amniocentesis and then chorionic villus sampling, and, more recently, on screening for single gene mutations and chromosome abnormalities. From a prospective patient perspective, there are three questions that must be addressed: first, “How much information do I want?”; second, “At what time in pregnancy?”; and third, “What will I do with the information in regard to the pregnancy?” This chapter will describe the current status of prenatal testing and preimplantation genetic screening and diagnosis as well as critique anticipated developments in the field of reproductive genetics.

Prenatal Carrier Screening for Single Gene Mutations Carrier screening for selected genetic disorders is now offered routinely to all pregnant women and their partners or those contemplating pregnancy. The American College of Obstetricians and Gynecologists and the American College of Medical Genetics now recommend that all women be offered carrier screening for cystic fibrosis [1], while the latter also recommends testing for spinal muscle atrophy [2]. The risk of being a carrier varies considerably with ethnicity as well as family history (Table 7.1). In general, the risk of being a carrier for any genetic disorder should exceed 1 in 100, in order to be considered for population-wide screening. It is a standard practice to offer carrier screening for sickle-cell disease for African Americans (carrier risk: 1 in 12), thalassemia for persons of Mediterranean parentage (carrier risk: 1 in 20), and for three additional disorders among the Ashkenazi Jewish, Tay-Sachs disease (carrier risk: 1 in 28), familial dysautonomia (carrier risk: 1 in 30), and Canavan disease (carrier risk: 1 in 57). Although carrier screening for only four disorders has been recommended in the case of the Ashkenazim, panels now range from 9 to as many as 22 different genetic conditions [3], including some whose clinical effects can be significantly ameliorated postnatally, e.g., maple syrup urine disease (Table 7.2). The standard for which genetic disorders should comprise a screening panel has not been determined. Questions have not been resolved about widespread Ashkenazi screening for genetic disorders that impact the quality of life (e.g., nonsyndromic sensorineural hearing loss) or are of clinical significance in adulthood (e.g., familial hypercholesterolemia). Patients undergoing carrier testing for cystic fibrosis, spinal muscle atrophy, and other genetic disorders usually believe that such testing definitively identifies them either as a “carrier” or as a “noncarrier.” Genetic testing to determine whether one is a carrier of a specific Mendelian mutation does not provide an absolute answer. Rather genetic testing will reduce the likelihood of being a carrier of a significant gene change. Therefore, there is a residual risk for every carrier screening test result which is significantly influenced by the panel of gene mutations selected for testing. For example, more than 1,700 pathological mutations have been identified in CFTR, the gene associated with cystic fibrosis, and most panels detect from 32 to 99 of the most common mutations, causing the most severe forms of disease. These panels

7

Prenatal Testing: Screening, Diagnosis, and Preimplantation Genetic Diagnosis

149

Table 7.1 Carrier risk, residual risk, and mutation panel detection rate for cystic fibrosis and spinal muscle atrophy according to ethnicity Number of Carrier Residual Detection Disorder Ethnicity mutations risk risk rate Cystic 97 fibrosis Caucasian 1/25 1/290 ~90% Ashkenazim 1/26 1/650 ~95% Hispanic 1/46 1/156 ~81% African American 1/65 1/292 ~78% Asian American 1/90 1/182 ~49% Spinal muscle 1 atrophy Caucasian 1/35 1/632 95% Ashkenazim 1/41 1/350 ~90% Hispanic 1/117 1/1061 ~91% African American 1/66 1/121 ~71% Asian American 1/53 1/628 ~93%

Table 7.2 Carrier frequency and mutation panel detection rate for 22 genetic disorders frequent in the Ashkenazim (AJ) and Sephardic (SH) Jewish population Disease Carrier frequency Detection rate Alpha-1-antitrypsin 1 in 30 ~95% Cystic fibrosiscau 1 in 25 ~95% Tay-Sachs diseaseAJ 1 in 28 >93% Canavan diseaseAJ 1 in 57 >99% Familial dysautonomiaAJ 1 in 30 >99.5% Bloom’s syndromeAJ 1 in 100 >97% Fanconi’s anemiaAJ 1 in 89 >99% Factor XI deficiencyAJ 1 in 23 >96% Familial hyperinsulinemiaAJ 1 in 89 ~90% Familial hypercholesterolemiaAJ 1 in 56 >90% Familial Mediterranean feverSH 1 in 5 ~90% Gaucher’s diseaseAJ 1 in 13 90% Glycogen storage disease type IaAJ 1 in 71 94% Glycogen storage disease type IIIaSH 1 in 35 Lipoamide dehydrogenase deficiencyAJ 1 in 94 ~90% Maple syrup urine diseaseAJ 1 in 80 ~99% Mucolipidosis type IVAJ 1 in 100 >95% Nemaline myopathyAJ 1 in 108 >95% Niemann-Pick type AAJ 1 in 80 ~95% Nonsyndromic sensorineural hearing lossAJ 1 in 25 ~60% Torsion dystoniaAJ 1 in 900 >95% Usher syndrome type 1AJ 1 in 70

150

E. Pergament

are decidedly biased, focusing predominately on mutations frequent in the Caucasian populations, giving the false impression that this is a disease primarily present in such populations and that the carrier risk to others, e.g., African Americans and Hispanics, is considerably less. The cystic fibrosis mutations selected for carrier testing in fact determine the detection rate and the residual risk. Depending on which mutations are detected in carrier parents, the clinical effects in offspring can vary significantly from “nonclassical” cystic fibrosis, e.g., congenital absence of the vas deferens, to “classical” cystic fibrosis. This requires appropriate and detailed genetic counseling for at-risk couples. Another unique and growing problem is those instances where only one member of a reproductive partnership is of Ashkenazi Jewish ancestry. In such cases, carrier testing is conducted on the Ashkenazi individual for population-specific mutations; if a mutation is detected, complete DNA sequencing of that gene should then be considered for the non-Ashkenazi member. Whereas the carrier risk for different diseases among the Ashkenazim has been defined, the risk of being a carrier in other ethnicities is reduced but certainly not zero. This approach provides the means to identify mutations within a gene other than those common to the Ashkenazim. While a negative DNA sequencing result reduces the risk of carrying a pathological mutation, it does not necessarily identify all mutations capable of causing an adverse pregnancy outcome. Testing for the fragile X syndrome, while currently not a standard practice, is under consideration for general population screening and increasingly being requested by patients and physicians. The frequency of carriers of the premutation, i.e., 55–200 CGG repeats, has been estimated to be as high as 1 in 60 [4]. Intermediate fragile X alleles, i.e., 45–54 CGG repeats, occur in 1 in 57. Invasive prenatal diagnosis is warranted for those women with a fragile X allele containing 55 or more CGG repeats; invasive prenatal diagnosis for fragile X syndrome is not indicated in the case of intermediate alleles [5]. With the introduction of “next-generation” sequencing, it is anticipated that carrier testing for recessive diseases will enormously expand to eventually include all of the 1,139 disorders with suspected Mendelian recessive inheritance [6]. Mendelian disorders collectively account for 20% of infant mortality and over 10% of pediatric hospitalization, in turn, contributing to considerable health expenditures and family suffering. The remarkable decline in the incidence of such severe childhood disorders as Tay-Sachs disease is soon to be eclipsed by technologies which permit hundreds of genetic disorders to be screened in a timely, cost-effective, and accurate manner that will greatly expand parental decision making. There is also a potential downside to the introduction of such technologies, particularly the possibility of generating sequence data for which clinical outcomes are unknown. There are the added moral and ethical dilemmas that all preconceptual carrier screenings generate when both prospective parents are found to be carriers of severe childhood recessive diseases that lack medical treatment. With “next-generation” sequencing, it is very likely that everyone will be found to be a carrier of at least one mutation for a severe genetic disorder. Since the reproductive risk ranges from 25% for autosomal recessive disorders, when both parents are carriers, to as high as 50% for autosomal

7


151

dominant disorders and X-linked disorders, one would predict that eventually a standard practice for all pregnancies would either be invasive testing by chorionic villus sampling and amniocentesis or noninvasive prenatal diagnosis through analyses of fetal cells or cell-free fetal DNA in the maternal circulation. Expanded technologies are now projected to provide an unparalleled ability to define an individual’s genome. Such technologies promise to be accurate and cost effective as well as widely available. A recurrent concern, yet to be resolved, is what to do with the information generated by carrier screening. If prospective parents are found to be at increased risk for a having a child with a genetic disorder that compromises quality and/or quantity of life, reproductive choices are limited either to preimplantation or prenatal genetic diagnosis. Preimplantation genetic diagnosis requires undergoing conventional in vitro fertilization, carries an emotional burden that is not easily measureable, is costly, and does not promise success. Prenatal diagnosis either by first trimester chorionic villus sampling or midtrimester amniocentesis carries an obstetrical risk of losing a normal pregnancy as a direct consequence of the procedure and, if a genetic abnormality is present, offers only two solutions: continue the pregnancy and deliver an affected offspring or terminate the pregnancy. Despite all the promises of stem cells, this conundrum of choices continues to be the bane of prenatal diagnosis, as gene therapies for almost all genetic disorders are either unavailable or insubstantial. What most critics of prenatal testing appear to fail to recognize is that prenatal testing has directly contributed toward increasing the total number of pregnancies, encouraging at-risk couples to undertake a pregnancy, and at the same time reassuring the overwhelming majority of prospective parents undergoing carrier screening for a specific set of genetic disorders that their pregnancy is not at increased risk. For many at-risk couples, the moral dilemma surrounding pregnancy termination still remains.

Prenatal Screening for Chromosome Abnormalities Historically, the first introduction of widespread prenatal genetic screening began in the early 1980s and involved monitoring maternal serum alpha fetoprotein (AFP). Elevated levels of maternal serum AFP in the second trimester of pregnancy are an indication of an increased risk for an open neural tube defect such as anencephaly or spina bifida as well as other adverse pregnancy outcomes. This was extended several years later when it was noted that pregnancies with trisomy 18 (Edwards syndrome) and trisomy 21 (Down syndrome) were associated with reduced levels of maternal serum AFP. A number of statistical approaches have been applied to standardize the clinical application of maternal serum AFP screening so as to enhance the discrimination between affected and unaffected pregnancies, thereby making maternal serum AFP screening accessible to all pregnant women in the second trimester of pregnancy. These statistical considerations included sources of error, repeat testing policies, effect of gestational age at the time of screening, and determination of a uniform cutoff level. The latter in particular determines the detection

152

E. Pergament

rate (DR), the false-positive rate (FPR), and the positive predictive value (the odds of being affected for individuals with positive results), in order to justify further diagnostic testing by amniocentesis which carries obstetrical risks to mother and fetus. Those with positive screening results will have had their risk increased by DR/FPR, a factor known as the likelihood ratio (LR). Second trimester maternal serum screening for trisomies 21 and 18 was expanded in the late 1980s to the “triple screen” to include AFP, unconjugated estriol (mE3), and total human chorionic gonadotropin (hCG). In trisomy 21, AFP and mE3 levels are reduced, and the level of hCG is increased; in trisomy 18, all levels are reduced. Triple screen was routinely offered to women less than 35 years of age, and the detection rate, at a false-positive rate of 5%, ranged between 55% and 60%, using the risk of trisomy 21 to a 35-year-old at term as the cutoff and the risk of trisomy 18 to a 40-year-old at term. The relatively recent addition of maternal serum inhibin A, elevated in trisomy 21, was added to comprise the “quadruple” screen, thereby increasing the detection rate in Down syndrome in the second trimester to over 70%. First trimester screening was first introduced in the United Kingdom under the guidance of Kypros Nicolaides at King College [7], followed by an NIH-sponsored multicenter clinical trial in the United States in 1998 [8]. The American College of Obstetrics and Gynecology in 2007 proposed that all pregnant women should be offered screening as well as invasive diagnostic testing regardless of age [9]. First trimester screening for Down syndrome and trisomies 13 and 18 is based on likelihood ratios derived from four independent factors: age, nuchal translucency thickness, maternal serum PAPP-A (pregnancy-associated plasma protein A), and maternal serum free beta or total hCG (human chorionic gonadotropin); this is referred to as the “combined test.” Assessment of the fetal nasal bone improved the performance of first trimester screening: the nasal bone was absent in 60% with Down syndrome, 53% with trisomy 18 and 45% with trisomy 13, and only 2.6% of euploid fetuses [10]. Important sonographic markers for chromosomal abnormalities include fetal growth restriction, tachycardia, abnormal flow in the ductus venosus, megacystis, exomphalos, and single umbilical artery [11]. First trimester aneuploidy screening is associated with very high detection rates, approaching 90% [12] for a false-positive rate of 5% [13]. Following first trimester screening, a number of options are available to the prospective parents: if the calculated risk for Down syndrome exceeds that of a 35 year old, which is 1 in 270, the screening result is considered positive and invasive testing by chorionic villus sampling may be recommended; if the screening result is less than the age-related risk of a 35 year old, second trimester screening is offered. The latter is referred to as “contingency” screening. It is possible then to combine the results of first and second trimester screenings for trisomy 21 (“integrated screening”), which increases the detection rate and thereby lowers the false-negative rate. In a comparison of seven possible screening options for trisomy 21, “contingent sequential” testing (combined sequential: combined first and only those with risk between 1 in 30 and 1 in 1,500 have quad screen) was the most cost effective [14]. One study has reported that when there is a moderate risk for Down syndrome, ranging between 1 in 51 and 1 in 270, the majority of patients, (97%), did not pursue an invasive procedure based on their

7


153

first trimester screening results but completed the sequential screening before making any decisions regarding invasive testing [15]; this would appear to depend rather considerably on the form and direction of the genetic counseling provided. Screening using ultrasound and maternal markers in the first trimester has changed the rate of invasive prenatal diagnosis, particularly in the case of women 35 years of age and older [16], the gestational age of prenatal diagnosis and abortion, and the identification of certain major structural abnormalities. With increasing application of first trimester screening, the number of invasive procedures has declined [17–19]. In addition, the levels of both first trimester maternal serum markers have been shown to affect pregnancy outcome, e.g., PAPP-A levels below 0.3 MoM have been associated with premature rupture of membranes, intrauterine growth reduction, and low birth weight [20]. Increasing numbers of women 35 years of age and greater now rely on the individually adjusted risk figure for Down to make a decision about invasive testing. The application of first and second trimester screenings has led to corresponding reduction in the rates of invasive testing by chorionic villus sampling at 10–14 weeks’ gestation and midtrimester amniocentesis, with fewer losses of fetuses with normal karyotypes [21]. Assessment of secondary ultrasound markers in the first trimester has been reported to reduce the need for chorionic villus sampling by 30%, although this estimate needs further study and confirmation [22].

Prenatal Genetic Diagnosis Prenatal genetic diagnosis exemplifies the effective integration of theoretical and clinical medicine. Milestones in its history include the development of methodologies in cytogenetics and molecular genetics as well as advances in ultrasonography. First and second trimester invasive prenatal diagnoses are most commonly performed to assess the fetal genome for aneuploidy, while increasing numbers of Mendelian diseases can be diagnosed prenatally, particularly with technologies that make it possible to define the nature of specific mutations at the molecular level. Polygenic and multifactorial disorders cannot be reliably diagnosed by genetic testing at present. As obstetricians have gained experience in performing chorionic villus sampling and amniocentesis, there have been continuous improvements in the safety of these invasive procedures. Most obstetricians in the United States will quote the risk of invasive testing, i.e., the risk of losing a pregnancy as a direct result of the obstetrical procedure, as being around 1 in 300, whereas in Western European countries, a risk of 1 in 100 is commonly applied when counseling prospective parents about invasive testing. The miscarriage rates, i.e., spontaneous loss and procedure-related loss, after chorionic villus sampling and amniocentesis were reported to be 1.9% and 1.4%, respectively; this difference was attributed to difference in gestational age at the time of the procedures [23]. However, the miscarriage rate was inversely correlated with the experience of the obstetricians performing invasive testing. There are studies which have concluded that the fetal loss rate after CVS was not significantly different from the group that had no procedure [24].

154

E. Pergament

Cells representing the fetal genotype are obtained either by transcervical or transabdominal chorionic villus sampling performed from 10 to 14 weeks’ gestation or amniocentesis usually performed from 16 to 18 weeks’ gestation. In the majority of women undergoing prenatal genetic diagnosis, the focus of the genetic analysis is a karyotype following cultures of villi or amniocytes. Tissue culturing usually requires 5–12 days before harvesting, and as a preliminary first step, many laboratories have used FISH or quantitative real-time polymerase chain reaction (qRTPCR) for identifying the ploidy state of chromosomes 13, 18, 21, X, and Y [25]. Indications for invasive diagnostic testing have characteristically included advanced maternal age (e.g., 35 years of age and older in the United States), a previous chromosomally abnormal conception, or one parent a carrier of a structural chromosome abnormality, e.g., a translocation. In the case of parents who are both carriers of a Mendelian recessive gene mutation, with a reproductive risk of 25%, a variety of molecular technologies are available to define whether the conception is clinically normal, i.e., homozygous for the nonmutant gene or a carrier like the parents, or is clinically affected. Whereas in the past it may have only been possible to identify specific “common” mutations, the availability of techniques such as automated sequencing and novel technologies including mutation scanning techniques, multiplex ligation dependent probe amplification (MLPA), and array technologies has markedly improved the diagnostic efficiency of molecular testing [26]. Similar molecular technologies can also be applied in the case of a dominant or X-linked disorder. The American College of Obstetrics and Gynecology now recommends that invasive prenatal diagnostic testing be made available to all pregnant women, regardless of age or prenatal screening results [9]. Although maternal age is a major factor in evaluating the need for invasive prenatal diagnosis of aneuploidy, there is no evidence that paternal age is contributing to the risk of a chromosome abnormality [27]. Finally, the detection of a fetal chromosome abnormality in the course of first and second trimesters diagnostic testing presents moral, ethical, and legal issues concerning pregnancy termination. In the United States, the recently enacted federal law, the Prenatally and Postnatally Diagnosed Conditions Awareness Act (United States Public Law 110–374), seeks to improve opportunities for parents and pregnant women to anticipate and understand the likely life course of children born with Down syndrome and other unspecified conditions [28]. This law is likely to contribute to increased public and political debate concerning the legal, social, and ethical issues associated with pregnancy termination in the United States. For the past 30 years, conventional chromosome analysis using the G-banded karyotype has been the method of choice for prenatal genetic diagnosis, accurately detecting chromosome abnormalities larger than ~5 Mb. Conventional chromosome analysis is not capable of identifying submicroscopic deletions or duplications that often are associated with congenital malformations and mental retardation. Subtle molecular genetic abnormalities which cannot be detected with the conventional G-banded karyotype can be identified by array-based comparative genomic hybridization (aCGH). Array CGH has been established for the fast and accurate detection of unbalanced structural and numerical chromosome abnormalities in postnatal diagnosis, allowing characterization of syndromes, phenotype, and genotype

7


155

correlation, prevention, prognosis, and better clinical management. The detection rate of array CGH in postnatal diagnosis ranges between 7% and 11% in children with mental retardation and/or multiple congenital abnormalities with normal or no karyotype [29–31]. At the present time, aCGH has had limited use in prenatal diagnosis, and a National Institute of Health multicenter sponsored study is in progress to determine its efficacy. The application of aCGH in prenatal genetic diagnosis has several obvious advantages: higher resolution independent of the ability of cell to grow and/or generating a metaphase spread, less than 100 Kb resolution versus the standard karyotype of 5 Mb resolution, direct mapping of aberrations to the genome sequence, amenable to automation and quality control procedures, and higher throughput and shorter reporting times. Even though the higher resolution of analysis allows the identification of genetic imbalances at the molecular level, the presence of polymorphisms representing microdeletions/microduplications either of unknown clinical significance or of “benign variation” challenges the clinical introduction into the field of prenatal genetic diagnosis. An unresolved issue is the question as to which aCGH platform should be applied in prenatal diagnosis: a targeted array or a genome-wide array. Target arrays focus on specific genomic disorders as well as subtelomeric and pericentromeric regions. Genome-wide arrays depend on uniform distribution of probes along the lengths of each chromosome. In one study comparing targeted versus genome-wide aCGH applied to prenatal specimens, aCGH identified all numerical and structural aberrations in pregnancies with known cytogenetic abnormalities; clinically, significant copy number variations (CNVs), i.e., changes not detectable by conventional chromosome analysis, ranged from 1% in targeted arrays to 2.7% in genome-wide arrays [32]. However, detection of benign CNVs as well as CNVs of unclear clinical significance was also a serious issue, the former being as high as 8% in both arrays, and the latter, 0.5% in the genome-wide array platform after parental analysis. In another study, targeted aCGH was applied in the evaluation of 300 prenatal samples: 58 CNVs were detected, 5% were clinically significant chromosome alterations, 1% were of uncertain clinical significance, and 13.3% were benign CNVs [33]. Conventional chromosome analysis performed on fetuses with structural anomalies identified at 18–20 weeks’ gestation reveals an abnormal karyotype in approximately 10% of cases. Applying aCGH to fetuses with a normal karyotype identified significant copy number alterations in 2% [34] and as high as 10.8% [35]. A similar study using a genome-wide platform (44 K oligonucleotide array) confirmed causative CNVs in 15% of fetuses with ultrasound-based structural abnormalities and a normal karyotype [36]. Further studies are required to more precisely identify which fetal structural anomalies warrant the additional cost and effort of the introduction of aCGH in prenatal diagnosis [37]. A reliable and convenient method for noninvasive prenatal diagnosis (NIPD) has long been sought to reduce the risk of miscarriage following invasive testing as well as to allow for testing that is early in pregnancy and universally available. Multiple studies have shown that both intact fetal cells and cell-free fetal nucleic acids (cffNA) cross the placenta and circulate in the maternal blood. Extensive investigations have been conducted in identifying and genotyping fetal cells present in the

156

E. Pergament

maternal circulation [38]. Various methods of fetal cell enrichment have been developed with varying degrees of success [39]. Results to date have been disappointing because of the scarcity of intact fetal cells in the maternal circulation (estimated to be around one cell per ml of maternal blood) [40], low efficiency of enrichment, and difficulties with chromosomal analysis associated with abnormally dense nuclei [41]. Certain fetal cells, especially nucleated red blood cells, have a relatively short lifespan in maternal circulation [42], and research into sophisticated cell sorting techniques is ongoing [43]. Most research on NIPD has focused on strategies for detecting cell-free fetal nucleic acids. The presence of cell-free fetal DNA in the maternal circulation was first reported by Lo et al. [44]. Cell-free fetal DNA apparently originates from apoptotic trophoblastic cells derived from the placenta [45, 46] and ranges from 3% of the total cellfree DNA in maternal blood during early pregnancy to as high as 6% late in pregnancy [47]; the remaining 94–97% is derived from maternal cell-free DNA. Cell-free fetal DNA consists predominately of short DNA fragments, as compared to whole chromosomes, and can reliably be detected from 7 weeks’ gestation onward, from the equivalent of 16 fetal genomes per ml of maternal blood in the first trimester to greater than 75 in the third trimester [48]. In addition, cell-free mRNA has also been detected in the maternal circulation, representing genes that are actively expressed in the placenta [49]. Although cell-free fetal RNA also has the potential for NIPD, up to the present time research has been directed toward the use of cell-free fetal DNA in the maternal circulation. Cell-free fetal DNA has been applied to families at significant reproductive risk for inheritable monogenic diseases. Applications include sex determination in cases at risk of X-linked diseases, detection of specific paternally inherited single gene disorders, and Rhesus factor status in RhD negative women. Using single molecule counting methods, including digital PCR and massively parallel sequencing, it has been claimed that noninvasive prenatal diagnosis for monogenic disorders and aneuploidy is possible [50]. Multimarker assays including genome-wide approaches are being applied in population-based, double-blind, large-scale clinical cohort trials to determine the sensitivity, specificity, accuracy, and precision of cell-free fetal DNA in noninvasive prenatal genetic diagnosis of trisomy 21. Noninvasive prenatal genetic diagnosis has been of interest for more than a quarter of a century and may in the future become part of routine obstetrical care for all pregnant couples for the identification of trisomy 21 and other common genetic disorders. The prospect that noninvasive prenatal genetic diagnosis will increase pregnancy terminations on the grounds of disability will again become a source of controversy [51]. Advances in ultrasonography have also enabled more reliable in utero identification of structural malformations. Ultrasound evaluation at 18–20 weeks’ gestation has been reported to detect between 22% and 74% of major structural anomalies [52]. Midtrimester soft sonographic markers have been associated with abnormal karyotypes. Significant soft markers are nuchal thickness, echogenic bowel, short limbs, and absence of nasal bone [53]. Soft markers that are only significant in combination with altered maternal serum markers, or in women of advanced maternal age, include intracardiac echogenic focus, choroid plexus cyst, pyelectasis, and

7


157

single umbilical artery. Other potentially useful soft signs identifying pregnancies with karyotypic abnormalities are hypoplasia of the fifth finger, sandal gap, ear size, brachycephaly, cervical cyst, and iliac angle. Risk assessment for trisomy 21 for each of six soft ultrasound markers (nuchal thickness, echogenic bowel, echogenic cardiac focus, short humerus, short femur, and major structural anomaly) can be integrated with risk based on maternal age and maternal serum markers to determine if invasive testing is warranted. Invasive testing in such cases is usually recommended when the risk assessment exceeds the risk of trisomy 21 at term for a 35 year old, namely, 1 in 380. The consequences of prenatally diagnosing structural congenital anomalies by ultrasound are profound. In such instances, prospective parents must be counseled either by a medical geneticist, maternal-fetal medicine specialist, and/or neonatologist concerning therapeutic options. Invasive testing is likely to be considered in order to rule out a chromosomal abnormality or a single gene mutation. In continuing pregnancies, serial ultrasound examination is necessary to assess the developmental progress of the original structural anomaly(ies) as well as the identification of other, previously unidentified anomalies, all of which have the potential of altering the genetic counseling and clinical management. If pregnancy termination, stillbirth, or neonatal death occurs, a complete autopsy should be made available in order to determine the diagnosis and etiology of the structural anomaly(ies). In those pregnancies coming to term, newborn evaluation is also essential in determining diagnosis, etiology, prognosis, and recurrence risk.

Preimplantation Genetic Diagnosis and Screening Preimplantation genetic testing is an alternative to prenatal diagnosis for the detection of genetic disorders in couples at risk of transmitting a genetic condition to their offspring. Preimplantation genetic diagnosis is made possible by three integrated technologies: conventional in vitro fertilization (IVF), micromanipulation of single cells, and genetic analysis of the single cell. In the course of conventional in vitro fertilization, three sources are available for genotyping: (1) the first and second polar bodies, (2) the day 3 blastomere, and/or (3) the trophoblast at day 5. Most clinical programs provide preimplantation genetic testing through biopsy of the day 3 blastomere; in certain settings, particularly where embryonic testing is legally constrained, genetic analysis of the first and second polar bodies is used. There are several important components of an IVF program providing preimplantation genetic diagnosis: genetic counseling emphasizing the benefits and limitations of all available options for preimplantation and prenatal diagnosis, the genetic abnormality for which the couple is at reproductive risk can be identified with tests on a single cell, and the possibility of a false-negative result requires recommending confirmation by invasive testing. A frequent use of preimplantation genetic diagnosis involves a prospective parent who is a carrier of a translocation and is, therefore, at significant risk for pregnancy failure (see Figs. 7.1 and 7.2).

