Cancer Epidemiology and Prevention

Cancer Epidemiology and Prevention Associate Editors Graham A. Colditz, M.D., Dr.P.H. Department of Medicine Harvard ...

Author: David Schottenfeld | Joseph F. Fraumeni Jr.

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Cancer Epidemiology and Prevention

Associate Editors Graham A. Colditz, M.D., Dr.P.H. Department of Medicine Harvard Medical School Department of Epidemiology Harvard School of Public Health Boston, Massachusetts Jonathan M. Samet, M.D. Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland Alice S. Whittemore, Ph.D. Department of Health Research and Policy Stanford University School of Medicine Stanford, California

Cancer Epidemiology and Prevention Third Edition Edited by

DAVID SCHOTTENFELD, M.D. John G. Searle Professor Emeritus of Epidemiology University of Michigan School of Public Health Ann Arbor, Michigan

JOSEPH F. FRAUMENI, JR., M.D. Director, Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

1 2006

3 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam

Copyright © 2006 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Cancer epidemiology and prevention / edited by David Schottenfeld, Joseph F. Fraumeni Jr.—3rd ed. p. ; cm. Includes bibliographical references and index. ISBN-13: 978-0-19-514961-6 ISBN-10: 0-19-514961-0 1. Cancer—Prevention. 2. Cancer–Epidemiology. I. Schottenfeld, David. II. Fraumeni, Joseph F. [DNLM: 1. Neoplasms–epidemiology. 2. Neoplasms—prevention & control. QZ 220.1 C215 2006] RC268.C354 2006 616.99¢4—dc22 2005051838 The science of medicine is a rapidly changing field. As new research and clinical experience broaden our knowledge, changes in treatment and drug therapy do occur. The authors and the publisher of this work have checked with sources believed to be reliable in their efforts to provide information that is accurate and complete, and in accordance with the standards accepted at the time of publication. However, in light of the possibility of human error or changes in the practice of medicine. neither the authors, nor the publisher, nor any other party who has been involved in the preparation or publication of this work warrants that the information contained herein is in every respect accurate or complete. Readers are encouraged to confirm the information contained herein with other reliable sources, and are strongly advised to check the product information sheet provided by the pharmaceutical company for each drug they plan to administer.

9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper

To Rosalie and Tricia

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Preface

The third edition of Cancer Epidemiology and Prevention represents a comprehensive update of information that has increased dramatically since the publication of the second edition in 1996. Once again the volume addresses the global burden of cancer, the complex interrelationship of environmental and genetic factors involved in the induction and progression of a broad spectrum of malignancies, and the current priorities and challenges in cancer epidemiology and prevention research. Cancer incidence and mortality is universal, but the burden of cancer classified by morphology and organ site is distributed unevenly in different populations around the world. In developing countries the populations are disproportionately affected by cancers related to infectious agents, while in industrialized countries a large percentage of cancers are associated with lifestyle factors including smoking, obesity, and physical inactivity. The heavy worldwide toll of cancer can be gauged in a survey of the International Agency for Research on Cancer, which estimated that in 2002 there were 10.9 million cancers diagnosed, 6.7 million cancer deaths, and 24.6 million persons living with cancers detected within the previous 5 years. In the United States, cancer mortality in men and women younger than 85 years has surpassed heart disease as the leading cause of death since 1999, although cancer mortality is still less common than heart disease after age 85. This volume maintains the structure of previous editions, with chapters grouped into five major sections: Basic Concepts, The Magnitude of Cancer, The Causes of Cancer, Cancer by Tissue of Origin, and Cancer Prevention and Control. We have undertaken an extensive revision, adding new chapters in each section to expand the scope of coverage and keep pace with the striking progress in our understanding of cancer biology and etiology over the past decade. The introductory chapters under Basic Concepts have been amended to highlight the advances in genomic and molecular sciences that are increasingly incorporated into epidemiologic research designed to uncover the environmental and heritable determinants of cancer development and progression. The section now includes chapters on the molecular as well as morphologic classification of cancer, the critical genetic events that provoke normal cells to malignant transformation and tumor invasion, the origins and natural history of cancer precursors, and the epidemiologic application of emerging molecular and biochemical biomarkers that sharpen our measures of carcinogenic exposures, susceptibility genes, and intermediate outcomes. The section on the magnitude of cancer highlights the enormous variation in cancer incidence that occurs around the world, and the major shifts in incidence within one or two generations among migrant populations that have provided important leads into the lifestyle and cultural determinants of cancer risk. The patterns of cancer incidence, mortality and survival in the United States are reviewed for various racial and ethnic groups, while a complementary chapter dealing with the substantial socioeconomic disparities in cancer incidence and mortality is added. Also covered is the economic impact of cancer in a chapter that estimates the direct medical

viii

Preface

costs of treating specific types of cancer, costs to the family and community in providing rehabilitative services or palliative care, and costs to society as a result of premature death or lost productivity. The section on the causes of cancer provides the foundation for viewing the multifactorial origins of human cancer. Whereas the neoplastic process at the cellular level results from cumulative genetic perturbations in the mechanisms that regulate proliferation, differentiation, apoptosis and DNA repair, these events appear to be triggered or propagated by lifestyle and other environmental exposures that are critically evaluated in this section. To keep abreast of the rapid expansion of epidemiologic literature, separate chapters are devoted to the cancer risks associated with obesity and with physical activity in addition to dietary and nutritional factors, while a new chapter on electromagnetic fields and radiofrequencies evaluates the current epidemiologic evidence for this widely studied exposure that remains a speculative risk factor for cancer. To highlight the exciting recent progress made in identifying mechanisms of genetic susceptibility, chapters are devoted to the uncommon but important hereditary neoplastic syndromes, and to the extensive ongoing search for common susceptibility or modifier genes that may play a major role in cancer development through interactions with environmental exposures. The section on cancer by tissue of origin provides a comprehensive epidemiologic survey covering a wide variety of cancers, including childhood tumors and multiple primary cancers. New chapters on pleural and peritoneal malignancies, as well as anal cancer, are included. Each chapter systematically addresses the demographic, environmental and host factors that influence cancer risk, and efforts are made to integrate developments from clinical and laboratory sciences into concepts of carcinogenesis, current strategies aimed at cancer prevention, and future directions in illuminating causal mechanisms and assessing the benefits of preventive interventions. The concluding section on cancer prevention and control addresses the methods and applications for translating the results of epidemiologic, clinical, and laboratory research into preventive interventions that will contribute to the eventual goal of eliminating suffering and death due to cancer. It is generally understood that preventing cancer is far preferable to experiencing aggressive treatment, and many strategies are currently available to accomplish this objective. Emphasis is given to approaches aimed at identifying, quantifying, and ultimately reducing the prevalence of cancer risk factors in diverse populations. Special attention is placed on measuring the impact of lifestyle and behavioral interventions, health-promoting practices, as well as governmental policies that regulate environmental carcinogens. The benefits and challenges of cancer screening practices and the use of chemopreventive agents are also reviewed. Although further understanding of causal factors and pathways through epidemiologic and other research will contribute to improvements in preventive strategies, it is important to capitalize on the panoply of preventive measures that are already at hand. Through the shared efforts of innovative research programs, a supportive health care system, a cooperative network of national and international agencies, and an informed public, we can expect to see a rapid acceleration of evidence-based public health policies and clinical interventions that are designed to keep cancer from developing and escaping control.

Contents

I BASIC CONCEPTS 1. Cause and Cancer Epidemiology Steven N. Goodman and Jonathan M. Samet

3

2. Morphologic and Molecular Classification of Human Cancer Thomas J. Giordano

10

3. Cancer Precursors Thomas E. Rohan, Donald E. Henson, Eduardo L. Franco, and Jorge Albores-Saavedra

21

4. Molecular and Genetic Events in Neoplastic Transformation Ayse E. Erson and Elizabeth M. Petty

47

5. Risk Assessment of Carcinogenic Hazards Leslie T. Stayner, Paolo Boffetta, and Harri Vainio

65

6. Application of Biomarkers in Cancer Epidemiology Montserrat García-Closas, Roel Vermeulen, Mark E. Sherman, Lee E. Moore, Martyn T. Smith, and Nathaniel Rothman

70

7. Genetic Concepts and Methods in Epidemiologic Research Neil J. Risch and Alice S. Whittemore

89

II THE MAGNITUDE OF CANCER 8. International Patterns of Cancer Incidence and Mortality D. Maxwell Parkin and Freddie I. Bray

101

9. Cancer Incidence, Mortality, and Patient Survival in the United States Lynn A.G. Ries and Susan S. Devesa

139

10. Socioeconomic Disparities in Cancer Incidence and Mortality Ichiro Kawachi and Candyce Kroenke

174

11. Migrant Studies Laurence N. Kolonel and Lynne R. Wilkens

189

12. Economic Impact of Cancer in the United States Martin L. Brown and K. Robin Yabroff

202

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Contents

III THE CAUSES OF CANCER 13. Tobacco Michael J. Thun and S. Jane Henley

217

14. Alcohol James R. Marshall and Jo Freudenheim

243

15. Ionizing Radiation John D. Boice, Jr.

259

16. Solar Radiation Adele C. Green and David C. Whiteman

294

17. Electromagnetic Fields and Radiofrequency Radiation David A. Savitz and Anders Ahlbom

306

18. Occupation Jack Siemiatycki, Lesley Richardson, and Paolo Boffetta

322

19. Air Pollution Jonathan M. Samet and Aaron J. Cohen

355

20. Water Contaminants Kenneth P. Cantor, Mary H. Ward, Lee E. Moore, and Jay H. Lubin

382

21. Diet and Nutrition Walter C. Willett

405

22. Obesity and Body Composition Rachel Ballard-Barbash, Christine Friedenreich, Martha Slattery, and Inger Thune

422

23. Physical Activity I-Min Lee and Yuko Oguma

449

24. Exogenous Hormones James V. Lacey, Jr., Graham A. Colditz, and David Schottenfeld

468

25. Pharmaceuticals Other Than Hormones Laurel A. Habel and Gary D. Friedman

489

26. Infectious Agents Nancy E. Mueller, Brenda M. Birmann, Julie Parsonnet, Mark H. Schiffman, and Sherri O. Stuver

507

27. Immunologic Factors Gareth J. Morgan, Martha S. Linet, and Charles S. Rabkin

549

28. Hereditary Neoplastic Syndromes Noralane M. Lindor, Carl J. Lindor, and Mark H. Greene

562

29. Genetic Modifiers of Cancer Risk Neil E. Caporaso

577

IV CANCER BY TISSUE OF ORIGIN 30. Cancers of the Nasal Cavity and Paranasal Sinuses Alyson J. Littman and Thomas L. Vaughan

603

31. Nasopharyngeal Cancer Mimi C. Yu and Jian-Min Yuan

620

32. Cancer of the Larynx Andrew F. Olshan

627

33. Cancer of the Lung Margaret R. Spitz, Xifeng Wu, Anna Wilkinson, and Qingyi Wei

638

xi

Contents

34. Pleural and Peritoneal Neoplasms Paolo Boffetta and Leslie T. Stayner

659

35. Cancers of the Oral Cavity and Pharynx Susan T. Mayne, Douglas E. Morse, and Deborah M. Winn

674

36. Esophageal Cancer William J. Blot, Joseph K. McLaughlin, and Joseph F. Fraumeni, Jr.

697

37. Stomach Cancer Atsuko Shibata and Julie Parsonnet

707

38. Cancer of the Pancreas Kristin E. Anderson, Thomas M. Mack, and Debra T. Silverman

721

39. Liver Cancer W. Thomas London and Katherine A. McGlynn

763

40. Biliary Tract Cancer Ann W. Hsing, Asif Rashid, Susan S. Devesa, and Joseph F. Fraumeni, Jr.

787

41. Cancers of the Small Intestine Jennifer L. Beebe-Dimmer and David Schottenfeld

801

42. Cancers of the Colon and Rectum Edward Giovannucci and Kana Wu

809

43. Anal Cancer Morten Frisch and Mads Melbye

830

44. The Leukemias Martha S. Linet, Susan S. Devesa, and Gareth J. Morgan

841

45. Hodgkin Lymphoma Nancy E. Mueller and Seymour Grufferman

872

46. Non-Hodgkin Lymphoma Patricia Hartge, Sophia S. Wang, Paige M. Bracci, Susan S. Devesa, and Elizabeth A. Holly

898

47. Multiple Myeloma Anneclaire J. De Roos, Dalsu Baris, Noel S. Weiss, and Lisa J. Herrinton

919

48. Bone Cancer Robert W. Miller, John D. Boice, Jr., and Rochelle E. Curtis

946

49. Soft Tissue Sarcoma Marianne Berwick

959

50. Thyroid Cancer Elaine Ron and Arthur B. Schneider

975

51. Breast Cancer Graham A. Colditz, Heather J. Baer, and Rulla M. Tamimi

995

52. Ovarian Cancer Susan E. Hankinson and Kim N. Danforth

1013

53. Endometrial Cancer Linda S. Cook, Noel S. Weiss, Jennifer A. Doherty, and Chu Chen

1027

54. Cervical Cancer Mark H. Schiffman and Allan Hildesheim

1044

55. Cancers of the Vulva and Vagina Margaret M. Madeleine and Janet R. Daling

1068

56. Choriocarcinoma Julie R. Palmer and Colleen M. Feltmate

1075

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Contents

57. Renal Cancer Joseph K. McLaughlin, Loren Lipworth, Robert E. Tarone, and William J. Blot

1087

58. Bladder Cancer Debra T. Silverman, Susan S. Devesa, Lee E. Moore, and Nathaniel Rothman

1101

59. Prostate Cancer Elizabeth A. Platz and Edward Giovannucci

1128

60. Testicular Cancer Aruna V. Sarma, Julie C. McLaughlin, and David Schottenfeld

1151

61. Penile Cancer Louise Wideroff and David Schottenfeld

1166

62. Nervous System Susan Preston-Martin, Reema Munir, and Indro Chakrabarti

1173

63. Cutaneous and Ocular Melanoma Stephen B. Gruber and Bruce K. Armstrong

1196

64. Keratinocyte Carcinomas (Basal and Squamous Cell Carcinomas of the Skin) Margaret R. Karagas, Martin A. Weinstock, and Heather H. Nelson

1230

65. Cancers in Children Julie A. Ross and Logan G. Spector

1251

66. Multiple Primary Cancers David Schottenfeld and Jennifer L. Beebe-Dimmer

1269

V CANCER PREVENTION AND CONTROL 67. Principles and Applications of Cancer Prevention and Control Interventions Robert A. Hiatt and Barbara K. Rimer

1283

68. Increasing the Contribution of Epidemiology to the Primary Prevention of Cancer Beverly Rockhill and Douglas Weed

1292

69. Cancer Risk Communication and Comprehension Karen M. Emmons, Cara Cuite, and Erika Waters

1303

70. Principles of Screening Bernard Levin and Philip C. Prorok

1310

71. Cancer Chemoprevention Jaye L. Viner, Ernest Hawk, and Scott M. Lippman

1318

72. Regulating Carcinogens Jonathan M. Samet, Thomas A. Burke, and Lynn Goldman

1341

Index

1355

Contributors

Anders Ahlbom, PhD

Marianne Berwick, PhD, MPH

Division of Epidemiology National Institute of Environmental Medicine Karolinska University Stockholm, Sweden

Division of Epidemiology and Cancer Prevention University of New Mexico Albuquerque, New Mexico

Jorge Albores-Saavedra, MD

Brenda M. Birmann, ScD

Department of Pathology Louisiana State University Shreveport, Louisiana

Department of Epidemiology Harvard School of Public Health Boston, Massachusetts

Kristin E. Anderson, PhD, MPH

William J. Blot, PhD

Division of Epidemiology University of Minnesota School of Public Health Minneapolis, Minnesota

International Epidemiology Institute Rockville, Maryland

Bruce K. Armstrong, MBBS, DPhil

International Agency for Research on Cancer Lyon, France

School of Public Health University of Sydney Sydney, Australia

Heather J. Baer, ScD Department of Medicine Brigham and Women’s Hospital Harvard Medical School Channing Laboratory Boston, Massachusetts

Paolo Boffetta, MD, MPH

John D. Boice, Jr., ScD International Epidemiology Institute Rockville, Maryland

Paige M. Bracci, MSc, MPH Department of Epidemiology and Biostatistics University of California San Francisco San Francisco, California

Rachel Ballard-Barbash, MD, MPH Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Dalsu Baris, MD, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Freddie I. Bray, BSc, MSc The Cancer Registry of Norway Oslo, Norway

Martin L. Brown, PhD Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Jennifer L. Beebe-Dimmer, MPH, PhD Departments of Epidemiology and Urology University of Michigan School of Public Health and University of Michigan Medical School Ann Arbor, Michigan

Thomas A. Burke, PhD, MPH Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland

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Contributors

Kenneth P. Cantor, PhD, MPH

Jennifer A. Doherty, PhD

Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, Washington

Neil E. Caporaso, MD

Karen M. Emmons, PhD


Division of Community-Based Research Dana Farber Cancer Institute Boston, Massachusetts

Indro Chakrabarti, MD, MPH

Ayse E. Erson, PhD

Department of Neurosurgery Keck School of Medicine University of Southern California Los Angeles, California

Colleen M. Feltmate, MD

Chu Chen, PhD Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, Washington

Aaron J. Cohen, MPH, DSc

Department of Internal Medicine University of Michigan Medical School Ann Arbor, Michigan Department of Obstetrics and Gynecology Brigham and Women’s Hospital Harvard Medical School Boston, Massachusetts

Eduardo L. Franco, DrPH, MPH

Health Effects Institute Boston, Massachusetts

Departments of Epidemiology and Oncology McGill University Montreal, Quebec, Canada

Graham A. Colditz, MD, DrPH

Joseph F. Fraumeni, Jr., MD, MSc

Department of Medicine Harvard Medical School Department of Epidemiology Harvard School of Public Health Boston, Massachusetts


Linda S. Cook, PhD Department of Community Health Sciences University of Calgary Calgary, Alberta, Canada

Cara Cuite, PhD Food Policy Institute Rutgers University New Brunswick, New Jersey