158

E. Pergament

Fig. 7.1 Metaphase figure demonstrating translocation between the short arm of chromosome 5 and the long arm of chromosome 10. FISH probes for the telomere regions of the short arm of chromosome 5 and the long arm of chromosome 10

Preimplantation screening for aneuploidy has been proposed for a variety of clinical conditions preventing a successful pregnancy, including recurrent pregnancy loss, recurrent IVF failure, and advanced maternal age; however, the benefits of this approach using FISH technology have been questioned [54]. Aneuploid screening by fluorescence in situ hybridization (FISH) for nine chromosomes is currently undergoing transition to a more sophisticated genetic evaluation, aCGH. Whole genome amplification (WGA) and subsequent DNA analysis on high-resolution array platforms has the potential to be a universal, off-the-shelf protocol that would lead to the replacement of FISH and multiplex polymerase chain reaction (mPCR). Array CGH has also undergone transition from the use of BAC (bacterial artificial chromosomes) probes and oligonucleotide probes to single nucleotide polymorphism (SNP) arrays; the latter has the advantage that loci can be interpreted for both chromosomal copy number and genotypic data [55, 56]. The accuracy of FISH applied to single blastomeres in assessing the preimplantation embryo’s genotype has been repeatedly questioned; recently, aCGH has been used to validate embryos diagnosed as abnormal by FISH [57]. FISH is also limited by the total number of chromosomes that can be analyzed in a single blastomere, usually chromosomes 13, 15, 16, 17, 18, 21, 22, X and Y, whereas with aCGH, ploidy (chromosome number) as well as microdeletions and microduplications along the complete

7


159

Fig. 7.2 Blastomere demonstrating balanced chromosome constitution based on two signals for the short arm of chromosome 5 and the long arm of chromosome 10

length of each chromosome can be assayed. Using aCGH, the false-negative rate was estimated to be 3.7%, which is in the ranges reported by FISH [57]. Randomized trials comparing FISH to aCGH, and the potential contribution of each to enhanced “take-home baby” rates, have yet to be reported. The biopsy of several trophectoderm cells from blastocysts followed by aCGH represents another strategy for detection of aneuploidy [58]. A source of concern with this approach is the rate of chromosome mosaicism which has the potential of leading to a misdiagnosis. In a study comparing cytogenetic analysis of human blastocysts using FISH, CGH, and aCGH, one-third of all embryos were mosaic of which 15.4% were found to be composed of a mixture of different aneuploid cell lines, while 17% contained both normal and aneuploid cells [59]. Furthermore, mosaic diploid-aneuploid blastocysts with greater than 30% normal cells accounted for less than 6% of embryos analyzed. These results indicated that meiotic and postzygotic errors leading to mosaicism are common in preimplantation embryos. In contrast, another study using aCGH found that 80% of blastocysts were euploid and that discordance between inner cell mass and trophectoderm occurred in 2 of 51 embryos (4%), but this involved structural chromosome abnormalities and not numerical aberrations [60]. The diagnostic accuracy of this approach has not been adequately studied, and the reliability and accuracy of the trophectoderm as a representative of the embryonic/fetal genome is still unresolved.

160

E. Pergament

Preimplantation genetic diagnosis for single gene disorders has been applied to a wide range of mutations. However, details concerning success rates are based on data voluntarily submitted by IVF programs to the European Society of Human Reproduction [60]. These reports do not represent studies that have been conducted under the sanction of objective oversight or evaluation by means of defined clinical trials. Preimplantation genetic diagnosis must still be considered a personal choice – not a standard of care – and at present still subject to confirmation by invasive testing.

References 1. American College of Obstetricians and Gynecologists, American College of Medical Genetics. Preconception and prenatal carrier screening for cystic fibrosis: clinical and laboratory guidelines. Washington, DC/Bethesda(MD): ACOG/ACMG; 2001. 2. Prior TW. Carrier screening for spinal muscular atrophy. Gene Med. 2008;10:840–2. 3. Schriver I, Kulm M, Gardner PI, et al. Comprehensive arrayed primer extension array for the detection of 59 sequence variants in 15 conditions prevalent among the (Ashkenazi) Jewish population. J Mol Diagn. 2007;9:228–36. 4. Hantash FM, Goos DM, Crossley B, et al. FMR1 premutation carrier frequency in patients undergoing routine population-based carrier screening: Insights into the prevalence of fragile X syndrome, fragile X-associated tremor/ataxia syndrome, and fragile X-associated primary ovarian insufficiency in the United States. Gene Med. 2011;13:39–45. 5. Cronister A, Teicher J, Rohlfs EM, et al. Prevalence and instability of fragile X alleles: implications for offering fragile X prenatal diagnosis. Obstet Gynecol. 2008;111:596–601. 6. Kuhlenbauer G, Hullmann J, Appenzeller S. Novel genomic techniques open new avenues in the analysis of monogenic disorders. Hum Mutat. 2011;32:144–51. 7. Kagan KO, Wright D, Valencia C, et al. Screening for trisomies 21, 18, and 13 by maternal age, fetal nuchal translucency, fetal heart rate, free b-hCG and pregnancy-associated plasma protein-A. Hum Reprod. 2008;23:1968–75. 8. Wapner R, Thom E, Simpson JL, et al. First-trimester screening for trisomies 21 and 18. N Engl J Med. 2003;349:1405–13. 9. ACOG Committee on Practice Bulletins. ACOG Practice Bulletin No 77: screening for fetal chromosomal abnormalities. Obstet Gynecol. 2007;109:217–27. 10. Kagen KO, Cicero S, Staboulidou I. Fetal nasal bone in screening for trisomies 21, 18 and 13 and Turner syndrome at 11–13 weeks of gestation. Ultrasound Obstet Gynecol. 2009;33: 259–64. 11. Nicoloaides KH. Nuchal translucency and other first-trimester sonographic markers of chromosomal abnormalities. Am J Obstet Gynecol. 2004;191:45–67. 12. Cicero S, Spencer K, Avgidou K, et al. Maternal serum biochemistry at 11–13(+6) weeks in relation to the presence or absence of the fetal nasal bone on ultrasonography in chromosomally abnormal fetuses: an updated analysis of integrated ultrasound and biochemical screening. Prenat Diagn. 2005;25:977–83. 13. Nicolaides KH, Spencer K, Avgidou K, et al. Multicenter study of first-trimester screening for trisomy 21 in 75,821 pregnancies: results and estimation of the potential impact of individual risk-oriented two-stage first-trimester screening. Ultrasound Obstet Gynecol. 2005;25:221–6. 14. Ball RH, Caughey AB, Malone FD, et al. First- and second-trimester evaluation of risk for Down syndrome. Obstet Gynecol. 2007;110:10–7. 15. Wagner D, Pargas C, Donnenfeld AE. Moderately increased risks of Down’s syndrome (1/51– 1/270) identified on first trimester sequential screening: what do patients do with this information. J Med Screen. 2010;17:4–7.

7


161

16. Nakata N, Wang Y, Bhatt S. Trends in prenatal screening and diagnostic testing among women referred for advanced maternal age. Prenat Diagn. 2010;30:198–206. 17. Kjaergaard Hahnemann JM, Skibsted L, et al. Prenatal diagnosis of chromosome aberrations after implementation of screening for Down’s syndrome. Ugeskr Laeger. 2008;170:1152–6. 18. Vestergaard CH, Lidegaard O, Tabor A. Invasive prenatal diagnostic practice in Denmark 1996 to 2006. Acta Obstet Gynecol Scand. 2009;88:362–5. 19. Fang YM, Benn P, Campbell W, et al. Down syndrome screening in the United States in 2001 and 2007: a survey of maternal-fetal medicine specialists. Am J Obstet Gynecol. 2009;201:97.e1–5. 20. Krantz D, Goetz L, Simpson JL, et al. Association of extreme first-trimester free human chorionic gonadotropin-beta, pregnancy-associated plasma protein A, and nuchal translucency with intrauterine growth restriction and other adverse pregnancy outcomes. Am J Obstet Gynecol. 2004;191:1452–8. 21. Nadel AS, Likhite ML. Impact of first-trimester aneuploidy screening in a high-risk population. Fetal Diagn Ther. 2009;26:29–34. 22. Molina Garcia FS, Carrillo Badillo MP, Zaragoza Garcia EA, et al. Analysis of secondary ultrasound markers in the first trimester before chorionic villus sampling. Prenat Diagn. 2010; 30:1117–20. 23. Tabor A, Vestergaard CH, Lidegaard O. Fetal loss rate after chorionic villus sampling and amniocentesis: an 11-year national registry study. Ultrasound Obstet Gynecol. 2009;34: 19–24. 24. Odibo AO, Dicke JM, Grey DL, et al. Evaluating the rate and risk factors for fetal loss after chorionic villus sampling. Obstet Gynecol. 2008;112:813–9. 25. Ciriglliano Voglino G, Ordonez E, et al. Rapid prenatal diagnosis of common chromosome aneuploidies by QF-PCR, results of 9 years of clinical experience. Prenat Diagn. 2009;29: 40–9. 26. Walter JH. Genes, patients, families, doctors – mutation analysis in clinical practice. J Inherit Metab Dis. 2009;32:441–6. 27. Sartorius GA, Nieschlag E. Paternal age and reproduction. Hum Reprod Update. 2010;16: 65–79. 28. Reilly PR. Commentary: the federal ‘Prenatally and Postnatally Diagnosed Conditions Awareness Act’. Prenat Diagn. 2009;29:829–32. 29. Shevell MI, Bejjani BA, Srour M, et al. Array comparative genomic hybridization in global developmental delay. Am J Med Genet B Neuropsychiatr Genet 2007;1101–8. 30. Moeschler JB. Medical genetics diagnostic evaluation of the child with global developmental delay or intellectual disability. Curr Opin Neurol. 2008;21:117–22. 31. Schoumans J, Rulvenkamp C, Holmberg E, et al. Detection of chromosomal imbalances in children with idiopathic mental retardation by array based comparative genomic hybridization (array-CGH). J Med Genet. 2005;42:699–705. 32. Coppinger J, Alliman S, Lamb AN, et al. Whole-genome microarray analysis in prenatal specimens identifies clinically significant chromosome alterations without increase in results of unclear significance compared to targeted microarray. Prenat Diagn. 2009;29:1156–66. 33. Van den Veyver IB, Patel A, Shaw CA, et al. Clinical use of array comparative genomic hybridization (aCGH) for prenatal diagnosis in 300 cases. Prenat Diagn. 2009;29:29–39. 34. Kleeman L, Bianchi DW, Shaffer LG, et al. Use of array comparative genomic hybridization for prenatal diagnosis of fetuses with sonographic anomalies and normal metaphase karyotype. Prenat Diagn. 2009;29:1213–7. 35. Hayashi S, Imoto I, Aizu Y, et al. Clinical application array-based comparative genomic hybridization by two-stage screening for 536 patients with mental retardation and multiple congenital anomalies. J Hum Genet. 2011;56:110–24. 36. Vialard F, Molina Gomes D, Leroy B, et al. Array comparative genomic hybridization in prenatal diagnosis: another experience. Fetal Diagn Ther. 2009;25:277–84. 37. Valduga M, Philippe C, Bach Segura P, et al. A retrospective study of oligonucleotide arrayCGH analysis in 50 fetuses with multiple malformations. Prenat Diagn. 2010;30:333–41.

162

E. Pergament

38. Jackson L. Fetal cells and DNA in maternal blood. Prenat Diagn. 2003;23:837–46. 39. Sekizawa A, Purwosunu Y, Matsuoka R, et al. Recent advances in non-invasive prenatal DNA diagnosis through analysis of maternal blood. J Obstet Gynaecol Res. 2007;33:747–64. 40. Bianchi DW, Williams JM, Sullivan LM, et al. PCR quantitation of fetal cells in maternal blood in normal and aneuploid pregnancies. Am J Hum Genet. 1997;61:822–9. 41. Babochkina T, Mergenthaler S, De Napoli G, et al. Numerous erythroblasts in maternal blood are impervious to fluorescent in situ hybridization analysis, a feature related to a dense compact nucleus with apoptotic character. Haematologica. 2005;90:740–5. 42. Lurie S, Mamet Y. Red blood cell survival and kinetics during pregnancy. Eur J Obstet Gynecol Reprod Biol. 2000;93:185–92. 43. Wright CF, Burton H. The use of cell-free fetal nucleic acids in maternal blood for non-invasive prenatal diagnosis. Hum Reprod Update. 2009;15:139–51. 44. Lo YMD, Corbetta N, Chamberlain PF, et al. Presence of fetal DNA in maternal plasma and serum. Lancet. 1997;350:485–7. 45. Tjoa ML, Cindrova-Davies T, Spasic-Boskovic O, et al. Trophoblastic oxidative stress and the release of cell-free feto-placental DNA. Am J Pathol. 2006;169:400–4. 46. Alberry M, Maddocks D, Jones M, et al. Free fetal DNA in maternal plasma in anembryonic pregnancies: confirmation that the origin is the trophoblast. Prenat Diagn. 2007;27:415–8. 47. Lo YMD, Tein MS, Lau TK, et al. Quantitative analysis of fetal DNA in maternal plasma and serum: implications for noninvasive prenatal diagnosis. Am J Hum Genet. 1998;62:768–75. 48. Birch L, English CA, O’Donoghue K, et al. Accurate and robust quantification of irculating fetal and total DNA in maternal plasma from 5 to 41 weeks of gestation. Clin Chem. 2005;51:312–20. 49. Poon LLM, Leung TN, Lau TK, et al. Presence of fetal RNA in maternal plasma. Clin Chem. 2000;46:1832–4. 50. Chiu RW, Cantor CR, Lo YM. Non-invasive prenatal diagnosis by single molecule counting technologies. Trends Genet. 2009;25:324–31. 51. Newson AJ. Ethical aspects arising from non-invasive fetal diagnosis. Semin Fetal Neonatal Med. 2008;13:103–8. 52. Fadda GM, Capobianco G, Balata A, et al. Routine second trimester ultrasound screening for prenatal detection of fetal malformations in Sassari University Hospital, Italy: 23 years of experience in 42,256 pregnancies. Eur J Obstet Gynecol Reprod Biol. 2009;144:110–4. 53. Ben-Ami M, Jadaon JE. The genetic sonogram. Harefuah. 2009;148:455–9. 474. 54. Audiert F, Wilson RD, Allen V, et al. Preimplantation genetic testing. J Obstet Gynaecol Can. 2009;31:761–75. 55. van Uum CMJ, Stevens SJC, Dreesen JCFM, et al. Snp array-based combination of copy number and genotype analyses to determine chromosomal imbalances in human blastomeres. Hum Reprod. 2010;25 Suppl 1:i61–3. 56. Handyside A, Gabriel A, Thornhill AR, et al. Preliminary validation of SNP genotyping and karyomapping for preimplantation genetic of fifty eight autosomal single gene defects. Hum Reprod. 2010;25 Suppl 1:i323–4. 57. Mir P, Rodrigo L, Cervero A, et al. Validation of arrayCGH on day-4 single blastomeres from day-3 embryos diagnosed as abnormal by FISH. Hum Reprod. 2010;25 Suppl 1:i63–4. 58. Schoolcraft WB, Fragouli E, Stevens J, et al. Clinical application of comprehensive chromosomal screening at the blastocyst stage. Fertil Steril. 2009;94(5):1700–6. 59. Johnson DS, Cinnioglu C, Ross R, et al. Comprehensive analysis of karyotypic mosaicism between trophectoderm and inner cell mass. Mol Hum Reprod. 2010;16:944–9. 60. Harper JC, Coonen E, De Rycke M, et al. ESHRE PGD consortium data collection X: cycles from January to December 2007 with pregnancy follow-up to October 2008. Hum Reprod. 2010;25:2685–707.

Chapter 8

Newborn Screening for Metabolic Disorders Marzia Pasquali and Nicola Longo

Introduction Metabolic disorders affect the transformation of nutrients in energy or in other compounds necessary for growth. They are due to deficiencies in enzymes or transporters involved in the metabolism of sugars, amino acids, fatty acids, and macromolecules. There is a wide spectrum of clinical presentation with onset of symptoms ranging from the newborn period to adult life. The consequences of untreated metabolic disorders can be extremely severe and devastating, independently from the age of onset. Early identification and treatment of these conditions before symptoms appear and irreversible damage has occurred can improve overall outcome and quality of life, with reduction of morbidity, mortality, and disabilities. In recent years, advances in technology, such as the introduction of tandem mass spectrometry, has enabled neonatal screening for many diseases caused by impaired metabolism of amino acids and fatty acids through multiplex analysis of several metabolites. This chapter will discuss some of the most common disorders of metabolism identified through universal newborn screening and how molecular genetics can integrate the screening and biochemical tests to reach a definite diagnosis of metabolic disorders in the asymptomatic patient.

M. Pasquali, Ph.D., FACMG (*) Department of Pathology, University of Utah and ARUP Laboratories, 500 Chipeta Way, Salt Lake City, UT 84108, USA e-mail: [email protected] N. Longo, M.D., Ph.D. Division of Medical Genetics, Department of Pediatrics, University of Utah, 2C412 SOM, 50 N Mario Capecchi Drive, School of Medicine, Salt Lake City, UT 84132, USA e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_8, © Springer Science+Business Media, LLC 2012

163

164

M. Pasquali and N. Longo

Newborn Screening Newborn screening is a public health activity that started in the early 1960s, thanks to Dr. Robert Guthrie, who developed a bacterial inhibition assay to screen for phenylketonuria using newborns’ blood spotted and dried on filter paper [1]. Since then, millions of infants in the United States have been screened for a variety of genetic and congenital disorders. Each state in the United States decides the panel of disorders included in their newborn screening programs. The use of tandem mass spectrometry (MS/MS), detecting simultaneously amino acids and acylcarnitines in the same blood spot, has greatly increased the number of disorders amenable to newborn screening [2, 3]. In 2005, the American College of Medical Genetics (ACMG) released a report, commissioned by the Maternal and Child Health Bureau (MCHB) of HRSA (Health Resources and Services Administration), with recommendations for a uniform panel for newborn screening [4]. This report recommended screening all newborns in the United States for 29 conditions, including five fatty acid oxidation disorders, nine organic acidemias, six aminoacidopathies, three hemoglobinopathies, and six other disorders (Table 8.1). Of these conditions, 20 are screened for using tandem mass spectrometry, while the others use more “traditional” methods (isoelectrofocusing, immunoassay, HPLC, etc.). At the beginning of 2011, the addition of SCID (severe combined immunodeficiency) to the uniform panel was recommended and introduced the first DNA-based screening test in newborn screening [5]. Two main classes of metabolites are detected by tandem mass spectrometry: amino acids and acylcarnitines. Amino acids become elevated in certain aminoacidopathies and urea cycle defects (phenylketonuria, tyrosinemia, maple syrup urine disease, etc.), while the study of the acylcarnitine profile can identify defects of fatty acid oxidation (medium-chain acyl-CoA dehydrogenase deficiency, very-long-chain acyl-CoA dehydrogenase deficiency, and others) and organic acidemias (propionic acidemia, methylmalonic acidemia, glutaric acidemia type I, etc.). Disorders of carbohydrate metabolism (such as galactosemia) cannot yet be detected by MS/MS; however, methods are in development [6–8]. Blood is collected from a heel stick from each newborn and spotted on a filter paper card, and then the card is sent to a centralized laboratory where testing occurs. Typically, the sample is collected prior to discharge from the hospital of birth, between 24 and 48 h of life. This allows enough time to have a build-up of abnormal metabolites as a result of a “full” feeding, necessary to identify amino acidopathies, yet maintaining the ability to identify fatty acid oxidation disorders, which require a more “stressed” sample. Abnormal results, i.e., results outside the range observed for the normal population, are followed up with diagnostic tests to confirm or exclude a diagnosis of a metabolic disorder. Typically, biochemical genetics tests (amino acids, organic acids, acylcarnitines analyses in blood, and/or urine) represent the first line of testing and are sufficient to diagnose or exclude a metabolic disorder. This is especially true when patients are symptomatic and their biochemical phenotype is grossly abnormal. With the expansion of newborn screening, metabolic disorders are now identified before the

8

Newborn Screening for Metabolic Disorders

165

Table 8.1 Recommended uniform screening panel (core conditions) of the Secretary’s Advisory Committee on Heritable Disorders in Newborns and Children ACMG code Core condition Organic acidemia PROP Propionic acidemia MUT Methylmalonic acidemia (methylmalonyl-CoA mutase) Cbl A,B Methylmalonic acidemia (cobalamin disorders) IVA Isovaleric acidemia 3-MCC 3-Methylcrotonyl-CoA carboxylase deficiency HMG 3-Hydroxy-3-methylglutaric aciduria MCD Holocarboxylase synthase deficiency ßKT ß-Ketothiolase deficiency GA1 Glutaric acidemia type I CUD MCAD VLCAD LCHAD TFP

Fatty acid oxidation Carnitine uptake defect/carnitine transport defect Medium-chain acyl-CoA dehydrogenase deficiency Very-long-chain acyl-CoA dehydrogenase deficiency Long-chain L-3 hydroxyacyl-CoA dehydrogenase deficiency Trifunctional protein deficiency

ASA CIT MSUD HCY PKU TYR I

Amino acidopathy Argininosuccinic aciduria Citrullinemia, type I Maple syrup urine disease Homocystinuria Classic phenylketonuria Tyrosinemia, type I

CH CAH

Endocrinopathy Primary congenital hypothyroidism Congenital adrenal hyperplasia

Hb SS Hb S/ßTh Hb S/C

Hemoglobinopathy S,S disease (sickle cell anemia) S, b-thalassemia S,C disease

Others BIOT Biotinidase deficiency CCCHD Critical cyanotic congenital heart disease (pending secretary approval) CF Cystic fibrosis GALT Classic galactosemia HEAR Hearing loss SCID Severe combined immunodeficiencies The nomenclature for conditions is based on the report “Naming and counting disorders (conditions) included in newborn screening panels” Pediatrics 2006;117(5) Suppl:S308-14

patients become symptomatic, at least in the majority of the cases. This brings an additional level of complexity, as the biochemical phenotype is now less obvious, and the final diagnosis may require additional tests such as enzyme/receptor/transporter assays and/or molecular analysis. In addition, for some metabolic conditions, heterozygotes may show the same abnormalities as the affected individuals;

166


therefore, the inclusion of molecular analysis in confirmatory algorithms (see, e.g., those recommended by the American College of Medical Genetics: http://www. acmg.net/AM/Template.cfm?Section=NBS_ACT_Sheets_and_Algorithms_ Table&Template=/CM/HTMLDisplay.cfm&ContentID=5072) is increasingly used. This chapter will review the groups of metabolic disorders identified by newborn screening, with special emphasis on the molecular diagnosis of these conditions.

Disorders of Amino Acid Metabolism The concentrations of free amino acids in physiological fluids reflects the balance between exogenous intake, endogenous release from the catabolism of proteins, the kidney function responsible for filtration and reabsorption, and the body utilization to synthesize proteins or produce energy. Changes in any of these steps can result in accumulation or deficiency of one or more amino acids. The aminoacidopathies phenylketonuria, maple syrup urine disease, homocystinuria, tyrosinemia type I, and the two defects of the urea cycle, citrullinemia and argininosuccinic aciduria, are the amino acid disorders included in the core panel of conditions recommended by the American College of Medical Genetics to be screened for in each infant (Table 8.1). The characteristic amino acids elevated in these conditions are identified by tandem mass spectrometry. Other disorders of the urea cycle (such as ornithine transcarbamylase deficiency and carbamyl phosphate synthase deficiency), characterized by low concentrations of citrulline and arginine, are not included in this panel because the reliability of newborn screening in identifying analytes present at low concentrations is not optimal. To overcome this problem, the use of ratios of amino acids seems promising. Table 8.2 lists selected disorders of amino acid metabolism identifiable by newborn screening and the strategy for confirming or excluding the diagnosis.

Phenylketonuria Phenylketonuria (PKU) results from the impaired conversion of phenylalanine to tyrosine leading to increased concentration of phenylalanine in body fluids. Phenylketonuria is caused by a deficiency of phenylalanine hydroxylase, the enzyme converting phenylalanine into tyrosine, in about 98% of cases. In the remaining 2% of the cases, elevated phenylalanine is due to a defect in the synthesis or recycling of tetrahydrobiopterin, the essential cofactor of phenylalanine hydroxylase. The combined incidence of these conditions is about 1:10,000 to 1:20,000 live births. In addition to accumulation of phenylalanine, some of its metabolites, such as phenyllactate and phenylpyruvate (phenylketones), can also accumulate and be excreted in the urine. The pathophysiology is due to the neurotoxic effect of high concentrations of phenylalanine and deficiency of tyrosine, which interferes with the synthesis of neurotransmitters [10].

Citrulline (might be normal at birth) ASL Citrulline Argininosuccinic acid PAA plasma amino acids, UOA urine organic acids

SLC25A13

BCKDHA (E1a) BCKDHB (E1b) DBT (E2) ASS1

Citrulline

Leucine

CBS

Homocystinuria (Classic) (HCY) Maple Syrup Urine Disease (MSUD)

Citrullinemia type I (CIT) Citrullinemia type II (citrin deficiency) Argininosuccinic aciduria (ASA)

Succinylacetone ( Tyrosine not reliable) Methionine

FAH

Tyrosinemia type I (TYR-I)

Fibroblasts

Citrulline, argininosuccinic acid (PAA)

Full gene sequencing


Not indicated

Citrulline (and other amino acids) (PAA)



Targeted mutational analysis


Fibroblasts

Liver (DNA testing preferable) Fibroblasts

Fibroblasts

Leucine, valine, isoleucine Alloisoleucine (PAA) Citrulline (PAA)

Succinylacetone (UOA) Tyrosine (and other amino acids) (PAA) Methionine Homocystine (PAA)

Table 8.2 Diagnostic strategy and DNA testing in selected disorders of amino acid metabolism identifiable by newborn screening First-line DNA Disease Gene Newborn screening Biochemical findings Enzyme assay testing Phenylketonuria PAH Phenylalanine Phenylalanine (PAA) Liver (not Full gene sequencing (PKU) usually Normal pterin profile done) Normal DHPR activity

NA

NA

NA

NA

NA


Additional DNA testing NA

8 Newborn Screening for Metabolic Disorders 167

168


Patients with phenylketonuria (PKU) do not show any symptoms at birth nor suffer any acute metabolic decompensation attacks. Delays in development, chronic eczema, and acquired microcephaly appear after a few months of life. Early identification and treatment (before 2 weeks of life) prevents mental retardation and other complications. For this reason, all infants are screened at birth for this condition. Newborn screening is done by measuring phenylalanine in blood spots collected usually 24–48 h after birth. The ratio phenylalanine/tyrosine is also used to differentiate false positive results, due to intravenous hyperalimentation or liver immaturity/disease, from true positives. The diagnosis of PKU is confirmed by plasma amino acid analysis. In all infants identified by newborn screening with elevated phenylalanine, even if the elevation is minimal, disorders of the cofactor (tetrahydrobiopterin) synthesis or recycling need to be excluded by measuring urine pterin profile and activity of dihydropterine reductase (DHPR) in red blood cells. Phenylalanine hydroxylase deficiency is diagnosed when plasma phenylalanine concentrations are above the normal reference interval and tyrosine is low or normal, with a normal urine pterin profile and normal DHPR activity. Dietary therapy for PKU consists of a special formula without phenylalanine but containing tyrosine. Therapy should be started as soon as possible and ideally before 3 weeks of age [9–11] and needs to be continued for life. Phenylalanine concentrations are monitored periodically and should remain between 60 and 360 mM (normal 30–80 mM) to assure adequate brain development. High concentrations of phenylalanine in the first years of life lead to mental retardation. Dietary control of phenylalanine is especially important for patients with PKU during pregnancy. In fact, phenylalanine at high concentrations is teratogenic and, depending on the period of exposure, leads to an increased risk of spontaneous abortion, congenital heart defects, facial dysmorphism, microcephaly, and developmental delay. Adverse pregnancy outcome in pregnant women with PKU can be minimized by maintaining phenylalanine concentrations A; c.554-1G>T/IVS6-1G>T; c.6076T>G/IVS7-6T>G; and p.P261L) is first performed, followed by full gene sequencing in case mutations have not been identified and the diagnosis is still clinically considered. The above panel will also identify the most common mutations in people of French-Canadian descent (c.1062+5G>A/IVS12+5G>A) and in Ashkenazi Jews (p.P261L) [24–28].

Homocystinuria Homocystinuria can be caused by several genetically different disorders. Methionine is converted into homocysteine through a series of S-adenosyl intermediates [29, 30]. Homocysteine condenses with serine via cystathionine beta-synthase to form cystathionine, which is then converted by cystathionase into cysteine. This pathway is also called the transsulfuration pathway. Homocysteine can also be remethylated back to methionine by the enzyme methionine synthase, which requires methylfolate and methylcobalamin as cofactors. Defects in transsulfuration or remethylation of homocysteine cause homocystinuria. The most common form is classic homocystinuria and is caused by reduced activity of cystathionine beta-synthase.

8


171

The incidence is approximately 1:200,000 to 1:350,000 live births, with the highest incidence in Qatar (1:1,800) [31, 32]. Clinical manifestations include failure to thrive, developmental delay, downward dislocation of the lens, myopia, bone abnormalities with marfanoid habitus and pectus excavatum, osteoporosis, mental retardation, and psychiatric disturbances. Thromboembolic episodes can be seen even in children and are a major cause of morbidity and mortality. The biochemical diagnosis is made when plasma amino acids analysis shows increased plasma concentrations of methionine (especially in children) and the presence of free homocystine, a homodimer derived from two molecules of homocysteine. Total plasma homocysteine (free and peptide-bound homocysteine) is also markedly increased in this condition. Defects of homocysteine remethylation such as 5,10-methylene-tetrahydrofolate reductase deficiency, methionine synthase (cblG) and methionine synthase reductase (cblE) deficiency, and 5-methyl-tetrahydrofolate-homocysteine-methyltransferase deficiency are characterized by markedly elevated total plasma homocysteine and free homocystine; however, in these conditions, the concentration of methionine is reduced. Methylcobalamin is a cofactor of methionine synthase; therefore, severe deficiency of vitamin B12 or defects in its metabolism (cblC, cblD, cblF, and cblH) will result in homocystinuria. Patients with cblC and cblF and some patients with cblD defects also have an impairment in the synthesis of adenosylcobalamin, the cofactor of methylmalonyl-CoA mutase, and have combined methylmalonic acidemia/homocystinuria [33]. The newborn screening marker for classic homocystinuria is elevated methionine. There are many different causes for elevated methionine in the newborn period, such as liver disease or liver immaturity, protein-rich diet, and other rare disorders of methionine metabolism (S-adenosylhomocysteine hydrolase deficiency, glycine N-methyltransferase deficiency, methionine adenosyltransferase (MAT) deficiency). In these latter cases, homocystine is absent and total plasma homocysteine is normal or only mildly increased. Depending on the cutoff used by the screening laboratories for methionine, classic homocystinuria may be missed because the concentration of methionine increases gradually, and it may not be abnormal at the time of the first newborn screen. Homocystinuria caused by defects in remethylation of homocysteine can be identified by newborn screening by very low concentrations of methionine [34]. Definitive confirmation of diagnosis requires DNA testing for most of these conditions. Complementation studies in fibroblasts may be required to identify the specific cause of a cobalamin synthetic defect. Treatment of classic homocystinuria requires a low-methionine, low-protein diet, high doses of pyridoxine (the cofactor of cystathionine beta-synthase), and administration of betaine that facilitates the remethylation of homocysteine by donating a methyl group. Defects of vitamin B12 metabolism require high doses of intramuscular hydroxycobalamin and oral betaine [33]. Mild genetic variations in the methylenetetrahydrofolate reductase gene are frequent in the general population and are responsible for mild elevation of total plasma homocysteine, a risk factor for vascular disease [35] and neural tube defects [36]. These genetic variations are not identified by newborn screening.

172


DNA Testing Over 150 mutations have been reported in the cystathionine beta-synthase (CBS) gene (http://cbs.lf1.cuni.cz/index.php) [CBS Mutation Database]. Several of these mutations are common or regional, although many of them are private [30]. One of the mutations especially common in Northern Europe and in the United States is the B6-responsive p.I278T mutation, while the p.G307S mutation frequently identified in Ireland is not. Diagnostic confirmation is important to exclude other disorders causing elevated methionine levels and sometimes to predict B6 responsiveness. Full gene sequencing identifies more than 95% of causative mutations. Targeted mutation analysis for p.I278T and p.G307S can identify less than 50% of mutations in affected patients. Different genes are involved in disorders of homocysteine remethylation leading to homocystinuria [33]. Biochemical studies and complementation studies in fibroblasts should be performed to identify the defect prior to DNA analysis. The genes for all of these conditions have been identified [33], and DNA testing is available for the most frequent types (cblC, cblD, MTHFR), whereas testing for other more rare variants (cblE, cblF, cblG) is only available at selected centers. It must be noted that there are still other genes in the metabolism of vitamin B12 that remain to be identified.

Maple Syrup Urine Disease Maple syrup urine disease derives its name from the characteristic odor of the urine of these patients due to the presence of branched-chain keto acids (keto-methylvaleric, keto-isocaproic, keto-isovaleric). The disease is caused by impaired activity of branched-chain a-keto acid dehydrogenase (BCKD), a complex enzyme requiring thiamine pyrophosphate as a cofactor [37], involved in the metabolism of the essential branched-chain amino acids leucine, isoleucine, and valine. The BCKD complex is composed of four subunits, E1a, E1b, E2, and E3. The E3 subunit is shared by two other dehydrogenases, pyruvate dehydrogenase and a-ketoglutarate dehydrogenase [37]. A defect of any component of the complex causes maple syrup urine disease (MSUD), an autosomal recessive disorder with an incidence of approximately 1:250,000 live births. There are several forms of this disease, depending on the severity of the mutations: (a) the classic form, which is the most severe and is characterized by very high plasma concentrations of branched-chain amino acids; (b) forms responsive to pharmacological amounts of thiamine (thiamine-responsive MSUD) [38]; (c) intermediate or intermittent forms triggered by high consumption of proteins or catabolic state; and (d) E3 deficiency with combined deficiency of pyruvate and a-ketoglutarate dehydrogenase [39]. Classic MSUD presents, following a normal birth and uneventful first few days of life, with poor feeding and vomiting followed by lethargy and coma. The increased concentrations of branched-chain amino acids, especially leucine, become toxic. Leucine accumulates within the brain causing cerebral edema which is responsible

8


173

for the progressive worsening of neurological symptoms. Routine laboratory tests may show only the presence of ketones in the urine. Depending on the mutation and residual enzyme activity, some patients can present with recurrent episodes of vomiting, developmental delays, and seizures even after 1 year of age. In the intermittent form of the disease, patients may have episodes of acute decompensation with vomiting, ataxia, and lethargy progressing to coma. They may or may not return to a normal status after recovery [39]. Newborn screening can identify an elevated concentration of leucine/isoleucine with normal concentrations of other amino acids (such as phenylalanine) whose metabolism is not affected. However, patients with milder forms of the disease can be missed by screening [40]. The diagnosis of MSUD is confirmed by plasma amino acid analysis that shows marked elevation of leucine (usually the prominent amino acid), isoleucine, and valine in addition to the pathognomonic presence of L-alloisoleucine. Urine organic acids show the presence of characteristic branchedchain keto acids and increased excretion of 2-hydroxyisovaleric acid during episodes of decompensation. The therapy consists of a low-leucine and low-protein diet. Supplementation with valine and isoleucine may be necessary. Some patients improve with pharmacological doses of thiamine.

DNA Testing MSUD can be caused by mutations in any of three genes that encode components of the BCKD complex (E1alpha, E1beta, and E2) [41–44]. Mutations in the E3 subunit produce a different phenotype that is not usually confused with MSUD. The genotype has prognostic value to some extent; therefore, DNA analysis is important to predict long term outcome and treatment adherence. DNA testing can allow for prenatal studies if the proband’s genotype is known. In general, diagnosis is confirmed by enzyme assay in cultured skin fibroblasts. Western blot analysis in fibroblasts can sometimes identify the defective protein and direct subsequent DNA testing. Full gene sequencing of the genes encoding the E1alpha, E1beta, and E2 subunits is usually necessary, starting from the one suggested by the results of Western blot analysis. In selected populations with a high frequency of MSUD due to a founder effect, targeted mutation analysis can be used as a first-line DNA testing.

Urea Cycle Defects Amino acids are metabolized to gluconeogenic and/or ketogenic precursors after their amino group has been disposed of by the urea cycle through several enzymes and mitochondrial transporters (Fig. 8.1). The ammonia generated from the nitrogen groups of the amino acids is used in conjunction with CO2, ATP, and the enzyme

174


UTP CTP acetyl-CoA+glutamate NAG synthase N-acetyl-glutamate CPS-1 carbamylphosphate CO2+NH3+2ATP + ornithine MITOCHONDRION OTC aspartate citrulline CYTOSOL

orotic acid

CPS ornithine ORC1 (HHH)

ORC1 CITRIN aspartate + citrulline ASA synthase argininosuccinic acid ASA lyase

Arginase

UREA

arginine fumarate

Fig. 8.1 The urea cycle. The urea cycle leads to the formation of urea starting from ammonia (NH3). It requires many enzymes and mitochondrial transporters, any of which can be defective and impair the function of the urea cycle. Argininosuccinate lyase: ASA lyase (ASL); argininosuccinate synthase: ASA synthase (ASS); arginase: ARG; aspartate/glutamate exchanger: citrin; carbamyl phosphate: CP; carbamyl phosphate synthase 1: CPS-1; cytidine triphosphate: CTP; N-acetylglutamate (NAG) synthase (NAGS); ornithine/citrulline mitochondrial transporter: ORC1; ornithine transcarbamylase: OTC; uridine triphosphate: UTP

carbamyl phosphate synthase 1 (CPS-1) to generate carbamyl phosphate. The allosteric activator of CPS-1 is N-acetylglutamate, which is synthesized by N-acetylglutamate synthase (NAGS). Carbamyl phosphate combines with ornithine, which enters the mitochondria through the specific transporter ORC1, by the action of the enzyme ornithine transcarbamylase (OTC). This reaction generates citrulline, which exits the mitochondria through the ORC1 transporter (ornithine/citrulline exchanger) [45]. Aspartate, exported from mitochondria by the citrin transporter (aspartate/ glutamate exchanger), combines with citrulline in the cytoplasm to form argininosuccinate through the enzyme argininosuccinate synthase (ASS). Argininosuccinate lyase (ASL) converts argininosuccinate into arginine and fumarate. In the last step of the urea cycle, arginase (ARG) generates urea and ornithine to restart the cycle. Defects in any of these enzymes or transporter will result in a block of the urea cycle and hyperammonemia. Newborn screening can identify elevated citrulline in citrullinemia type I (ASS deficiency) or II (citrin deficiency) and, in addition to argininosuccinate, in argininosuccinate lyase deficiency (ASL deficiency) and elevated arginine in arginase deficiency. Elevated ornithine and homocitrulline are theoretically the markers for hyperammonemia, hyperornithinemia, and homocitrullinuria syndrome (ORC1 deficiency/HHH syndrome), but it is unclear whether newborns with this condition show elevations of these amino acids. Low citrulline and arginine with elevated glutamine can be found in NAGS, CPS-1, and OTC deficiency; however,

8


175

newborn screening by MS/MS has not allowed yet the consistent identification of these disorders [46]. Patients with urea cycle defects may present at any age [47, 48], shortly after birth or later in infancy or adulthood. The pathophysiology is due to hyperammonemia and the accumulation of glutamine in the brain leading to poor feeding, vomiting, and lethargy progressing to coma and death. The late onset presentation is often triggered by excess protein intake or catabolic state. In arginase deficiency, the clinical picture is of spastic diplegia frequently diagnosed as cerebral palsy [49], while hyperammonemia is not a consistent finding. The dominant laboratory findings in urea cycle defects are hyperammonemia and abnormalities of liver functions, with variably elevated transaminases and PT/ PTT. Plasma amino acids, urine orotic acid, and, in some cases, urine amino acids are necessary for the diagnosis of these disorders. In the differential diagnosis of a patient with hyperammonemia, other conditions, such as organic acidemias and fatty acid oxidation defects, need to be considered. They can be excluded by urine organic acids and plasma acylcarnitine analyses. The treatment of urea cycle defects consists of a low-protein diet, in addition to scavengers (benzoate, phenylacetate, phenylbutyrate) that combine mainly with glutamine and glycine and facilitate their excretion, reducing the nitrogen pool. Citrulline (in OTC or CPS-1 deficiencies) and/or arginine (in ASS and ASL deficiencies) supplementation provides substrates of the urea cycle downstream from the specific block to allow protein synthesis to resume [47, 48]. Citrullinemia type II (citrin deficiency) can present in the neonatal period as neonatal intrahepatic cholestasis. Infants with citrin deficiency often have elevated galactose, methionine, and/or phenylalanine in their newborn screen. Citrulline may not be elevated in the first days after birth, when the newborn screen is collected, but increases with time. The treatment for citrin deficiency, different from the other urea cycle defects, consists of a high-protein diet [50–56]. For this reason, it is very important to differentiate citrullinemia type I from type II. DNA analysis is currently the best procedure for this task.

DNA Testing DNA testing should be directed by the biochemical findings. Unfortunately, proximal disorders of the urea cycle (ornithine transcarbamylase deficiency, carbamyl phosphate synthetase 1 deficiency, and N-acetyl glutamate synthase deficiency) present the same biochemical findings, although the urinary excretion of orotic acid is increased only in OTC deficiency. Some of the urea cycle enzymes are expressed only in the liver; therefore, enzyme assays can be performed only on a liver biopsy. For this reason, DNA testing is becoming the procedure of choice to confirm the diagnosis. Outside of close communities, there are no predominant mutations, and, in most patients, full gene sequencing is the procedure of choice. One of the challenges in the past few years has been the identification of patients with a mild form of

176


citrullinemia type I. These patients have only mild but persistent elevation of citrulline levels. It is still unclear whether they require therapy or not. DNA testing in these patients usually reveals at least one missense mutation either of unknown clinical significance or associated with significant residual enzyme activity. It is important to follow these patients over time since liver failure might develop even in adult age in citrullinemia type I [57]. In the case of arginase deficiency, the enzyme is expressed in red blood cells and can be assayed prior to DNA testing.

Organic Acidemias Organic acidemias are inborn errors of metabolism characterized by the accumulation of intermediates in the catabolic pathways of amino acids. The onset of symptoms is usually in the first 48–72 h of life with vomiting, refusal of feeds, and lethargy progressing to coma. The accumulation of organic compounds results in metabolic acidosis (pH = 6.85–7.30) with low bicarbonate (T, p.A282V) associated with residual enzyme activity [63]. Treatment requires a low-protein diet, special formulas without leucine, glycine (250 mg/kg/day), and carnitine (50–100 mg/kg/day) supplements. The prognosis is usually good if acute metabolic decompensation is prevented.

DNA Testing The biochemical profile observed in the urine organic acids is usually characteristic for this condition. However, DNA testing by full gene sequencing confirms the diagnosis and defines the presence of mutations associated with the milder outcome, possibly not requiring strict therapy. Enzyme assay in fibroblasts can be obtained but remains difficult to perform.

180


3-Methylcrotonyl-CoA Carboxylase Deficiency 3-Methylcrotonyl-CoA carboxylase is a biotin-dependent mitochondrial enzyme involved in the catabolism of leucine [64, 65] converting 3-methylcrotonyl-CoA to 3-methylglutaconyl-CoA. This enzyme is composed of a larger alpha-subunit that binds biotin, bicarbonate, and ATP and a smaller beta-subunit containing the binding site for 3-methylcrotonyl-CoA [66, 67], encoded by two different genes, MCCA and MCCB. Deficiency of 3-methylcrotonyl-CoA carboxylase is inherited as an autosomal recessive trait, and heterogeneous mutations in the MCCA and MCCB genes have been reported in affected patients [68]. Patients have variable phenotypes, ranging from neurological involvement and developmental delays, to recurrent attacks of metabolic decompensation followed by complete recovery, to asymptomatic adults [68–73]; there is no clear correlation between genotype and phenotype [68]. Treatment consists of fasting avoidance and prompt treatment of fever and illnesses with IV fluids containing glucose. Carnitine supplements are also indicated since most of these patients develop severe carnitine deficiency. Patients with 3-methylcrotonyl-CoA carboxylase deficiency can be identified through newborn screening by MS/MS because of an increased concentration of C5-OH (3-hydroxyisovaleryl-carnitine) [69, 74, 75]. Newborn screening has also identified asymptomatic affected mothers of heterozygous babies [69]. Some of these mothers had a history of recurrent vomiting requiring IV fluids after minor illnesses or high-protein meals [69]. Diagnosis is confirmed by urine organic acid analysis showing a markedly increased excretion of 3-hydroxyisovaleric acid and the presence of 3-methylcrotonylglycine that increases during episodes of acute decompensation. Urine acylglycine analysis might help in cases where 3-methylcrotonylglycine is only mildly elevated. Quantitative analysis of acylcarnitine profiles in plasma and urine provides further supporting evidence, showing a marked increase in C5-OH carnitine and the absence of other acylcarnitine species seen in other disorders such as 3-ketothiolase deficiency, 3-hydroxy-3-methylglutaryl-CoA lyase deficiency, or multiple carboxylase deficiency. An isolated increase in C5-OH carnitine can be seen with biotinidase deficiency that can be easily excluded by measurement of serum enzyme activity. The diagnosis is confirmed by measuring enzyme activity in white blood cells.

DNA Testing Full gene sequencing of both the MCAA and MCCB genes is clinically available. However, since there is an easy enzyme assay in white blood cells, this procedure is rarely used for diagnostic confirmation.

8


181

3-Hydroxy-3-Methylglutaric Aciduria 3-Hydroxy-3-methylglutaric aciduria is a disorder of ketogenesis and L-leucine catabolism caused by mutations in the 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL) gene. The clinical acute symptoms include vomiting, convulsions, metabolic acidosis, hypoketotic hypoglycemia, and lethargy, usually triggered by fasting or catabolic state resembling in many aspects a fatty acid oxidation defect rather than an organic acidemia. Urine organic acids show elevated excretion of 3-hydroxy-3methylglutaric acid, 3-methylglutaconic acid, and 3-methylglutaric acid, with occasional elevation of 3-hydroxyisovaleric acid and 3-methylcrotonylglycine; ketone bodies are not (or only minimally) increased. In the plasma acylcarnitine profile, there is characteristic elevation of 3-methylglutarylcarnitine (C6-DC) in addition to elevated C5OH-carnitine. Treatment consists of fasting avoidance, prompt treatment of decompensation with IV solution containing glucose, cornstarch and carnitine supplements as needed, and a diet moderately restricted in leucine.

DNA Testing Heterogeneous mutations in the HMGCL gene have been reported in affected patients, and full gene sequencing is the procedure of choice to confirm the diagnosis [76]. Enzyme assay, although effective, is not easily available.

Holocarboxylase Synthase Deficiency Holocarboxylase synthase deficiency (or multiple carboxylase deficiency, MCD) leads to impaired activity of four biotin-dependent enzymes: acetyl-CoA carboxylase, propionyl-CoA carboxylase, 3-methylcrotonyl-CoA carboxylase, and pyruvate carboxylase (Fig. 8.3). This enzyme attaches biotin covalently to the active site of the different carboxylases. The clinical symptoms include feeding and breathing difficulties, hypotonia, seizures, and lethargy, and there sometimes is progression to developmental delay or coma. Some children exhibit skin rash and alopecia. Affected children exhibit metabolic acidosis, organic aciduria, and mild to moderate hyperammonemia. The organic aciduria includes elevated concentrations of 3-hydroxyisovalerate, 3-methylcrotonylglycine, 3-hydroxy-propionate, methylcitrate, lactate, and tiglylglycine, reflecting impaired activity of biotin-dependent enzymes. Plasma acylcarnitines show increased C5:1, C3, C5-OH acylcarnitines. The enzyme defect has been demonstrated in lymphocytes, cultured fibroblasts, and cultured lymphoblasts from affected children. Therapy consists of the administration of high doses (up to 100 mg twice a day) of oral biotin and prompt treatment of fever, infections, and catabolic state with intravenous fluids containing glucose.

182

M. Pasquali and N. Longo Free Biotin Biotinidase Biocytin

Dietary Biotin

Protein Recycling

Holocarboxylase Synthase Acetyl CoA Propionyl CoA Pyruvate β-Methylcrotonyl CoA carboxylases

Holocarboxylases

Fig. 8.3 Biotin metabolism. Dietary biotin is attached to the epsilon amino group of lysine in proteins. After digestion, biocytin is generated and biotin can be detached by the action of biotinidase. Free biotin can then be incorporated into the active site of carboxylase enzymes by the action of holocarboxylase synthase. When these enzymes are recycled, biocytin is generated again, and biotin can be recycled by the action of biotinidase

DNA Testing Heterogeneous mutations have been identified in patients with multiple carboxylase deficiency. Specific mutations (such as c.1519+5G>A/IVS10+5G>A prevalent in the Faroe Islands; p.L237P and c.780delG predominant in Japanese patients) have ethnic predilection, but in general, full gene sequencing is required for diagnostic confirmation [77]. There is genotype-phenotype correlation; patients with two severe mutations have an earlier presentation, and patients with higher residual activity present after the neonatal period and show a better clinical response to biotin therapy.

3-Ketothiolase Deficiency 3-Ketothiolase (mitochondrial acetoacetyl-CoA thiolase, T2) is an enzyme involved in isoleucine catabolism and ketone bodies utilization. Its deficiency is characterized by intermittent episodes of severe ketoacidosis, usually with normoglycemia or hyperglycemia (for which it can be confused with diabetes mellitus), that can result in hyperventilation, dehydration, lethargy, coma, and death. Episodes are usually associated with severe vomiting and are triggered by infections or other illnesses. Analysis of urine organic acids during acute episodes reveals high excretion of 2-methyl-3-hydroxybutyrate, 2-methylacetoacetate, and tiglylglycine with large amounts of 3-hydroxy-butyrate and acetoacetate. Analysis of acylcarnitines by MS-MS in whole blood or in plasma shows increased concentrations of C5-OH (2-methyl-3-hydroxybutyryl) carnitine and C5:1 (tiglyl) carnitine. The diagnosis can be confirmed by enzyme assay in cultured fibroblasts or more easily by DNA testing. Therapy consists of mild protein restriction to limit

8


183

the intake of isoleucine, avoidance of fasting, supplementation with carnitine, avoidance of prolonged fasting, and prompt treatment of illnesses that can precipitate acute attacks.

DNA Testing The gene coding for this enzyme, ACAT1, maps to 11q22.3-23.1, and heterogeneous mutations have been reported in patients with 3-ketothiolase deficiency [78–81]. Two groups of patients have been identified based on the presence of null mutations in both alleles or of mutations leaving some residual activity in at least one of the mutant alleles [82, 83]. Although there is no correlation between phenotype and genotype, in patients with milder mutations, the biochemical phenotype can normalize between episodes [78, 82]. For this reason, full gene sequencing should be performed in dubious cases.

Glutaric Acidemia Type I Glutaric acidemia type I (GA-I) is caused by a defect in the catabolic pathway of lysine, hydroxylysine, and tryptophan due to impaired activity of glutaryl-CoA dehydrogenase. Affected individuals may have macrocephaly at birth or develop it in the first year of life [84]. They have mild hypotonia and they can develop acute dystonia and spasticity with metabolic decompensation, usually triggered by fever or other illnesses. Brain imaging of affected individuals demonstrates frontotemporal atrophy, and after a crisis, the characteristic striatal degeneration with shrinkage of the caudate and Putamen [84]. Hypotonia followed by spasticity, abnormal movements, seizures, mild hypoglycemia, and metabolic acidosis have also been associated with acute events. Diagnosis of this condition is confirmed by urine organic acids showing massive excretion of glutaric and 3-hydroxyglutaric acids. This is associated with increased concentrations of glutarylcarnitine (C5-DC) in plasma and increased urinary excretion of glutarylcarnitine. Some patients, referred to as low excretors, show a more subtle biochemical phenotype with normal or mildly elevated glutaric acid and mildly increased excretion of 3-hydroxyglutaric acid [84]. In these patients, the concentration of plasma glutarylcarnitine can be normal, although they tend to have elevated urinary excretion of glutarylcarnitine [85]. Infants with GA-I have usually elevated glutarylcarnitine identifiable by newborn screening, although some low excretors can be missed. Glutaryl carnitine can also be elevated in patients with kidney failure, but usually in association with other acylcarnitine species. The treatment consists of a diet restricted in lysine and tryptophan and carnitine supplementation. Prompt treatment of infections or other illnesses and prevention of metabolic decompensation is also critical in this disease.

184


DNA Testing Full gene sequencing of the GCDH gene is the procedure of choice to confirm the diagnosis in a patient with abnormal newborn screening when the biochemical phenotype is ambiguous. Enzyme assay in cultured fibroblasts is also available, but low excretors usually retain significant residual enzyme activity, and differentiation from carriers is sometimes difficult.