Rochelle E. Curtis, MA Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Janet R. Daling, PhD Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, Washington

Kim N. Danforth, MPH Department of Epidemiology Harvard School of Public Health Boston, Massachusetts

Anneclaire J. De Roos, MPH, PhD Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, Washington

Jo Freudenheim, PhD School of Medicine and Biomedical Sciences State University of New York at Buffalo Buffalo, New York

Christine Friedenreich, PhD Alberta Cancer Board Calgary, Alberta, Canada

Gary D. Friedman, MD, MS Department of Health Research and Policy Stanford University School of Medicine Stanford, California

Morten Frisch, MD, PhD Danish Epidemiology Science Center State Serum Institute Copenhagen, Denmark

Montserrat García-Closas, MD, DrPH Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Thomas J. Giordano, MD, PhD Department of Pathology University of Michigan Medical School Ann Arbor, Michigan

Edward Giovannucci, MD, ScD Departments of Nutrition and Epidemiology Harvard School of Public Health Boston, Massachusetts

Susan S. Devesa, PhD

Lynn Goldman, MD, MPH, MS


Department of Environmental Health Sciences Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland

Contributors

Steven N. Goodman, MD, PhD

Elizabeth A. Holly, PhD, MPH

Department of Oncology Johns Hopkins University School of Medicine Baltimore, Maryland

Division of Cancer Epidemiology University of California San Francisco School of Medicine San Francisco, California

Adele C. Green, MBBS, PhD

Ann W. Hsing, PhD

Population Studies and Human Genetics Division Queensland Institute of Medical Research Brisbane, Queensland, Australia


Mark H. Greene, MD

Margaret R. Karagas, PhD


Department of Community and Family Medicine Norris Cotton Cancer Center Dartmouth Medical School Lebanon, New Hampshire

Stephen B. Gruber, MD, PhD, MPH Division of Molecular Medicine & Genetics University of Michigan Medical School Ann Arbor, Michigan

Seymour Grufferman, MD, DrPH Department of Pathology University of New Mexico School of Medicine Albuquerque, New Mexico

Laurel A. Habel, PhD Division of Research Kaiser Permanente Oakland, California

Susan E. Hankinson, ScD Department of Medicine Brigham and Women’s Hospital Harvard Medical School Boston, Massachusetts

Patricia Hartge, ScD

Ichiro Kawachi, MD, PhD Department of Society, Human Development, and Health Harvard School of Public Health Boston, Massachusetts

Laurence N. Kolonel, MD, PhD Cancer Research Center of Hawaii University of Hawaii Honolulu, Hawaii

Candyce Kroenke, ScD, MPH Department of Medicine Harvard Medical School Boston, Massachusetts

James V. Lacey, Jr., PhD, MPH Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

I-Min Lee, MBBS, ScD


Department of Medicine Brigham and Women’s Hospital Harvard Medical School Boston, Massachusetts

Ernest Hawk, MD, MPH

Bernard Levin, MD

National Cancer Institute Bethesda, Maryland

S. Jane Henley, MSPH American Cancer Society Atlanta, Georgia

Donald E. Henson, MD Department of Pathology George Washington University Medical Center Washington, District of Columbia

Lisa J. Herrinton, PhD Division of Research Kaiser Permanente Oakland, California

Robert A. Hiatt, MD, PhD University of California San Francisco Comprehensive Cancer Center San Francisco, California

Allan Hildesheim, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Division of Cancer Prevention MD Anderson Cancer Center Houston, Texas

Carl J. Lindor, BA University of St. Thomas St. Paul, Minnesota

Noralane M. Lindor, MD Department of Medical Genetics Mayo Clinic College of Medicine Rochester, Minnesota

Martha S. Linet, MD, MPH Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Scott M. Lippman, MD Department of Clinical Cancer Prevention MD Anderson Cancer Center Houston, Texas

Loren Lipworth, ScD International Epidemiology Institute Rockville, Maryland

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Contributors

Alyson J. Littman, PhD

Douglas E. Morse, DDS, PhD

Department of Epidemiology School of Public Health and Community Medicine University of Washington Seattle, Washington

Department of Epidemiology and Health Promotion New York University College of Dentistry New York, New York

W. Thomas London, MD Fox Chase Cancer Center Philadelphia, Pennsylvania

Department of Epidemiology Harvard School of Public Health Boston, Massachusetts

Jay H. Lubin, PhD

Reema Munir, MD


Thomas M. Mack, MD, MPH Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles, California

Margaret M. Madeleine, PhD, MPH Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, Washington

James R. Marshall, PhD Cancer Prevention and Population Sciences Roswell Park Cancer Institute Buffalo, New York

Susan T. Mayne, PhD Department of Epidemiology and Public Health Yale University School of Medicine New Haven, Connecticut

Katherine A. McGlynn, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Nancy E. Mueller, ScD

Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles, California

Heather H. Nelson, PhD, MPH Department of Environmental Health Harvard School of Public Health Boston, Massachusetts

Yuko Oguma, MD Department of Epidemiology Harvard School of Public Health Boston, Massachusetts

Andrew F. Olshan, PhD Department of Epidemiology School of Public Health University of North Carolina Chapel Hill, North Carolina

Julie R. Palmer, ScD Slone Epidemiology Center Boston University Boston, Massachusetts

D. Maxwell Parkin, MD


Clinical Trials Service Unit, and Epidemiological Studies Unit Nuffield Department of Clinical Medicine University of Oxford Oxford, United Kingdom

Julie C. McLaughlin, MPH, MS

Julie Parsonnet, MD

Department of Urology University of Michigan Ann Arbor, Michigan

Departments of Medicine, and Health Research and Policy Stanford University School of Medicine Stanford, California

Mads Melbye, MD, PhD

Elizabeth M. Petty, MD

Danish Epidemiology Science Center State Serum Institute Copenhagen, Denmark

Departments of Internal Medicine and Human Genetics University of Michigan Medical School Ann Arbor, Michigan

Robert W. Miller, MD, DrPH*

Elizabeth A. Platz, ScD

Joseph K. McLaughlin, PhD


Lee E. Moore, PhD, MPH Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Gareth J. Morgan, MD, PhD Haemato-Oncology Unit Royal Marsden Hospital Sutton, Surrey, United Kingdom * deceased

Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland

Susan Preston-Martin, PhD Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles, California

Philip C. Prorok, PhD Division of Cancer Prevention National Cancer Institute Bethesda, Maryland

Contributors

Charles S. Rabkin, MD, MSc

Mark H. Schiffman, MD, MPH



Asif Rashid, MD, PhD

Arthur B. Schneider, MD, PhD

Department of Pathology MD Anderson Cancer Center Houston, Texas

Department of Medicine University of Illinois at Chicago Chicago, Illinois

Lesley Richardson, MSc

David Schottenfeld, MD, MSc

International Agency for Research on Cancer Lyon, France

Departments of Epidemiology and Internal Medicine University of Michigan School of Public Health and University of Michigan Medical School Ann Arbor, Michigan

Lynn A.G. Ries, MS Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Barbara K. Rimer, DrPH School of Public Health University of North Carolina Chapel Hill, North Carolina

Neil J. Risch, PhD

Mark E. Sherman, MD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Atsuko Shibata, MD, PhD Amgen Inc. Thousand Oaks, California

Center for Human Genetics University of California San Francisco San Francisco, California

Jack Siemiatycki, PhD

Beverly Rockhill, PhD

Debra T. Silverman, ScD

Department of Epidemiology School of Public Health University of North Carolina Chapel Hill, North Carolina


Thomas E. Rohan, MD, PhD

University of Utah Health Research Center Salt Lake City, Utah

Department of Epidemiology and Population Health Albert Einstein College of Medicine Bronx, New York

CRCHUM—Population Health Montreal, Quebec, Canada

Martha Slattery, PhD, MPH

Martyn T. Smith, PhD


Environmental Health Sciences Division School of Public Health University of California at Berkeley Berkeley, California

Julie A. Ross, PhD

Logan G. Spector, PhD

Department of Pediatrics University of Minnesota Minneapolis, Minnesota

Department of Pediatrics University of Minnesota Minneapolis, Minnesota

Nathaniel Rothman, MD, MPH

Margaret R. Spitz, MD, MPH


Department of Epidemiology MD Anderson Cancer Center Houston, Texas

Jonathan M. Samet, MD

Leslie T. Stayner, PhD, MSc

Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland

University of Illinois at Chicago School of Public Health Chicago, Illinois

Aruna V. Sarma, PhD, MHA

Sherri O. Stuver, ScD

Departments of Urology and Epidemiology University of Michigan School of Public Health and University of Michigan Medical School Ann Arbor, Michigan

Department of Epidemiology Boston University School of Public Health Boston, Massachusetts

David A. Savitz, PhD

Department of Medicine Harvard Medical School Channing Laboratory Boston, Massachusetts

Elaine Ron, PhD, MPH

Department of Community and Preventive Medicine Mount Sinai School of Medicine New York, New York

Rulla M. Tamimi, ScD

xvii

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Contributors

Robert E. Tarone, PhD

David C. Whiteman, BMedSc, MB, PhD


Population Studies and Human Genetics Division Queensland Institute of Medical Research Brisbane, Queensland, Australia

Michael J. Thun, MD American Cancer Society Atlanta, Georgia

Inger Thune, MD, PhD Norwegian Cancer Society University of Tromsø Tromsø, Norway

Harri Vainio, PhD International Agency for Research on Cancer Lyon, France

Thomas L. Vaughan, MD, MPH Department of Epidemiology School of Public Health and Community Medicine University of Washington Seattle, Washington

Roel Vermeulen, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Jaye L. Viner, MD, MPH National Cancer Institute Bethesda, Maryland

Sophia S. Wang, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, Maryland

Mary H. Ward, PhD Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD

Erika Waters, MS Rutgers University New Brunswick, New Jersey

Douglas Weed, MD, PhD Division of Cancer Prevention National Cancer Institute Bethesda, Maryland

Qingyi Wei, MD, PhD Department of Epidemiology MD Anderson Cancer Center Houston, Texas

Martin A. Weinstock, MD, PhD Brown University School of Medicine Dermatoepidemiology Unit Providence, Rhode Island

Noel S. Weiss, MD, DrPH Department of Epidemiology School of Public Health and Community Medicine University of Washington Seattle, Washington

Alice S. Whittemore, PhD Department of Health Research and Policy Stanford University School of Medicine Stanford, California

Louise Wideroff, PhD Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Lynne R. Wilkens, DrPH Cancer Research Center of Hawaii University of Hawaii Honolulu, Hawaii

Anna Wilkinson, PhD Department of Epidemiology MD Anderson Cancer Center Houston, Texas

Walter C. Willett, MD, DrPH Department of Nutrition Harvard School of Public Health Boston, Massachusetts

Deborah M. Winn, PhD Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Kana Wu, MD, PhD Department of Nutrition Harvard School of Public Health Boston, Massachusetts

Xifeng Wu, MD, PhD Department of Epidemiology MD Anderson Cancer Center Houston, Texas

K. Robin Yabroff, PhD Division of Cancer Control and Population Sciences National Cancer Institute Bethesda, Maryland

Mimi C. Yu, PhD Cancer Center University of Minnesota Minneapolis, Minnesota

Jian-Min Yuan, MD, PhD Cancer Center University of Minnesota Minneapolis, Minnesota

I BASIC CONCEPTS

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1

Cause and Cancer Epidemiology STEVEN N. GOODMAN AND JONATHAN M. SAMET

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he prevention of disease has long been based implicitly on taking action on the assumption that a disease is caused by a factor that can be controlled. Early examples include the experimental evidence generated by Lind, showing that consumption of oranges and lemons prevented scurvy, and Snow’s observations on cholera occurrence in London, showing a disease pattern consistent with water-borne transmission (Rosen, 1993). In these examples, preventive steps followed: After Lind’s experiment, the diets of the British navy were supplemented with citrus fruits; and after Snow’s observational study, steps were taken to ensure that the source of water was changed in the affected areas of London. Over the ensuing centuries, infectious agents were causally linked to specific diseases, and prevention was accomplished by interrupting transmission and by vaccines. During the twentieth century, public health was threatened by parallel epidemics of chronic diseases, including cancer; and as the causal agents were identified, a broad range of preventive strategies were implemented. The concept of causation has long had a central role in the application of epidemiologic evidence for controlling cancer. The designation of a risk factor as “causal” has been the starting point for initiating cancer prevention programs based on reducing exposure to the risk factor. Although the concept of causation itself remains a matter of continuing discussion among philosophers and others, use of the term in public health implies that the evidence supporting causality of association has reached a critical threshold of certainty and that reduced exposure can be expected to be followed by reduced disease occurrence. Over the last 50 years, identification of the causes of cancer has been the primary focus of most epidemiologic research on cancer; only recently has attention shifted toward identifying genetic determinants of susceptibility and markers of the early stages of carcinogenesis. There are numerous examples of how identifying a cause of cancer has led to intervention and reduction of cancer occurrence. Tobacco use and cancer of the lung is a notable example for its historical precedence and for the framework applied to the scientific evidence as the causality of the association was evaluated (US Department of Health Education and Welfare—DHEW, 1964; White, 1990). The range of causal risk factors for cancer is broad, including infectious agents (e.g., human papillomavirus and cervical cancer), physical agents (e.g., ionizing radiation and leukemia), inhaled agents (e.g., radon and lung cancer), pharmaceutical agents and hormones (e.g., diethylstilbestrol and adenocarcinoma of the vagina), food contaminants (e.g., aflatoxin and liver cancer), workplace exposures (e.g., asbestos), life stylerelated exposures (e.g., alcohol consumption), and genetic mutations (e.g., Li-Fraumeni syndrome). These and other factors considered to be causes of cancer have been given this label only after the accumulation of sufficient evidence, in most instances derived from both epidemiologic and laboratory research. This chapter provides an overview of causal inference with a focus on the interpretation of epidemiologic data on cancer risk. It begins with an introduction to the centuries-old discussion on cause and causation and next considers the epidemiologic concept of causation, setting the discussion in the context of current understanding of carcinogenesis as a multistep process. The criteria for causation, often attributed to the British medical statistician Sir Austin Bradford Hill (Hill, 1965) or to the 1964 Report of the U.S. Surgeon General on tobacco (US Department of Health Education and Welfare—DHEW, 1964), have provided a framework for evaluating evidence to judge the causality of associations.

These criteria are addressed in depth, and their application is illustrated with the example of smoking, both active and passive, and lung cancer. The chapter concludes with a consideration of emerging issues concerned with causation, including the interpretation of data coming from the new technologies of contemporary “molecular epidemiology” and new approaches to evaluating causation.

CONCEPTS OF CAUSATION At its foundation, “cause” is not knowable with certainty. This fact underlies much of the methodologic and conceptual confusion that often swirls around claims of causation based on scientific data. The fundamental intuition underlying the causal concept is that event “A” somehow produces another event, “B.” However, the “production” of “B” is not observable. The philosopher Bryan Magee summarized this conundrum eloquently. It seems to be impossible for us to form any conception of an ordered world at all without the idea of there being causal connections between events. But when we pursue this idea seriously we find that causal connection is not anything we ever actually observe, nor ever can observe. We may say that Event A causes Event B, but when we examine the situation we find that what we actually observed is Event A followed by Event B. There is not some third entity between them, a casual link, which we also observe. . . . So we have this indispensable notion of cause at the very heart of our conception of the world, and of our understanding of our own experience, which we find ourselves quite unable to validate by observation or experience . . . It actually purports to tell us how specific material events are related to each other in the real world, yet it is not derived from, nor can it be validated by, observation of that world. This is deeply mysterious. (Magee, 2001) The fact that causation is not directly observable means that scientists and philosophers have had to develop a set of constructs and heuristics by which to define a “cause” operationally. These constructs typically have two components: a predictive or associational one, determined empirically, and an explanatory one, based on a proposed underlying mechanism. All causal claims rest on these twin pillars; an association with no plausible mechanistic basis is typically not accepted as causal, and a proposed mechanism, however well founded, cannot be accepted as the basis for a causal claim without empirical demonstration that the effect occurs more often in the presence of the purported cause than in its absence. However, these components need not contribute equally, and various causal claims may rest on quite different balances of contributions of empirical and mechanistic information. Underlying any operational definition of causality must be an ontologic one: that is, how a cause is defined in principle. A particularly useful, widely accepted definition in both philosophy and epidemiology is the “counterfactual” notion of causation. This concept had its origins at least as far back as the English philosopher David Hume (1711–1776) (Hume, 1739). During the twentieth century, this concept was further developed and applied by statisticians, philosophers, and epidemiologists (Bunge, 1959; Lewis, 1973; Rubin, 1974; Robins, 1986, 1987; Greenland, 1990; Neyman, 1990; Greenland et al., 1999;

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Pearl, 2000). The counterfactual definition holds that something is a cause of a given outcome if, when the same individual is observed with and without a purported cause and without changing any other characteristic of that individual, a different outcome would be observed. For example, the counterfactual state for a smoker is the same individual never having smoked. The state that cannot be observed is called the counterfactual state: literally, counter to the observed facts. The impossibility of observing the counterfactual state is what makes all causal claims subject to uncertainty. The above definition is deterministic; that is, the outcome always occurs in the presence of the cause and never occurs without it. However, health research rarely deals with either a cause that inevitably produces certain outcomes, or outcomes that cannot occur absent specific causes. For example, smokers do not always get lung cancer, and never-smokers do develop this malignancy. Therefore, the counterfactual definition must be expanded to encompass the notion of a probabilistic outcome. That is, the formal definition of a cause in epidemiology requires that a factor X be associated with a difference in the probability of an outcome. For example, if X may take on two different values, y or z: Condition 1: observed association Pr(outcome | X = y) π Pr(outcome | X = z) Properly designed studies provide a scientific basis for inferring what the outcome of the counterfactual state would be and permit the related uncertainty to be quantified. In a laboratory, scientists are able to predict the outcome in this counterfactual state, generally with a high degree of confidence, by repeating an experimental procedure with every factor tightly controlled, varying only the factor of interest. In observational studies of humans, however, researchers must try to infer what the outcome would be in a counterfactual state by studying another group of persons who, at least on average, are substantively different from the exposed group in only one variable: the exposure under study. The outcome of this second group is used to represent what would have occurred in the original group if it were observed with an exposure different from that which actually existed (Greenland, 1990). In the case of smoking and disease, this comparison is between disease risk in smokers and nonsmokers. Simply observing a difference in the probability of an outcome between two groups that differ on X is not sufficient condition for causation because it does not distinguish between causation and spurious or indirect association, produced by “confounders,” or ancillary causes. The notion of “causation” requires that the cause somehow actively “produce” its effect, which is captured operationally by the requirement that active manipulation of the cause should produce a change in the probability of the outcome. For example, if one saw that students with poor visual acuity typically sat closer to the front of a classroom, one would not call the seating arrangement a “cause” of their poor eyesight unless it could be shown that seating them farther back improved it. The notation that captures this idea is one that introduces an operator, not part of traditional statistical notation “Set (X = x),” which corresponds to actively setting a risk factor X equal to some value x, rather than simply observing that the factor is equal to x. Thus the counterfactual notion of probabilistic causation for a risk factor X requires condition 2. Condition 2: no confounding Pr(outcome | set[X = x)] = Pr(outcome | X = x) If we put together condition 1—that there is an observed association between cause and effect—with condition 2—that there is no other indirect cause responsible for the observed effect—we have the counterfactual condition for probabilistic causation, expressed as follows. Condition 1 + Condition 2 = Causality condition Pr[outcome | set(X = y)] π Pr[outcome | set(X = z)] This condition states that if the probability of an outcome changes when risk factor X is actively changed from z to y, then X is regarded as a cause of the outcome.