Disorders of the Carnitine Cycle and Fatty Acid Oxidation Carnitine plays an essential role in the transport of long-chain fatty acids inside mitochondria for beta-oxidation. During periods of fasting or high energy demands, the main substrates for energy production in the liver, cardiac muscle, and skeletal muscle are fatty acids which are released by the adipose tissue. The brain cannot directly utilize fatty acids, because they cannot cross the blood–brain barrier; however, it can oxidize ketone bodies derived from beta-oxidation of fatty acids in the liver [86]. If fatty acid oxidation is impaired, fatty acids will still be released by the adipose tissue during fasting or high energy demands, but they will accumulate in the liver, heart, or skeletal muscle. In the liver, impaired oxidation of fatty acids will result in steatosis and decreased production of ketones. Ketones are used by the heart, skeletal muscle, and brain as energy source sparing glucose, while in the liver, the increased acetyl-CoA, derived from beta-oxidation of fatty acids stimulates gluconeogenesis (production of glucose), mostly from the carbon skeleton of amino acids released from the muscle. In patients with fatty acid oxidation disorders, the excessive utilization of glucose and lack of gluconeogenesis cause hypoglycemia that, in addition to decreased availability of ketones, decreases the energy supply for the brain causing loss of consciousness, seizures, and coma. Fats can also accumulate in the heart and skeletal muscle causing cardiomyopathy and myopathy in addition to fatal arrhythmias. Long-chain fatty acids, released from the adipose tissue, travel in blood bound mostly to albumin. There is a complex system involving membrane transporters and vectorial acylation by the action of acyl-CoA synthase that drives fatty acids inside cells. Once inside the cell, the carnitine cycle is essential to allow fatty acids to enter mitochondria for subsequent beta-oxidation (Fig. 8.4). This cycle requires enzymes and transporters that accumulate carnitine within the cell (OCTN2 carnitine transporter), conjugate it with long-chain fatty acids to form long-chain acylcarnitines (carnitine palmitoyl transferase 1, CPT1), transfer the acylcarnitines across the inner plasma membrane (carnitine-acylcarnitine translocase, CACT), and conjugate the fatty acids back to coenzyme A for subsequent beta-oxidation (carnitine palmitoyl transferase 2, CPT2). Medium- and short-chain fatty acids can cross the mitochondrial membrane independently from the carnitine cycle. In the mitochondrial matrix, long-chain fatty acids undergo a series of enzymatic reactions to progressively shorten their chain by two carbon units (acetyl-CoA). Dehydrogenases with different carbon chain length specificity (from C18 to C4, very-long-chain acyl-CoA dehydrogenase (VLCAD), long-chain acyl-CoA dehydrogenase (LCAD), medium-chain

8


185

COOH FATTY CH3 ACID Albumin + OH H3C N COO− COOH HOOC CH3 CARNITINE FABP pm OUT

FATP OCTN2

IN

COOH FA CoASH

ACS CH3 OH H3C N COO− Vectorial Acylation CH3 Acyl-S-CoA COOH COOH Acyl CH3 + O H3C N COO− CH3

CH3 OH COO− CH3

H3C N

FABPc

ACBP CoA

Plasma Membrane

Caveolin CD 36

ω ,ω-1oxidation MICROSOMES PEROXISOMES MEDIUM CHAIN DICARBOXYLIC ACIDS

CoA

CPT-1 CH3 CPT-2 OH Acyl-S-CoA H3C N COO− CH3 Acyl CH3 β-OXIDATION CACT − O H3C N COO− MITOCHONDRION CH3

Fig. 8.4 The carnitine cycle in fatty acid oxidation. The carnitine cycle is responsible for delivering long-chain fatty acid to the mitochondrial matrix for subsequent beta-oxidation. Fatty acids are bound to albumin or other proteins in plasma. They enter the cells through fatty acid transporters (FATP: fatty acid transporter protein) that interact with other proteins (membrane-bound fattyacid-binding proteins (FABPpm), caveolin, CD-36) to allow the transfer of the fatty acid to the transporter. Once inside the cell, fatty acids are conjugated with coenzyme A by acyl-CoA synthases (ACS) maintaining a constant gradient for free fatty acid to flow from the outside to the inside of the cells. In the cytoplasm, free fatty acids attach to fatty-acid-binding proteins (FABPc) or acyl-CoA-binding proteins (ACBP). FA: fatty acid; CPT-1: carnitine palmitoyl transferase 1; CPT-2: carnitine palmitoyl transferase 2; CACT: carnitine-acylcarnitine translocase

acyl-CoA dehydrogenase (MCAD), and short-chain acyl-CoA dehydrogenase (SCAD)) introduce a double bond between C2 and C3. A trifunctional protein (TFP) adds water and cleaves two carbon atoms from the long-chain fatty acid. This is done through the sequential action of a hydratase (enoyl-CoA hydratase), a dehydrogenase (long-chain 3-OH-acyl-CoA dehydrogenase, LCHAD), and a thiolase (acyl-CoA acetyltransferase). The two carbon units generated can be completely oxidized in the muscle to CO2 or generate ketone bodies in the liver that can be exported to provide energy to other organs. Deficiency in any of these steps will lead to impaired energy production, in most cases aggravated by fasting or catabolic state. The characteristics of disorders of the carnitine cycle and of fatty acid oxidation included in the core panel of conditions to be screened in the newborns are discussed in more detail below. Table 8.4 lists selected disorders of fatty acid oxidation identifiable by newborn screening and the strategy for confirming or excluding the diagnosis.

Table 8.4 Diagnostic strategy and DNA testing in selected disorder of fatty acid oxidation and the carnitine cycle identifiable by newborn screening Newborn First-line DNA Additional DNA Disease Gene screening Biochemical findings Enzyme assay testing testing Carnitine uptake defect/ SLC22A5 ¯ C0 ¯ C0, C3, C16 (free and Fibroblasts Full gene Deletions/ carnitine transport defect total carnitine, sequencing duplications (CUD) acylcarnitines) Very-long-chain acyl-CoA ACADVL C14:1 C14:1 (acylcarnitines) Fibroblasts (fluxes, Full gene Deletions/ dehydrogenase deficiency oxidation, sequencing duplications Can be normal (VLCAD) acylcarnitine) Long-chain L-3 hydroxyacyl- HADHA C16-OH C16-OH, C18:1-OH, Fibroblasts Targeted Full gene CoA dehydrogenase and other long-chain mutational sequencing C18:1-OH deficiency (LCHAD) acylcarnitines analysis Trifunctional protein defiHADHA C16-OH C16-OH, C18:1-OH, Fibroblasts Full gene NA ciency (TFP) and other long-chain sequencing HADHB C18:1-OH acylcarnitines Medium-chain acyl-CoA ACADM C8 C8, C6, C10:1 Fibroblasts Targeted Full gene dehydrogenase (acylcarnitines) mutational sequencing deficiency (MCAD) analysis Hexanoylglycine (UOA, acylglycines) UOA urine organic acids, acylcarnitines plasma acylcarnitine profile, acylglycines urine acylglycine profile

186 M. Pasquali and N. Longo

8


187

Carnitine Uptake Defect (CUD) Carnitine uptake defect or primary carnitine deficiency (OMIM 212140) is an autosomal recessive disorder of the carnitine cycle affecting fatty acid oxidation. It is due to the lack of functional OCTN2 carnitine transporter. Primary carnitine deficiency has a frequency of about 1:40,000 newborns in the general population, but it has a much higher incidence in the Faroe Islands (1:720) [87]. The lack of the plasma-membrane carnitine transporter results in urinary carnitine wasting, low serum carnitine concentrations (0–5 mM, normal 25–50 mM), and decreased intracellular carnitine accumulation. Patients with primary carnitine deficiency lose carnitine in urine, resulting in decreased plasma concentration and subsequent tissue deficiency. Patients can present with hepatic encephalopathy or cardiomyopathy triggered by fasting or infection. Some patients have been completely asymptomatic for all of their lives and have been diagnosed following the birth of an affected child [88]. Routine laboratory studies can show hypoglycemia with minimal or no ketones in urine, hyperammonemia with variably elevated liver function tests and, sometimes, elevated creatine kinase. Newborn screening can identify reduced free carnitine (C0) in blood spots. Low carnitine concentrations are also seen in infants of mothers with low carnitine due to carnitine uptake defect or secondary to an undiagnosed organic acidemia or fatty acid oxidation defect. Diagnosis is further suspected by finding extremely reduced plasma concentration of free, total, and acylated carnitine (free carnitine < 9 mM, normal 25–50 mM) and relatively increased urinary excretion of free and total carnitine, with unremarkable urine organic acids. Diagnosis is confirmed by demonstrating reduced carnitine transport (C, p.E474Q) causes isolated LCHAD deficiency [93]. In this case, only part of the reaction occurs, and the intermediates that accumulate are toxic for the body. A genotype-phenotype correlation has also emerged for TFP deficiency, with residual enzyme activity being associated with a milder, later-onset phenotype. In general, targeted mutation analysis is first performed in a patient with an elevation of long-chain hydroxylated acylcarnitines, followed by full gene sequencing of the two genes to confirm trifunctional protein deficiency if the first testing is normal. Unlike VLCAD deficiency, abnormal acylcarnitines are almost always present in this condition, unless the child is under optimal treatment.

Medium-Chain Acyl-CoA Dehydrogenase Deficiency Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency is the most common disorder of fatty acid oxidation, with an estimated frequency of 1:6,000 to 1:10,000 Caucasian births [95, 96]. The symptoms of the disease are variable, from completely

190


asymptomatic to hypoglycemia, lethargy, coma, and sudden death; they are usually triggered by prolonged fasting or illness. Although the majority of patients present in the first year of life, clinical symptoms can occur at any time, and patients can even die in the newborn period before diagnosis [97]. Patients with MCAD deficiency are identified by MS/MS newborn screening because of the characteristic acylcarnitine profile, with increased concentration of C6- (hexanoyl), C8- (octanoyl), and C10:1- (decenoyl) carnitine and elevated C8/C2 and C8/C10 ratios. Urine organic acids and urine acylglycines analyses during metabolic crisis show increased excretion of dicarboxylic acids (adipic, suberic, sebacic), saturated and unsaturated, little or absent ketones, and increased excretion of hexanoylglycine and suberylglycine. When patients are metabolically stable, the urinary concentration of these analytes is greatly reduced, although hexanoylglycine and suberylglycine usually remain detectable. The abnormal plasma acylcarnitine profile is almost always present. The treatment consists of avoidance of fasting, low-fat diet, carnitine supplementation, and institution of an emergency plan in case of illness or other metabolic stress. Early diagnosis through newborn screening and early initiation of treatment leads to improved outcome.

DNA Testing The diagnosis can be confirmed by DNA analysis. Among patients presenting with clinical symptoms, 98% carry at least one copy of the common mutation K304E (also known as p.K329E), with 80% being homozygous for this mutation [98]. In newborns detected prospectively by newborn screening, the Y42H (also known as p.Y67H) mutation has been found frequently in association with K304E [98]. The Y42H mutation has not yet been reported in patients with clinical symptoms [98] and is associated with lower concentrations of diagnostic metabolites in blood and urine [99]. In the presence of the characteristic abnormal acylcarnitine profile and the absence of the two mutations above, DNA sequencing can usually identify both causative mutations.

Galactosemia Galactosemia is a disorder of carbohydrate metabolism that results in the accumulation of galactose in blood. In theory, galactosemia can result in the deficiency of one of three enzymes: galactose-1-phosphate-uridyltransferase deficiency (GALT), galactokinase deficiency (GALK), and epimerase deficiency (GALE) (Fig. 8.5). Usually, we refer to GALT deficiency as classic galactosemia, with inability to metabolize galactose to glucose [1]. Galactose derives from lactose in milk. This is digested by disaccharidases in the brush border of intestinal cells to generate glucose and galactose that enter cells by the action of the sodium-dependent glucose transport SGLT1. Galactose is then phosphorylated to galactose-1-phosphate by the

8

Newborn Screening for Metabolic Disorders DIET Aldose reductase

191

Galactitol+Galactonate Galactose dehydrogenase

Galactose + ATP Endogenous production Gal-1-P + UDPGlu

Gal-1-P + ADP

galactokinase (GALK)

Glu-1-P + UDPGal

galactose-1-phosphate uridyl transferase (GALT) UDPGalactose

UDPGlucose uridine diphosphate galactose-4-epimerase (GALE)

Fig. 8.5 Galactose metabolism. Galactose is obtained from the diet or endogenous production. Galactokinase (GALK), galactose-1-phosphate uridyl transferase (GALT), and uridine diphosphate galactose-4-epimerase (GALE) are required for converting galactose into glucose

action of galactokinase (GALK). Galactose-1-phosphate uridyl transferase (GALT) then transforms galactose-1-phosphate and UDP glucose to glucose-1-phosphate and UDP galactose. Finally, the epimerase (uridine diphosphate galactose-4-epimerase, GALE) converts UDP galactose to UDP glucose. In classic galactosemia, elevated levels of galactose-1-phosphate are toxic for cells. Affected infants present at 3–14 days of age with poor feeding, vomiting, diarrhea, jaundice, lethargy progressing to coma, and abdominal distension with hepatomegaly usually followed by progressive liver failure. Patients with galactosemia are also at increased risk for E. coli or other gram-negative neonatal sepsis. Diagnosis is made by measuring GALT enzyme activity in red blood cells. Treatment consists of the removal of galactose from the diet. Early identification and intervention prevents most early and serious complications, although even with treatment some children develop speech dyspraxia and learning disabilities; girls may experience ovarian failure with primary amenorrhea or early menopause.

DNA Testing The diagnosis of galactosemia is confirmed by enzyme assay that can be performed in red blood cells. This is ideally integrated with concomitant DNA testing for common mutations (p.Q188R, p.S135L, p.K285N, p.T138M, p.L195P, p.Y209C, and IVS2-2 A>G) and two variants (p.N314D and p.L218L). DNA testing is essential in distinguishing people who have Duarte-variant galactosemia, a condition in which there is significant residual enzyme activity that usually requires no treatment or treatment for only the first year of life [100]. These patients have the p.N314D variant in association with a deletion in the promoter region of the gene markedly reducing gene expression and enzyme activity. The same p.N314D variant has been found in

192


association with increased enzyme activity (in cis with the L218L variant and normal promoter activity, Los Angeles variant) and rarely in cis with mutations abolishing enzyme activity. For this reason, measurement of enzyme activity is essential to the correct interpretation of DNA testing. Full gene sequencing can identify mutations in patients with markedly reduced enzyme activity but no common mutations.

Biotinidase Deficiency Biotinidase deficiency is a disorder of biotin recycling that can cause seizures, hypotonia, ataxia, breathing problems, hearing loss, optic atrophy, developmental delay, skin rash, and alopecia. The clinical expression of the disorder is variable, with onset from weeks to several years of age. Therapy consists of the administration of oral biotin for life. Biotinidase deficiency can be identified by newborn screening, using a colorimetric method. The disease is confirmed by measurement of enzyme activity in serum.

DNA Testing Biotinidase deficiency can be complete or partial. In the complete deficiency syndrome, biotinidase activity is reduced to less than 10% of normal. This complete impairment is associated with mutations that completely abolish enzyme activity. Targeted mutation analysis (c.98_104del7ins3 (G98d7i3), p.Q456H, p.R538C, and the double mutation p.A171T:p.D444H) can identify a good percentage of the causative mutations [101]. Partial biotinidase deficiency is associated with significant residual enzyme activity. In most cases, patients carry at least one copy of the p. D444H mutation that, without another change in cis, is associated with partial deficiency. Full gene sequencing can identify mutations in all other patients with absent enzyme activity and only one mutation detected by the common panel.

References 1. Guthrie R, Susi A. A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics. 1963;32:338–43. 2. Chace DH. Mass spectrometry in newborn and metabolic screening: historical perspective and future directions. J Mass Spectrom. 2009;44(2):163–70. 3. Chace DH, Kalas TA. A biochemical perspective on the use of tandem mass spectrometry for newborn screening and clinical testing. Clin Biochem. 2005;38(4):296–309. 4. Newborn screening: toward a uniform screening panel and system. Genet Med. 2006;8 Suppl 1:1S–252S.

8


193

5. SCID added to national newborn screening standards. Am J Med Genet A. 2010; 152A(8):ix. 6. Ko DH, Jun SH, Park KU, Song SH, Kim JQ, Song J. Newborn screening for galactosemia by a second-tier multiplex enzyme assay using UPLC-MS/MS in dried blood spots. J Inherit Metab Dis. 2011;34(2):409–14. 7. Ko DH, Jun SH, Park HD, et al. Multiplex enzyme assay for galactosemia using ultraperformance liquid chromatography-tandem mass spectrometry. Clin Chem. 2010;56(5):764–71. 8. Jensen UG, Brandt NJ, Christensen E, Skovby F, Norgaard-Pedersen B, Simonsen H. Neonatal screening for galactosemia by quantitative analysis of hexose monophosphates using tandem mass spectrometry: a retrospective study. Clin Chem. 2001;47(8):1364–72. 9. Phenylketonuria (PKU): screening and management. NIH Consens Statement. 2000;17(3): 1–33. 10. Verkerk PH, van Spronsen FJ, van Houten M, Smit GP, Sengers RC. Predictors of mean phenylalanine levels during the first five years of life in patients with phenylketonuria who were treated early. Dutch National PKU Steering Committee. Acta Paediatr Suppl. 1994;407:70–2. 11. Viau KS, Wengreen HJ, Ernst SL, Cantor NL, Furtado LV, Longo N. Correlation of agespecific phenylalanine levels with intellectual outcome in patients with phenylketonuria. J Inherit Metab Dis. 2011;34(4):963–71. 12. Koch R, Trefz F, Waisbren S. Psychosocial issues and outcomes in maternal PKU. Mol Genet Metab. 2010;99 Suppl 1:S68–74. 13. Widaman KF, Azen C. Relation of prenatal phenylalanine exposure to infant and childhood cognitive outcomes: results from the International Maternal PKU Collaborative Study. Pediatrics. 2003;112(6 Pt 2):1537–43. 14. Koch R, Azen C, Friedman E, et al. Research design, organization, and sample characteristics of the Maternal PKU Collaborative Study. Pediatrics. 2003;112(6 Pt 2):1519–22. 15. Longo N. Disorders of biopterin metabolism. J Inherit Metab Dis. 2009;32(3):333–42. 16. Dobrowolski SF, Heintz C, Miller T, et al. Molecular genetics and impact of residual in vitro phenylalanine hydroxylase activity on tetrahydrobiopterin responsiveness in Turkish PKU population. Mol Genet Metab. 2011;102(2):116–21. 17. Koch R, Moseley K, Guttler F. Tetrahydrobiopterin and maternal PKU. Mol Genet Metab. 2005;86 Suppl 1:S139–41. 18. Aguado C, Perez B, Garcia MJ, et al. BH4 responsiveness associated to a PKU mutation with decreased binding affinity for the cofactor. Clin Chim Acta. 2007;380(1–2):8–12. 19. Desviat LR, Perez B, Belanger-Quintana A, et al. Tetrahydrobiopterin responsiveness: results of the BH4 loading test in 31 Spanish PKU patients and correlation with their genotype. Mol Genet Metab. 2004;83(1–2):157–62. 20. Scott CR. The genetic tyrosinemias. Am J Med Genet C Semin Med Genet. 2006;142C(2): 121–6. 21. Magera MJ, Gunawardena ND, Hahn SH, et al. Quantitative determination of succinylacetone in dried blood spots for newborn screening of tyrosinemia type I. Mol Genet Metab. 2006;88(1):16–21. 22. Schlump JU, Mayatepek E, Spiekerkoetter U. Significant increase of succinylacetone within the first 12 h of life in hereditary tyrosinemia type 1. Eur J Pediatr. 2010;169(5):569–72. 23. Turgeon C, Magera MJ, Allard P, et al. Combined newborn screening for succinylacetone, amino acids, and acylcarnitines in dried blood spots. Clin Chem. 2008;54(4):657–64. 24. Arranz JA, Pinol F, Kozak L, et al. Splicing mutations, mainly IVS6–1(G>T), account for 70% of fumarylacetoacetate hydrolase (FAH) gene alterations, including 7 novel mutations, in a survey of 29 tyrosinemia type I patients. Hum Mutat. 2002;20(3):180–8. 25. Dreumont N, Poudrier JA, Bergeron A, Levy HL, Baklouti F, Tanguay RM. A missense mutation (Q279R) in the fumarylacetoacetate hydrolase gene, responsible for hereditary tyrosinemia, acts as a splicing mutation. BMC Genet. 2001;2:9. 26. Grompe M, al-Dhalimy M. Mutations of the fumarylacetoacetate hydrolase gene in four patients with tyrosinemia, type I. Hum Mutat. 1993;2(2):85–93.

194


27. Grompe M, St-Louis M, Demers SI, al-Dhalimy M, Leclerc B, Tanguay RM. A single mutation of the fumarylacetoacetate hydrolase gene in French Canadians with hereditary tyrosinemia type I. N Engl J Med. 1994;331(6):353–7. 28. St-Louis M, Leclerc B, Laine J, Salo MK, Holmberg C, Tanguay RM. Identification of a stop mutation in five Finnish patients suffering from hereditary tyrosinemia type I. Hum Mol Genet. 1994;3(1):69–72. 29. Orendac M, Zeman J, Stabler SP, et al. Homocystinuria due to cystathionine beta-synthase deficiency: novel biochemical findings and treatment efficacy. J Inherit Metab Dis. 2003;26(8):761–73. 30. Mudd SH. Hypermethioninemias of genetic and non-genetic origin: a review. Am J Med Genet C Semin Med Genet. 2011;157(1):3–32. 31. Gan-Schreier H, Kebbewar M, Fang-Hoffmann J, et al. Newborn population screening for classic homocystinuria by determination of total homocysteine from Guthrie cards. J Pediatr. 2010;156(3):427–32. 32. Zschocke J, Kebbewar M, Gan-Schreier H, et al. Molecular neonatal screening for homocystinuria in the Qatari population. Hum Mutat. 2009;30(6):1021–2. 33. Watkins D, Rosenblatt DS. Inborn errors of cobalamin absorption and metabolism. Am J Med Genet C Semin Med Genet. 2011;157(1):33–44. 34. Tortorelli S, Turgeon CT, Lim JS, et al. Two-tier approach to the newborn screening of methylenetetrahydrofolate reductase deficiency and other remethylation disorders with tandem mass spectrometry. J Pediatr. 2010;157(2):271–5. 35. Clarke R, Lewington S. Homocysteine and coronary heart disease. Semin Vasc Med. 2002;2(4):391–9. 36. Steegers-Theunissen RP, Boers GH, Trijbels FJ, Eskes TK. Neural-tube defects and derangement of homocysteine metabolism. N Engl J Med. 1991;324(3):199–200. 37. Danner DJ, Doering CB. Human mutations affecting branched chain alpha-ketoacid dehydrogenase. Front Biosci. 1998;3:d517–24. 38. Fernhoff PM, Lubitz D, Danner DJ, et al. Thiamine response in maple syrup urine disease. Pediatr Res. 1985;19(10):1011–6. 39. Chuang DT, Chuang JL, Wynn RM. Lessons from genetic disorders of branched-chain amino acid metabolism. J Nutr. 2006;136(1 Suppl):243S–9. 40. Bhattacharya K, Khalili V, Wiley V, Carpenter K, Wilcken B. Newborn screening may fail to identify intermediate forms of maple syrup urine disease. J Inherit Metab Dis. 2006;29(4):586. 41. Fernandez-Guerra P, Navarrete R, Weisiger K, et al. Functional characterization of the novel intronic nucleotide change c.288+9C>T within the BCKDHA gene: understanding a variant presentation of maple syrup urine disease. J Inherit Metab Dis. Published online 30 Apr. 2010. 42. Flaschker N, Feyen O, Fend S, Simon E, Schadewaldt P, Wendel U. Description of the mutations in 15 subjects with variant forms of maple syrup urine disease. J Inherit Metab Dis. 2007;30(6):903–9. 43. Quental S, Macedo-Ribeiro S, Matos R, et al. Molecular and structural analyses of maple syrup urine disease and identification of a founder mutation in a Portuguese Gypsy community. Mol Genet Metab. 2008;94(2):148–56. 44. Rodriguez-Pombo P, Navarrete R, Merinero B, Gomez-Puertas P, Ugarte M. Mutational spectrum of maple syrup urine disease in Spain. Hum Mutat. 2006;27(7):715. 45. Palmieri F. Diseases caused by defects of mitochondrial carriers: a review. Biochim Biophys Acta. 2008;1777(7–8):564–78. 46. Cavicchi C, Malvagia S, la Marca G, et al. Hypocitrullinemia in expanded newborn screening by LC-MS/MS is not a reliable marker for ornithine transcarbamylase deficiency. J Pharm Biomed Anal. 2009;49(5):1292–5. 47. Smith W, Kishnani PS, Lee B, et al. Urea cycle disorders: clinical presentation outside the newborn period. Crit Care Clin. 2005;21(4 Suppl):S9–17.

8


195

48. Nassogne MC, Heron B, Touati G, Rabier D, Saudubray JM. Urea cycle defects: management and outcome. J Inherit Metab Dis. 2005;28(3):407–14. 49. Scaglia F, Lee B. Clinical, biochemical, and molecular spectrum of hyperargininemia due to arginase I deficiency. Am J Med Genet C Semin Med Genet. 2006;142C(2):113–20. 50. Kobayashi K, Saheki T. Citrin deficiency. In: Pagon RA, Bird TD, Dolan CR, Stephens editors. Gene Reviews. Seattle (WA): University of Washington, Seattle; 1993–2005 [updated 2008 June 01]. 51. Saheki T, Inoue K, Tushima A, Mutoh K, Kobayashi K. Citrin deficiency and current treatment concepts. Mol Genet Metab. 2010;100 Suppl 1:S59–64. 52. Fukushima K, Yazaki M, Nakamura M, et al. Conventional diet therapy for hyperammonemia is risky in the treatment of hepatic encephalopathy associated with citrin deficiency. Intern Med. 2010;49(3):243–7. 53. Dimmock D, Kobayashi K, Iijima M, et al. Citrin deficiency: a novel cause of failure to thrive that responds to a high-protein, low-carbohydrate diet. Pediatrics. 2007;119(3):e773–7. 54. Ohura T, Kobayashi K, Tazawa Y, et al. Clinical pictures of 75 patients with neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD). J Inherit Metab Dis. 2007;30(2): 139–44. 55. Tazawa Y, Kobayashi K, Abukawa D, et al. Clinical heterogeneity of neonatal intrahepatic cholestasis caused by citrin deficiency: case reports from 16 patients. Mol Genet Metab. 2004;83(3):213–9. 56. Tamamori A, Fujimoto A, Okano Y, et al. Effects of citrin deficiency in the perinatal period: feasibility of newborn mass screening for citrin deficiency. Pediatr Res. 2004;56(4): 608–14. 57. Salek J, Byrne J, Box T, Longo N, Sussman N. Recurrent liver failure in a 25-year-old female. Liver Transpl. 2010;16(9):1049–53. 58. Deodato F, Boenzi S, Santorelli FM, Dionisi-Vici C. Methylmalonic and propionic aciduria. Am J Med Genet C Semin Med Genet. 2006;142C(2):104–12. 59. Desviat LR, Perez B, Perez-Cerda C, Rodriguez-Pombo P, Clavero S, Ugarte M. Propionic acidemia: mutation update and functional and structural effects of the variant alleles. Mol Genet Metab. 2004;83(1–2):28–37. 60. Perez B, Angaroni C, Sanchez-Alcudia R, et al. The molecular landscape of propionic acidemia and methylmalonic aciduria in Latin America. J Inherit Metab Dis. 2010;33 Suppl 2:S307–14. 61. Bikker H, Bakker HD, Abeling NG, et al. A homozygous nonsense mutation in the methylmalonyl-CoA epimerase gene (MCEE) results in mild methylmalonic aciduria. Hum Mutat. 2006;27(7):640–3. 62. Dobson CM, Gradinger A, Longo N, et al. Homozygous nonsense mutation in the MCEE gene and siRNA suppression of methylmalonyl-CoA epimerase expression: a novel cause of mild methylmalonic aciduria. Mol Genet Metab. 2006;88(4):327–33. 63. Vockley J, Ensenauer R. Isovaleric acidemia: new aspects of genetic and phenotypic heterogeneity. Am J Med Genet C Semin Med Genet. 2006;142C(2):95–103. 64. Gallardo ME, Desviat LR, Rodriguez JM, et al. The molecular basis of 3-methylcrotonylglycinuria, a disorder of leucine catabolism. Am J Hum Genet. 2001;68(2):334–46. 65. Baumgartner MR, Almashanu S, Suormala T, et al. The molecular basis of human 3-methylcrotonyl-CoA carboxylase deficiency. J Clin Invest. 2001;107(4):495–504. 66. Sweetman L, Williams JC. Branched chain organic acidurias, vol. 2. 7th ed. New York: McGraw-Hill; 2001. 67. Holzinger A, Roschinger W, Lagler F, et al. Cloning of the human MCCA and MCCB genes and mutations therein reveal the molecular cause of 3-methylcrotonyl-CoA: carboxylase deficiency. Hum Mol Genet. 2001;10(12):1299–306. 68. Dantas MF, Suormala T, Randolph A, et al. 3-Methylcrotonyl-CoA carboxylase deficiency: mutation analysis in 28 probands, 9 symptomatic and 19 detected by newborn screening. Hum Mutat. 2005;26(2):164.

196


69. Koeberl DD, Millington DS, Smith WE, et al. Evaluation of 3-methylcrotonyl-CoA carboxylase deficiency detected by tandem mass spectrometry newborn screening. J Inherit Metab Dis. 2003;26(1):25–35. 70. Bannwart C, Wermuth B, Baumgartner R, Suormala T, Weismann UN. Isolated biotin-resistant deficiency of 3-methylcrotonyl-CoA carboxylase presenting as a clinically severe form in a newborn with fatal outcome. J Inherit Metab Dis. 1992;15(6):863–8. 71. Bartlett K, Bennett MJ, Hill RP, Lashford LS, Pollitt RJ, Worth HG. Isolated biotin-resistant 3-methylcrotonyl CoA carboxylase deficiency presenting with life-threatening hypoglycaemia. J Inherit Metab Dis. 1984;7(4):182. 72. Baykal T, Gokcay GH, Ince Z, et al. Consanguineous 3-methylcrotonyl-CoA carboxylase deficiency: early-onset necrotizing encephalopathy with lethal outcome. J Inherit Metab Dis. 2005;28(2):229–33. 73. Visser G, Suormala T, Smit GP, et al. 3-methylcrotonyl-CoA carboxylase deficiency in an infant with cardiomyopathy, in her brother with developmental delay and in their asymptomatic father. Eur J Pediatr. 2000;159(12):901–4. 74. Roschinger W, Millington DS, Gage DA, et al. 3-Hydroxyisovalerylcarnitine in patients with deficiency of 3-methylcrotonyl CoA carboxylase. Clin Chim Acta. 1995;240(1):35–51. 75. van Hove JL, Rutledge SL, Nada MA, Kahler SG, Millington DS. 3-Hydroxyisovalerylcarnitine in 3-methylcrotonyl-CoA carboxylase deficiency. J Inherit Metab Dis. 1995;18(5):592–601. 76. Menao S, Lopez-Vinas E, Mir C, et al. Ten novel HMGCL mutations in 24 patients of different origin with 3-hydroxy-3-methyl-glutaric aciduria. Hum Mutat. 2009;30(3):E520–9. 77. Suzuki Y, Yang X, Aoki Y, Kure S, Matsubara Y. Mutations in the holocarboxylase synthetase gene HLCS. Hum Mutat. 2005;26(4):285–90. 78. Fukao T, Scriver CR, Kondo N. The clinical phenotype and outcome of mitochondrial acetoacetyl-CoA thiolase deficiency (beta-ketothiolase or T2 deficiency) in 26 enzymatically proved and mutation-defined patients. Mol Genet Metab. 2001;72(2):109–14. 79. Fukao T, Yamaguchi S. 3-ketoacyl-CoA thiolase deficiency. Nippon Rinsho. 2002;60 Suppl 4:738–42. 80. Fukao T, Nakamura H, Nakamura K, et al. Characterization of six mutations in five Spanish patients with mitochondrial acetoacetyl-CoA thiolase deficiency: effects of amino acid substitutions on tertiary structure. Mol Genet Metab. 2002;75(3):235–43. 81. Mrazova L, Fukao T, Halovd K, et al. Two novel mutations in mitochondrial acetoacetyl-CoA thiolase deficiency. J Inherit Metab Dis. 2005;28(2):235–6. 82. Fukao T, Zhang GX, Sakura N, et al. The mitochondrial acetoacetyl-CoA thiolase (T2) deficiency in Japanese patients: urinary organic acid and blood acylcarnitine profiles under stable conditions have subtle abnormalities in T2-deficient patients with some residual T2 activity. J Inherit Metab Dis. 2003;26(5):423–31. 83. Zhang GX, Fukao T, Rolland MO, et al. Mitochondrial acetoacetyl-CoA thiolase (T2) deficiency: T2-deficient patients with “mild” mutation(s) were previously misinterpreted as normal by the coupled assay with tiglyl-CoA. Pediatr Res. 2004;56(1):60–4. 84. Hedlund GL, Longo N, Pasquali M. Glutaric acidemia type 1. Am J Med Genet C Semin Med Genet. 2006;142C(2):86–94. 85. Tortorelli S, Hahn SH, Cowan TM, Brewster TG, Rinaldo P, Matern D. The urinary excretion of glutarylcarnitine is an informative tool in the biochemical diagnosis of glutaric acidemia type I. Mol Genet Metab. 2005;84(2):137–43. 86. Longo N, Amat di San Filippo C, Pasquali M. Disorders of carnitine transport and the carnitine cycle. Am J Med Genet C Semin Med Genet. 2006;142C(2):77–85. 87. Lund AM, Joensen F, Hougaard DM, et al. Carnitine transporter and holocarboxylase synthetase deficiencies in The Faroe Islands. J Inherit Metab Dis. 2007;30(3):341–9. 88. Schimmenti LA, Crombez EA, Schwahn BC, et al. Expanded newborn screening identifies maternal primary carnitine deficiency. Mol Genet Metab. 2007;90(4):441–5. 89. Gregersen N, Andresen BS, Corydon MJ, et al. Mutation analysis in mitochondrial fatty acid oxidation defects: exemplified by acyl-CoA dehydrogenase deficiencies, with special focus on genotype-phenotype relationship. Hum Mutat. 2001;18(3):169–89.

8


197

90. Boneh A, Andresen BS, Gregersen N, et al. VLCAD deficiency: pitfalls in newborn screening and confirmation of diagnosis by mutation analysis. Mol Genet Metab. 2006;88(2): 166–70. 91. Andresen BS, Olpin S, Poorthuis BJ, et al. Clear correlation of genotype with disease phenotype in very-long-chain acyl-CoA dehydrogenase deficiency. Am J Hum Genet. 1999;64(2): 479–94. 92. Carpenter K, Pollitt RJ, Middleton B. Human liver long-chain 3-hydroxyacyl-coenzyme A dehydrogenase is a multifunctional membrane-bound beta-oxidation enzyme of mitochondria. Biochem Biophys Res Commun. 1992;183(2):443–8. 93. Spiekerkoetter U, Sun B, Khuchua Z, Bennett MJ, Strauss AW. Molecular and phenotypic heterogeneity in mitochondrial trifunctional protein deficiency due to beta-subunit mutations. Hum Mutat. 2003;21(6):598–607. 94. Strauss AW, Bennett MJ, Rinaldo P, et al. Inherited long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency and a fetal-maternal interaction cause maternal liver disease and other pregnancy complications. Semin Perinatol. 1999;23(2):100–12. 95. Derks TG, Boer TS, van Assen A, et al. Neonatal screening for medium-chain acyl-CoA dehydrogenase (MCAD) deficiency in The Netherlands: the importance of enzyme analysis to ascertain true MCAD deficiency. J Inherit Metab Dis. 2008;31(1):88–96. 96. Horvath GA, Davidson AG, Stockler-Ipsiroglu SG, et al. Newborn screening for MCAD deficiency: experience of the first three years in British Columbia, Canada. Can J Public Health. 2008;99(4):276–80. 97. Iafolla AK, Thompson Jr RJ, Roe CR. Medium-chain acyl-coenzyme A dehydrogenase deficiency: clinical course in 120 affected children. J Pediatr. 1994;124(3):409–15. 98. Andresen BS, Dobrowolski SF, O’Reilly L, et al. Medium-chain acyl-CoA dehydrogenase (MCAD) mutations identified by MS/MS-based prospective screening of newborns differ from those observed in patients with clinical symptoms: identification and characterization of a new, prevalent mutation that results in mild MCAD deficiency. Am J Hum Genet. 2001;68(6): 1408–18. 99. Waddell L, Wiley V, Carpenter K, et al. Medium-chain acyl-CoA dehydrogenase deficiency: genotype-biochemical phenotype correlations. Mol Genet Metab. 2006;87(1):32–9. 100. Ficicioglu C, Thomas N, Yager C, et al. Duarte (DG) galactosemia: a pilot study of biochemical and neurodevelopmental assessment in children detected by newborn screening. Mol Genet Metab. 2008;95(4):206–12. 101. Pindolia K, Jordan M, Wolf B. Analysis of mutations causing biotinidase deficiency. Hum Mutat. 2010;31(9):983–91.

Chapter 9

The Role of Genetic Counseling in Everyday Medical Practice Kimberly J. Hart, Erin E. Baldwin, and D. Hunter Best

Introduction Genetic counseling is a relatively new discipline in medicine that emerged with the advancement of genetic diagnostics and molecular medicine. Genetic counselors are experts at translating the complex language of genomic medicine into terms that are easy to understand for patients, their relatives, and the medical support team. In general, the genetic counseling protocol involves diagnosis, management, and support for individuals and their families at risk for a genetic disorder. The role of genetic counselors as part of the medical team is based on their unique ability to communicate complex scientific and clinical findings in a way individual patients, commonly referred to as clients, and their families can easily understand. Traditionally, genetic counseling is a service intended to provide clients with sufficient information in a nondirective manner to aid with decisions regarding testing options, help with the management of genetic conditions, and provide support for clients in coping with post-counseling and testing realities. Genetic counselors are not exclusively educators nor are they solely psychosocial counselors. Biomedical knowledge is essential, but not sufficient for helping clients understand and make decisions regarding their situations. As such, genetic counselors must balance

K.J. Hart, M.S. • E.E. Baldwin, M.S. Department of Genetics, ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA D. Hunter Best, Ph.D., FACMG (*) Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112-0565, USA e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_9, © Springer Science+Business Media, LLC 2012

199

200

K.J. Hart et al.

attention to science with attention to psychosocial concerns (i.e., the client’s beliefs, values, family relationships, and social support networks). The field of genetic counseling is growing rapidly due to increased utilization of molecular diagnostics. Clinically available genetic tests are also growing in complexity. This has resulted in expanded needs for specialized laboratory genetic counselors that aid with the initial diagnostic step by acting as a bridge between the diagnostic laboratory and the healthcare provider. In this chapter, we define and explain the processes of genetic counseling, and describe areas in which Genetic counselors specialize, with a focus on the diagnostic laboratory. We also discuss common ethical issues that are routinely addressed by genetic counselors.

Definition and History of Genetic Counseling Genetic counseling was first defined in 1975 by the American Society of Human Genetics (ASHG) as a multistep communication process to help individuals or families [1]. To encompass the growing practice of genetic counseling, the National Society of Genetic Counselors (NSGC) in 2006 redefined genetic counseling as the process of helping people understand and adapt to the medical, psychological, and familial implications of genetic contributions to the disease by (1) integrating family and medical history; (2) providing education regarding inheritance, testing, management, prevention, recourses, and research; and (3) counseling to promote informed choices and adaptation to the risk or condition [2]. In 1979, the NSGC was incorporated with approximately 200 members. Subsequently, in 1992, the American Board of Genetic Counseling (ABGC) was established to certify genetic counselors, a role that had been previously performed by the American Board of Medical Genetics (ABMG). To be eligible for the genetic counseling board exam, the ABGC requires candidates to have graduated from an accredited genetic counseling training program. Typically, ABGC-accredited genetic counseling programs are masters-level graduate programs that include a combination of both didactic and clinical training. In most programs, the coursework includes studies in human and molecular genetics, related biological sciences, and psychosocial counseling with clinical application. Graduates must demonstrate clinical skills in a variety of settings with diverse patient populations to be eligible for board certification.

The Genetic Counseling Process Genetic counseling involves several key elements including information gathering, establishing or verifying a diagnosis, risk assessment, education, and psychological counseling. These steps are described in detail below.

9 The Role of Genetic Counseling in Everyday Medical Practice

201

Key Male Female Sex unknown Affected Proband Obligate carrier Deceased

Fig. 9.1 Pedigree showing an autosomal recessive inheritance pattern. Parents of the affected child are obligate carriers of the recessive condition

Information Gathering The exchange of information is a central part of genetic counseling and includes assessment of a patient’s family history, which, along with general physical and mental health, is recorded in the form of a pedigree to document the possible transmission of a condition from one generation to the next. Accurate, detailed, and relevant information is important for construction of the pedigree, which aids in making a diagnosis, determining the risks and likelihoods of being affected with or a carrier of a certain disease and assessing the needs for patient education and psychosocial support. Adherence to conventional symbols notating gender, biologic relationships, pregnancy outcomes, and genotypic information assures that a pedigree can be readily and accurately interpreted [3]. An example of a schematic family history is illustrated in Fig. 9.1.

Diagnosis In addition to the medical and family history, a physical exam performed by a clinical geneticist or another specialist is often necessary to establish a diagnosis. Further testing, such as imaging studies, blood or urine tests, examination of other family members, or genetic testing, may be necessary to confirm a suspected diagnosis.

202

K.J. Hart et al. Table 9.1 Example calculation of conditional probability by Bayesian analysis Probability Carrier Not a carrier Prior (based on family history) 1 1 2 2 1 Conditional (2 healthy sons) ⎛ 1⎞ ⎛ 1⎞ ⎜⎝ ⎟⎠ ⎜⎝ ⎟⎠ 2 2 1 1 Joint (prior × conditional) 8 2 1 Posterior probability 8 =1 1 1 5 + 8 2

Risk Assessment Once a diagnosis is established, genetic counselors are able to discuss the chance of recurrence in a couple’s future children and can discuss the probabilities that other family members might be affected by the same condition. Recurrence risk is based on the specific inheritance pattern of the disorder and on specific family history. While determining recurrence risks for single-gene disorders that follow a Mendelian inheritance pattern is often straightforward, it can be complicated by a variety of factors. The calculation of the recurrence risk for a disorder with a singlegene inheritance is illustrated in Fig. 9.1. In the example, a child is affected with cystic fibrosis (CF), an autosomal recessive disorder caused by mutations in single gene (known as CFTR). Based on family history and based on basic Mendelian principles, the parents are obligate carriers of a CFTR gene mutation, and the chance of the couple having another affected child is one in four with each subsequent pregnancy (assuming there is no misattributed paternity). Risk calculations can be complicated by reduced penetrance (not everyone with a disease-causing mutation has symptoms of the disease) or variable expressivity (not everyone with the same mutation has the same severity of disease), often seen in X-linked and autosomal dominant disorders. In these circumstances, a conditional probability can be applied to account for specific family history that may increase or decrease an underlying risk. A conditional probability is calculated by Bayesian analysis which considers prior probabilities of an event as well as conditional probabilities. Table 9.1 demonstrates an example of a female patient who is at risk of being a carrier of an X-linked disorder (e.g., hemophilia). In this example, the patient’s mother is a known carrier of the X-linked disorder, and, therefore, the patient has a one in two risk of being a carrier herself. This prior risk, however, can be modified by introducing conditional factors, such as this patient having two unaffected sons. Based on this information, the patient’s risk of being a carrier is reduced from one in two to one in five. Complex traits and multifactorial conditions are not caused by a defect affecting a single gene; instead disease manifestation results from interaction between several


203

loci and/or environmental influences. Like single Mendelian traits, they can have strong genetic components and exhibit familial clustering. Examples include cleft lip and palate, congenital heart disease, and psychiatric illnesses. For these conditions, an empirical recurrence risk can be provided. Empiric risks to first-degree relatives are generally increased over the risk to the general population, but the risk is not at a level observed with autosomal dominant or recessive disorders. Calculated conditional probabilities guide the recommendations of further genetic tests.

Education and Psychosocial Counseling In general, genetic counselors do not tell clients what decisions to make regarding testing or management options; rather, clients are provided with sufficient information and support to make an informed decision appropriate for their situation. Typically, the information provided to patients includes the features, natural history, and variability of the condition in question; its genetic (or nongenetic) basis; diagnoses, management, and anticipatory guidance; risk of occurrence in other family members; economic, social, and psychosocial impact; and available resources. Genetic counselors help clients adapt to the impact of a disorder through positive regard, empathy, trust in the individual’s ability to participate in the resolution of their problems, and the centrality of the counselor-patient relationship [4]. The process is “nondirective” and in contrast to a paternalistic approach where the specialist makes the decision.

Genetic Counseling Specialties The utilization of genetic counselors is increasing as genetic testing and genetic medicine become more commonplace. Historically, genetic counselors have worked mainly in prenatal and pediatric genetics settings, but over time, the role of a genetic counselor has expanded to become relevant in nearly all areas of medicine, including laboratory diagnostics. Below we describe some of the many roles of a genetic counselor in providing genetic services across several clinical subspecialties.

Prenatal and Perinatal All women who are pregnant or planning a pregnancy are candidates for genetic counseling. The various screening and testing options available during pregnancy can be complex and overwhelming for both the patient and her healthcare provider. Defining high- and low-risk pregnancies is becoming increasingly complex as

204

K.J. Hart et al.

detailed evaluation of the fetus and genetic testing are becoming more commonplace. Genetic counselors are trained to help patients and their healthcare providers understand the spectrum of specific screening and diagnostic tests, their sequential application, as well as the risks, benefits, and limitations. Pregnant women or couples are referred to a prenatal genetic counselor to discuss either the potential risks and outcomes of a current pregnancy or a specific fetal diagnosis affecting the current pregnancy [5]. Other indications for prenatal genetic counseling include (but are not limited to): repeated pregnancy loss, infertility, advanced maternal or paternal age, family history, or teratogen exposure. Preconception genetic counseling may also be important for clients planning a pregnancy who are interested in discussing potential risks to a pregnancy relating to personal or family medical history, identifying and understanding prenatal or preconception testing options, or learning about carrier screening. Following the above outlined process of a genetic counseling session, the genetic counselor will create a detailed and personalized risk assessment based on the information collected. The genetic counselor will then guide patients regarding appropriate screening and diagnostic tests and provide support explaining test results and their implications. In the event of abnormal test results, this may include a discussion of the risks to the pregnancy, natural history of the condition, referral to support groups, and preparation for a child with special needs. Additionally, the prenatal genetic counselor can explore family building alternatives. Once all available options are considered, the genetic counselor and patient determine a management plan that includes medical and emotional support.

Pediatric Medical Genetics Pediatric genetics clinics primarily serve children with known or suspected genetic conditions or birth defects, though some pediatric genetics clinics may also serve adult patients. Patients may be referred to the pediatric genetics clinic from a variety of sources, including newborn screening programs, primary care providers, specialty clinics, inpatient consultations, or following a prenatal diagnosis. The genetics clinic team typically includes a physician specialized in medical genetics, genetic counselor, dietician, social worker, and nurse who aid with diagnosis, education, and support. Some genetics services offer multidisciplinary clinics, including specialists from a variety of disciplines. From the genetic counselor’s perspective, a typical pediatric genetics evaluation will follow the above-described genetic counseling process. The physical exam of the patient is provided by a physician geneticist, and, as relevant, other history information may be collected by the genetics nurse, dietician, and social worker. Following the assessments, findings are discussed among the team, and a diagnosis or list of potential diagnoses may be assimilated, and testing recommendations may be made. The team will then discuss further diagnostic steps with the family. If a diagnosis, or tentative diagnosis, can be made, the genetic counselor and/or geneticist


205

present the family with information on the inheritance pattern, natural history, prognosis, management, and etiology of the condition. The genetic counselor will also discuss issues of recurrence risk and provide emotional support. If further genetic testing is recommended, the genetic counselor will guide the family regarding relevant genetics concepts, test benefits, limitations, and risk to promote informed decision making, and help coordinate testing. This may include selecting a specialized diagnostic laboratory, coordinating the logistics of sample collection, and writing letters of medical necessity to the patient’s insurance provider. In accordance with the counseling process, the genetic counselor will also guide patients and family members on post-testing realities. The interpretation of genetic testing is often complex. In the event of a negative test result, it may be necessary to educate the family on the limitations of the testing. While a positive result confirms a diagnosis, a negative or inconclusive test result does not always mean that a condition can be excluded. Such outcomes can be challenging for both the genetic counselor and the patient, as their meaning is unknown. Additional testing may be required. In this case, the genetic counselor will often work closely with the genetic counselor at the performing laboratory. Depending on the condition, a patient may be seen only once in a genetics clinic or may require long-term follow-up. Sometimes, patients leave the clinic with a known diagnosis; however, very often, the genetics workup may take months to years to complete.

Specialty Clinics With the advances in genetics and technology, genetic testing has become more routine in general medical practice. This has generated opportunities and increased the need for genetic counselors working with specialty clinics, disease foundations, and support groups. Specialty clinics can include, but are not limited to, ophthalmology, cardiology, neurology, audiology, and endocrinology. Genetic counselors can become experts in their field, serving as resources not only to their patients and colleagues, but also providing education to the general medical and patient communities. Genetic counselors may also be heavily involved in genetic research protocols, assisting in gene discovery and clinical research.

Adult Onset Diseases Genetic counselors also provide services for conditions which develop later in life, such as cancer or neurological disorders. With the availability of molecular testing for certain adult onset diseases, healthy individuals who are at risk for developing an adult-onset condition may elect to pursue genetic testing to learn the chance they may develop the condition later in life. This knowledge may aid with making

206

K.J. Hart et al.

informed medical decisions and help individuals and families reduce anxiety and remove uncertainty. For example, a cancer genetic counselor may evaluate an individual with early onset colon cancer or breast cancer to determine if the cancer may be due to a hereditary predisposition. Based upon the family history, the patient’s personal medical history, and the nature of the cancer diagnosis, the genetic counselor will discuss the risks of an inherited cancer syndrome. If genetic testing is available, the genetic counselor can identify the appropriate testing as well as the most appropriate family member to whom testing should be initially offered (this may or may not be the patient). Post-testing, the genetic counselor will provide education on screening, testing, and management recommendations for the patient and at-risk family members. For some adult onset conditions, however, treatment or prevention may not be available. Examples here include Huntington disease and many other neurodegenerative diseases. In these situations, the decision to pursue genetic testing may be particularly complex, and it is important for individuals seeking this testing to be offered genetic counseling and for informed consent to be provided. This issue is discussed in more detail in a separate chapter in this volume. Due to the nature of hereditary disease, genetic counseling and testing is a multistep process. As discussed in the next section, to assure the testing process is performed seamlessly, having a relationship with the performing diagnostic lab can be extremely advantageous.

Diagnostic Laboratory With a limited number of medical geneticists and genetic counselors available, nongeneticists play a major role in ordering genetic tests, communicating with patients about genetic issues, and referring patients to appropriate specialists. Limited genetic knowledge among healthcare providers is a global problem [6]. One solution for healthcare providers is to utilize genetic counselors employed by diagnostic laboratories. In diagnostic laboratories, genetic counselors are most commonly involved with pre- and post-analytic processes, often fielding calls from healthcare providers trying to select the most appropriate test for their patients or aiding in the interpretation of completed tests [7]. Genetic counselors in diagnostic laboratories are available to discuss testing strategies with healthcare providers, aid with pedigree assessment, and discuss the benefits or limitations of testing. Genetic counselors often review genetic test orders upon receipt in the laboratory to confirm that the appropriate test has been ordered, obtain any missing information that may aid with test interpretation, and assist in prioritizing samples that may be clinically urgent. After the testing is completed, genetic counselors can help healthcare professionals understand results in the context of their patient’s clinical picture, as well as recommend additional testing if applicable. If available, genetic counselors can discuss research studies which may


207

better characterize an identified mutation or aid in establishing a genotype/phenotype correlation. Even with these efforts, genetic tests are frequently misordered, which can delay medical decision making, increase healthcare costs, and prolong the anxiety that a family experiences. Genetic diagnostic tests can be complex, often integrating labor intensive methodologies, extended turnaround times, variable test sensitivity, and multifaceted interpretations that incorporate a patient’s medical history, ethnicity, and family history. Molecular testing is a complicated and often collaborative process, and correlating clinical information with molecular test results is critical to providing proper test interpretation. Creating a dialog and maintaining a relationship with your laboratory can be extremely important and beneficial, enabling laboratories and clinicians to provide quality, patient-centered care.

Other Genetic Subspecialties in Laboratory Diagnostics While genetic counselors are becoming an integral part in diagnostic laboratories, they are certainly not the only genetics specialist. In fact, many physicians are probably already utilizing the services of some geneticists without realizing it. In a laboratory setting, medical directors are individuals with specialized genetic training involved in everything from cancer diagnosis to newborn screening. Most of these individuals have doctoral degrees (M.D., Ph.D.) and complete rigorous training programs that are accredited by the American Board of Medical Genetics (ABMG) in order to become eligible for board certification examinations. Subspecialties with training programs, relevant to laboratory diagnosis, that are recognized by the ABMG include: Biochemical Genetics: Individuals with training in this specialty are able to perform and interpret data from biochemical analyte analysis and genetic testing related to inherited biochemical disorders (i.e., galactosemia, PKU). Biochemical geneticists are routinely involved in interpreting the results obtained from newborn screening programs, as well as the follow-up testing performed when the initial abnormal results are obtained. Cytogenetics: Individuals with training in this specialty are involved in the performance and interpretation of cytogenetic testing. This encompasses a wide spectrum of genetic tests including, but certainly not limited to, routine karyotypes, microarray-based genetic testing, and fluorescence in situ hybridization (FISH). Individuals with training in cytogenetics are involved in testing for inherited genetic disorders and acquired conditions such as cancer. Molecular Genetics: Individuals in this area of expertise specialize in the performance and interpretation of molecular genetic testing as it relates to both inherited and acquired disease. Molecular geneticists review test results ranging from DNA sequencing of entire genes to screening panels for common genetic disorders (such as cystic fibrosis).