In the randomized controlled trial, a risk factor is actively manipulated. Understanding the role of randomization can deepen insights into the interpretation of nonrandomized designs used in epidemiology. Randomization has two critical consequences: (1) it makes exposure to a proposed causal factor independent of potentially confounding factors; and (2) it provides a known probability distribution for the potential outcomes in each group under a given mathematical hypothesis (i.e., the null) (Greenland, 1990). Randomization does not necessarily free the inference from an individual randomized study from unmeasured confounding (it does so only on average). Randomization does imply that measures of uncertainty about causal estimates from randomized studies have an experimental foundation. In the absence of randomization, uncertainty about causal effects depends in part on the confidence that all substantive confounding has been eliminated or controlled by either the study design or the analysis. The level of confidence is ultimately based on scientific judgment and consequently is subject to uncertainty and questioning. One way to increase that confidence is to repeat the study. Similar results in a series of randomized studies make it increasingly unlikely that unmeasured confounding is accounting for the findings, as the process of randomization makes the mathematical probability of such confounding progressively smaller as the sample size or number of studies increase. In observational studies, however, increasing the number of studies may reduce the random component of uncertainty, but not necessarily the systematic component attributable to confounding. Without randomization, there is no mathematical basis for assuming that an imbalance of unknown confounders decreases with an increase in the number of studies. However, if observational studies are repeated in different settings with different persons, different eligibility criteria, and/or different exposure opportunities, each of which might eliminate another source of confounding from consideration, the confidence that unmeasured confounders are not producing the findings is increased. How many studies need to be done, how diverse they need to be, and how relevant they are to the question at hand are matters of scientific judgment, and explicit criteria cannot be offered. Confidence that unmeasured confounding is not producing the observed results is further increased by understanding the biologic process by which the exposure might affect the outcome. This understanding allows better identification and measurement of relevant confounders, making it more unlikely that unmeasured factors are of concern. Biologic understanding can also serve as the basis for a judgment that the observed difference in outcome frequency could be produced only by an implausible degree of confounder imbalance between exposed and unexposed groups. Thus, causal conclusions from observational studies typically require more and stronger biologic evidence to support plausibility and to exclude confounding than is needed for causal inferences based on randomized studies.

COMPONENT CAUSE MODEL Causes defined in the manner described above can be viewed as working together in many ways. In 1976 Rothman proposed a useful framework for considering multiple-cause diseases (Rothman, 1976) that has ready extension to the causation of cancer, as most cancers have several causes. Rothman proposed that a disease may have several sufficient causes, each accounting for some proportion of the cases in the population, and that each cause may have several components (Fig. 1–1) (Rothman, 1976). With this model, each component of the three complete causes must be present for disease to develop. For example, sufficient cause I would be incomplete if A were not present; and because A is a component of each of the three complete causes, it is a necessary cause. Rothman’s model is useful for considering causation of malignancy, particularly for most of the cancers for which multiple genetic and environmental risk factors may play a role in a multistage process that transforms a normal cell into a malignant cell. There may be multiple ways to complete this sequence of changes, involving the actions of different complexes of factors, analogous to complete causes I, II, and III in Figure 1–1. The sufficient causes might include multiple envi-

Cause and Cancer Epidemiology Sufficient Cause I

Sufficient Cause II

Sufficient Cause III

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Table 1–1. Henle-Koch Postulates 1. The parasite occurs in every case of the disease in question and under circumstances that can account for the pathologic changes and clinical course of the disease. 2. It occurs in no other disease as a fortuitous and nonpathogenic parasite. 3. After being fully isolated from the body and repeatedly grown in pure culture, it can induce the disease anew. Source: Evans (1993).

Figure 1–1. Conceptual scheme for the causes of a hypothetical disease. (Source: Rothman, 1976.)

ronmental and genetic risk factors, including environmental exposures and genes determining carcinogen metabolism and DNA repair. Individual cases would result from having the full complement of components for a complete cause. We know, for example, that cigarette smoking is a powerful cause of lung cancer, but not all smokers develop lung cancer, implying that this factor may need to act in combination with other factors, perhaps genetic, to complete one sufficient cause for lung cancer. Some sufficient causes for lung cancer do not include smoking, as some percentage of lung cancer cases occur in never-smokers (about 5%–10% of cases in the United States at present) (Alberg and Samet, 2003). Rothman’s model has one significant implication for considering the burden of cancer attributable to a particular risk factor, a calculation often made when assigning priorities to prevention initiatives. The presence of several components in the same complete causes (e.g., A and B in causes I and II) implies that the cases associated with these causes might be prevented by eliminating exposure to either A or B. The burden of disease to be prevented exceeds 100%, as the attributable risk estimates for A and B would include some of the same cases. In some past reviews of the burden of preventable cancer, the assumption was made incorrectly that the attributable risks associated with various causal factors should add up to 100% (Samet and Lerchen, 1984).

CRITERIA FOR CAUSALITY Epidemiologists and other public health researchers have needed pragmatic definitions of causation to support the translation of research evidence into interventions directed at reducing the exposure to causal risk factors (Susser, 1973, 1991; Olsen, 2003). The epidemiologic literature has consequently placed great emphasis on the approach to evaluating evidence to determine if a factor can be considered to cause disease. The approaches that have been developed for evaluating causality of associations also draw on multiple lines of scientific evidence; epidemiologic evidence alone is generally not regarded as sufficient for establishing causality (Last, 2000). Making causal inferences from observational data, in combination with other relevant forms of data, can be a challenging task that requires expert judgment regarding the likely sources and magnitude of confounding, together with judgment about how well the existing constellation of study designs, results, and analyses address this potential threat to inferential validity. This judgment also needs to incorporate a broader assessment of the evidence, evaluating whether a causal effect has support in the existing knowledge of the underlying biologic process. To aid this judgment, criteria for determining a cause have been proposed by many philosophers and scientists over the centuries. In biomedical research, the first criteria came following the discovery of bacteria during the nineteenth century. A method was then needed for judging if an organism caused a particular disease. The first criteria put forward for making this judgment are generally attributed to Robert Koch and his mentor Jacob Henle, although Koch also acknowledged Eugene Klebs. Evans (1993) provided a full accounting of the elaboration of these criteria, now referred to as the Henle-

Koch postulates (Table 1–1). The criteria proved valuable for linking infectious agents to infectious diseases, which often have specific clinical features and unique, specific causal agents (e.g., pulmonary tuberculosis and Mycobacterium tuberculosis). These criteria, however, proved unsuitable for establishing the causes of the epidemics of “chronic disease,” including coronary heart disease, chronic lung disease, and cancer, that became the dominant causes of death spanning the twentieth century, as infectious diseases were controlled. Unlike many infectious diseases, these diseases were often found to be associated with multiple factors, and many cases could not be clearly linked to any risk factors. The limitations of the Henle-Koch postulates were recognized as the results of the first wave of epidemiologic studies on the chronic diseases were reported. In 1959, Yerushalmy and Palmer proposed criteria for evaluating possible etiologic risk factors for chronic diseases that acknowledged the need for evidence of increased risk in exposed persons and for handling the nonspecific causation of these diseases. Lilienfeld (1959) and Sartwell (1960), discussing the article, added the consideration of dose-response, the strength of the association, its consistency, and its biologic plausibility. The most widely cited criteria in epidemiology and public health more generally were set forth by Sir Austin Bradford Hill in 1965 (Table 1–2) (Hill, 1965). Five of the nine criteria he listed were also put forward in the 1964 Surgeon General’s report (US Department of Health Education and Welfare—DHEW, 1964) as the criteria for causal judgment: consistency, strength, specificity, temporality, and coherence of an observed association. Hill also listed biologic gradient (dose-response), plausibility, experiment (or natural experiment), and analogy. Many of these criteria had been cited in earlier epidemiologic writings (Lilienfeld, 1959; Yerushalmy and Palmer, 1959; Sartwell, 1960); and Susser and others have refined them extensively by exploring their justification, merits, and interpretations (Susser, 1973, 1977; Kaufman and Poole, 2000). Hill (1965) clearly stated that these criteria were not intended to serve as a checklist. Here are then nine different viewpoints from all of which we should study association before we cry causation. What I do not believe . . . is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect. None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis, and none can be required as a sine qua non. What they can do, with greater or less strength, is to help us to make up our minds on the fundamental

Table 1–2. Sir Austin Bradford Hill’s Causal Criteria: Aspects of Association to Be Considered Before Deciding on Causation Strength Consistency Specificity Temporality Biologic gradient Plausibility Coherence Experiment Analogy Source: Hill (1965).

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PART I: BASIC CONCEPTS question—is there any other way of explaining the facts before us, is there any other answer equally, or more, likely than cause and effect? (Hill 1965, p. 299)

All of these criteria were meant to be applied to evidence related to an already established statistical association; if no association has been observed, these criteria are not relevant. Hill explained how if a given criterion were satisfied it strengthened a causal claim. Each of these nine criteria served one of two purposes: as evidence against competing noncausal explanations or as positive support for causal ones. Noncausal explanations for associations include chance; residual or unmeasured confounding; model misspecification; selection bias; errors in measurement of exposure, confounders, or outcome; and issues regarding missing data (which can also include missing studies, such as publication bias). The criteria are discussed below.

Consistency The criterion consistency refers to the persistent finding of an association between exposure and outcome in multiple studies of adequate power and carried out by different investigators in studies involving different persons, places, circumstances, and times. Consistency can have two implications for causal inference. First, consistent findings make unmeasured confounding an unlikely alternative explanation to causation for an observed association. Such confounding would have to persist across diverse populations, exposure opportunities, and measurement methods. The confounding is still possible if the exposure of interest were strongly and universally tied to an alternative cause, as was claimed in the form of the “constitutional hypothesis” put forward in the early days of the smoking-disease debate (US Department of Health Education and Welfare—DHEW, 1964). This hypothesis held that there was a constitutional (i.e., genetic) factor that made people more likely to both smoke and develop cancer. Thus, consistency serves mainly to exclude the possibility that the association is produced by an ancillary factor that differs across studies but not one factor that is common to all or most of them (Rothman and Greenland, 1998). The second implication of the consistency criterion is to reduce the possibility of a chance effect by increasing the statistical strength of an association through the accumulation of a large body of data. Consistency does not include the qualitative strength of such studies, which Susser subsumed under his subsidiary concept of “survivability,” relating to the rigor and severity of tests of association (Susser, 1991).

Strength of Association Strength of association includes two dimensions: the magnitude of the association and its statistical strength. An association strong in both aspects makes the alternative explanations of chance and confounding unlikely. The larger the measured effect, the less likely it is that an unmeasured or poorly controlled confounder could account for it completely. Associations that have a small magnitude or weak statistical strength are more likely to reflect chance, a modest degree of bias, or unmeasured weak confounding. However, the magnitude of association is reflective of underlying biologic processes and should be consistent with understanding the role of the risk factor in these processes. Either a strong or a weak effect might be considered plausible based on knowledge of the underlying processes. In the example of active smoking and lung cancer, the relative risks listed in the first Surgeon General’s Report (US Department of Health Education and Welfare—DHEW, 1964) were notably elevated in men, reaching as high as 10 or more. At that time, other causes of lung cancer, including air pollution and occupational agents, had been identified. However, for the general population, the risks from these factors were far lower, making them unsatisfactory as potential confounders, leading to the observed association of active smoking with lung cancer. Passive smoking, by contrast, has a far smaller effect on lung cancer risk. Comparing persons with greater and lesser exposures (e.g., never-

smoking women married to smokers compared with never-smoking women married to never-smokers). The 1986 report of the U.S. Surgeon General (US Department of Health and Human Services— USDHHS, 1986) concluded that passive smoking does cause lung cancer. The magnitude of the effect was small in most of the studies, as anticipated on a biologic basis, but within a plausible range. The relative risk associated with marriage to a smoker has been estimated to be 1.2 in a recent meta-analysis (International Agency for Research on Cancer—IARC, 2002).

Specificity Specificity has been interpreted to mean both a single (or few) effect(s) of one cause or no more than one possible cause for one effect. In addition to specific infectious diseases caused by specific infectious agents, other examples include asbestos exposure and mesothelioma and thalidomide exposure during gestation and the resulting unusual constellation of birth defects. This criterion is rarely used as it was originally proposed, having been derived primarily from the HenleKoch postulates for infectious causes of disease (Susser, 1991). When specificity exists, it can strengthen a causal claim, but its absence does not weaken it (Sartwell, 1960). For example, most cancers are known to have multifactorial etiologies; many cancer-causing agents can cause several types of cancer, and these agents can also have noncancerous effects. When considering specificity in relation to the smoking–lung cancer association, the 1964 Surgeon General’s report (US Department of Health Education and Welfare—DHEW, 1964) provides a rich discussion of this criterion. The committee recognized the linkage between this criterion and strength of association and offered a symmetrical formulation of specificity in the relation between exposure and disease; that is, a particular exposure always results in a particular disease, and the disease always results from the exposure. The committee acknowledged that smoking does not always result in lung cancer and that lung cancer has other causes. The report noted the extremely high relative risk for lung cancer in smokers and the high attributable risk, and it concluded that the association between smoking and lung cancer has “a high degree of specificity.”

Temporality Temporality refers to the occurrence of a cause before its purported effect. Temporality is the sine qua non of causality, as a cause clearly cannot occur after its purported effect. Rothman (1986) emphasized that temporality is the only one of the criteria that must be fulfilled for an association to be considered causal. Any question about a temporal sequence seriously weakens a causal claim; but establishing temporal precedence is by itself not strong evidence in favor of causality.

Coherence, Plausibility, and Analogy Although the original definitions of coherence, plausibility, and analogy were subtly different, in practice they have been treated essentially as one idea: that a proposed causal relation should not violate known scientific principles, and that it be consistent with experimentally demonstrated biologic mechanisms and other relevant data, such as ecologic patterns of disease (Rothman and Greenland, 1998). In addition, if biologic understanding can be used to set aside explanations other than a causal association, it offers further support for causality. Together, these criteria can serve to both support a causal claim (by supporting the proposed mechanism) and refute it (by showing that the proposed mechanism is unlikely). Biologic understanding, of course, is always evolving as scientific advances make possible ever deeper exploration of disease pathogenesis. For example, in 1964 the Surgeon General’s committee found the causal association of smoking with lung cancer to be biologically plausible based on knowledge of the presence of carcinogens in tobacco smoke and animal experiments. Nearly 40 years later, this association remains biologically plausible, but that determination rests not only on the earlier evidence but on more recent findings that address the

Cause and Cancer Epidemiology genetic and molecular basis of carcinogenesis, providing a level of understanding that could not have been anticipated in 1964.

Biologic Gradient (Dose-Response) The finding of a graded increase in effect with an increase in the strength of the possible cause provides strong positive support in favor of a causal hypothesis. This is not just because such an observation is predicted by many cause-and-effect models and biologic processes but, more importantly, because it makes most noncausal explanations highly unlikely. If some factor other than that of interest explains the observed gradient, the unmeasured factor must change in the same manner as the exposure of interest. Except for confounders that are closely related to a causal factor, it is extremely difficult for such a pattern to be created by virtually any of the noncausal explanations for an association listed earlier. The finding of a dose-response relation has long been a mainstay of causal arguments in smoking investigations; virtually all health outcomes causally linked to smoking have shown an increase in risk and/or severity with an increase in the lifetime smoking history. This criterion is not based on any specific shape of the dose-response relation.

Experiment The criterion “experiment” refers to situations where natural conditions might plausibly be thought to imitate conditions of a randomized experiment, producing a “natural experiment” whose results might have the force of a true experiment. An experiment is typically a situation in which a scientist controls who is exposed in a way that does not depend on any of the subject’s characteristics. Sometimes nature produces similar exposure patterns. The reduced risk after smoking cessation serves as one such situation that approximates an experiment; an alternative noncausal explanation might posit that an unmeasured causal factor of that health outcome was more frequent among those who did not stop smoking than among those who did. The causal interpretation is further strengthened if risk continues to decline in former smokers with increasing time since quitting. Similar to the dose-response criteria, observations of risk reduction after quitting smoking have the dual effects of making most noncausal explanations unlikely and supporting the biologic model that underlies the causal claim.