208

K.J. Hart et al.

Ethical Concerns and Genetic Testing Genetic testing, genetic test results, and all genetic information pose great ethical challenges to patients, healthcare providers, and insurance providers. While everyone agrees that advances in knowledge and technology must be used responsibly, the principles of (1) autonomy, or safeguarding an individual’s rights to control their medical care and medical information, (2) beneficence, or to do good for the patient, (3) nonmaleficence, or to do no harm, and (4) justice, or to ensure that all patients are treated equally and fairly, guides the genetic counseling profession in their professional conduct. A code of ethics is outlined by the National Society of Genetic Counselors [8]. The NSGC codes are influenced by the ethical and moral position of medicine and offer genetic counselors a framework of guidance when conflicts arise. As in other disciplines and professions, perceptions of ethics depend on cultural frameworks, personal experiences, and other life influences [9]. In the following section, we will discuss examples of the wide spectrum of potential conflicts that range from prenatal genetic testing to genetic discrimination and insurability of patients and family members.

Informed Consent An important part of genetic testing is obtaining informed consent. Informed consent establishes that a patient has understood the information, has not been coerced, and has autonomously agreed with testing procedures. Several states have specific requirements, such as obtaining written informed consent. Requirements regarding the content of the consent document often include the nature and purpose of the genetic test, its effectiveness and limitations, implications of the test results, future use of the sample taken from the patient and a list of those who will have access to the sample, and the person’s right to confidential treatment of the sample and associated information. Consent must truly be informed. Should a provider fail to provide information or fail to document the consent process, the provider risks allegations that care was rendered without informed consent should a problem occur.

Prenatal Genetic Testing Patients referred to a prenatal genetic counselor seek information to make an informed, autonomous decision regarding their reproductive choices. Individuals planning a family want healthy offspring. As the spectrum of genetic conditions that


209

can be diagnosed early and less invasively expands, prenatal screening and diagnosis is affecting more and more pregnancies. Carrier screening which targets high-risk populations (e.g., Tay-Sachs disease in the Ashkenazi Jewish population) will eventually touch most patients as more screening tests become routinely available. Couples aware of their carrier status may then decide to not have children, use donor gametes, use preimplantation genetic diagnosis, or adopt. Prenatal diagnosis and pregnancy termination may also be utilized. With early genetic testing, the boundaries of medical necessity become less imminent and ethical considerations more prevalent. At what point is it acceptable or unacceptable to use prenatal testing techniques? For example, ethical concerns may be raised when a patient indicates that if she learns she is pregnant with a girl, she plans to end the pregnancy. While there are certainly medical arguments for and against sex selection [10], this particular issue has highlighted the need for balancing clinical indications, laws, cultural issues, and ethical obligations.

Genetic Testing in Children Genetic testing for disease predisposition in children is a common source of ethical concern among both medical practitioners and family members. When considering genetic testing in a minor, there is no agreed upon time to test children for disorders in which medical intervention is available. However, it is appropriate to consider genetic testing at the time medical intervention or surveillance would be initiated. Certain genetic testing can be beneficial and important for implementing proper treatment and prophylactic screening protocols. For example, testing a child at risk for an APC gene mutation, a common cause of early onset colon cancer, can help determine if colonoscopies at the recommended age of 12 are indicated [11]. In contrast, testing a child for Huntington disease provides no preventive benefits and has the potential to lead to stigmatization, damage to self-esteem, and increased anxiety in anticipation of symptoms. It also interferes with the child’s right to decide whether to be tested as an adult. Here, the general consensus is that childhood genetic testing is not warranted unless clinically beneficial intervention is possible (see the Huntington’s Disease Society of America web site (www.hdsa.org) for additional information). In cases where disease onset typically occurs in adulthood, testing is generally not performed on individuals under the age of 18 [12]. In addition, the decision to pursue genetic testing for an adult onset condition can require that one understand complex medical implications and the variety of psychological, medical, and personal implications that the testing may mean for the patient. As such, obtaining informed consent directly from the patient (instead of a family member or guardian) is encouraged before genetic testing is performed for any adult onset condition.

210

K.J. Hart et al.

Duty to Warn Duty to warn applies to cases where the patient is a threat to society as a whole or has indicated intent to harm a specific individual. In these situations, the clinician must breach confidentiality to warn the identified target about the imminent danger. The application of duty-to-warn laws places clinicians in the uneasy and difficult situation of breaching the law of confidentiality. However, if the clinician has reasonable suspicion, the clinician is protected from prosecution [13]. The concept of “duty to warn” has been tested in practice in law, and been subjected to ethical debates in the USA in clinical psychology. Duty to warn originated from a legal case brought by the Tarasoff family after their daughter was murdered by a patient under psychological care in the university counseling center who confided his intent to kill Tatiana Tarasoff (Tarasoff v. Regents of the University of California, 551, p.2d 334, 1974). Like the psychologist who learns in a counseling session that a patient has concrete plans of harming others, the genetic counselor can be faced with similar dilemmas. For example, offering genetic testing to a patient with an identical twin will provide genetic information for both the patient and his/her twin, regardless of whether or not the twin has provided informed consent for the testing. If the twin has explicitly rejected the option of genetic testing for a potentially life-threatening condition, but the patient proceeds with such testing, information on the identical twins’ risk will also be revealed. This can introduce ethical conflicts and dilemmas that can be difficult for both clinicians and patients to navigate. Determining who is responsible for sharing a positive genetic result with relatives can also be a source of ethical debate. Often, in diseases with familial clustering of a causal mutation, genetic counselors will provide the patient with a letter which can be shared with at-risk family members. Other family members can then take the letter to their own providers and request appropriate testing. Written documentation that the patient can share with relatives also safeguards confidentiality as desired by patients. If a high-risk patient refuses to contact at-risk relatives, an ethics consult is an option [13]. Here, the genetic counselor consults with an expert in medical ethics and discusses the specifics of the case and underlying circumstances. Because genetic information is both individual and familial, conflicts can arise between maintaining confidentiality and the duty to warn. The ethical, legal, and statutory exceptions place limits on the principle of confidentiality and permit disclosure in specific circumstances. The ethical duty of healthcare professions to warn at-risk relatives will continue to be a topic of debate.

Genetic Information and Nondiscrimination Act (GINA) Public awareness, concerns, and interest led to the introduction of a federal legal framework that limits the potential of discrimination based on individuals’ genetic


211

makeup and knowledge thereof concerning insurability. The Genetic Information Nondiscrimination Act (GINA) is a federal law implemented in 2008 with the intent to regulate health insurance and employment discrimination in light of genetic information and disease predisposition. The law prohibits group health plans and health insurers from denying coverage to a healthy individual or charging that person higher premiums based solely on a genetic predisposition to developing a disease in the future. The legislation also prohibits employers from using individuals’ genetic information when making job placement or promotion decisions. Prior to GINA, the Health Insurance Portability and Accountability Act (HIPAA) was enacted. These guidelines regulate the use and disclosure of protected health information (PHI) for health service providers. PHI is defined as information held by a covered entity which concerns health status, provision of health care, or payment for health care that can be linked to an individual. In addition, many states have their own laws and regulations governing privacy and the use of genetic information. The frequency and variety of ethical challenges and issues that emerge during genetic counseling show the importance of flexible frameworks, the need to consider and revisit issues before they arise in practice, as well as an open discussion involving medical, legal, and ethics experts.

Conclusion As the implementation of genetic testing becomes more commonplace in the practice of medicine, clinicians will be responsible for identifying appropriate testing strategies, providing informed consent, and interpreting complex genetic test results. Genetic counselors are uniquely trained to aid clinicians in providing these services. Genetic counseling is a critical element in the incorporation of genetic and genomic services into medical practice, and the provision of genetic counseling can increase understanding and appropriate utilization of genetic testing as well as decrease liability and overall costs to both the patient and the healthcare system. Genetic counselors and other genetic subspecialists are experts at conveying complex genetic information in a way that can help patients and families understand the natural history, etiology, management, and inheritance of a genetic condition. They can help practitioners and patients understand testing options and results interpretation. In addition, they can aid patients in the adjustment to the social, psychological, and emotional implications of a genetic diagnosis. Genetic counselors are charged with the task of maintaining a current understanding of a rapidly changing field and translating that information in a way that can be readily understood by patients, healthcare providers, and the public. The knowledge and support that genetic counselors can bring to nearly all aspects of medicine is critical as health care evolves in the wake of the Human Genome Project and encroaches upon the practice of genomic medicine.

212

K.J. Hart et al.

Resources Local genetic counselors and genetic counselors at clinical laboratories can serve as a readily available resource to clinicians by helping to identify local genetic services and providing information on testing options, implications, and strategies. In addition, the following web sites harbor a wealth of information on clinical genetic services: • National Society of Genetic Counselors, www.nsgc.org: provides patient and practitioner information on genetic counseling and serves as a tool for locating a genetic counselor by zip code, name, and other information. • American College of Medical Genetics, www.acmg.net: provides general medical genetics resources, including a search engine for locating clinical genetics services. • GeneTests, www.genetests.org: provides comprehensive reviews and laboratory testing information for a variety of genetic conditions. • Genetic Alliance, www.geneticalliance.org: provides information on genetic conditions, health insurance, and links to lay advocacy groups. • National Institutes of Health Office of Rare Diseases, http://rarediseases.info. nih.gov: provides information on more than 6,000 rare diseases.

References 1. Ad Hoc Committee on Genetic Counseling. Genetic counseling. Am J Hum Genet. 1975; 27(2):240–2. 2. Resta R, Biesecker BB, Bennett RL, et al. A new definition of Genetic Counseling: National Society of Genetic Counselors’ Task Force report. J Genet Couns. 2006;15(2):77–83. 3. Bennett RL, French KS, Resta RG, Doyle DL. Standardized human pedigree nomenclature: update and assessment of the recommendations of the National Society of Genetic Counselors. J Genet Couns. 2008;17(5):424–33. 4. Veach PM, Bartels DM, Leroy BS. Coming full circle: a reciprocal-engagement model of genetic counseling practice. J Genet Couns. 2007;16(6):713–28. 5. Ciarleglio LJ, Bennett RL, Williamson J, Mandell JB, Marks JH. Genetic counseling throughout the life cycle. J Clin Invest. 2003;112(9):1280–6. 6. Baars MJ, Scherpbier AJ, Schuwirth LW, et al. Deficient knowledge of genetics relevant for daily practice among medical students nearing graduation. Genet Med. 2005;7(5):295–301. 7. Scacheri C, Redman JB, Pike-Buchanan L, Steenblock K. Molecular testing: improving patient care through partnering with laboratory genetic counselors. Genet Med. 2008;10(5):337–42. 8. National Society of Genetic Counselors. The code of ethics of the National Society of Genetic Counselors. J Genet Couns. 2006;15(5):309–11. 9. Weil J. Genetic counselling in the era of genomic medicine. As we move towards personalized medicine, it becomes more important to help patients understand genetic tests and make complex decisions about their health. EMBO Rep. 2002;3(7):590–3. 10. Ethics Committee of the American Society for Reproductive Medicine. Preconception gender selection for nonmedical reasons. Fertil Steril. 2001;75(5):861–4.


213

11. Giardiello FM, Brensinger JD, Petersen GM. AGA technical review on hereditary colorectal cancer and genetic testing. Gastroenterology. 2001;121(1):198–213. 12. National Society of Genetic Counselors (NSGC). Position statement: Genetic Testing for Adult-Onset Disorders. 1997. 13. Disclosure ASoHGSISoF. Professional disclosure of familial genetic information. Am J Hum Genet. 1998;62(2):474–83.

Chapter 10

Direct-to-Consumer Genetic Testing Molecular Genetics and Personalized Medicine Caroline F. Wright and Daniel G. MacArthur

Introduction What Is Direct-to-Consumer Testing? Medical tests that are both marketed and sold directly to the public, without the supervision of a health-care professional, are not new. Until recently the market consisted primarily of self-test kits for various blood or urine analytes, many of which are available direct-to-consumer (DTC) over-the-counter at pharmacies or via the Internet. Most notable among these are glucose monitors, which form an integral part of diabetes management, home pregnancy testing kits, and blood cholesterol tests. These kits combine a simple assay device with rudimentary interpretation, whereby the results of the test are displayed on the device itself. More recently, this market has expanded to include DNA testing services that are available DTC over the Internet. In this case, instead of purchasing a testing device, the customer sends a sample of tissue away for testing and their results are made available online. Although some companies offer (or require) the involvement of a qualified health-care professional before and/or after testing, the ethos is broadly one in which the consumer has direct access to his or her own genome so that they can take charge of their own health. The development of this “consumer genomics” industry has fuelled international debate about the implications of widespread, medically unsupervised access to genetic information. There are numerous players in the field offering a multitude of different tests and services (Fig. 10.1). A regularly updated list of tests and providers is available

C.F. Wright Ph.D. (*) • D.G. MacArthur Ph.D. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK e-mail: [email protected] D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5_10, © Springer Science+Business Media, LLC 2012

215

216

C.F. Wright and D.G. MacArthur

Fig. 10.1 Timeline from 2007 to 2011 showing key events affecting the DTC genetic testing industry. Actions from the companies in the industry (including company launches, and changes in the price of whole genome sequencing in USD) are shown on the left, and events related to regulation, media coverage, and financial changes are shown on the right. Months within each year are indicated by tick marks

online from the US Genetics and Public Policy Center, and a report published in 2010 from the UK Human Genetics Commission (HGC) identified 11 different categories of DTC genetic tests (see Table 10.1) [1]. While specialist providers exist that offer just a single analysis (e.g., APOE genotyping, paternity testing), because

10 Direct-to-Consumer Genetic Testing

217

Table 10.1 Types of tests covered by the UK Human Genetics Common Framework of Principles for Direct-to-Consumer Genetic Testing Services # Test type Description 1 Diagnostic Tests intended to diagnose a medical condition in a person with symptoms and/or signs 2 Presymptomatic/ Tests intended to predict with a high probability that an predictive asymptomatic person will develop a condition (e.g., BRCA testing for breast cancer, mutation testing for monogenic conditions) 3 Carrier Tests intended to show that a person is a carrier of a recessive condition, so that although they are not themselves affected, there is a risk they may have affected children 4 Prenatal Tests intended to identify medical information about a fetus, or to establish fetal sex or paternity, during pregnancy 5 Susceptibility/ Tests intended to provide an indication of the absolute predisposition lifetime risk and/or relative risk of an individual developing a condition compared with the general population (e.g., APOE testing) 6 Pharmacogenetic Tests intended to predict the response profile of an individual to a drug or course of therapy 7 Nutrigenetic Tests intended to provide information about an individual’s responsiveness to a particular nutrient or diet and how this affects metabolism, health status, and risk of disease 8 Lifestyle/behavioral Tests intended to provide information about an individual’s behavioral propensities, performance capacities (physical or cognitive), or response to certain environmental conditions, which are designed to assist the individual to modify the outcomes of any of these by elective changes in behavior (excluding the administration of prescribed medicines) 9 Phenotype Tests intended to provide information about how an individual’s phenotype is conditioned by their genotype (e.g., height, eye color) 10 Genetic relatedness Tests intended to determine and/or provide information about a genetic relationship, including paternity tests 11 Ancestry Tests intended to provide information about relatedness to a certain ancestor or ancestral group and/or how much of an individual’s genome is likely to have been inherited from ancestors from particular geographical areas or ethnic groups Adapted and used with permission from the HGC [1]

of the nature of genomic analysis and the development of affordable high-throughput technologies, increasingly a company may offer multiple tests on one sample as a single service. This breadth of analysis makes questions around the evaluation and regulation of these services much more complicated than those associated with individual medical tests, whether available DTC or otherwise.

218


Focus on Health Although much of the genome is medically uninformative, and many DTC genetic tests sold are for nonmedical purposes (particularly ancestry and relatedness testing), in this chapter, we focus on DTC genetic tests that purport to provide healthrelevant information. These include the first six categories listed by the HGC (see Table 10.1); of these, the first four categories include tests that are commonly used in clinical genetics services for families with a history of a particular inherited condition, while the next two are more speculative applications of genetic testing in health aimed at a wider range of patients and individuals. Much of what follows is concerned specifically with the fifth category of tests – susceptibility testing for multifactorial diseases – since this is where much of the controversy surrounding DTC genetic testing services has resulted. These tests offer risk predictions based on a recent plethora of findings from genome-wide association studies (GWAS) of common variants that impart a small genetic susceptibility to a variety of common, complex diseases – such as type 2 diabetes or rheumatoid arthritis. As such, the tests relate to diseases that many individuals will encounter during their lifetimes, and have a high public health burden. The tests estimate the probability (or absolute risk) of an individual getting a disease over his or her lifetime by combining the relative risks associated with multiple genetic variants with the age and sex-specific population incidence of the disease [2]. The results are not intended to be diagnostic, but to provide an indication of the likelihood than an individual will develop various conditions in the future, so that they may act upon the information by undertaking behaviors that mitigate the risk (e.g., diet, exercise, smoking cessation, regular screening).

Key Players and Timeline The birth of the modern consumer genomics industry can be dated to November 2007, which saw the back-to-back launches of DTC genome scan products by two companies: Google-backed Silicon Valley–based start-up and current market leader 23andMe [www.23andme.com], and deCODEme [www.decodeme.com], a subsidiary of Icelandic biotech giant deCODE Genetics. A third genome scan product was launched by another California-based start-up, Navigenics [www.navigenics.com], in April 2008. All three companies offered products that were extremely similar. Customers were sent a kit allowing them to submit a saliva sample by mail, from which their DNA was extracted and then analyzed on a commercial genotyping array or “SNP chip” from either Illumina or Affymetrix, the two major suppliers of genotyping technologies to research groups. The arrays provide information on customer’s sequence at between half a million and a million sites of known genetic variation throughout the genome. Since launch the companies have varied their prices (ranging


219

between US $400 and $2,500), as well as the number of sites assayed (from a few thousand to over a million) and the level of professional advice provided to consumers by the companies. Genome scan products tend to target two categories of genetic variants. Firstly, large numbers of common single-nucleotide polymorphisms (SNPs, with a population frequency typically above 5%) scattered throughout the genome, some of which have been associated with (typically very small) increases in the risk of complex diseases through genome-wide association studies. Secondly, additional markers may be included targeting variants that are either of interest to genetic genealogists, or that represent rare strongly disease-associated point mutations. While based on similar technology, the major players in the DTC genome scan market have sought to differentiate themselves in other ways. 23andMe has consistently emphasized the recreational aspects of genetic testing, tempting customers with colorful instructional videos and intuitive interfaces to explore both ancestry and disease risk information. deCODEme marketing has focused on the academic credentials of its parent company, deCODE Genetics, which has leveraged its genetic database to identify and publish many of the trait-associated variants subsequently tested by other DTC providers. Finally, Navigenics has portrayed itself as the more serious player in the field, focusing exclusively on actionable disease risk variants and eschewing more “frivolous” analyses of ancestry and nondisease traits. A newer entrant to the consumer genotyping market, Counsyl [www.counsyl. com], has focused exclusively on a very different target audience to other genome scan companies: it offers a custom genotyping array that targets several hundred extremely rare genetic variants associated with severe monogenic diseases. The company markets the test to prospective parents; by determining whether one or both parents are carriers of autosomal (or X-linked) recessive diseases, the test can be used to generate pre- and posttest probabilities that a couple’s offspring will be affected by any of over 100 diseases. While genotyping array technology has been the primary tool of the DTC personal genomics industry, other companies have gambled on offering a higher-end product at a massively higher price: whole genome sequencing, i.e., the provision of an individual’s entire genetic code at nearly every position along the roughly 6 billion sites in their genome. The first company to fill this niche, Knome [www.knome. com], launched just 2 months after 23andMe and deCODEme in November 2008, offering wealthy customers the sequence of their genomes for a staggering US $350,000 [3]. Knome took advantage of a revolution in genomic technology: the development of massively parallel methods for analyzing DNA, referred to collectively as “second-generation” sequencing, as opposed to the “first-generation” capillary sequencing methods used for the Human Genome Project. Second-generation technologies have been the venue for furious innovation over the last few years, driven by the massive potential market for clinical genome sequencing [4]. As sequencing technology has improved, the retail cost of complete genome sequences has plummeted. In April 2009, Knome auctioned a genome sequence to an anonymous individual on eBay for US $68,000 and subsequently established this as its retail price point – a fivefold drop over just 12 months. Two months later,

220


genetic technology company Illumina launched its own personal genome sequencing service for US $48,000, and a year later dropped its price to US $19,500. In addition to the more visible DTC genomics providers, including those listed above, numerous less well-publicized companies exist, many of which have launched over the last 3 years and have relied on similar genotyping technologies to 23andMe, deCODEme, and Navigenics (e.g., SeqWright and Gene Essence). Other companies offer targeted assays of one or a few disease-associated variants, such as the baldness prediction tests offered by HairDX, or use genetic tests as a marketing tool to sell customers “personalized” nutrition supplements. These companies vary substantially both in the degree of scientific evidence supporting their products (see, e.g., the report in 2006 on nutrigenomics tests from the US Government Accountability Office) and the intuitiveness of their customer interfaces. Given the novelty of DTC genomics and the varying quality of tests offered by the industry, it is unsurprising that DTC genomics companies have been subjected to periodic regulatory scrutiny in the USA and elsewhere (see section on “Regulation”). In June 2008, the California Department of Public Health sent “cease and desist” letters to 13 genetic testing companies operating in the state; as a result, several companies ceased offering their products DTC, while others (such as 23andMe) altered their testing procedures to comply with the state’s regulations. More recently, in May to July 2010, the industry was the subject of unprecedented regulatory attention, beginning with the announcement that Pathway Genomics intended to offer its testing kits on the shelves of pharmacy giant Walgreens. This announcement triggered a letter to the company from the US Food and Drug Administration (FDA), which was followed over the next 2 months by letters to a further 19 genetic testing companies. In July 2010, a Congressional hearing into the DTC genetics industry was held, featuring a report from the US Government Accountability Office that denounced the industry as “misleading” and “deceptive.” At the time of writing, the regulatory future of the industry in the USA remains unclear. The continued regulatory uncertainty surrounding DTC genetics has resulted in a number of providers switching to alternative business models. Following receipt of a letter from California regulators in 2008, baldness genetics company HairDX dropped its DTC product line. The more aggressive actions of regulators in 2010 coincided with the decisions of Counsyl, Pathway Genomics, and Navigenics to abandon DTC marketing in favor of provision entirely through clinicians and (in the case of Navigenics) large-scale contracts with companies to provide testing to employees. Importantly, while the USA remains the epicenter of the DTC genetic testing industry – being both the location of most of the major testing companies, with the notable exception of the Icelandic deCODEme, and the largest source of customers – the market is becoming steadily more international. Direct-to-consumer genome scans are currently offered by companies operating in China [www.mygene23. com], Russia [www.i-gene.ru], Australia [www.lumigenix.com], the UK [www. genetic-health.co.uk], and Finland [www.geenitesti.fi], for instance. Companies and customers outside the USA face a variety of regulatory regimes, ranging from


221

extremely permissive environments throughout most of East Asia to a complete ban on DTC genetic testing in Germany and Australia. The ease of shipping saliva samples and genetic data across national borders limits the ability of nations to regulate access to testing by their own citizens, making it likely that overly restrictive regulations implemented in the USA would result in a shift of the industry to more lenient legislative environments in Asia.

Issues and Evidence Numerous commentators have raised various concerns in relation to DTC personal genomics, particularly in association with risk prediction for common multifactorial diseases. These include ensuring informed consent, validity and utility of the tests, privacy and confidentiality, nonconsensual testing, and the knock-on effect on health systems. These issues are extremely pertinent to the regulation of such services, and the extent to which there is a need to protect unwary consumers from harm. Here we discuss three overarching issues in more detail: information provision and informed consent; evaluating accuracy, validity, and utility; and issues around privacy and confidentiality.

Information Provision and Informed Consent The provision of accurate and transparent information is crucial for all medical tests, particularly those available DTC, to ensure that the individual is able, firstly, to make an informed decision about whether or not to have a test and, secondly, to correctly interpret the results. Information and support from all the major consumer genomics companies is provided predominantly or entirely online (as opposed to via personal interaction with a health-care professional) in the form of information about the conditions, references to relevant scientific literature, guidance about how to interpret the results, online forms to allow consumers to ask questions of the company, and links to external professional and charitable organizations for additional support or advice. The gold standard in medical ethics of obtaining informed consent prior to testing is pursued by most companies through a lengthy consent and legal/service agreement prior to purchasing a test and/or accessing the results. Although it is unlikely that consent is truly fully informed, due to the fast-changing nature of genomics and the enormous amount of information that needs to be digested, the extensive consent agreement obtained by 23andMe for participants in their research study has recently been assessed by external ethical advisors. They concluded that it met “minimal legal requirements” with “sufficient information for participants to realize that they are participating in genetic research… [and] that there are associated risks” [5]. However, as the journal editors commented, it is in the nature of

222


DTC testing that the testing process is clearly undertaken entirely voluntarily, assuming the buyer owns (or has legal authority over) the sample (a requirement which is stipulated in the agreement). Thus, the traditional ethical principle of informed consent may be less applicable in these “minimum risk” contexts, and reforms to structural governance may be more appropriate [6]. Nonetheless, a major continuing concern among physicians and commentators is that consumers will misinterpret their test results, believe that the results are equivalently accurate to standard medical tests, and potentially suffer psychological distress or even physical harm due to ill-advised treatments or behavioral change [7]. However, there is little doubt that the leading companies have made every effort to develop clear and transparent methods for presenting risk information, and have largely succeeded in making an enormous amount of complex genomic data both accessible and understandable. In practice, most physicians as well as consumers will need help or guidance with the interpretation of genetic susceptibility tests, and hence some commentators and policymakers have argued that these tests should require the direct involvement of qualified specialists (such as genetic counselors) [8]. However, because genomic risk prediction tests for common multifactorial diseases are only weakly predictive and are not yet used in clinical practice, it has also been argued that they are not truly medical tests, and hence it is overly paternalistic and potentially misleading to the consumer to require the involvement of a medical professional [9].

Evaluating Accuracy, Validity, and Utility The accuracy and usefulness of any health-related test is critical to its performance. The ACCE framework provides a systematized approach for genetic test evaluation, by assessing the analytical validity, clinical validity, clinical utility, and ethical, social, and legal implications of a DTC genetic testing service [10–13].