APPLYING THE CAUSAL CRITERIA The greater the extent to which an association fulfills the previous criteria, the more difficult it is to offer a more compelling alternative explanation. Which of these criteria may be more important and whether some can be unfulfilled and still justify the causal claim is a matter of judgment. Temporality, however, cannot be violated. When there is a still incompletely understood pathogenic mechanism, the causal claim might still be justified by strong direct empirical evidence of higher lung cancer rates in smokers (i.e., strong, consistent associations). Moderate associations (e.g., relative risk of 1–2) in only a few studies, without adequate understanding of potential confounders or with weak designs, might result in a suspicion of causal linkage. The process of applying the criteria extends beyond simply lining up the evidence against each criterion, although there is evidence that epidemiologists tend to use the evidence in neither a consistent nor comprehensive manner (Weed and Gorelic, 1996). Rather, the criteria should be used to integrate multiple lines of evidence coming from chemical and toxicologic characterizations of tobacco smoke and its components, epidemiologic approaches, and clinical investigations. Those applying the criteria weigh the totality of the evidence in a decision-making process that synthesizes and, of necessity, involves a multidisciplinary judgment. The 1964 Surgeon General’s report still stands as one of the finest examples of the power of applying these criteria systematically and comprehensively. Starting with the criterion for consistency, the committee noted that all 29 retrospective studies (i.e., case-control) and 7

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prospective studies (i.e., cohort) at the time reported highly significant smoking–lung cancer relations. They further noted that all of the studies comparing smokers to nonsmokers showed high relative risks for lung cancer (~10). Dose-response effects were also observed in every prospective study and in all retrospective studies where it could be calculated. The temporal sequence was reported to be not absolutely certain but seemed highly unlikely to be in the lung cancer–smoking direction, as cancer typically appears many years or decades after the onset of smoking. With regard to coherence of the association with known facts, the studies noted the ecologic increase in lung cancer rates with increased smoking in the population; the gender differential in lung cancer, which at the time was consistent with more smoking by men; an urban-rural difference, which air pollution could not completely explain; socioeconomic differentials in lung cancer, for which smoking seemed to be the strongest explanation; and the localization of cancer in the respiratory tract in relation to the type of smoking. The studies also cited the known reduction in risk among former smokers, with greater risk reductions correlated with more time spent not smoking. These observations, in combination with histopathologic evidence, basic biologic observations, and an in-depth discussion of each competing non-smoking-related explanation (e.g., occupation, constitutional hypothesis, infections, environmental factors such as pollution), produced a case for causation that proved irrefutable. The 1986 Report of the Surgeon General (US Department of Health and Human Services—USDHHS, 1986) concluded that passive smoking causes lung cancer, a conclusion that has proved momentous in its implications. This report also based its evaluation of the evidence on the causal criteria. A clear distinction was made between the evidence on active smoking and that expected from the much lower carcinogen doses arising from passive smoking. Biologic plausibility was emphasized, including the substantial evidence on lung cancer risk in active smokers. This causal conclusion has been reaffirmed in all subsequent reports (Samet and Wang, 2000; International Agency for Research on Cancer—IARC, 2002).

EMERGING ISSUES IN CAUSAL INFERENCE AND CANCER Perhaps the most challenging and exciting issue facing cancer scientists now and in the future is the prospect of understanding the processes of cancer development at the molecular level. However, with a richer understanding of basic mechanisms comes concomitant complexity in the concept and determination of cause. Biomarkers can serve as indicators of exposure, dose, susceptibility, or effect, each of which can elevate the cancer risk, albeit via quite different routes (Links et al., 1995). Similarly, the mechanisms by which various genes affect cancer risk are diverse, from genes that directly modulate tumor growth to others responsible for cellular homeostasis, DNA repair, genetic stability, or a host of interrelated functions that protect the cell against damage from somatic or environmental factors or affect its repair capacity when damage occurs (Vineis and Porta, 1996; Hussain and Harris, 1998). It is interesting to consider the implications of this kind of knowledge for causal inference in cancer. The most obvious change is that we now understand the biologic basis of action of long-established carcinogens, such as smoking, chemotherapy, and various chemical agents. Mechanistic explanations of how environmental exposures have a carcinogenic effect provide the basis for increased confidence that any given association between the exposure and cancer incidence is justifiably labeled causal. Molecular “signatures” of specific exposures (e.g., p53 CpG hotspots) (Greenblatt et al., 1994) are defining the relevant effects of certain exposures more precisely and are also making increasingly possible what could not be done before: establish causal connections between exposure and disease on the individual level. Second, by understanding better what mediates risk due to exposures, we are increasingly able to identify subpopulations of individuals at substantially different degrees of risk from an exposure (Shields

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and Harris, 2000). This risk heterogeneity can be due to genetic or somatic variations that affect the absorption, metabolism, or cellular effect of an agent or the host’s susceptibility to those effects (Links et al., 1995). The smaller size of these subpopulations, however, poses greater challenges to epidemiologic approaches to risk assessment: the finer the risk stratification based on mechanistic criteria, the more difficult it is to confirm it with epidemiologic methods (Slattery, 2002; Moore et al., 2003). Third, with the opening of the cancer “black box” comes greater recognition of the extraordinary complexity of the carcinogenic process at the molecular level. This is seen not only with external agents dependent on genes for their effects but with environmental modification of gene expression (e.g., through DNA methylation) (Moore et al., 2003), gene functions interacting in myriad ways, and perhaps most intriguingly genetic determinants of exposure, as shown with developing understanding of the biologic bases of nicotine, alcohol, and drug addiction (Shields et al., 1993; Greenblatt et al., 1994; Hemby, 1999). This complexity poses significant problems for population-based risk assessments and causal inference because it raises, more acutely than with conventional risk factors, questions about which genes lie in the causal pathway (those that do should not be considered covariates), which genes or biomarkers are necessary for the effects or function of others (i.e., there may be many biologically based high level interaction terms), whether an observed association represents a direct or indirect causal effect, and whether it is meaningful to talk about binary exposure–outcome or gene–outcome relations when the components of these complex networks either cannot be changed individually or changed at all (Taioli and Garte, 2002). Fourth, although the functions of many genes and gene products are understood, many more are not. The current ability to perform mass screening of potential causal factors via high throughput, genomic and proteomic technologies is far outstripping our ability to explain what we find; and severe problems related to multiplicity (“data dredging”) arise in many of these investigations (Reiner et al., 2003). Epidemiologists have long faced this problem but never on this scale, with literally hundreds or even thousands of potential markers measured and analyzed, in all combinations, sometimes in only tens or hundreds of subjects. The weakness of findings subject to these problems is not often fully appreciated, and researchers are making claims about possibilities of screening, treatment, or prevention before findings are replicated in independent data sets. A final issue that is often neglected in this rush toward exploring new potential causes is that of measurement (Little et al., 2002). Many of the techniques and assays used to identify metabolic products, genes, and gene products are relatively new and not standardized. Understanding the reliability and validity of these techniques is an arduous process that often does not get sufficient attention, yet it is critical for distinguishing likely spurious claims from those that are well grounded. As we come closer to understanding cancer on the molecular level, many have raised the possibility of individual risk prediction (Vineis, 1997; Hussain et al., 2001). Some commentators have suggested that mechanistic understanding may ultimately render population studies irrelevant. However, it is instructive to consider the case of infectious diseases. Understanding the basic mechanisms of infectious disease has not eliminated the need to study disease patterns on a population level, and the same will likely be true for cancer (Nevins et al., 2003). There are many reasons to believe that population-based studies will be as important in the future as they are now, albeit perhaps focused on different kinds of questions. To understand a mechanism after a cancer occurs is not to predict it; early steps in the process, when the disease can be prevented, will necessarily be less than 100% predictive; and defining optimal groups for screening and early intervention will still require population-based data. Risk groups must be defined using far fewer factors than we know are operating at the molecular level, the latter being almost unique for an individual. The molecular revolution in cancer may ultimately force a merging of two “schools” of causal inference: the probabilistic, chronic disease model that the Hill criteria addressed and the more mechanistic, deter-

ministic models used for infectious disease, for which the Henle-Koch criteria were devised (Fredericks and Relman, 1996). Nowhere is this seen more clearly than with viral carcinogenesis. The Henle-Koch criteria were based on the nineteenth century understanding of bacterial disease causation and are poorly suited for viral disease mechanisms or even for infectious disease outcomes. Efforts to refashion the HenleKoch criteria for the new era of molecular medicine (Fredericks and Relman, 1996; Vineis and Porta, 1996) have shown that it is extraordinarily difficult to outline a set of experimental conditions that all known pathogens—not to mention new pathogens with different mechanisms—must satisfy to justify causal claims for new infectious diseases. In the case of viral carcinogenesis, the situation is even more complex because the final disease is not a direct manifestation of an infectious process. Numerous viral agents have been linked to cancer with varying degrees of certainty: Epstein-Barr virus and Burkitt’s lymphoma (Pagano, 1999), human papillomavirus and cervical cancer (Bosch and de Sanjose, 2003), SV40 and mesothelioma (Carbone et al., 1997; Klein et al., 2002). The evidential basis of these claims includes findings that might implicate the virus in individual cancer cases (e.g., finding viral genetic sequences or viral-specific proteins in tumor tissue) and traditional epidemiologic evidence (e.g., high incidence in persons with evidence of viral exposure). However, as both these and other examples have shown, there is no single molecular finding that definitively implicates a virus as a cause of cancer. The variety and complexity of mechanisms by which viruses can directly (by inducing oncogenic changes) or indirectly (by increasing host susceptibility to exogenous agents) raise cancer risk seems to defy a set of causal criteria designed specifically for viral agents or that are based on any specific mechanism. Therefore, from the standpoint of causal inference, it may be best to consider viral agents under the same umbrella as toxic exposures and other environmental causes of cancer. The fact that the pathways from viral infection to cancer appearance often share components (e.g., p53 inactivation) with those of noninfectious carcinogenic exposures supports this view. It seems unlikely that the need for population-based risk estimates and causal inference will disappear from cancer research, just as it has not in infectious disease. However, we are entering an era where the relative strength of the “twin pillars” of causal inference—knowledge derived from empirical, population-based patterns and that based on understanding of biologic mechanisms in individuals—will tilt farther toward the mechanistic end, requiring less proof from populations and more from the laboratory. One challenge for causal inference in the future will be how best to integrate these various forms of evidence and how to assemble groups with the sufficient interdisciplinary expertise to assess them. References Alberg AJ, Samet JM. 2003. Epidemiology of lung cancer. Chest 123:21S–49S. Bosch FX, de Sanjose S. 2003. Chapter 1: Human papillomavirus and cervical cancer—burden and assessment of causality. J Natl Cancer Inst Monogr 31:3–13. Bunge M. 1959. Causality: The Place of the Causal Principle in Modern Science. Cambridge, MA: Harvard University Press. Carbone M, Rizzo P, Pass HI. 1997. Simian virus 40, poliovaccines and human tumors: a review of recent developments. Oncogene 15:1877–1888. Evans AS. 1993. Causation and Disease: A Chronological Journey. New York: Plenum. Fredericks DN, Relman DA. 1996. Sequence-based identification of microbial pathogens: a reconsideration of Koch’s postulates. Clin Microbiol Rev 9:18–33. Greenblatt MS, Bennett WP, Hollstein M, Harris CC. 1994. Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Cancer Res 54:4855–4878. Greenland S. 1990. Randomization, statistics, and causal inference. Epidemiology 1:421–429. Greenland S, Robins JM, Pearl J. 1999. Confounding and collapsibility in causal inference. Stat Sci 14:29–46. Hemby SE. 1999. Recent advances in the biology of addiction. Curr Psychiatry Rep 1:159–165. Hill AB. 1965. The environment and disease: association or causation? Proc R Soc Med 58:295–300.

Cause and Cancer Epidemiology Hume D. 1739. A Treatise of Human Nature. London: Oxford University Press. Hussain SP, Harris CC. 1998. Molecular epidemiology of human cancer: contribution of mutation spectra studies of tumor suppressor genes. Cancer Res 58:4023–4037. Hussain SP, Hofseth LJ, Harris CC. 2001. Tumor suppressor genes: at the crossroads of molecular carcinogenesis, molecular epidemiology and human risk assessment. Lung Cancer 34(Suppl 2):S7–S15. International Agency for Research on Cancer (IARC). 2002. Tobacco smoke and involuntary smoking. IARC Monograph 83. Lyon: IARC. Kaufman JS, Poole C. 2000. Looking back: causal thinking in the health sciences. Annu Rev Public Health 21:101–119. Klein G, Powers A, Croce C. 2002. Association of SV40 with human tumors. Oncogene 21:1141–1149. Last JM. 2000. A Dictionary of Epidemiology. New York: Oxford University Press. Lewis D. 1973. Counterfactuals. Cambridge, MA: Harvard University Press. Lilienfeld AM. 1959. On the methodology of investigations of etiologic factors in chronic diseases: some comments. J Chronic Dis 10:41–46. Links JM, Kensler TW, Groopman JD. 1995. Biomarkers and mechanistic approaches in environmental epidemiology. Annu Rev Public Health 16:83–103. Little J, Bradley L, Bray MS, Clyne M, Dorman J, Ellsworth DL, Hanson J, Khoury M, Lau J, O’Brien TR, Rothman N, Stroup D, Taioli E, Thomas D, Vainio H, Wacholder S, Weinberg C. 2002. Reporting, appraising, and integrating data on genotype prevalence and gene-disease associations. Am J Epidemiol 156:300–310. Magee B. 2001. The Great Philosophers: An Introduction to Western Philosophy. Oxford, UK: Oxford University Press. Moore LE, Huang WY, Chung J, Hayes RB. 2003. Epidemiologic considerations to assess altered DNA methylation from environmental exposures in cancer. Ann NY Acad Sci 983:181–196. Nevins JR, Huang ES, Dressman H, Pittman J, Huang AT, West M. 2003. Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Hum Mol Genet 12(Spec. No. 2):R153–R157. Neyman J. 1990. On the application of probability theory to agricultural experiments: essay on principles (1923). Stat Sci 5:463–572. Olsen J. 2003. What characterises a useful concept of causation in epidemiology? J Epidemiol Community Health 57:86–88. Pagano JS. 1999. Epstein-Barr virus: the first human tumor virus and its role in cancer. Proc Assoc Am Physicians 111:573–580. Pearl J. 2000. Causality: Models, Reasoning and Inference. Cambridge, UK: Cambridge University Press. Reiner A, Yekutieli D, Benjamini Y. 2003. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368–375. Robins J. 1986. A new approach to causal inference in mortality studies with sustained exposure periods: applications to control of the healthy worker survivor effect. Mathematical Modelling 7:1393–1512. Robins J. 1987. A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. J Chronic Dis 40:139S–161S.

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Rosen G. 1993. A History of Public Health. Baltimore, MD: The Johns Hopkins University Press. Rothman KJ. 1976. Causes. Am J Epidemiol 104:587–592. Rothman KJ. 1986. Interactions Between Causes. Modern Epidemiology. Boston: Little, Brown. Rothman KJ, Greenland S. 1998. Modern Epidemiology. Philadelphia: Lippincott-Raven. Rubin D. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66:688–701. Samet JM, Lerchen ML. 1984. Proportion of lung cancer caused by occupation: a critical review. In: Bernard J, Gee L, Keith W, Morgan C, Brooks SM, editors. Occupational Lung Disease. New York: Raven Press, pp. 55–67. Samet JM, Wang SS. 2000. Environmental tobacco smoke. In: Lippmann M, editor. Environmental Toxicants: Human Exposures and Their Health Effects. New York: Van Nostrand Reinhold, pp. 319–375. Sartwell PE. 1960. On the methodology of investigations of etiologic factors in chronic diseases: further comments. J Chronic Dis 11:61–63. Shields PG, Harris CC. 2000. Cancer risk and low-penetrance susceptibility genes in gene-environment interactions. J Clin Oncol 18:2309–2315. Shields PG, Caporaso NE, Falk RT, Sugimura H, Trivers GE, Trump BF, Hoover RN, Weston A, Harris CC. 1993. Lung cancer, race, and a CYP1A1 genetic polymorphism. Cancer Epidemiol Biomarkers Prev 2:481–485. Slattery ML. 2002. The science and art of molecular epidemiology. J Epidemiol Community Health 56:728–729. Susser M. 1973. Causal Thinking in the Health Sciences: Concepts and Strategies in Epidemiology. New York: Oxford University Press. Susser M. 1977. Judgement and causal inference: criteria in epidemiologic studies. Am J Epidemiol 105:1–15. Susser M. 1991. What is a cause and how do we know one? A grammar for pragmatic epidemiology. Am J Epidemiol 133:635–648. Taioli E, Garte S. 2002. Covariates and confounding in epidemiologic studies using metabolic gene polymorphisms. Int J Cancer 100:97–100. US Department of Health and Human Services (USDHHS). 1986. The Health Consequences of Involuntary Smoking: A Report of the Surgeon General. DHHS Publ. No. (CDC) 87-8398. Washington, DC: U.S. Government Printing Office. US Department of Health Education and Welfare (DHEW). 1964. Smoking and Health. Report of the Advisory Committee to the Surgeon General. DHEW Publ. No. (PHS) 1103. Washington, DC: U.S. Government Printing Office. Vineis P. 1997. Sources of variation in biomarkers. IARC Sci Publ 142: 59–71. Vineis P, Porta M. 1996. Causal thinking, biomarkers, and mechanisms of carcinogenesis. J Clin Epidemiol 490:951–956. Weed DL, Gorelic LS. 1996. The practice of causal inference in cancer epidemiology. Cancer Epidemiol Biomarkers Prev 5:303–311. White C. 1990. Research on smoking and lung cancer: a landmark in the history of chronic disease epidemiology. Yale J Biol Med 63:29–46. Yerushalmy J, Palmer C. 1959. On the methodology of investigations of etiologic factors in chronic diseases. J Chronic Dis 10:27–40.

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Morphologic and Molecular Classification of Human Cancer THOMAS J. GIORDANO

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umor morphology, assessed by light microscopic examination of stained tissue sections (the essence of surgical pathology practice), has been the foundation for the pathologic assessment of human cancer for more than 100 years. The durability of morphology and surgical pathology in medicine can be attributed to many factors, including its efficiency and reproducibility. However, the predominant reason for its persistence as a worthwhile diagnostic tool is its significant predictive power and its ability to dictate therapy. No other single medical test can provide as much clinically useful information as a well documented surgical pathology examination of a resected neoplasm. Using morphology alone, a tumor’s grade, stage (primary and nodal status), type of differentiation, and other informative morphologic features, can be determined with accuracy, efficiency, and reproducibility. Using this information, oncologists and other cancer therapists can design and implement therapy and predict a patient’s outcome with reasonable accuracy. Despite the power of morphologic assessment for diagnosis and prognosis, there is still a need to provide additional and better predictive information about a particular patient’s disease. For example, the standard of care for patients with stage 1 adenocarcinoma of the lung is surgery followed by careful follow-up. These patients generally do well, with most of them alive and free of disease 5 years following surgery. Although the morphology can be helpful for identifying the patients who will do poorly, it is not adequate. Similar arguments can be made for some patients with node-negative breast carcinoma. Thus, there is considerable enthusiasm about new molecular approaches that might be able to stratify these patients into groups with varying risks of poor outcome, thereby affording the opportunity to treat them differently. Furthermore, it is hoped that molecular approaches will lead to the discovery of new subclasses of tumors not appreciable by morphology. In fact, early work on breast cancer and lymphoma suggests that this is indeed the case (see Breast Carcinoma; Hematologic Malignancies). As molecularly targeted therapeutics become increasingly available, it will be important to evaluate specific therapeutic targets in tumors to determine the appropriateness of a particular therapy for a given patient. The most illustrative paradigm is breast cancer, for which every case of invasive carcinoma is evaluated for expression of estrogen receptors (ERs) and progesterone receptors (PRs) and overexpression of the erbB2/Her2/neu gene. This information is used to classify patients further and guide decisions regarding antiestrogenic (tamoxifen) and anti-erbB2 therapies (trastuzumab). In this instance, morphology is augmented by information regarding expression of specific target genes to select the most appropriate therapy. Determining the sequence of the human genome, together with the development of powerful new molecular biology techniques that permit comprehensive and parallel assessment of gene expression, has sparked a genomic revolution in the assessment of human cancer. Using this gene expression approach, it is anticipated that a new cancer classification based on gene expression will be developed that, together with traditional morphology, will lead to a more informative classification of human cancer. In this chapter, an overview of the morphologic assessment of cancer along with the progress made to date for select tumor types in the molecular classification revolution are presented. Detailed description of all the types of tumor and their morphology is clearly beyond the scope of this chapter. Rather, the goal here is to present a selected

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overview of tumor morphology that also provides a foundation for the discussion of molecular classification approaches. Readers seeking additional information are directed to the many volumes dedicated to the morphologic description of tumors, such as the Atlas of Tumor Pathology published by the Armed Forces Institute of Pathology and the WHO Classification of Tumours series published by the World Health Organization.

HISTORICAL PERSPECTIVE The historical aspects of morphology and human cancer classification are rich and fascinating yet clearly beyond the scope of this chapter. Interested readers are referred to the writings of Juan Rosai (Rosai, 1997) and others (Gal, 2001; Acs et al., 2002).

MORPHOLOGIC CLASSIFICATION OF CANCER The morphologic approach to cancer assessment is based on the pathologist’s ability to analyze myriad histologic patterns and synthesize the information into a rational classification scheme. The neural network that is the human brain is amazingly well suited for this task of pattern recognition. The art and science of surgical pathology is the recognition of patterns common to tumors and the assembly of classification schemes based on those patterns. In the ideal setting, a classification scheme would group patients with similar diseases and thereby provide the basis for a rational approach to therapy. For example, the classification of lung cancer into two large groups, smallcell carcinoma and non-small-cell carcinoma, with four main subtypes—adenocarcinoma, squamous cell carcinoma, small-cell carcinoma, large-cell carcinoma—resulted from the histologic examination of many lung tumors and recognition of these common morphologic patterns. This classification scheme is clinically informative regarding the clinical course and response to therapy and is largely the determining factor in the choice of therapy. Applying this approach to all cancers, surgical pathologists over the years have assembled a comprehensive classification of human cancer. An abbreviated classification of human cancer is presented in Table 2–1.

OVERVIEW OF CURRENT NOMENCLATURE OF HUMAN CANCER Consistent and informative cancer nomenclature is vital to effective patient care and all types of cancer research. Thus, much effort over the years has been invested in developing systemized cancer nomenclatures, resulting in two that are commonly used. The Systematized Nomenclature of Human and Veterinary Medicine (SNOMED) (College of American Pathologists, 1993), also called SNOMED International, traces its beginnings to back the publication of the Systematized Nomenclature of Pathology (SNOP) (College of American Pathologists, 1965). SNOP was expanded into the Systematized Nomenclature of Medicine (SNOMED) (College of American Pathologists, 1979). SNOMED assigns terms to one of eleven independent systematized modules, including those for site of tumor origin (topography) and histologic type (morphology). Within each of the 11

Table 2–1. Simplified Organ-Based Classification of Primary Human Cancers 1. Skin A. Epidermis a. Basal cell carcinoma b. Squamous cell carcinoma B. Adnexae a. Malignant versions of numerous tumors with eccrine, apocrine and sebaceous differentiation, along with tumors of the hair follicle 2. Oral cavity and oropharynx A. Squamous cell carcinoma B. Malignant tumors of minor salivary glands 3. Mandible and maxilla A. Odontogenic tumors a. Ameloblastoma and ameloblastic carcinoma 4. Nasopharynx and sinuses A. Squamous cell carcinoma B. Nasopharyngeal carcinoma C. Olfactory neuroblastoma 5. Lung A. Squamous cell carcinoma B. Adenocarcinoma C. Large cell carcinoma D. Small cell carcinoma E. Carcinoid 6. Pleura A. Mesothelioma 7. Mediastinum A. Thymoma and thymic carcinoma B. Neuroendocrine neoplasms C. Malignant lymphoma D. Neurogenic tumors 8. Thyroid A. Papillary carcinoma B. Follicular carcinoma C. Hurthle cell carcinoma D. Poorly differentiated carcinoma E. Anaplastic (undifferentiated) carcinoma F. Medullary carcinoma 9. Parathyroid A. Parathyroid carcinoma 10. Esophagus A. Adenocarcinoma B. Squamous cell carcinoma 11. Stomach A. Adenocarcinoma a. Intestinal type b. Diffuse type B. Neuroendocrine tumors C. Stromal tumors D. Malignant lymphoma 12. Small intestine A. Adenocarcinoma B. Neuroendocrine tumors 13. Appendix A. Adenocarcinoma B. Carcinoid 14. Large intestine A. Adenocarcinoma B. Carcinoid and other neuroendocrine tumors 15. Anus A. Malignant melanoma B. Adenocarcinoma C. Squamous cell carcinoma 16. Liver A. Hepatocellular carcinoma B. Cholangiocarcinoma C. Angiosarcoma 17. Gallbladder A. Adenocarcinoma 18. Pancreas A. Ductal adenocarcinomas B. Anaplastic carcinoma C. Endocrine tumors

19. Salivary glands A. Mucoepidermoid carcinoma B. Acinic cell carcinoma C. Adenoid cystic carcinoma D. Ductal carcinoma E. Malignant lymphoma 20. Adrenal gland A. Adrenocortical carcinoma B. Pheochromocytoma C. Neuroblastoma 21. Kidney A. Renal cell carcinoma and related tumors B. Wilms’ tumor 22. Bladder and renal pelvis A. Urothelial carcinoma B. Neuroendocrine carcinoma 23. Prostate A. Adenocarcinoma 24. Testis A. Germ cell tumors a. Seminoma b. Mature and immature teratoma c. Yolk sac tumor d. Choriocarcinoma e. Teratocarcinoma 25. Sex cord-stromal tumors A. Leydig cell tumor 26. Penis A. Squamous cell carcinoma 27. Vulva A. Squamous cell carcinoma B. Extramammary Paget’s disease C. Malignant melanoma 28. Vagina A. Squamous cell carcinoma B. Adenocarcinoma C. Botryoid rhabdomyosarcoma 29. Cervix A. Squamous cell carcinoma B. Neuroendocrine carcinoma C. Adenocarcinoma 30. Uterus A. Endometrial adenocarcinoma B. Malignant mixed müllerian tumors C. Leiomyosarcoma 31. Fallopian tube A. Adenocarcinoma 32. Ovary A. Surface epithelial tumors a. Serous adenocarcinoma b. Endometrioid adenocarcinoma c. Mucinous adenocarcinoma d. Clear cell carcinoma B. Germ cell tumors a. Dysgerminoma b. Yolk sac tumor c. Choriocarcinoma d. Mature and immature teratoma C. Sex cord-stromal tumors a. Granulosa cell tumor b. Sertoli-Leydig cell tumor 33. Placenta A. Hydatidiform mole B. Placental site trophoblastic tumor C. Choriocarcinoma 34. Mammary gland A. In situ carcinoma a. Ductal carcinoma in situ b. Lobular carcinoma in situ B. Invasive carcinoma a. Invasive ductal carcinoma b. Invasive lobular carcinoma c. Tubular carcinoma d. Mucinous carcinoma e. Medullary carcinoma f. Metaplastic carcinoma

(continued)

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Table 2–1. (cont.) C. Stromal sarcoma (phylloides tumors) D. Angiosarcoma 35. Lymph nodes A. Hodgkin’s lymphoma a. Classic Hodgkin’s lymphoma b. Nodular lymphocyte-predominant Hodgkin’s lymphoma c. Nodular sclerosis Hodgkin’s lymphoma d. Mixed cellularity Hodgkin’s lymphoma e. Lymphocyte depletion Hodgkin’s lymphoma B. Non-Hodgkin’s lymphoma a. Precursor B-cell leukemia/lymphoma b. Mature (peripheral) B-cell leukemia/lymphoma c. Precursor T-cell leukemia/lymphoma d. Mature (peripheral) T-cell leukemia/lymphoma 36. Spleen A. Malignant lymphoma B. Leukemia C. Systemic mastocytosis 37. Bone marrow A. Leukemia a. Chronic myeloid leukemia b. Chronic lymphoid leukemia c. Acute lymphoblastic leukemia d. Acute myeloid leukemia B. Malignant lymphoma C. Histiocytosis D. Plasma cell neoplasms 38. Bone A. Osteosarcoma B. Aggressive and malignant osteoblastoma C. Chondrosarcoma D. Ewing’s sarcoma E. Other sarcomas 39. Soft tissue cancers A. Fibrosarcoma B. Malignant fibrous histiocytoma C. Malignant schwannoma D. Liposarcoma E. Angiosarcoma F. Leiomyosarcoma G. Rhabdomyosarcoma H. Clear cell sarcoma I. Epithelioid sarcoma J. Alveolar soft part sarcoma 40. Heart A. Sarcoma a. Angiosarcoma b. Rhabdomyosarcoma 41. Pericardium and peritoneum A. Malignant mesothelioma 42. Central nervous system A. Glial tumors a. Astrocytoma and glioblastoma multiforme b. Oligodendroglioma c. Malignant ependymoma B. Primitive neuroepithelial tumors a. Medulloblastoma C. Meningothelial tumors a. Malignant meningioma D. Lymphoid tumors a. Malignant lymphoma 43. Pituitary gland A. Invasive adenoma and pituitary carcinoma

modules, terms are placed into hierarchies, and a five- or six-digit alphanumeric code is designated. The latest version of SNOMED incorporates nomenclature from the other commonly used cancer nomenclature, the International Classification of Diseases for Oncology (ICD-O). The ICD-O traces its origins back to the formation of the World Health Organization (WHO), when after World War II it assumed responsibility for coding diseases. The original classifications

of neoplasms were based entirely on topographic site and behavior (benign or malignant). Tumor morphology was first incorporated with publication in 1951 of the Manual of Tumor Nomenclature and Coding (MOTNAC) (American Cancer Society, 1968). The ICD-O was initially published in 1976 (WHO, 1976) and then revised in 1990 (WHO, 1990). ICD-O is a dual classification and coding scheme that incorporates information regarding tumor topography and morphology. The third and latest edition of ICD-O (WHO, 2000) is similar to the second edition, with revision of the classification of leukemias and lymphomas to reflect the WHO classification of these diseases.

MOLECULAR METHODS FOR TUMOR CLASSIFICATION Molecular stratification of tumors for classification purposes has recently developed into a burgeoning field. Yet this approach has been used for more than two decades in hematopathology, starting with the realization that many of the hematolymphoid malignancies contained specific cytogenetic abnormalities that were characteristic for a particular disease. Recent excitement about gene expression profiling (Chung et al., 2002) can be directly attributed to technologic advances (e.g., serial analysis of gene expression, or SAGE, and DNA microarrays) combined with novel computational approaches to biology. Certainly in this postgenome era, the sequencing of the human genome and the consequent identification of most of the genes greatly amplifies the power of the comparative gene expression approach provided by these new technologies. A short overview of them is presented.

Cytogenetic Techniques Traditional cytogenetics employs chromosomal banding techniques of chromosomes (so-called G banding for the Giemsa stain, which is commonly used) to identify gross abnormalities such as deletions, inversions, isochromosomes, and translocations. The sensitivity for detecting relatively small abnormalities such as small deletions is limited. However, this technique is well suited for uncovering and mapping balanced reciprocal translocations (Sandberg, 1991; Sozzi et al., 1999), such as those present in sarcomas (Sreekantaiah et al., 1994) and hematolymphoid malignancies (Kaneko, 1990; Ambinder and Griffin, 1991; Clare and Hansen, 1994; Rowley, 1999). Fine mapping of the breakpoints in these translocated chromosomes has led to a greater understanding of the neoplastic pathogenesis for many tumor types by leading to the discovery of several oncogenes, such as c-myc.

Fluorescence In Situ Hybridization Fluorescence in situ hybridization (FISH) and the more recently developed chromogenic in situ hybridization (CISH) are molecular cytogenetic techniques that permit identification of specific nucleic acid sequences in intact cells in either metaphase or interphase (Muhlmann, 2002). Hybridization of labeled nucleic acid probes results in a detectable fluorescent signal, which can be quantitated along with a distinctly colored chromosome-specific control probe. FISH offers several advantages over traditional cytogenetics. First and importantly, it works on cells in interphase as well as metaphase. Second, the results are visually striking and easy to comprehend. Third, unlike traditional cytogenetics, which requires fresh tissue to culture and arresting the cells in metaphase, FISH is fully compatible with archival paraffin-embedded tissue, thus permitting significant retrospective analyses. Finally, FISH is flexible and adaptable to other techniques, expanding its usefulness. One of the disadvantages of the FISH technique is its lack of a discovery component, as the probe sequence must be known in advance, in contrast to comparative genomic hybridization (see below). Using FISH, cytogenetic abnormalities have been discovered and defined for most hematologic malignancies (Martin-Subero et al., 2003) and many solid tumors (Poetsch et al., 2000). However, given the extreme aneuploidy present in some solid tumors, most

Morphologic and Molecular Classification of Human Cancer FISH-detectable abnormalities have not yet been clinically adapted and do not yet represent the means for useful classification. Two exceptions are FISH analysis for erbB2/Her2/neu amplification in breast cancer (Kallioniemi et al., 1992) and for loss of 1p and 19q in oligodendrogliomas (Gelpi et al., 2003) (see Breast Carcinoma; Gliomas). Despite this limited clinical acceptance to date, much effort is being expended to develop novel FISH-based diagnostic tests, including assays for detecting recurrent bladder carcinoma using cells in urine.

Comparative Genomic Hybridization Comparative genomic hybridization (CGH), like cDNA microarrays (see below), involves competitive hybridization of differentially labeled DNAs (test and reference samples) to normal metaphase chromosomes to measure chromosome imbalances across the entire genome (Kallioniemi et al., 1993). Thus, CGH is useful for identifying tumor-specific chromosomal gains and losses (regions of chromosomal amplifications or deletions). By analyzing numerous related tumors in parallel, it is possible to identify regions of gain or loss common to the set of tumors being studied, thereby identifying alterations that are likely related to tumorigenesis. Comparative genomic hybridization, although not a particularly high-throughput technology, has been used successfully to classify human cancers, including sarcomas (Chibon et al., 2003), lung carcinoma (Aliferis et al., 2002), and breast carcinoma (Wessels et al., 2002). Yet CGH is not likely to become a clinically accepted diagnostic test.

DNA Microarrays DNA microarray technology has become one of the most robust methods for the parallel measurement of expression of numerous genes, presenting an alternative approach to existing techniques, such as differential display (Liang, 2002) and SAGE (see below). DNA microarrays exist in two fundamental types, each with advantages and disadvantages (Ramsay, 1998; Cheung et al., 1999; Khan et al., 1999; Gershon, 2002). cDNA microarrays (also called spotted microarrays because DNA is spotted onto glass slides) contain thousands of cDNA clones precisely arrayed on a slide. RNAs from two related tissues or cell lines (experimental and reference) are competitively hybridized to the spotted DNAs. Labeling the RNAs with different fluorescent molecules allows measurement of the relative amounts of a specific RNA in the experimental sample compared to the reference; the results are expressed as a ratio. Unlike spotted cDNA microarrays, oligonucleotide microarrays are synthesized in situ by means of photolithography; the technique utilizes inherent probe redundancy to measure RNA transcript levels. Affymetrix (Santa Clara, CA) manufactures one of the most widely used oligonucleotide microarrays (GeneChips), although chips from other manufacturers are gaining in use. With GeneChips (Affymetrix), each probe set for a given gene consists of a series of short oligonucleotides that are complementary to that gene. Both perfectly matched (PM) oligonucleotides and mismatched oligonucleotides (MM), which differ by a central base, are incorporated into the microarrays. The intensity signal derived from the MM is subtracted from the PM intensity signal, and the net signal from the entire series of probes for an individual gene is an indication of its RNA level. The presence of hundreds of thousands of oligonucleotides on a single chip allows the parallel assessment of transcripts levels for more than 22,000 probe sets. Advantages of cDNA microarrays include a lower cost and their flexibility of design; their disadvantages include the need for a reference RNA and the quality control aspects of producing spotted microarrays. Advantages of oligonucleotide microarrays include the absence of a required reference RNA (which permits more robust cross-experiment and cross-laboratory comparisons), commercial standards and quality assurance for chip manufacturing, and the independence from establishing and maintaining a spotted microarray facility. The major disadvantage of oligonucleotide microarrays is their high cost. For thorough discussions of cDNA and oligonucleotide

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microarrays, see several recent reviews (Holloway et al., 2002; Yang and Speed, 2002; Stears et al., 2003).