Analytical Validity The first step is to consider the raw analytical accuracy of the assay, i.e., whether the measured genotype is correct. The genotyping arrays currently used most widely by DTC genomics companies are technically robust assays. According to the manufacturers’ web sites, the analytical validity of most of these genotyping platforms is very high (>99.9% reproducibility). In a comparison between the results on a single individual from both 23andMe and deCODEme (unpublished results; raw genotype data are available at www.genomesunzipped.org), of the 567,338 common SNPs tested by both companies, there were 10,805 cases where a variant was called by one company but not the other (1.90%) but only 35 cases of disagreement about the genotype (0.01%). However, analytical validity is a function not only of the assay itself but also of the quality of the laboratory in which it is used. As a result, the leading DTC testing


223

services have taken steps to ensure quality assurance and good laboratory practice, for example, using laboratories that are certified under the US federal Clinical Laboratories Improvement Act of 1988 (CLIA). Nonetheless, mistakes have occurred. The quality of the process run by 23andMe was called into question by a mix-up of results caused by misalignment of a 96-well plate [14]. A smaller slip-up was also made by deCODEme in 2009, in which an isolated software glitch produced a nonhuman mitochondrial DNA sequence in one customer’s profile [15]. It should be noted, however, that similar errors are known to occur in clinical laboratories, and that in both cases the problems were appropriately and quickly resolved by the companies involved. Indeed, commentators have argued that the DTC nature of the tests allowed the 23andMe sample error to be detected more quickly than it might have been if results had been returned via clinicians [16]. Notwithstanding these isolated incidents, in general the analytical validity of genotyping assays appears to be extremely high. However, it is worth noting that as DTC companies begin to offer products based on emerging DNA sequencing technologies, which are less mature and standardized than genotyping arrays and applied across a much larger number of genetic sites, there will inevitably be higher numbers of technical false positives in customer data (see the section “Future of DTC Genomics”).

Clinical Validity Another important factor to consider is the clinical validity of genomic risk prediction for multifactorial diseases. Clinical validity is a measure of the ability of a test to discriminate between individuals who have (or will develop) a disease from those who do not have and will not develop the same disease. This concept can usefully be separated into two related but independent domains: scientific validity and clinical test performance [13]. The former is a measure of the robustness of the genedisease association and is a necessary but not sufficient attribute for clinical validity; the latter is a measure of the performance of the test or multifactorial risk model in the population of interest in the relevant context (evaluated by measures of sensitivity, specificity, predictive values, calibration, discrimination, etc.). Evidence of gene-disease association cannot be used as a direct measure of performance; the presence of an association, particularly one where the odds ratio is small (OR < 2), does not translate directly into a useful clinical discriminator [17, 18]. Despite this, a valid gene-disease association is an absolute requirement for a test to be scientifically valid, and extensive guidance has been published on the verification and replication of genome-wide association findings [19, 20]. In many cases, the literature provides robust evidence of scientific validity relating to the use of a SNP-based approach for risk prediction among the population(s) in whom the association has been found, with an increasing number of robust and reproducible genedisease associations [21]. Nonetheless, an appraisal in 2008 of seven DTC companies offering predictive genomic profiling revealed that less than 60% of the gene-disease associations were reviewed by meta-analysis, and of those that were, only 38% were

224


found to be statistically significant [22]. The major genome scan companies now have stringent criteria regarding the use of robustly validated variants for risk prediction relating to factors such as the study sample size and independent validation. Despite evidence that a particular variant is associated with a disease, problems may occur by assuming that the SNP genotyped in the assay can be used to predict the SNP that has been associated with disease. Although the two SNPs may be in statistical linkage disequilibrium (LD) across a population, this may not hold true in individuals, or in other populations. Some commentators have recommended that companies should only report the actual SNPs associated with disease, rather than using another SNP in LD as a proxy [23]; in practice, this will require a trade-off between theoretical accuracy and the number of markers that can be reported to customers for a given price. Moreover, even the “hit SNP” reported by a GWAS is unlikely to correspond to the actual causal variant at that location, but rather to a marker that is in LD with the causal SNP at the population level but not in particular individuals. Unless the genotyped SNP itself has been experimentally demonstrated to be the causal variant – which is true for only a tiny fraction of reported GWAS hit SNPs – this issue is impossible to resolve using SNP-based genotyping. Major differences exist between personal genomics providers in the number of SNPs used to calculate risk for a given disease [24], which partly reflects differences between the SNP chips used, and therefore the known genotypes, and partly reflects differences in policies relating to updating and evidence required for an association to be added. Nonetheless, this difference can have a substantial effect on the combined relative risk, potentially resulting in calculated relative risks that are of very different magnitudes or even in opposite direction from the two services. The question of how often to update the interpretation of a test to include (previously measured) variants with (new) disease associations, and what level of evidence is required in order to do so, is a general problem that exists for all genomic services and may cause an individual’s risk estimate to vary over time [25]. Most personal genomics companies use odds ratios for individual SNPs (sometimes from different GWAS in different underlying populations) and derive a multiplicative model by combining multiple markers together into an overall multigenic risk profile. Although the services perform the calculation in subtly different ways, relating to when in the process individual odds ratios are converted to a relative risk (as detailed by the Personalized Medicine Coalition) [26], the models rest on the same assumptions of independence and the services are largely in agreement over the relative risks associated with each individual genetic variant (except a few cases where the number of SNPs measured varies, as discussed above). However, there can be enormous variation in the absolute risk estimates, due to underlying differences in disease incidence statistics caused by founder effects as well as enormous environmental and social differences [24, 27]. This could be easily remedied by stating the source of the data and indicating to which underlying demographic population (age, sex, ethnicity, or country) it refers. At the time of writing, most services do not attempt to combine individual genetic data with other risk factors, such as weight or smoking habits. Although this renders the final absolute risk prediction incomplete, the limitation is clearly stated, and additional environmental


225

and behavioral risk factors are generally listed. Moreover, the services make clear that they are only estimating genetic risk, and thus explicitly discount an individual’s true overall risk. However, potential future medical application of genetic risk prediction in a clinical setting would need to also include traditional risk factors and combine the information in a contextual, Bayesian manner – for example, through the use of likelihood ratios to calculate posttest probability of disease [28]. As has been noted previously [23], to make sense of genetic risk information, it is necessary to know what proportion of the disease is genetic in origin, and what proportion of the disease is explained by the variants actually measured by the test [29]. The former can be approximated by heritability, which is quoted for some diseases by some companies. However, the latter is absent from most if not all services. Given the large fraction of genetic disease risk currently unexplained by known variants for most common diseases (so-called “missing heritability”) [30], providing this information to customers is presumably an unattractive prospect for DTC companies. However, it would not only allow consumers to weigh up the importance of the genetic risk information relative to other nongenetic risk factors for each disease, but would also highlight instances where variants detected by – or absent from – the genome scan are likely to play a major role. Finally, the performance of each test must be assessed separately, through measuring its sensitivity (true positive rate), specificity (true negative rate), and positive and negative predictive values, as well as the goodness of fit of the risk model (calibration) and its ability to distinguish high-risk subjects from low-risk subjects (discrimination). Importantly, these performance characteristics vary with the context, population, and the purpose for which a test is used and hence cannot be directly inferred from gene-disease association studies. Numerous studies have investigated the performance of risk prediction algorithms for various specific complex diseases based on genetic variants, either alone or in addition to standard risk factors or models (reviewed by Janssens et al [31].). Well-studied examples include cardiovascular disease [32, 33], type 2 diabetes [34], macular degeneration [35], and breast [36] and prostate [37, 38] cancer. Overall, with a few notable exceptions, the performance of these models has been disappointing for complex diseases, with only very limited improvements in calibration or discrimination, either incrementally or in total [39]. This does not exclude the application of such models for stratifying populations into risk categories, and targeting preventative interventions, such as screening, at those in the highest (genetic) risk group [40]; nor does it exclude the potential incremental value of genetic information for diagnostic purposes in addition to other clinical tests [28]. However, it does call into question the validity of using multigenic risk models for disease prediction in individuals.

Clinical Utility Ultimately, what lies at the heart of personal genomics is the ability of a test to lead to an improved health outcome; does risk prediction allow prevention or early detection of disease through vigilance and appropriate intervention? Evaluating clinical

226


utility involves a complex matrix of benefits and harms, which depend on a variety of different factors such as the availability and effectiveness of therapeutic options. To date, there are no genetic risk prediction models for complex diseases based on common variants that have proven clinical utility; moreover, many diseases have no proven preventative or therapeutic interventions. Despite the lack of systematic evidence of clinical utility, there is little doubt that genetic risk prediction could in principle motivate positive behavior changes, such as improved diet and increased exercise as a result of a high risk of type 2 diabetes. It has recently been suggested that the concept of utility with respect to DTC tests should be reframed to include not only clinical utility relating to direct medical actions, but also personal utility and even social utility relating to indirect health-related and other nonmedical benefits [41]. In a recent study of over 1,000 customers of 23andMe, deCODEme, and Navigenics, 58% of consumers said they learned information that would help improve their health, and as a result of testing, 34% said they were being more careful about their diet and 14% were exercising more [42]. Several studies have also indicated that genetic susceptibility testing may be minimally harmful. In a recent survey of over 1,000 customers of 23andMe, Navigenics, and deCODEme, 88% reported that their risk results were easy to understand, and only 4–7% misinterpreted two example risk reports presented in the survey [42]. Another survey of over 2,000 customers of Navigenics found “no clinically significant test-related distress” in over 97% of subjects; sharing results with a genetic counselor was not associated with test-related distress or changes in anxiety level, dietary fat intake, or exercise behavior at follow-up [43]. These surveys are also notable for the absence of reported harms associated with the tests, which is supported by evidence from relatives of sufferers of Alzheimer disease in whom APOE testing for susceptibility to future disease did not cause lasting psychological distress [44]. However, there is scant evidence that providing risk information, whether genetic or otherwise, actually results in lasting behavior modification [45] or psychological harm, so further research is needed to investigate whether reported behavioral changes were transient or permanent.

Privacy and Confidentiality One major category of risk raised by critics of DTC genetic testing is the possibility of deliberate or accidental disclosure of genetic information from customers to third parties. Deliberate disclosure of genetic information by a testing company might be motivated either by profit or by legal coercion. Potential profit motivations might include the sale of combined genetic and phenotypic data collected from customers, from whom individual identity might conceivably be uncovered, to a pharmaceutical company; or, more seriously, the sale of identity-linked genetic information to an insurance company for use in calculating premiums. Legal coercion might occur


227

in cases where a criminal suspect is thought to have contributed DNA to a testing company, and a warrant is thus issued to obtain the corresponding information to compare with a crime scene sample. Accidental disclosure is a potential risk for any entity dealing with large, complex datasets: information might be lost through unapproved access (“hacking”) of a company’s database, or simply through inadvertent release of data online. The risk of these events can be dramatically mitigated through the same careful data security policies practiced by other entities such as banks, and most well-known DTC genomics companies appear to have adopted reasonable protections against these threats. An alternative mode of data loss – the forced purchase of a company’s database by a third party without the same data protection policies – received considerable attention following the announcement of the legal bankruptcy of deCODEme’s parent company deCODE Genetics in November 2009. There is also the possibility that DTC companies may be used to obtain genetic information from individuals without their knowledge or consent. Surreptitious genetic testing is already familiar in a forensic context and for paternity testing, but as the utility of genetic testing increases, it will become possible to infer a growing amount of information about a subject’s ancestry and health risks from a covert sample. Nonconsensual genetic testing is illegal in the UK under the Human Tissue Act 2004 [46], but its legal status varies between other countries and between US states. Some genome scan companies (e.g., 23andMe) have argued that the risks of covert testing are reduced by their sample collection method, which requires 2 ml of saliva. DTC testing could also be used to obtain genetic information about individuals who cannot legally consent to the process, such as minors. The ethical status of presymptomatic testing of children or adolescents for adult-onset conditions is contentious, and not recommended in clinical practice unless preventative action can be taken in childhood [47], and this practice is recommended against by most national guidelines [48]. However, performing genome scans on children has been actively encouraged by some DTC companies [49]. This seems likely to be a point of major contention between DTC genetic testing companies and the clinical genetics community, especially as DTC companies expand further into the genotyping of large-effect variants affecting adult-onset disorders and carrier testing for recessive disease mutations. Legal protection for individuals affected by disclosure of genetic information varies widely between jurisdictions. In the USA, the Genetic Information Nondiscrimination Act (GINA) [50] protects individuals from discrimination by health insurance companies or employers on the basis of genetic information, although life insurance and long-term care insurance are not covered by the legislation. UK citizens have no equivalent legal protection, but UK insurance companies have agreed to a voluntary moratorium on the use of genetic information that is due to expire in 2014. However, opinions on the use of genetic information by insurance companies are divided, and arguments often depend upon the insurance model itself. There are broadly two models of pooled insurance: mutual insurance, which is generally an optional, private payment that relates to the estimated risk an individual brings to the pool, and solidaristic or social insurance, which is generally a compulsory, state run payment that is independent of the level of risk brought to the pool.

228


While the latter can operate even if the insurer and insured have different levels of information about the individual’s level of risk (such as genetic risk), the former is only stable under conditions of informational symmetry. Therefore, for a risk-based insurance industry to be sustainable, any useful risk information must be available to both the individual and the insurance company, or neither [51]. Thus, it is hard to sustain a coherent argument to justify withholding only “genetic” information from insurers, but not other types of information relevant for risk assessment. Nonetheless, there continues to be widespread concern that individuals with inherited conditions may be treated unfairly in countries lacking a universal health-care system. Concern about the privacy of genetic information is likely to grow over the short term, thanks to two ongoing trends. First, technological advances will make it increasingly easy to obtain large-scale genetic data from smaller DNA samples, and second, dissemination of large DNA datasets via the Internet will become increasingly more straightforward. Some commentators have argued that these advances mean the very notion of genetic privacy is doomed, and that the most pragmatic approach is thus to embrace the open release of such data, with substantial potential benefits for both basic and medical research [52]. The risks and benefits of this radical approach are currently being explored by a number of projects internationally, the most visible being the Personal Genome Project [www.personalgenomes.org], which seeks to recruit 100,000 volunteers willing to share their genetic and medical data without guarantee of anonymity. A smaller project, Genomes Unzipped [www. genomesunzipped.org] (of which both authors of this chapter are founding members), has also freely released genetic data from participants to help illustrate the uses and limitations of genetic information. Data may be voluntarily disclosed by customers themselves, and DTC genomics customers already have a number of venues available for sharing their own genetic data. For instance, 23andMe customers are able to customize privacy settings to share their genotypes for variants of medical or genealogical interest with other customers, although this sharing precludes access to genotypes for mutations in BRCA1 and BRCA2 associated with hereditary breast cancer and to raw genotype data files. Many DTC companies allow customers to download their raw data, which customers may then voluntarily or inadvertently disclose to third parties. A noncommercial site, SNPedia [www.snpedia.com/index.php/Genomes], permits DTC genomics customers to upload their entire raw genetic data files for public sharing. In the Facebook era, it seems likely that any web site offering free analysis of raw genetic data from DTC companies would be able to harvest substantial numbers of genetic profiles. There is little that DTC companies could do to prevent such disclosure, short of preventing customers from accessing their own raw data. While a complete abandonment of the concept of genetic privacy seems implausible and is clearly inappropriate in a clinical setting, there is little doubt that the growing availability of genomic technology will result in increasing instances of both voluntary and involuntary disclosure of genetic information. However, this process will not be restricted to DTC genomics companies: academic researchers and health-care systems are likely to be even more vulnerable to data loss than private companies. As such, the most prudent solution would seem to be to focus on


229

legislation to protect individuals from potential harms of genetic disclosure from any source, as is the case for GINA in the USA, rather than on excessive tightening of the data security policies of DTC companies.

Regulation In the few years since the launch of DTC genome scans by 23andMe and deCODEme, there has been extensive discussion about how to regulate such services [53–58]. Although various organizations are engaged in developing codes of conduct and best practice guidelines [8], different jurisdictions have already taken different approaches to the problem, ranging from banning DTC access to genetic testing outright, to allowing the market to develop entirely unchecked. In Europe, a number of states have signed additional voluntary legislation relating to genetic tests in the Convention on Human Rights and Biomedicine [59]. The protocol states that a genetic test for health purposes “may only be performed under individualized medical supervision,” and with the provision of relevant information and nondirective genetic counseling in the case of predictive, susceptibility, or carrier testing. If widely adopted within Europe, these provisions could have significant implications for DTC testing [60]. However, it is notable that to date several member states including Germany and the UK have signed or ratified the Convention or the Additional Protocol, and Germany has now banned direct access to genetic tests by consumers [61]. In the USA, a lack of federal oversight has led to different states taking alternative approaches toward the regulation of DTC genetic testing. Most of the activity has focused on who can order a genetic test, and the question of whether DTC genetic testing services are practicing medicine without a license. In particular, the states of California and New York have tried to regulate DTC genetic testing services directly, notifying companies that they need to meet the specific requirements of the state in order to be licensed to receive DNA samples from residents for analysis. In mid-2010, the US FDA took a more active interest in these services and sent enforcement letters to the major US-based DTC personal genomics providers, equating the services with medical devices under section 201(h) of the Federal Food, Drug, and Cosmetic Act [62]. At this time, however, it is unclear what form FDA oversight of these services will take. Our view is that regulators should weigh the evidence about risks and benefits of testing, and strive to develop a regulatory environment that fosters innovation while avoiding unnecessary and unwarranted genetic exceptionalism [63]. There is little doubt that some form of regulation is needed in this area, to protect the public from inaccurate products, unqualified service providers, and fraudulent claims. However, the extent to which formal statutory regulation is required is debateable [64], and many of the issues facing DTC genetic testing services may be better dealt with through consumer protection legislation and ensuring accuracy and transparency of information. Data on validity and utility could be stored in a centrally located and

230


publicly accessible database, such as the US Genetic Testing Registry [65, 66]. In addition, laboratory quality assurance and analytical validity could be formerly regulated, so that consumers have confidence in the raw genotype results. In the USA, the Centers for Medicare & Medicaid Services already regulate all clinical laboratory testing through CLIA certification, which should be a formal requirement for DTC testing labs; in Europe, oversight of laboratory certification is generally country-specific, but the Organization for Economic Co-operation and Development (OECD) has produced a set of guidelines for quality assurance in molecular genetic testing [67]. Importantly, we do not believe that DTC genetic testing services need a special or unduly burdensome regulatory regime simply by virtue of the fact that they analyze DNA. Rather, they should be regulated appropriately and proportionately based on their true potential to cause harm to consumers. Just as in any sphere of life, it may be impossible to prevent vindictive individuals intent on causing harm, but this does not provide adequate reason to limit access to these services; many jurisdictions already have laws against DNA theft and nonconsensual testing, for example, which should be pursued and vigorously upheld. Ultimately, we passionately believe that consumers should not be prevented from accessing their own personal genetic information, even in the absence of proven utility.

The Future of DTC Genomics It is currently unclear whether DTC genomics will be widely used in the future, or if it is just a temporary and financially unsustainable blip in the market. The future of the industry will be dictated primarily by several forces: technology, scientific understanding, public perception, and government regulation. While the trajectory of the first force can be fairly confidently predicted based on historical change over the last decade, the direction the other forces will take is more difficult to anticipate.

Technology and Science Over the last decade, genomic technology has been subject to an astonishing rate of advance, with the cost of analyzing a single base of DNA following an exponential decline (see Fig. 10.2). This technology boom has been fuelled by substantial investment in the field: as one example, sequencing technology start-up Pacific Biosciences had raised around US $370 million in venture capital and US $200 million in a public share offering as of October 2010. With dozens of companies and academic research teams investigating different approaches to DNA sequencing, it seems likely that a continued decline in costs will results in whole genome sequencing reaching a plausible price point for widespread consumer genomics by 2012.


231

Fig. 10.2 Historical trends in storage prices versus DNA sequencing costs. Blue squares describe the historic price of data storage in megabytes per US dollar. The long-term trend (blue line, which is a straight line here because the plot is logarithmic) shows exponential growth in storage per dollar with a doubling time of roughly 1.5 years. The cost of DNA sequencing, expressed in base pairs per US dollar, is shown by the red triangles. It follows an exponential curve (yellow line) with a doubling time slightly slower than disk storage until 2004, when next-generation sequencing (NGS) causes an inflection in the curve to a doubling time of less than 6 months (red line). These curves are not corrected for inflation or for the “fully loaded” cost of sequencing and disk storage, which would include personnel costs, depreciation, and overhead. Originally published by BioMed Central in Genome Biology [74] (Republished under BMC’s Open Access Charter)

The degree to which these technological advances will translate into products offering substantial medical utility, particularly in the prediction of complex disease risk in currently healthy individuals, remains uncertain. Certainly the explosion in the number of genetic variants associated with many common diseases, such as cardiovascular disease and type 2 diabetes, has contributed only incremental improvements in the prediction of individual risk [68]. However, risk variants discovered to date have been common variants with a typically small effect on disease risk; improving sequencing technology is expected to uncover low-frequency variants with larger effects, which will have substantially greater utility for individual prediction. The ultimate predictive value of complete genome sequences will depend on the genetic architecture underpinning each disease, which in most cases is still unclear [69], and will vary substantially between individuals and diseases. At the same time, genome sequencing will uncover a multitude of variants of unknown significance in every genome for which the effects on disease status are difficult or impossible to estimate. To illustrate the extent of this challenge, the genome of J. Craig Venter contains approximately 1,500 genetic variants predicted to have a deleterious effect on the function of protein-coding genes, but the actual phenotypic consequence of nearly all of these variants is currently impossible to predict [70]. As the technology available to DTC genetic testing companies

232


continues to outstrip scientific understanding, early-access customers will be faced with a huge amount of potentially useful information, almost none of which can actually be confidently interpreted given current scientific knowledge. In addition, each new wave of genomic technology will bring with it the potential for novel sources of error. New DNA sequencing technologies have a substantially higher per-base error rate than genotyping arrays, and the error rate tends to be inflated for variants predicted to have the largest functional effects [71]. In the absence of large-scale experimental validation of all putative disease-causing variants in an individual genome – an exercise that would dramatically increase the cost – it seems likely that early adopters will face the uncertainty of substantial technical error as well as challenging functional interpretation.

Consumer Demand and Regulation A crucial area of uncertainty affecting the future of DTC genomics is the size of the potential market. Sales to date of health-related DTC genomics products have been modest, with perhaps only 20–30,000 tests purchased in 2009 [72], despite aggressive marketing campaigns from the major companies. It is possible that consumer demand for DTC health-related genomics products will remain low even if the medical utility of these products increases substantially. Increasing demand will require that the industry escape three potential obstacles: perception of DTC genetic testing companies as “scams,” simple lack of interest from consumers, and offering of equivalent utility from other sources (for instance, the mainstream medical establishment). Avoiding a broad perception from the public that the industry is fraudulent will require active engagement from the more responsible companies in the field in developing industry-wide standards for the reporting of test results, as well as action from consumer protection bodies against instances of false advertising or unethical marketing practices in the industry. Attracting interest from a broader section of the public than the “early adopter” and “genetic hobbyist” markets currently captured by the industry will also be crucial if DTC genomics is to become something more than a fringe activity. To a large extent, this will depend on the ability of DTC companies to offer unique and useful services. The industry will need to offer products with utility that potential customers cannot obtain elsewhere. One challenge here is that any product with genuine medical utility is likely to be gradually but systematically incorporated into mainstream medical testing, leaving DTC companies with whatever remains. However, there will still be areas where DTC companies can potentially enjoy an untroubled competitive advantage: for instance, genome-wide information and user-friendly interfaces for exploring genetic information (as opposed to more targeted clinicianfacing tests likely to be adopted by medical systems), or tests with substantial utility for a fraction of individuals that nonetheless fail to meet the requirements for population utility set by large health-care systems. Alternatively, genetic genealogy products based on similar assays to the more health-oriented services may drive the market by engaging more effectively with consumers.


233

A Source of Innovation Regardless of the future drivers and restraints for personal genomics, DTC companies have already proven to be effective drivers of novel ideas and innovative practices which may eventually be transferred into other fields. For example, the use of computer-based interfaces that present complex risk information in a clear and userfriendly manner could be extremely valuable in a clinical context. One DTC genomics company, 23andMe, has also demonstrated the utility of participant-driven research by identifying novel gene-trait associations using its own customer database [73], in which individuals voluntarily provide phenotype information through online surveys. The advantages of having an actively engaged participant cohort for long-term studies are clear, and if the 23andMe model continues to prove fruitful, data return to research participants may become attractive to both academic researchers and individuals volunteering for studies. Balancing all of these conflicting forces, what is the most likely outcome for DTC genomics? In the short to medium term, we suspect that there will be growth in specific applications where there is clear utility or broad public interest, such as carrier testing, but that many DTC genomics applications will retain a relatively small market of technology savvy early adopters. Ultimately, the future of the personal genomics industry will depend heavily on the approach taken by regulatory bodies. At one extreme, inadequate or poorly focused regulation of the industry, allowing companies making blatant false claims to persist, could have a profound negative effect on public perception of genetic testing. At the other extreme, placing excessively paternalistic regulatory obstacles in the path of DTC genomics companies could have a stifling effect on innovation in the field. Fostering an industry that behaves responsibly to consumers, without placing unnecessary obstacles in the path of innovation, will require careful action from both companies and regulators alike to ensure that individuals can choose to explore their own genomes.

References 1. Human Genetics Commission. Common framework of principles for direct-to-consumer genetic testing services. 2010. 2. Edelman E, Eng C. A practical guide to interpretation and clinical application of personal genomic screening. BMJ. 2009;339:b4253. 3. Harmon A. Gene Map becomes a luxury item. New York Times. 2008. http://www.nytimes. com/2008/03/04/health/research/04geno.html. Accessed 6 Aug 2011. 4. Metzker ML. Sequencing technologies – the next generation. Nat Rev Genet. 2010;11(1):31–46. 5. Gibson G, Copenhaver GP. Consent and internet-enabled human genomics. PLoS Genet. 2010;6(6):e1000965. 6. Knoppers BM. Consent to ‘personal’ genomics and privacy. EMBO Rep. 2010;11(6):416–9. 7. Eng C, Sharp RR. Bioethical and clinical dilemmas of direct-to-consumer personal genomic testing: the problem of misattributed equivalence. Sci Transl Med. 2010;2(17):17cm5. 8. American College of Medical Genetics. ACMG statement on direct-to-consumer genetic testing. 2008. http://www.acmg.net/StaticContent/StaticPages/DTC_Statement.pdf. Accessed 6 Aug 2011. 9. MacArthur D, Wright CF. Don’t stand in the way of genomes for all. New Sci. 2010;2774:59.