Serial Analysis of Gene Expression Serial analysis of gene expression (SAGE) permits quantitative and simultaneous assessment of expressed genes by sequencing small specific sequence tags (Velculescu et al., 1995). The relative abundance of a specific tag reflects the relative expression of the gene that corresponds to the tag. SAGE offers some benefits over DNA microarrays, such as not having to know the genes to be placed on a microarray and the ability to detect and measure low abundance transcripts. Using SAGE, expression profiles of many types of cancer have been elucidated (e.g., Zhang et al., 1997; Oue et al., 2004; Weeraratna et al., 2004), and these profiles have been used to identify novel diagnostic markers and to develop an expression-based classification map (Buckhaults et al., 2003). SAGE data have been effectively combined with DNA microarray data to focus the list of differentially expressed genes by identifying genes common to both techniques in pancreas cancer (Iacobuzio-Donahue and Hruban, 2003).

Single Marker Approaches to Molecular Classification Immunohistochemical assessment of protein expression in tissue sections represents the most elementary form of “molecular” testing for stratifying cancers. Although immunohistochemistry is generally not viewed as a molecular test, the immunologic detection of specific protein molecules in tissue sections does fit its broad definition. The best example and one of the most clinically accepted tests is the immunohistochemical assessment of ER expression in invasive breast carcinoma (Fig. 2–1a). With this single immunohistochemical assay, invasive carcinomas can be roughly divided into two broad categories (ER-positive and ER-negative) with profound clinical significance (Osborne et al., 1981; Berger et al., 1991). Studies of the erbB2/Her2/neu gene in invasive breast cancer have shown that assessment of this gene, for genomic amplification or protein expression (Fig. 2–1b), is significantly associated with the prognosis as well as the response to anti-Her2 therapy. This correlation was first observed during the late 1980s (McGuire, 1987; Slamon et al., 1987; Berger et al., 1988; Guerin et al., 1988; Tandon et al., 1989; Wright et al., 1989), and since then numerous studies have examined this relation from a variety of angles including various assay methods (DiLeo et al., 2002; Ross et al., 2003). Regardless of the method used, it is clear that erbB2/Her2/neu is informative for breast and probably ovarian carcinoma. Lobular and ductal types of in situ breast carcinoma are usually distinguished by morphology with little difficulty (see Breast, below). However, a small number of in situ lesions share morphologic features of both types. E-cadherin expression as assessed by immunohistochemistry, however, can assist with this distinction in a high percentage of cases, as loss of E-cadherin expression is an early event in lobular carcinoma (Moll et al., 1993; Vos et al., 1997; Jacobs et al., 2001; Wahed et al., 2002). The type-specific translocations observed in soft tissue sarcomas and many leukemias and lymphomas are prime examples of a single molecular abnormality functioning as the defining feature of a specific tumor type. Although translocations are rare in epithelial tumors, thyroid tumors of follicular cell origin do display some specific chromosomal rearrangements (Tallini, 2002). A high percentage of papillary thyroid carcinomas display rearrangements of the RET proto-oncogene (Tallini and Asa, 2001; Nikiforov, 2002), whereas some follicular carcinomas contain rearrangements of the PAX8peroxisome proliferator-activated receptor gamma genes (Kroll et al., 2000; Marques et al., 2002; Nikiforova et al., 2002; Cheung et al., 2003; Dwight et al., 2003). These rearrangements are thought to be crucial to the development of the neoplastic state (Kim et al., 2003; Puxeddu et al., 2003).

A

B

C

D

E

F

G

H

Figure 2–1. A, Invasive breast ductal carcinoma with strong nuclear immunoreactivity for estrogen receptors, seen by immunohistochemistry using formalin-fixed, paraffin-embedded tissue sections. B, Invasive breast ductal carcinoma with strong membranous immunoreactivity for cerbB2/Her2/neu, as seen by immunohistochemistry using formalin-fixed, paraffin-embedded tissue sections. C, Ordinary invasive ductal carcinoma of breast showing occasional duct or tubule formation. D, Invasive lobular

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carcinoma of breast with classic growth pattern. E, Invasive colorectal adenocarcinoma with stroma and necrosis. F, Squamous cell carcinoma of lung with focus of clear-cut squamous differentiation. G, Biphasic synovial sarcoma with spindle cell and epithelioid components. H, High-grade leiomyosarcoma with fascicles of spindle cells running in different directions. C–H: H&E.

Morphologic and Molecular Classification of Human Cancer

Single Marker versus Multimarker Approaches to Molecular Classification With the arrival of technologies for the serial assessment of expression of numerous genes (e.g., SAGE and DNA microarrays), it is now possible to use gene expression patterns as a tumor classification tool. As expected, this multigene approach provides greater analytic power than single gene approaches. For example, the diagnosis of small-cell lung carcinoma is routinely made with light microscopy and immunohistochemistry (IHC) for neuroendocrine markers, such as chromogranin A and synaptophysin. Although this approach routinely works well, expression of these markers can be variable, so a panel of IHC markers is sometimes needed for the highest diagnostic accuracy. DNA microarray analysis of lung carcinomas has detected many additional marker genes associated with the neuroendocrine phenotype of smallcell carcinoma (Bhattacharjee et al., 2001). Similarly, DNA microarray analysis of papillary thyroid carcinoma has detected numerous potential marker genes that, when combined into a panel of marker genes, effectively assists in the diagnosis of this tumor (unpublished results). Given the morphologic complexity of some tumors (e.g., lung carcinoma), it is not unexpected that multigene marker approaches are more informative and powerful than single-marker gene approaches.

TUMOR-SPECIFIC SIGNATURES AND MULTITUMOR CLASSIFICATIONS Several groups have developed gene expression profiles of morphologically related tumors (carcinomas or adenocarcinomas) from different organ systems to develop tumor-specific signatures and have used those signatures as a classification tool. One of the first attempts profiled acute leukemia and successfully rediscovered the tumor classes of lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) (class discovery). It further correctly predicted the classification of an independent set of cases (class prediction) using gene expression data alone (Golub et al., 1999). A similar approach using expression profiles of adenocarcinomas of colon, lung, and ovary correctly classified 152 tumors and detected two tumors that were misclassified (Giordano et al., 2001). Importantly, this approach yielded a list of marker genes for each class that contained markers already accepted into surgical pathology practice (e.g., cytokeratin 20 as a marker of colon carcinoma), providing some validation of the signature approach. Others (Ramaswamy et al., 2001; Su et al., 2001) have expanded this approach to include more tumor types, although with fewer tumors comprising each cohort. Collectively, these studies demonstrate the feasibility of constructing gene expression-based cancer classification maps and discovering new marker genes that can be implemented in the reverse transcription-polymerase chain reaction (RT-PCR) (Buckhaults et al., 2003) and IHC assays (Moskaluk et al., 2003). Data analysis is one of the most significant challenges presented by the voluminous gene expression data generated by DNA microarray analysis. There are numerous ways to approach the analysis for tumor classification, ranging from sophisticated computer learning programs such as artificial neural networks (Khan et al., 2001) and support vector machines (Furey et al., 2000; Ramaswamy et al., 2001; Lee and Lee, 2003) to more direct approaches such as decision trees (Shedden et al., 2003). Although many approaches successfully classify cancers, the simpler, more direct approaches offer the advantages of being transparent in terms of which genes drive the classification and of more closely mimicking the approach used by practicing surgical pathologists.

ORGAN-SPECIFIC MORPHOLOGIC AND MOLECULAR CLASSIFICATION OF SELECT TUMORS Breast Carcinoma In many respects, breast carcinoma serves as the paradigm for the approach to classifying many human cancers. Carcinomas of the breast

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are divided into in situ and invasive types, depending on whether the neoplastic cells are confined to the ductal system of the breast (in situ) or have left the ductal system to involve the supporting stroma (invasive). The same approach is taken with many epithelial tumors, including prostatic and pancreatic adenocarcinomas. In situ breast carcinomas are divided into ductal and lobular types, depending on the morphology of the neoplastic cells in the ducts. Invasive breast carcinomas are also divided into two large categories based on the morphologic appearance of the invasive tumor cells. Invasive ductal carcinomas are tumors that show evidence of glandular differentiation (Fig. 2–1c) and comprise most breast carcinomas. Invasive lobular carcinomas lack glandular differentiation and show a striking uniformity of tumor cells (Fig. 2–1d). Many other types and variants of both ductal and lobular carcinoma, some with prognostic significance, have been described. For example, tubular carcinoma is a well differentiated carcinoma that consists exclusively of tubules and is associated with a favorable prognosis (Kader et al., 2001; Kitchen et al., 2001; Cabral et al., 2003). The current evaluation of all invasive carcinomas includes assessment of hormone (estrogen and progesterone) receptor status, assessment of erbB2/Her2/neu expression, and/or genomic amplification. In fact, the separation of ER-positive and ER-negative breast carcinomas represents one of the earliest informative molecular classifications. Breast carcinomas that express high levels of these hormonal receptors usually respond to antihormonal therapy and have a more favorable prognosis (Osborne et al., 1981; Berger et al., 1991). Conversely, tumors with erbB2/Her2/neu amplification and overexpression that lack hormonal receptor expression have a poorer prognosis (Tandon et al., 1989; Press et al., 1997). Much recent effort has been invested in developing new molecular classifications of invasive ductal carcinomas using gene expression profiling approaches (Perou et al., 2000; Sorlie et al., 2001; Sorlie et al., 2003). These efforts by several groups have largely been successful in stratifying breast carcinoma into subsets. Interestingly, this approach reinforces the power of the ER and erbB2/Her2/neu status as useful classifying factors. Using hierarchical clustering algorithms to analyze gene expression in a cohort of breast carcinomas, the tumors were divided into two broad categories—ERnegative and ER-positive—each further containing additional subsets. The subsets were named based on specific gene expression. The ERnegative group consisted of the basal-like, the erbB2-positive, and the normal breast-like subtypes, whereas the ER-positive group consisted of the luminal subtypes A, B, and C. The basal-like subtype was characterized by high expression of keratins 5 and 17, laminin, and fatty acid-binding protein 7. The erbB2-positive subtype was characterized by high expression of erbB2 and other associated genes in its amplicon at 17q22.24. The normal breast-like subtype expressed genes related to adipose tissue and other mesenchymal cells. The luminal subtypes showed expression of genes common to luminal-type epithelial cells and genes associated with ER activation, with slight variation among the three subtypes. Importantly, this molecular classification scheme has been reproduced across several data sets (Sorlie et al., 2003). Because molecular classification schemes are useful only if they provide incremental information above and beyond that provided by morphology, the survival of breast carcinoma patients was examined to determine if the molecular subtype correlated with the outcome. The basal-like and erbB2-positive subtypes were associated with the shortest survival times and relapse-free survival times compared with the other subtypes. The association of erbB2 amplification and poor prognosis is well documented, thereby reinforcing the gene expression profiling approach to class discovery. The basal-like subtype expresses keratins 5 and 17, and expression of these proteins was shown to have prognostic significance as assessed by IHC (van de Rijn et al., 2002). The major significance of this work related to breast carcinoma classification lies in the discovery of the basal-like subtype. Although it can be argued that this subtype could be deduced by elimination using standard IHC methods (ER- and erbB2-negative tumors), the clear delineation of this subtype based on gene expression and the

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development of positive IHC markers (keratins 5 and 17) should permit routine identification of this subtype in pathology practice. Moreover, knowledge of gene expression in the basal-like subtype should allow new therapeutic approaches.

Colorectal Carcinoma Most primary colorectal tumors are adenocarcinomas. Much work in this field led to the development of the adenoma–carcinoma sequence with an understanding of the accumulation of mutations during this histologic progression (Kinzler and Vogelstein, 1996). In addition to adenocarcinoma, tumors with neuroendocrine differentiation (e.g., carcinoids) are common in the colon and rectum as well as throughout the gastrointestinal tract. Colorectal adenocarcinoma is relatively morphologically homogeneous, especially when compared with adenocarcinomas of other organs (e.g., lung), as most tumors are well to moderately differentiated and have other characteristic features, such as mucin production and a prominent desmoplastic response. However, histologic subtypes have been developed, such as mucinous carcinoma, signet-ring cell carcinoma, and medullary carcinoma. There is a correlation between morphologic type and the underlying pathogenesis, as it is now appreciated that colorectal carcinomas develop through at least two distinct molecular genetic pathways (Jass et al., 1999). The bulk of the tumors develop through the chromosomal instability or the wnt signaling pathway and exhibit the typical, or ordinary, phenotype. The remaining tumors are associated with microsatellite instability owing to underlying defects in DNA mismatch repair, and they have a different phenotype that includes the mucinous and medullary carcinomas and is characterized by being either well or poorly differentiated and having a right-sided location, a host response with tumorinfiltrating lymphocytes, and mucinous differentiation (Greenson et al., 2003). Microsatellite instability status, which has gained clinical acceptance, is often included in the workup of patients with colorectal carcinoma and is determined by examining a panel of microsatellite markers (Boland et al., 1998) or documenting loss of DNA mismatch repair enzymes in tumor tissue sections by IHC (Lindor et al., 2002). Microarray analysis of colon cancer has not yet yielded any significant insights. However, distinct transcriptional profiles of microsatellite stable and unstable tumors have been derived.

Prostate Carcinoma Prostate cancer is almost invariably adenocarcinoma and can be divided into two major categories: adenocarcinoma of peripheral ducts and acini and large duct adenocarcinoma. Rare primary urothelial carcinomas of the prostate have also been recognized, as have several morphologic variants of adenocarcinoma. Grading prostatic adenocarcinoma uses the clinically accepted and preferred Gleason grading scheme, developed in association with the Veterans Administration Cooperative Urology Research Group (Gleason and Mellinger, 1974). Gleason grading is based on the degree of glandular differentiation and is assigned a score of 1–5, with 1 being well differentiated and 5 being nearly undifferentiated. The predominant and secondary patterns in a given tumor are each graded, and the two grades are added to obtain the Gleason score. Studies have demonstrated high interobserver reproducibility of Gleason grading (Mills et al., 1990; Allsbrook et al., 2001a,b). The acceptance of Gleason grading can be attributed to its documented high correlation with a variety of clinical and pathologic parameters, most significantly with survival (Mills et al., 1990). Despite the success of clinical staging and pathologic grading for predicting the outcome of patients with prostatic adenocarcinoma, methods to improve outcome prediction are needed. Several studies have employed gene expression profiling as a tool to develop novel single gene prognostic biomarkers (Dhanasekaran et al., 2001) and using clusters of small numbers of genes to predict the clinical course of prostate carcinoma (Glinsky et al., 2004).

Lung Carcinoma Lung tumors are overwhelmingly epithelial (carcinomas) and are broadly divided into two types: small-cell and non-small-cell carcinomas. Small-cell carcinoma belongs to the larger family of neuroendocrine (NE) neoplasms, which also includes carcinoid, atypical carcinoid, and large-cell NE carcinoma (numerous classifications schemes have been proposed for the classification of NE tumors of the lung). Small-cell carcinoma represents the most undifferentiated form of NE carcinoma, yet it does retain some morphologic and/or IHC evidence of NE differentiation. Conversely, carcinoid represents the most differentiated NE tumor. Non-small-cell carcinomas include adenocarcinoma and squamous cell carcinoma and less common tumors such as large-cell carcinoma, which is believed to represent an undifferentiated form of either adenocarcinoma or squamous cell carcinoma. The molecular classification of lung carcinoma has been the focus of much recent work. Using a DNA microarray-based approach with hierarchical clustering, Bhattacharjee et al. (2001) recapitulated the recognized classification of lung carcinomas and further subdivided the adenocarcinoma cohort into four subclasses. Interestingly, using this approach, several presumed primary lung adenocarcinomas were discovered to be metastases from colon, breast, and liver. A study by Garber et al. (2001), using a similar approach with a cDNA microarray, yielded similar results and showed a correlation between adenocarcinoma subsets and patient survival. Work from our group (Beer et al., 2002) focused on subclassification of lung adenocarcinoma and identified three subtypes based on hierarchical clustering. Using a statistical risk index based on expression data for 16 genes, stage I adenocarcinomas with good and poor prognoses were identified. These results provide the opportunity to treat patients with early high risk disease further using adjuvant therapy after surgery. The collective lung carcinoma profiling work is currently being reproduced and expanded via a multiinstitutional National Cancer Institute (NCI)funded project. In preparation for this project, the NCI organized an interlaboratory comparability study of gene expression profiling using DNA microarrays to assess the feasibility of combining microarray data generated in various laboratories (K. Dobbin, personal communication).

Gliomas The basic classification of glial brain tumors (diffuse gliomas), the most common primary brain tumors, rests on morphology, with qualifiers used to describe patterns of differentiation (astrocytic, oligodendroglial, oligoastrocytic). Tumor grade is important for prognosis and therapy and is crudely based on the histologic degree of malignancy (WHO grades II–IV). Astrocytic neoplasms include the welldifferentiated diffuse astrocytoma (WHO grade II), the anaplastic astrocytoma (WHO grade III), and the specially designated glioblastoma multiforme (WHO grade IV). Oligodendroglial tumors include oligodendroglioma (WHO grade II) and anaplastic oligodendroglioma (WHO grade III). Although tumor grade and differentiation provide a useful framework for glioma classification, difficulties persist. For example, grading gliomas strives to create discrete categories when in fact the tumors represent a continuous spectrum of neoplastic evolution. Furthermore, this evolutionary process is essentially genetic and is driven by the underlying accumulation of molecular events. Thus, the potential role of alternative approaches is considerable, and significant progress has been made toward a molecular glioma classification (Louis et al., 2001). Glioblastomas have been molecularly subclassified using the genes encoding p53 (TP53), the epidermal growth factor receptor (EGFR) (von Deimling et al., 1993). Tumors with EGFR genomic amplification do not contain a mutation of TP53 or allelic loss of its chromosomal location (17p); the converse relation is also observed (Watanabe et al., 1996). Division of glioblastomas into these two molecular subtypes (EGFR-amplified, TP53 wild-type, EGFR wild-type, TP53 mutated) correlates with the clinical features. Patients with a TP53 mutation are younger, and their tumors are associated with lower grade

Morphologic and Molecular Classification of Human Cancer astrocytomas (Reifenberger et al., 1996). Patients with EGFR genomic amplification are older, and their tumors appear to arise de novo without an associated low grade glioma (Watanabe et al., 1996). Molecular subtyping by cytogenetic approaches of oligodendrogliomas has become part of the standard diagnostic workup of these tumors (Ino et al., 2000; Smith et al., 2000; Thiessen et al., 2003; van den Bent et al., 2003). Based on chromosomal loss of 1p and 19q, it is possible to identify patients who will respond to combined chemotherapy (procarbazine, lomustine, and vincristine). Thus, patients with tumors who have loss of 1p and 19q have a much longer survival than those with intact 1p and mutation of the TP53, PTEN, or CDKN2A genes. Clinical implementation of this molecular approach to a “clinical laboratory improvement amendment” (CLIA)certified environment has been readily accomplished by FISH testing for 1p and 19q (Gelpi et al., 2003; Perry et al., 2003). DNA microarray studies of gliomas have defined gene expression signatures that correlate with tumor location (Mueller et al., 2002) and that can be more informative for survival than morphologic classification alone (Nutt et al., 2003).

Sarcoma The morphologic classification of sarcoma, particularly the spindle cell type, represents one of the most significant challenges for surgical pathologists. Based purely on morphology, the distinction between the various sarcoma subtypes is fraught with subjectivity and interobserver variability despite the availability of useful IHC diagnostic markers. Fortunately, an understanding of the underlying pathogenesis of these tumors, specifically the presence of type-specific chromosomal translocations, has led to some significant advances in sarcoma classification. Sarcomas can be broadly divided into spindle cell and epithelioid types, yet some tumors, such as the biphasic synovial sarcoma, which displays a spindle cell and an epithelioid component, defy this distinction. Any evidence of cellular differentiation, either morphologic or immunohistochemical, can be used to classify these mesenchymal tumors. For example, leiomyosarcomas often display a characteristic fascicular pattern that resembles smooth muscle and are immunoreactive for actins and desmin. Thus, the usual approach to spindle cell sarcomas includes careful histologic evaluation for any evidence of cellular differentiation along with a battery of IHC stains directed against type-specific proteins. Cytogenetic analysis has revealed two molecular subtypes of sarcoma: those with complex karyotypic abnormalities resulting in aneuploidy and those with specific chromosomal translocations and the relative absence of aneuploidy. During the late 1970s and early 1980s, cytogenetic approaches were applied to sarcomas, and some of the first histologic-cytogenetic associations were made (Aurias et al., 1984; de Chadarevian et al., 1984; Trent et al., 1985; Limon et al., 1986; Turc-Carel et al., 1986a,b; Douglass et al., 1987; Griffin and Emanuel, 1987). By the late 1980s, the diagnostic significance of specific translocations for sarcoma classification was beginning to emerge (Karakousis et al., 1987). As detailed in Table 2–2, specific recurrent chromosomal translocations have been detected for many types of sarcoma. Current molecular pathology practice includes routine Table 2–2. Translocations in Sarcoma with Diagnostic Utility Tumor Type

Cytogenetics

Fusion Genes and Proteins

Ewing’s sarcoma Synovial sarcoma Myxoid/round cell liposarcoma Alveolar rhabdomyosarcoma Extraskeletal myxoid chondrosarcoma Ewing’s sarcoma Clear cell sarcoma Desmoplastic small round blue cell tumor

t(11;22) t(X;18) t(12;16) t(2;13) t(9;22)

EWS/ETS SYT/SSX1 or SSX2 FUS/CHOP FKHR/PAX3 or PAX7 EWS/TEC

t(11;22) t(12;22) t(11;22)

EWS/FLI1 EWS/ATF1 EWS/WT1

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translocation identification for all cases of uncertain classification by RT-PCR-based and/or interphase FISH-based methods. One of the most fascinating aspects of sarcoma translocations is the opportunity they provide to understand their biology and pathogenesis. As these translocations create novel fusion proteins involved in tumorigenesis, it is possible to identify the individual genes and explore the role of the resulting fusion proteins. In many cases, the genes involved encode DNA-binding proteins with transcriptional regulation activity (transcription factors), often developmentally regulated (e.g., the PAX genes). The cytogenetics of some sarcomas, malignant fibrous histiocytoma, and pleomorphic liposarcoma, for example, show complex chromosomal rearrangements without specific recurrent abnormalities. Because of this karyotypic heterogeneity, the development of molecular pathology assays is not practicable for these tumors. However, some have attempted to understand and classify these tumors using gene expression profiling approaches (Schofield and Triche, 2002). Molecular classification by comparative gene expression profiling has confirmed many of the existing sarcoma categories with consistent, distinct, homogeneous expression profiles. Such tumors include (not surprisingly based on their cytogenetic data) synovial sarcoma, round cell/myxoid liposarcoma, clear-cell sarcoma, and gastrointestinal stromal tumors.

Hematologic Malignancies The classification of hematologic malignancies (myeloid neoplasms, lymphoid neoplasms, posttransplant lymphoproliferative disorders, myelodysplastic syndromes, mast cell diseases, histiocytic and dendritic cell neoplasms) is vast and complicated, and a complete discussion of their current classification is beyond the scope of this chapter. However, the current classification recently published by WHO (Jancar, 2000; Jaffe et al., 2001) incorporates existing immunologic, cytogenetic (Martin-Subero et al., 2003), and molecular genetic information, thereby providing a complete framework for the classification of these neoplasms. Efforts to validate the classification, especially for the myelodysplastic syndromes, have been successful (Germing et al., 2000). Despite the success of the WHO classification, there is still a need to define the classification of hematologic neoplasms more precisely; and several recent studies using DNA microarrays have been performed with this goal in mind. A landmark study (Alizadeh et al., 2000) examined gene expression in diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and chronic lymphocytic leukemia/lymphoma (CLL), as well as normal lymphocytes and lymphoma cell lines using a DNA microarray with 17,856 cDNA clones representing genes selected from various lymphoid cDNA libraries. These three lymphoma types were distinguishable using this approach, with the lower grade types (CLL and FL) sharing similar gene expression patterns with resting B cells. Interestingly, there was significant expression heterogeneity in the DLBCL group, and it was possible to define subgroups, designated germinal center B-like DLBCL and activated B-like DLBCL. Importantly, these subgroups have prognostic significance. This study was one of the first to demonstrate that gene expression profiling could define new cancer subtypes not discernible by existing morphologic and IHC approaches. A similar study of gene expression in DLBCL also defined subgroups that predicted outcome (Shipp et al., 2002), although the informative genes differed, suggesting that many factors contribute to DLBCL’s response to therapy and that much work remains to be done before clinical implementation of gene expression profiling.

CONCLUSIONS Despite the development of better prognostic markers, which is a large component of the current cancer genomics revolution, there is a need to predict which patients will respond to therapy. The ability to use an effective second- or third-line therapy and sidestep an ineffective, potentially toxic first-line therapy would greatly improve the care of

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3

Cancer Precursors THOMAS E. ROHAN, DONALD E. HENSON, EDUARDO L. FRANCO, AND JORGE ALBORES-SAAVEDRA

C

ancer is thought to arise as a result of a lengthy (up to several decades), multistep process involving changes in a number of genes following the initial clonal expansion of a mutated stem cell (Nowell, 1976; Kern, 1993; Vogelstein and Kinzler, 1993; Boone et al., 1999). These genotypic changes are accompanied by changes in cell and tissue morphology, characterized by loss of cellular differentiation and increased cytologic atypia. As a result, the tissue assumes progressively more of the morphologic characteristics of cancer. Although it is usually not difficult to make a clinical diagnosis of cancer, from the perspective of tumor biology the demarcation between what is unmistakably cancer and what precedes cancer is not entirely clear (Pontén, 1998). Perhaps not surprisingly, therefore, various definitions of precursor lesions have been proposed. For example, early during the last century it was suggested that a useful definition of a cancer precursor is “a condition which may be associated with development of cancer” (Stout, 1932). Subsequently, the term “precancerous lesions” was used to describe “visible steps in a dynamic process of neoplasia” that “may or may not undergo progression to a more advanced stage of neoplasia” (Foulds, 1958). The apparent intent of these definitions is to suggest that, even if a tissue abnormality at a specific anatomic site is associated with cancer development at that site, cancers at that site do not necessarily develop from such lesions, and that such abnormalities do not necessarily progress to cancer. In this chapter, the term “cancer precursors” is used to refer to all morphologic lesions on the pathway from normal tissue to cancer, up to but not including invasive cancer itself. That is, essentially we have adopted a morphologic definition of cancer precursors, and we have included within this definition carcinomas in situ, which are lesions with morphologic hallmarks of invasive cancer but that are confined to the thickness of the epithelium and do not penetrate the basement membrane. (Our focus here is on precursors of epithelial malignancies because precursors of other types of malignancy [mesenchymal, hematopoietic, lymphoid] are not well defined.) Other terms used to denote similar conditions include “incipient neoplasia” (Henson and Albores-Saavedra, 2001), “precancer” (Pontén, 1998), and “intraepithelial neoplasia” (O’Shaughnessy et al., 2002). Less commonly, the term “precancerous states” has been used to denote all conditions up to but not including carcinoma in situ (Carter, 1984), the latter thought to represent preinvasive cancer rather than a precancerous lesion. Increasing attention is being devoted to the study of cancer precursors, as evidenced by a number of recent publications on the topic (Pontén, 1998; Srivastava et al., 1999; Henson and Albores-Saavedra, 2001; Franco and Rohan, 2002). As stated in one of those publications, “Until now there has been such a heavy emphasis on cancer that we are only at a beginning in understanding precancer” (Pontén, 1998). In part, this reflects the difficulty of defining the natural history of cancer and of establishing, therefore, whether a given lesion is a cancer precursor. Indeed, validation of cancer precursors as intermediate end points for invasive cancer is challenging, as tissue sampling interrupts or alters the natural history of the neoplastic process. Furthermore, it is often difficult, if not impossible, to undertake the repeated tissue sampling that is required to study progression (and indeed regression to earlier stages), although in some cases alternative

Portions of this chapter were adapted with permission from Cancer Precursors: Epidemiology, Detection, and Prevention. E.L. Franco and T.E. Rohan, editors. Copyright © 2002. Springer-Verlag New York Inc., www.springer-ny.com

sampling techniques (e.g., Papanicolaou smears) might provide the possibility of doing so without encountering such problems. However, cytopathologic and even histologic examinations are prone to error owing to inadequate sampling and/or to incorrect microscopic interpretation. Nevertheless, the study of cancer precursors is important for several reasons. First, elucidation of the etiology of precursors provides insight into the etiology of the corresponding cancer, because if the precursor represents an intermediate stage in the causal pathway between exposure and the development of invasive cancer, etiologic factors for the former must be a subset of those for the latter. Second, if etiologic investigation of cancer precursors identifies potentially modifiable risk factors, it can provide opportunities for the primary prevention of both the precursors and the corresponding cancer. Third, if cancer precursors are clearly defined, they can provide targets for screening and hence early detection of those at increased risk of cancer, with obvious implications for the clinical management of individuals identified with such lesions. Finally, study of the molecular and genetic changes that occur with cancer precursors can provide fundamental insights into the nature of the carcinogenic process and may have practical benefits with respect to the classification of lesions and their clinical management. Our knowledge of the etiology of cancer precursors varies considerably by anatomic site. In part, this might reflect the relative inaccessibility of a site to tissue sampling (e.g., ovary and pancreas) and hence the difficulty of detecting and diagnosing precursors at that site. For some cancers (e.g., ovary), it might also be indicative of a relatively short premalignant phase, which therefore eludes detection. Furthermore, for some anatomic sites, it might also reflect the fact that it is only recently that we have identified putative precursors for the corresponding invasive cancer, as in the case of precursors of prostatic and pancreatic cancer (i.e., prostatic and pancreatic intraepithelial neoplasia, respectively). Nevertheless, as implied earlier, clues to the etiology of such conditions come in part from knowledge of the etiology of the corresponding cancer, which often is easier to study. Given the uneven state of knowledge of the etiology of cancer precursors by anatomic site, in this chapter we describe in some detail a few of the more well studied sites, for which there is substantial knowledge of the histopathology, epidemiology, and natural history. To date, relatively few studies have been undertaken to test the effect of preventive interventions on the risk of cancer precursors. However, with increasing recognition of the advantages that intermediate (or surrogate) end points offer for the study of preventive strategies in terms of reductions in time, sample size, and cost compared with the corresponding requirements for trials involving cancer as an end point, we are witnessing a burgeoning of activity in this area. One important approach is cancer chemoprevention, a relatively new area of research that involves the use of “agents that prevent cancer by either preventing or treating premalignant lesions” (Lippman et al., 1998). Although screening is usually targeted to the detection of cancerous lesions at a relatively early stage, screening at some sites (in particular, the cervix and colon) results in detection of a substantial proportion of cancer precursors. In principle, treatment or removal of such lesions results in a reduced risk of subsequent invasive cancer. For example, detection and ablative treatment of cancer precursors in the uterine cervix have resulted in a marked reduction in the incidence

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of invasive squamous cell carcinoma (Miller et al., 2000); and removal of colorectal polyps has been associated with reduced risk of subsequent colorectal cancer (Muller and Sonnenberg, 1995). With further development of or improvement in screening modalities, it can be anticipated that detection of lesions at relatively early stages of carcinogenesis will increase. In some cases, this might pose diagnostic and therapeutic dilemmas, the former because of the need to establish that newly identified conditions (e.g., at sites that were previously inaccessible) are indeed cancer precursors and the latter because of concern over unnecessary treatment of lesions that might never progress. Indeed, the advent of new, sensitive technologies for cervical cancer screening has led to a debate as to the possibility that highgrade cervical lesions detected by human papillomavirus testing, for example, are less likely to progress than those detected by the traditional Papanicolaou test. Ongoing randomized trials are presently attempting to resolve this issue (Franco, 2003). The advent of the genetic era has spawned many studies of the molecular changes that characterize histologically defined cancer precursors (Srivastava et al., 1999). Such studies should provide insight into the progressive accumulation of the fundamental molecular changes leading to cancer. In addition, the results of such studies should lead to the development of new, molecular-based classifications of cancer precursors. Furthermore, they might have clinical implications (Ahrendt and Sidransky, 1999) because for women who are identified as being at increased risk of progression to invasive cancer (based on their status with respect to one or more molecular markers) close follow-up and early intervention might be warranted (Rohan et al., 1998). Also, such studies may lead to the identification of chemopreventive agents that target cellular or molecular alterations in preinvasive lesions (Franco and Rohan, 2002; Kelloff et al., 2003). Although it is only relatively recently that cancer precursors have been subjected to systematic study, it is clear that considerable progress has been made already in our understanding of these lesions. Our purpose in this chapter is to describe some of that progress.

TERMINOLOGY As indicated earlier, the term “cancer precursor” refers to specific morphologic changes that precede the development of cancer. The term does not imply that cancer is inevitable; rather, it refers to histologic changes associated with an increased probability or risk for cancer. These histologic changes are designated by morphologic terms that convey a mixture of diagnostic, prognostic, and etiologic significance. Such terms have included “atypical hyperplasia,” “mild, moderate, or severe dysplasia,” “epithelial atypia,” “high grade or low grade intraepithelial lesion,” “in situ carcinoma,” “intramucosal carcinoma,” “borderline tumor,” “grade one-half carcinoma,” “intraepithelial neoplasia,” and “minimal cancer.” Terms such as “actinic keratosis” and “arsenical keratosis” reflect the etiology of these cutaneous in situ carcinomas (Salasche, 2000). The most common diagnostic terms applied to precursors are “dysplasia” and “intraepithelial neoplasia,” which are often used synonymously. Dysplasia literally means disorganized cell proliferation and is usually characterized by abnormal epithelial maturation. For some sites, “intraepithelial neoplasia” is standard (e.g., “prostatic intraepithelial neoplasia” and “cervical intraepithelial neoplasia”). Unless the diagnosis is qualified by “mild,” “moderate,” “severe,” or some other modifier such as high-grade or low-grade, these terms alone do not provide information about the risk of progression or how advanced the lesion is, which are important parameters useful to clinicians and investigators.

abnormality, the risk of cancer increases. It is this outcome that allows us to infer that agents that induce precursor lesions are carcinogens. Morphologically, precursor lesions exhibit a continuum of histologic abnormalities. Moreover, this morphologic variability accompanies biologic variability. Not only do precursor lesions vary according to the tissue of origin, they also vary in size, rate of progression, rate of regression, rate of development, clinical presentation, and molecular profile. They may arise sporadically or may be genetically determined through germline mutations. Lesions may be multifocal or diffuse, often arising over wide areas of an epithelial surface.

Age of Onset Precursors arise in younger age groups than do invasive cancers. For most sporadic tumors, the peak age for the precursor is, on average, 10 years before the peak age for the invasive cancer. Thus, progression is a slow process, although there is variation among patients and presumably among histologic types of cancer. Many exceptions exist; for instance, there is evidence that progression to invasive carcinoma in the pancreas can take as long as 29 years (Brockie et al., 1998). On the other hand, genetically determined cancers tend to occur at an even younger age, often during early adult life. With familial adenomatous polyposis (FAP), adenomas appear at a mean age of 25 years, cancer at 39 years, and death due to the cancer at 42 years.

Location Precursor lesions are found primarily along epithelial surfaces, such as in the breast, uterine cervix, and prostate; throughout the gastrointestinal tract; along the respiratory mucosa; and in the urinary bladder. In contrast to precursors in epithelial tissues, the concept of cancer precursors is not well defined for mesenchymal and lymphoid tissues primarily because these tissues lack a basement membrane and their tumors are likely to be invasive from inception. However, progress has been made in identifying some of the precursor lesions for these sites, such as follicular hyperplasia in the stomach that may progress to malignant lymphoma when associated with Helicobacter pylori infection (Isaacson, 1999). Lymphoid hyperplasia associated with autoimmune diseases, viral infections such as Epstein-Barr virus infection, or immunodeficiency syndromes may also progress to malignant lymphoma. Follicular lymphoma in situ has been defined as involvement of single or scattered follicles in an otherwise normal lymph node (Beaty and Jaffe, 2001).