234


10. Haddow JE, Palomaki GE, Khoury MJ, Little J, Burke W. ACCE: a model process for evaluating data on emerging genetic tests. In: Human genome epidemiology. Oxford: Oxford University Press, New York; 2004. p. 217–33. 11. Kroese M, Zimmern RL, Farndon P, Stewart F, Whittaker J. How can genetic tests be evaluated for clinical use? Experience of the UK Genetic Testing Network. Eur J Hum Genet. 2007;15(9):917–21. 12. Teutsh SM, Bradley L, Palomaki G, et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP working group. Genet Med. 2009;11(1):3–14. 13. Wright CF, Kroese M. Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how? Hum Genet. 2010;127(2):125–34. 14. 23andme Community. Update from 23andMe. https://www.23andme.com/you/community/ thread/3621/. Accessed 4 June 2010. 15. Aldhous P. My ‘non-human’ DNA: a cautionary tale. New Scientist. 2009. http://www.newscientist.com/article/dn17683-my-nonhuman-dna-a-cautionary-tale.html. Accessed 6 Aug 2011. 16. Vorhaus D. Why 23andMe’s sample swap is actually an argument in favor of DTC genetic testing. Genomics Law Report. 2010. http://www.genomicslawreport.com/index.php/2010/06/09/23andmesample-swap. Accessed 6 Aug 2011. 17. Pepe MS, Feng Z, Huang Y, et al. Integrating the predictiveness of a marker with its performance as a classifier. Am J Epidemiol. 2008;167(3):362–8. 18. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159(9):882–90. 19. Chanock SJ. NCI-NHGRI Working Group on Replication in Association Studies. Replicating genotype-phenotype associations. Nature. 2007;447(7145):655–60. 20. Little J, Higgins JP, Ioannidis JP, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 2009;6(2):e22. 21. McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–69. 22. Janssens ACJW, Gwinn M, Bradley L, Oostra BA, van Duijn CM, Khoury MJ. A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions. Am J Med Genet. 2008;82:593–9. 23. Ng PC, Murray SS, Levy S, Venter JC. An agenda for personalized medicine. Nature. 2009;461(7265):724–6. 24. Swan M. Multigenic condition risk assessment in direct-to-consumer genomic services. Genet Med. 2010;12(5):279–88. 25. Mihaescu R, van Hoek M, Sijbrands EJG, et al. Evaluation of risk prediction updates from commercial genome-wide scans. Genet Med. 2009;11(8):588–94. 26. Personalized Medicine Coalition. Personal genomics and industry standards: scientific validity. 2008. 27. Yang Q, Flanders WD, Moonesinghe R, Ioannidis JPA, Guessous I, Khoury MJ. Using lifetime risk estimates in personal genomic profiles: estimation of uncertainty. Am J Hum Genet. 2009;85(6):786–800. 28. Morgan AA, Chen R, Butte AJ. Likelihood ratios for genome medicine. Genome Med. 2010;2(30). doi:10.1186/gm1151. 29. Wray NR, Yang J, Goddard ME, Visscher PM. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet. 2010;6(2):e1000864. 30. Maher B. The case of the missing heritability. Nature. 2008;456:18–21. 31. Janssens ACJW, van Duijn CM. Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet. 2008;17(R2):R166–73. 32. Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann Intern Med. 2009;150(2):65–72.


235

33. Tzoulaki I, Liberopoulos G, Ioannidis JPA. Assessment of claims of improved prediction beyond the Framingham risk score. JAMA. 2009;302(21):2345–52. 34. Talmud PJ, Hingorani AD, Cooper JA, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340(jan14_1):b4838. 35. Maller J, George S, Purcell S, et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat Genet. 2006;38(9):1055–9. 36. Gail MH. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst. 2009;101(13):959–63. 37. Li-Wan-Po A, Farndon P, Lithgow J. When is a genetic test suitable for prime time? Predicting the risk of prostate cancer as a case-example. Public Health Genomics. 2010;12:55–62. 38. Zheng SL, Sun J, Wiklund F, et al. Cumulative association of five genetic variants with prostate cancer. N Engl J Med. 2008;358(9):910–9. 39. Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE. Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers. PLoS Genet. 2009;5(2):e1000337. 40. Pharoah PDP, Antoniou AC, Easton DF, Ponder BAJ. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358(26):2796–803. 41. Foster MW, Mulvihill JJ, Sharp RR. Evaluating the utility of personal genomic information. Genet Med. 2009;11(8):570–4. 42. Researchers shed light on implications and impact of using direct-to-consumer and clinical genetic testing in disease risk assessment. Press release: American Society of Human Genetics. 20 October 2010. 43. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med. 2011;364(6):524–34. 44. Green RC, Roberts JS, Cupples LA, et al. Disclosure of APOE genotype for risk of Alzheimer’s disease. N Engl J Med. 2009;361(3):245–54. 45. McBride CM, Bowen D, Brody LC, et al. Future health applications of genomics: priorities for communication, behavioral, and social sciences research. Am J Prev Med. 2010;38(5):556–65. 46. UK. Human Tissue Act. 2004. 47. Borry P, Evers-Kiebooms G, Cornel MC, Clarke A, Dierickx K. Genetic testing in asymptomatic minors: background considerations towards ESHG recommendations. Eur J Hum Genet. 2009;17(6):711–9. 48. Borry P, Stultiens L, Nys H, Cassiman JJ, Dierickx K. Presymptomatic and predictive genetic testing in minors: a systematic review of guidelines and position papers. Clin Genet. 2006;70(5):374–81. 49. Mountain J. 23andKids: growing up genotyped. The spittoon. 2008. http://spittoon.23andme. com/2008/10/28/23andkids-growing-up-genotyped/. Accessed 6 Aug 2011. 50. United States of America. Genetic Information Nondiscrimination Act. Public Law. 2008:110–233. 51. O’Neill M. Genetic information, life insurance and social justice. Monist. 2006;89(4):567–92. 52. Lunshof JE, Chadwick R, Vorhaus DB, Church GM. From genetic privacy to open consent. Nat Rev Genet. 2008;9(5):406–11. 53. Ameer B, Krivoy N. Direct-to-consumer/patient advertising of genetic testing: a position statement of the American College of Clinical Pharmacology. J Clin Pharmacol. 2009;49(8):886–8. 54. Gurwitz D, Bregman-Eschet Y. Personal genomics services: whose genomes? Eur J Hum Genet. 2009;17:883–9. 55. Human Genetics Commission. More Genes Direct. 2007.

236


56. Hogarth S, Javitt G, Melzer D. The current landscape for direct-to-consumer genetic testing: legal, ethical, and policy issues. Annu Rev Genomics Hum Genet. 2008;9:161–82. 57. Hudson K, Javitt G, Burke W, Byers P, ASIC. ASHG statement* on direct-to-consumer genetic testing in the United States. Obstet Gynecol. 2007;110(6):1392–5. 58. Patch C, Sequeiros J, Cornel MC. Genetic horoscopes: is it all in the genes? Points for regulatory control of direct-to-consumer genetic testing. Eur J Hum Genet. 2009;17:857–9. 59. Council of Europe. Additional Protocol to the Convention on Human Rights and Biomedicine, concerning. Genetic Testing for Health Purposes (27.XI.2008). 2008. 60. Kaye J. The regulation of direct-to-consumer genetic tests. Hum Mol Genet. 2008;17(Review Issue 2):R180–3. 61. Bundesrat. Draft Human Genetic Examination Act (Genetic Diagnosis Act – GenDG). 2009. 62. FDA. FDA news release: FDA to host public meeting on oversight of laboratory-developed tests. 16 June 2010. 63. Green MJ, Botkin JR. “Genetic exceptionalism” in medicine: clarifying the differences between genetic and nongenetic tests. Ann Intern Med. 2003;138(7):571–5. 64. Wright CF, Hall A, Zimmern RL. Regulating direct-to-consumer genetic tests: what is all the fuss about? Genetics in Medicine. 2010. Publish ahead of print:10.1097/ GIM.1090b1013e3181f1069dd1092. 65. Javitt G, Katsanis SH, Scott J, Hudson K. Developing the blueprint for a genetic testing registry. Public Health Genomics. 2010;13(2):95–105. doi:10.1159/000226593. 66. NIH. Announces Genetic Testing Registry. 2010. http://www.nih.gov/news/health/mar2010/ od-18.htm. Accessed 6 Aug 2011. 67. OECD. Guidelines for Quality Assurance in Molecular Genetic Testing. 2007. 68. Kraft P, Hunter DJ. Genetic risk prediction – are we there yet? N Engl J Med. 2009;360(17):1701–3. 69. Singleton AB, Hardy J, Traynor BJ, Houlden H. Towards a complete resolution of the genetic architecture of disease. Trends Genet. 2010;26(10):438–42. 70. Ng PC, Levy S, Huang J, et al. Genetic variation in an individual human exome. PLoS Genet. 2008;4(8):e1000160. 71. MacArthur DG, Tyler-Smith C. Loss-of-function variants in the genomes of healthy humans. Hum Mol Genet. 2010;19(R2):R125–30. 72. Wright CF, Gregory-Jones S. Size of the direct-to-consumer genomic testing market. Genet Med. 2010;12(9):594. 73. Eriksson N, Macpherson JM, Tung JY, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 2010;6(6):e1000993. 74. Stein L. The case for cloud computing in genome informatics. Genome Biol. 2010;11(5):207.

Index

A Abacavir, 46–47 ABL1 gene, 76–81 Affymetrix, 106 Age-related macular degeneration, 110–111 ALK. See Anaplastic lymphoma kinase (ALK) Allopurinol, 46–47 Amino acid metabolic disorders diagnostic strategy and DNA testing, 166, 167 homocystinuria, 170–172 maple syrup urine disease, 172–173 phenylketonuria, 166–169 recommended uniform screening panel, 165, 166 tyrosinemia type I, 169–170 urea cycle defects, 173–176 Anaplastic lymphoma kinase (ALK), 70–72 Array comparative genomic hybridization (aCGH) applications of ASDs and dysmorphia, 27–28 cancer, 27 gene identification, 28 genetic testing of intellectual disability, 27 intragenic CNC detection, 28–29 prenatal testing, 28 SNP and hybrid arrays for genotyping, 29 array design, 24–25 BAC arrays, 25–26 CNC interpretation detected on constitutive aCGH, 29–31 detected on targeted aCGH, 32–33 constitutive vs. targeted arrays, 31

description, 23–24 human mutations and testing methodologies, 21–23 microarray based, 23–24 oligonucleotide, 26–27 steps used in, 24 ATP synthesis, 9 Autism spectrum disorders (ASDs), 27–28 Azathioprine, 45–46

B Bacterial artificial chromosome (BAC) arrays, 25–26 BCR-ABL1 gene, 76–81 Bioinformatics database summary, 113 disease trait information/comorbidities, 115 genome reference panels and SNP/gene information, 115–118 literature searches, 113–114 pathway information, 118–120 Body mass index, 109–110 BRAF gene mutations, 82–84 Brain tumors, genetic alterations, 89–90

C Cancer, aCGH, 27 Carbamazepine, 46–47 Carnitine uptake defect (CUD), 187 Center for Drug Education and Research (CDER), 40 CHARGE consortium, 111 Chronic myelogenous leukemia (CML), 76–77 Clopidogrel, 44–45

D.H. Best and J.J. Swensen (eds.), Molecular Genetics and Personalized Medicine, Molecular and Translational Medicine, DOI 10.1007/978-1-61779-530-5, © Springer Science+Business Media, LLC 2012

237

238 Codeine, 47 CYP2C19, 44–45 CYP2D6, 43–44

D DideoxyNTPs (ddNTPs), 7 Direct-to-consumer (DTC) genetic testing consumer demand and regulation, 232 on health, 218 innovation source, 233 issues analytical validity, 222–223 clinical utility, 225–226 clinical validity, 223–225 information provision and informed consent, 221–222 privacy and confidentiality, 226–229 key players and timeline, 218–221 regulation, 229–230 technology and science, 230–232 types of, 217 DNA-binding dyes, 6 Duchenne muscular dystrophy (DMD), 16 Dysmorphia, 27–28

E Education and psychosocial counseling, genetics, 203 EGFR (ERBB1), 62–64 EGFR variant III, 64 Electronic medical records and genomics (eMERGE) network, 112 EML4-ALK translocation, 72 Epidermal growth factor family of receptors EGFR (ERBB1), 62–64 EGFR variant III, 64 HER2 (ERBB2), 64–66 KIT, 66–67

F Fatty acid oxidation and carnitine cycle, 184 diagnostic strategy and DNA testing, 185–186 medium-chain acyl-CoA dehydrogenase deficiency, 189–190 trifunctional protein and long-chain acyl-CoA dehydrogenase deficiencies, 188–189 uptake defect, 187 very-long-chain acyl-CoA dehydrogenase deficiency, 187–188

Index FLT3 gene, 11 FLT3-internal tandem duplication (ITD) mutations, 69–70 Fluorescence in situ hybridization (FISH), 22 FMS-like tyrosine kinase 3 (FLT3), 69–70 Fragment analysis, 11–12

G Galactosemia, 190–192 Gastrointestinal stromal tumors (GISTs), 66 G-banding, 22 Gene identification, aCGH, 28 Gene ontology (GO), 120 Genetic counseling definition and history, 200 process diagnosis, 201 education and psychosocial counseling, 203 information gathering, 201 risk assessment, 202–203 specialties adult onset diseases, 205–206 biochemical genetics, 207 clinics, 205 cytogenetics, 207 diagnostic laboratory, 206–207 molecular genetics, 207 pediatric medical, 204–205 prenatal and perinatal, 203–204 testing and ethical concerns in children, 209 duty to warn, 210 Genetic Information and Nondiscrimination Act (GINA), 210–211 informed consent, 208 prenatal, 208–209 Genetic Information and Nondiscrimination Act (GINA), 210–211 Genetic testing in children, 209 direct-to-consumer (DTC) analytical validity, 222–223 clinical utility, 225–226 clinical validity, 223–225 consumer demand and regulation, 232 on health, 218 information provision and informed consent, 221–222

Index innovation source, 233 key players and timeline, 218–221 privacy and confidentiality, 226–229 regulation, 229–230 technology and science, 230–232 types of, 217 duty to warn, 210 Genetic Information and Nondiscrimination Act (GINA), 210–211 informed consent, 208 intellectual disability, 27 prenatal, 208–209 presymptomatic in children, 143 disorders, 131–133 hereditary hemochromatosis (HH), 136–137 hereditary nonpolyposis colon cancer (HNPCC), 137–140 Huntington disease (HD), 134–136 multiple endocrine neoplasia type 2 (MEN2), 140–143 Genome reference panels, 115–118 Genome-wide association studies (GWAS) bioinformatics in database summary, 113 disease trait information/ comorbidities, 115 genome reference panels and SNP/gene information, 115–118 literature searches, 113–114 pathway information, 118–120 vs. candidate gene studies, 105–106 common disease and variant, 104–105 description, 103–104 ethical issues, 120–122 genotyping platforms affymetrix, 106 illumina, 106–107 haplotype tagging, 104–105 large consortia conducting, 111–112 notable disease gene discoveries age-related macular degeneration, 110–111 body mass index, 109–110 Parkinson’s disease, 110 study designs and analytical considerations, 107–109 Genotyping, SNP and hybrid arrays, 29 GIANT consortium, 110 GJB2 gene, 8 Glioblastoma multiforme (GBM), 89–90 Glutaric acidemia type I, 183–184

239 H Haplotype tagging, 104–105 HapMap project, 115–117 Hepatitis C virus, 47–48 Hepatorenal tyrosinemia, 169–170 HER2 (ERBB2), 64–66 Hereditary hemochromatosis (HH), 136–137 Hereditary nonpolyposis colon cancer (HNPCC) colonoscopy screening, 140 diagnosis, 138 immunohistochemical analysis, 138 molecular genetic testing, 139 screening algorithms, 138–139 tumor surveillance programs and prophylactic measures, 137 High-density targeted oligoarray, 28–29 Holocarboxylase synthase deficiency, 181–182 Homocystinuria, 170–172 Huntington disease (HD) classification, 135 heritable neuropsychiatric disorder, 134 international testing protocols, 135 pretest counseling and evaluation, 136 single-gene disorder, 135 3-Hydroxy-3-methylglutaric acidemia, 181 Hypermethylation, promoter DNA methylation, 10 fragment analysis, 11–12 melting curve analysis, 14–15 MGMT, 10–11 multiplex PCR and fragment analysis, 12–14

I Illumina, 106–107 Intellectual disability, genetic testing of, 27 Irinotecan, 47 Isovaleric acidemia, 179

J Janus kinase 2 (JAK2), 81–82

K Karyotyping, 22 3-Ketothiolase deficiency, 182–183 KIT gene, 66–67 KRAS mutational analysis, 84–85 Kyoto Encyclopedia of Genes and Genomes (KEGG), 120

240 L Laboratory molecular analysis methodologies nucleic acid extraction, 2 polymerase chain reaction, 2–4 pyrosequencing, 9–10 qualitative analysis, 4–5 real-time PCR, 5–7 sequencing analysis, 7–8 Long-chain 3-hydroxy-acyl-CoA dehydrogenase (LCHAD) deficiency, 188–189

M Maple syrup urine disease, 172–173 Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, 189–190 Melting curve analysis, 14–15 6-Mercaptopurine, 45–46 Metabolic disorders amino acid diagnostic strategy and DNA testing, 166, 167 homocystinuria, 170–172 maple syrup urine disease, 172–173 phenylketonuria, 166–169 recommended uniform screening panel, 165, 166 tyrosinemia type I, 169–170 urea cycle defects, 173–176 biotinidase deficiency, 192 carnitine cycle and fatty acid oxidation, 184 diagnostic strategy and DNA testing, 185–186 medium-chain acyl-CoA dehydrogenase deficiency, 189–190 trifunctional protein and long-chain acyl-CoA dehydrogenase deficiencies, 188–189 uptake defect, 187 very-long-chain acyl-CoA dehydrogenase deficiency, 187–188 galactosemia, 190–192 organic acidemias diagnostic strategy and DNA testing, 176–177 glutaric acidemia type I, 183–184 holocarboxylase synthase deficiency, 181–182 3-hydroxy-3-methylglutaric acid, 181 isovaleric acid, 179 3-ketothiolase deficiency, 182–183 3-methylcrotonyl-CoA carboxylase deficiency, 180

Index propionic and methylmalonic acid, 176–179 MET gene, 75–76 3-Methylcrotonyl-CoA carboxylase deficiency, 180 Microsatellite instability (MSI) analysis algorithmic approaches, 87, 89 characteristic morphology, 87–88 fluorescent PCR analysis, 87–88 multiplex PCR, 13 Mismatch repair (MMR) genes, 87 Missense mutations, 75 Mitochondrial acetoacetyl-CoA thiolase, 182–183 Molecular genetic testing in genomic era, 1–2 laboratory molecular analysis methodologies nucleic acid extraction, 2 polymerase chain reaction, 2–4 pyrosequencing, 9–10 qualitative analysis, 4–5 real-time PCR, 5–7 sequencing analysis, 7–8 multiplex ligation-dependent probe amplification advantages, 16 molecular diagnostics, next generation of, 16–17 next-generation sequencing, clinical applications of, 17 promoter hypermethylation DNA methylation, 10 fragment analysis, 11–12 melting curve analysis, 14–15 MGMT, 10–11 multiplex PCR and fragment analysis, 12–14 Multiple endocrine neoplasia type 2 (MEN2) genotyping, 142 molecular genetic testing, 143 RET protein, 141 screening, 142 types, 140 Multiplex ligation-dependent probe amplification (MLPA) advantages, 16 molecular diagnostics, next generation of, 16–17 next-generation sequencing, clinical applications of, 17 Multiplex PCR and fragment analysis, 12–14

Index N Newborn screening amino acid metabolic disorders diagnostic strategy and DNA testing, 166, 167 homocystinuria, 170–172 maple syrup urine disease, 172–173 phenylketonuria, 166–169 recommended uniform screening panel, 165, 166 tyrosinemia type I, 169–170 urea cycle defects, 173–176 biochemical genetics tests, 164 biotinidase deficiency, 192 carnitine cycle and fatty acid oxidation, 184 diagnostic strategy and DNA testing, 185–186 medium-chain acyl-CoA dehydrogenase deficiency, 189–190 trifunctional protein and long-chain acyl-CoA dehydrogenase deficiencies, 188–189 uptake defect, 187 very-long-chain acyl-CoA dehydrogenase deficiency, 187–188 galactosemia metabolism, 190–192 organic acidemia metabolism diagnostic strategy and DNA testing, 176–177 glutaric acidemia type I, 183–184 holocarboxylase synthase deficiency, 181–182 3-hydroxy-3-methylglutaric acid, 181 isovaleric acid, 179 3-ketothiolase deficiency, 182–183 3-methylcrotonyl-CoA carboxylase deficiency, 180 propionic and methylmalonic acid, 176–179 recommended uniform screening panel, 164–165 Next-generation sequencing clinical applications of, 17 molecular diagnostics of, 16–17 Notable disease gene discoveries age-related macular degeneration, 110–111 body mass index, 109–110 Parkinson’s disease, 110 Nucleic acid extraction, 2 Nucleophosmin 1 (NPM1), 85–86

241 O Oligodendrogliomas, 89–90 Oligonucleotide array comparative genomic hybridization, 26–27 O6-Methylguanine methyltransferase (MGMT) methylation, 10–11 Online Mendelian Inheritance in Man (OMIM) database, 114 Organic acidemia metabolism diagnostic strategy and DNA testing, 176–177 glutaric acidemia type I, 183–184 holocarboxylase synthase deficiency, 181–182 3-hydroxy-3-methylglutaric acid, 181 isovaleric acid, 179 3-ketothiolase deficiency, 182–183 3-methylcrotonyl-CoA carboxylase deficiency, 180 propionic and methylmalonic acid, 176–179

P Parkinson’s disease, 110 Pharmacogenomic testing barriers to implementation of, 38–39 vs. companion testing, 38 definition, 37 drivers for, 40 genotyping, 38 specific drugs targeted for routine abacavir, carbamazepine, and allopurinol, 46–47 azathioprine and 6-mercaptopurine, 45–46 clopidogrel, 44–45 codeine, 47 hepatitis C virus, 47–48 irinotecan, 47 tamoxifen, 42–44 warfarin, 41–42 specific therapeutic decisions, 48–49 Phenylketonuria, 166–169 Platelet derived growth factor (PDGF), 67–69 Polymerase chain reaction, 2–4 Preimplantation genetic diagnosis and screening aneuploid, 158 blastomere, balanced chromosome constitution, 157, 159 chromosome mosaicism rate, 159 clinical trials, 160 components, in vitro fertilization, 157 sources, genotyping, 157

242 Preimplantation genetic diagnosis and screening (cont.) translocation, short and long arm chromosome, 157–158 whole genome amplification (WGA) and DNA analysis, 159 Prenatal testing aCGH in, 28 genetic diagnosis cell-free fetal DNA, 156 conventional chromosome analysis, 154 genome-wide arrays, 155 invasive testing, 157 maternal age, 154 miscarriage rate, 153 noninvasive prenatal diagnosis (NIPD), 155 structural malformations, 156 targeted arrays, 155 screening chromosome abnormalities, 151–153 single gene mutations, 148–151 Presymptomatic genetic testing in children, 143 disorders, 131–133 hereditary hemochromatosis (HH), 136–137 hereditary nonpolyposis colon cancer (HNPCC), 137–140 Huntington disease (HD), 134–136 multiple endocrine neoplasia type 2 (MEN2), 140–143 Pyrosequencing, 9–10

R Real-time PCR DNA-binding dyes, 6 nomenclature, 5 reaction, 5–6 target-specific methods, 6–7 Receptor tyrosine kinases, 62 RET proto-oncogene, 74–75

S Sequencing analysis, 7–8 Serine-threonine kinases, 82–84 Single gene mutations, screening carrier risk, residual risk, and mutation panel detection rate, 148–149 carrier testing, 150 mutation panel detection rate and carrier frequency, 148–149 preimplantation genetic diagnosis, 151

Index SNP/gene information, 115–118 Somatic alterations and targeted therapy anaplastic lymphoma kinase, 70–72 BRAF, 82–84 caveat of, 61 clinical molecular testing, 52–60 definition, 52 EGFR (ERBB1), 62–64 EGFR variant III, 64 epidermal growth factor family of receptors, 62–67 FMS-like tyrosine kinase 3, 69–70 genetic variations, 51 goals, 51–52, 54 HER2 (ERBB2), 64–66 human cancer, 52 intracellular tyrosine kinases ABL1, 76–81 JAK2, 81–82 KIT, 66–67 MET, 75–76 molecular markers genetic alterations, brain tumors, 89–90 KRAS, 84–85 microsatellite instability (MSI), 86–89 NPM1, 85–86 PDGF, 67–69 receptor tyrosine kinases, 62 RET, 74–75 serine-threonine kinases, 82–84 tyrosine kinase inhibitor (TKI), 61 tyrosine kinases, 54, 58 vascular endothelial growth factor, 73–74 Specialties, genetic counseling adult onset diseases, 205–206 biochemical genetics, 207 clinics, 205 cytogenetics, 207 diagnostic laboratory, 206–207 molecular genetics, 207 pediatric medical, 204–205 prenatal and perinatal, 203–204

T Tamoxifen, 42–44 TaqMan®, 7 Thermostable Taq polymerase, 3 Thermus aquaticus, 3 Thiopurine methyltransferase (TPMT), 45–46

Index Trifunctional protein (TFP) deficiency, 188–189 Tyrosinemia type I, 169–170

U UDP glucuronosyltransferase 1-A enzyme (UGT1A1), 47 University of California at Santa Cruz (UCSC) genome browser, 118 Urea cycle defects, 173–176

243 V Vascular endothelial growth factor (VEGF), 73–74 Very-long-chain acyl-CoA dehydrogenase (VLCAD) deficiency, 187–188

W Warfarin, 41–42

Molecular Genetics and Personalized Medicine

Molecular Genetics and Personalized Medicine

Integrative Neuroscience and Personalized Medicine

The Busy Physician's Guide To Genetics, Genomics and Personalized Medicine

Textbook of Personalized Medicine

Genetics and molecular biology

Genetics and Molecular Biology

Molecular Genetics of Hypertension (Human Molecular Genetics)

Methods in Molecular Genetics: Human Molecular Genetics

Methods in Molecular Genetics: Human Molecular Genetics

Molecular Endocrinology (Human Molecular Genetics)

Essentials of Genomic and Personalized Medicine

Molecular genetics of bacteria

Human Molecular Genetics 3

Molecular Genetics of Recombination

Molecular Genetics of Recombination

HUMAN MOLECULAR GENETICS

Molecular Genetics of Cancer

Molecular Genetics of Bacteria

Molecular Genetics of Liver Neoplasia (Cancer Genetics)

Advances in Genetics Volume 32 Incorporating Molecular Genetic Medicine

Molecular Genetics and the Human Personality

Human Molecular Genetics, Fourth Edition

Introduction To Molecular Genetics And Geonomics

Introduction To Molecular Genetics And Geonomics - hearts

Molecular Genetics and Ecology of Plant Adaptation

Molecular Genetics of Plant Development

Plant Adaptation: Molecular Genetics and Ecology

Cardiovascular Disease: Molecular Medicine

Principles of Molecular Medicine

Molecular Genetics, Gene Transfer, and Therapy

Molecular Genetics and Personalized Medicine