Size In general, precursor epithelial lesions are relatively small, occupying only the mucosal surface and, by definition, not violating the basement membrane. They may be flat or papillary. Often they are microscopic in size and not recognized on gross examination. Some lesions, however, can reach an unusually large size by growing along the epithelial surface. In situ carcinomas in the breast can be 5 cm in diameter as they wind their way through the mammary ducts. Flat in situ carcinomas of the urinary bladder may extend into the prostate gland and the seminal vesicles. Adenomas in the colon may measure 5 cm or larger in diameter. Mucinous cystic neoplasms of the pancreas of low malignant potential (borderline) are quite large but show only dysplastic and in situ changes along the surface epithelium. Some lesions can involve wide areas, such as diffuse metaplasia of the stomach. Lesions associated with germline mutations, such as with FAP, can extend along an entire epithelial surface.

Frequency GENERAL PROPERTIES OF CANCER PRECURSORS As morphologic risk factors for cancer, precursors have their own biology and natural history. Progression to invasive cancer is unpredictable and cannot be prognosticated reliably based on morphology. However, as a precursor progresses to successively higher grades of

Precursor lesions are more common than their corresponding invasive cancers. For example, sporadic colonic adenomas are more prevalent than the corresponding adenocarcinomas. Postmortem studies have shown that 25%–50% of the population have single or multiple adenomas in the colon by age 70 (Rickert et al., 1979; Williams et al., 1982). With the FAP syndrome, the colon contains hundreds of

Cancer Precursors adenomas, but only one (at most several) evolves into invasive cancer during the lifetime of the patient (Compton, 2001). In the lung, bronchial dysplastic changes are more common than invasive cancer (Kennedy et al., 1996; Park et al., 1999). In the skin, dysplastic and congenital nevi are more common than malignant melanomas, and actinic keratoses are more prevalent than squamous cell carcinomas (Schwartz, 1997). Most sporadic precursor lesions therefore do not progress to invasive cancer; progression may be more common with lesions secondary to germline mutations.

Multicentricity Precursor lesions are often multicentric and may occupy wide areas of a mucosal surface. Even a single microscopic lesion may indicate the existence of others nearby (Albores-Saavedra et al., 2000). In the lungs of smokers, for example, lesions are often multiple and bilateral. When multiple, they are often seen in various stages of development, which suggests that they do not all arise at the same time or progress at the same rate. Multiple lesions usually reflect chronic carcinogenic exposure and often arise in broad fields of exposed epithelium (Slaughter et al., 1953; Smith et al., 1996). Lobular carcinoma in situ of the breast is usually multicentric and frequently bilateral (Frykberg, 1999). Familial C-cell hyperplasia (medullary carcinoma in situ) of the thyroid is nearly always bilateral (Albores-Saavedra and Krueger, 2001). Precursor lesions may also coexist with invasive tumors. Pancreatic intraepithelial neoplasia is a multicentric lesion that often coexists with ampullary carcinoma, suggesting a field effect similar to that documented for urothelial carcinomas of the urinary bladder (Agoff et al., 2001).

Genomic Instability Most likely, progression is driven by an increased rate of unrepaired DNA damage with continued formation of abnormal genomic variants (Minna et al., 1997; Park et al., 1999; Hittelman, 2001). There is evidence that genetic changes antedate morphologic changes. For example, genetic changes have been found in normal-appearing mucosa of the lung and in the epithelium of the head and neck of smokers (Lydiatt et al., 1998; Park et al., 1999; Boyle et al., 2001). In the lung, these genetic changes resemble those seen in squamous cell carcinomas (Boyle et al., 2001). Genetic alterations have been found in colonic mucosa that shows no morphologic evidence of neoplastic transformation (Fearon and Vogelstein, 1990). In morphologically normal sun-exposed skin, multiple patches of keratinocytes with p53 mutations have been found (Ren et al., 1997). Approximately 100,000 times more common than dysplasia, these patches have practically no malignant potential. Molecular alterations associated with neoplasia that are found in normal tissues can be referred to as submorphologic precursors (Ren et al., 1997). Mutations are found in early precursor lesions. K-ras mutations, which are found in more than 80% of invasive carcinomas of the pancreas, have been detected in normal, hyperplastic, metaplastic, and neoplastic ductal pancreatic epithelium (Moskaluk et al., 1997; Sugio et al., 1997; Luttges et al., 1999). Genetic changes, especially 16p loss, have been found in cases of atypical hyperplasia of the breast (Gong et al., 2001). Genetic alterations have also been found in precursor lesions of the lung, especially loss of heterozygosity on chromosome 3 and mutations in p53 (Sozzi et al., 1992; Sundaresan et al., 1995). As a rule, genetic changes found in precursor lesions are also present in the corresponding invasive cancers. Alterations in gene expression and chromosome structure increase as lesions progress to invasive cancer (Wistuba et al., 1999). The more atypical lesions histologically are usually associated with more alterations (Wistuba et al., 1999).

Independence Multiple lesions arising over a broad mucosal surface are often independent because they have different genetic alterations. Independence has been extensively studied in the lung, head and neck, and urinary bladder (Sozzi et al., 1995; Barrera et al., 2001; Boyle et al., 2001;

23

Cheng et al., 2002). This independence may complicate chemopreventive interventions because different genetic alterations may indicate multiple pathways for malignant transformation, and it may not be possible to block all pathways with a single agent.

Heterogeneity Heterogeneity can be viewed as distinct subpopulations with differing selective growth advantages (Jotwani et al., 2001). These subpopulations, which reflect clonal evolution within a precursor lesion, are the result of genetic instability. Heterogeneity is reflected in all properties of precursor lesions. It applies to morphology, growth rate, karyotype, molecular abnormalities, surface antigens, and other biologic properties. As a result of heterogeneity, similar morphologic lesions may show variation in genetic alterations, progression rates, regression rates, and other biologic attributes. Furthermore, genetic changes that occur during progression of the precursor lesions to invasive cancer may differ among patients even though they have morphologically similar lesions. For instance, in dysplastic lung lesions, not all patients have similar genetic changes.

Preexisting Conditions Many preexisting conditions serve as risk factors because they may give rise to precursor lesions. Chronic inflammatory conditions (e.g., reflux esophagitis, ulcerative colitis, primary sclerosing cholangitis) and infections (e.g., those caused by oncogenic human papillomavirus types) may induce dysplastic changes that can progress to invasive carcinoma. Although these inflammatory or infectious conditions do not constitute neoplasia, they are risk factors for subsequent cancer and for this reason are considered precursors. Benign tumors such as colonic adenomas, which by definition are accompanied by dysplastic changes, may progress to in situ and intramucosal carcinoma. Proliferative lesions such as chronic lymphedema can give rise to lymphangiosarcomas. Immunodeficiency states may be associated with lymphoproliferative disorders and smooth muscle neoplasms (Monforte-Munoz et al., 2003). Rarely, other conditions may give rise to cancer as well. Fibrous dysplasia and Paget’s disease of bone, for example, may lead to osteogenic sarcoma (Unni and Dahlin, 1979; Fechner and Mills, 1993). Thus, the concept of precursors is broad, with multiple diverse conditions occasionally serving as a risk for cancer.

Progression Precursor lesions usually follow a morphologic sequence from hyperplasia or metaplasia through dysplasia to carcinoma in situ and subsequent invasion. Lesions that are more advanced along the sequence are likely to progress more rapidly than less advanced lesions. However, not all lesions follow the sequence, and some may never progress or may even reverse direction. There may be considerable variation among patients with respect to the time required for progression to invasive cancer. For instance, reports suggest that progression of precursor lesions of the pancreas may require many years—29 years in one case (Brat et al., 1998; Brockie et al., 1998). Progression of colonic adenomas to carcinoma has been estimated to require 10–15 years (Muto et al., 1975; Day and Morson, 1978). Progression may be rapid if in situ lesions already exist in the epithelial field. The rate of progression varies with the extent to which the cellular and architectural changes in the precursor lesion resemble those of the corresponding invasive cancer; such host factors as age, sex, ethnicity, and hormonal status; and the extent of genetic changes (Table 3–1). For acquired precursor lesions, there is currently no known specific genetic change that signals invasion. Indeed, constellations of molecular changes are often required for invasion. Rates of progression are difficult to estimate. Histologic grading of dysplastic lesions, for example, is subjective and often has low reproducibility. Also, changes in diagnostic criteria and terminology may affect the analysis of progression.

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Table 3–1. Changes Occurring with Progression from Normal Bronchial Mucosa to Invasive Carcinoma in Central Bronchial Carcinogenesis Parameter Hyperproliferation 3p LOH 9p LOH p53 overexpression Rb expression Cyclin D1 overexpression Telomerase overexpression Bcl-2 overexpression Aneuploidy p53 mutation p16 loss FHIT loss 13q and 17p LOH 5p and 5q LOH

Normal Epithelium

Squamous Metaplasia

Low Grade Dysplasia

High Grade Dysplasia

Carcinoma In Situ

Invasive Carcinoma

+ + + + +

++ + + + ++ + +

++ ++ ++ ++ ++ + + + +

++ ++ ++ ++ ++ ++ + + ++ +

+++ +++ +++ +++ ++ ++ + ++ ++

+++ +++ +++ +++ ++

+

+ +

++ ++ +++

+++ ++ +++ ++ +++ +++ +

Source: Kerr (2001). Reproduced with permission from the BMJ Publishing Group. LOH, loss of heterozygosity.

Dysplasia is the sine qua non precursor lesion. In most anatomic sites, high grade dysplasia is considered an irreversible change that is truly neoplastic and the morphologic forerunner of most invasive epithelial tumors. Clinically, a diagnosis of high grade, severe, or grade III dysplasia usually indicates persistence of the lesion or subsequent progression in a large proportion of untreated patients. In some sites, however, high grade dysplastic lesions may regress (e.g., in the cervix) (Holowaty et al., 1999). Often high grade dysplasia is difficult to distinguish morphologically from carcinoma in situ. For this reason, these two precursors are often grouped together. Carcinoma in situ represents a dysplastic change that extends through the full thickness of the epithelium without penetrating the basement membrane. For some anatomic sites, metaplasia presages cancer because it seems to make tissues more susceptible to malignant transformation. An acquired condition, metaplasia is the physiologic transformation of one type of differentiated tissue into another, usually in response to chronic irritation. Often considered a phenotypic reactive change, metaplasia always precedes cancer at these sites. In some sites, metaplasia is considered a premalignant lesion, as with Barrett’s esophagus. Mutations have been found in the nonneoplastic mucosa of stomach showing intestinal metaplasia (Ochiai et al., 1996). Metaplasia influences the type of invasive cancer. For instance, in the gallbladder intestinal-type metaplasia gives rise to intestinal-type carcinoma, and in the cervix squamous cell metaplasia gives rise to squamous cell carcinomas.

CANCER PRECURSORS AT SPECIFIC ANATOMIC SITES As indicated earlier, knowledge of the etiology of cancer precursors varies by anatomic site. In this section we describe in detail a few of the better-studied sites, for which there is substantial knowledge not only of the epidemiology but also of the histopathology and natural history.

Oral Cavity It has long been known that a group of intraepithelial lesions clinically defined as leukoplakia, erythroleukoplakia, erythroplakia, and oral submucous fibrosis generally precede the onset of squamous cell carcinoma of the oral cavity. The histologic assessment of such lesions frequently reveals areas of dysplasia and sometimes of hidden carcinoma (Sankaranarayanan and Somanathan, 2002; Silverman, 2003). Although the knowledge base on the pathology, natural history, and epidemiology of precursor lesions of malignancies of the oral cavity is considerable, much remains to be studied.

Pathology Cancer precursors in the oral mucosa are frequently multifocal. Microscopically, the most common lesions are hyperkeratosis, dysplasia, and

carcinoma in situ (Luna et al., 2001). The terms squamous intraepithelial neoplasia I, II, and III are occasionally used as synonyms for mild, moderate, and severe dysplasia, respectively. The clinical diagnosis of oral cancer precursors requires histologic assessment to exclude malignancy and to define the extent of epithelial involvement (Sankaranarayanan and Somanathan, 2002). Hyperkeratosis is most commonly seen in the buccal mucosa, alveolar ridge, hard palate, and dorsal surface of the tongue; it is characterized by increased surface keratin and thickening of the underlying epithelium. Clinically, these hyperkeratotic lesions are usually described as leukoplakia, although biopsy is required for a more precise diagnosis. The extent of surface keratin seen clinically does not reflect the underlying cellular changes. Recent guidelines define leukoplakia as predominantly whitish lesions of the oral mucosa that cannot be ascribed to any other definable lesion. There are two clinical and morphologic subtypes: homogeneous and nonhomogeneous (Axell et al., 1996). The former is predominantly white with a flat, thin appearance and may exhibit shallow cracks. Nonhomogeneous leukoplakia can be white only or white and red, in which case it is called erythroleukoplakia, a more clinically relevant lesion. It can be irregularly flat, nodular, or exophytic. The term erythroplakia is used to denote oral lesions that appear as red patches or plaques that cannot be ascribed to any clinically or pathologically defined condition. Dysplastic lesions are similar to those seen in other sites, such as the lung and uterine cervix (Lumerman et al., 1995). However, lesions in the oral mucosa are more likely to accumulate surface keratin in response to injury, which imparts the white color. As with most dysplastic lesions, there are major maturation epithelial abnormalities that become more pronounced with progression (Crissman and Zarbo, 1989).

Descriptive Epidemiology The prevalence and incidence of leukoplakia in the general population are not known. Limited information exists on the prevalence in specific populations based on informal surveys. Leukoplakia is uncommon in young persons. Fewer than 1% of men less than 30 years of age are found with these lesions, but the prevalence can reach 8% among those above age 70 (Bouquot et al., 1986). The leukoplakia prevalence in high risk populations varies between 0.2% and 12.0% (Gupta et al., 1980; Kleinman et al., 1991; Zain et al., 1997; Silverman, 2003). The most common sites affected are the buccal mucosa, alveolar mucosa, and lower lip; lesions in the floor of the mouth, lateral tongue, and lower lip tend to exhibit dysplastic or malignant changes more frequently (Neville and Day, 2002). In high risk areas in India, the age-specific prevalence peaks at around 8% at ages 55–60 and then declines to the same levels as those observed in young adults, at 4% (Mehta et al., 1969; Sankaranarayanan et al., 2000). True incidence

Cancer Precursors rates are known from a few large cohort studies that were started during the 1960s in India. The age-standardized (world population of 1960) annual incidence rate of leukoplakia was 3.3/1000 in men and 1.9/1000 in women among more than 20,000 villagers in Kerala, India (Mehta et al., 1976). The International Agency for Research on Cancer’s (IARC) ongoing oral cancer screening study in that area found annual incidence rates of leukoplakia to be 5.5/1000 in males and 3.6/1000 in females (Sankaranarayanan et al., 2000; Sankaranarayanan and Somanathan, 2002). The variability in study design and diagnostic criteria accounts for much of the observed variations in incidence rates and prevalence, although differences among populations with respect to risk factor prevalence clearly explain the major differences between high risk and low risk areas. Much less is known concerning the epidemiology of erythroplakia. It is found much less frequently than leukoplakia. Erythroleukoplakia is even less common, perhaps because of difficulties related to lack of standardization in recognizing such lesions. Oral submucous fibrosis (OSF) seems to be more common in high risk areas in India and Southeast Asia. The prevalence of OSF in India is in the range of 0.2%–1.2%; and the annual incidence rates are 8–21/100,000 in men and 29–46/100,000 in women (Sankaranarayanan and Somanathan, 2002). As with leukoplakia, variations in morbidity rates are largely attributable to variability in the prevalence of risk factors and in disease definition. The fact that these lesions are more common in women may be due to the greater prevalence of nutritional deficiencies among women in India. The presence of leukoplakia often indicates underlying areas of dysplasia or invasive lesions upon histologic assessment. The likelihood varies with the anatomic site and is highest for lesions in the floor of the mouth (43%) and lowest for those in the retromolar area (11%) (Waldron and Shafer, 1975). On the other hand, more than twothirds of erythroplakia lesions contain areas of histologically verifiable dysplasia or carcinoma (Sankaranarayanan and Somanathan, 2002), which underscores the clinical relevance of these lesions and the need to confirm all diagnoses via biopsy.

Etiology In general, risk factors for leukoplakia and its related lesions are the same as those documented for invasive cancers of the oral cavity. Tobacco smoking and chewing are well established as the major risk factors for leukoplakia. The association between oral leukoplakia and tobacco consumption satisfies all conventional criteria for causality, being strong, consistent, temporally verified, biologically plausible, and with a demonstrable dose-response relationship (IARC 1985, 1986). Moreover, lesion risk decreases significantly upon cessation of tobacco consumption (Mehta et al., 1982; Gupta et al., 1986). The various forms of smokeless and smoking tobacco products used in Southeast Asia [i.e., betel quid, bidis, and betel nut (arecanut)] have been shown to influence the risk of leukoplakia, with more intensive consumption habits being associated with a relative risk in the double digits (Sankaranarayanan and Somanathan, 2002). Arecanut and betel quid chewing have emerged as important risk factors for OSF in India (Maher et al., 1994). Tobacco smoking is an important risk factor for leukoplakia and oral epithelial dysplasia in Europe and North America (Morse et al., 1996; Jaber et al., 1999). Alcohol consumption appears to exert a moderate effect on the risk of oral leukoplakia, although study results have been inconsistent (Sankaranarayanan and Somanathan, 2002). It is still uncertain whether alcohol acts independently of tobacco or acts to potentiate the effects of tobacco (van der Waal et al., 1997). As with oral cancers, consumption of fruits and vegetables seems to protect against the risk of leukoplakia (Gupta et al., 1999). Serum levels of vitamins A, B12, and C and b-carotene were found to be inversely associated with the risk of leukoplakia in a case-control study in India (Ramaswamy et al., 1996). Body mass index has also been shown to be inversely associated with the risk of leukoplakia, but concerns about confounding effects due to low socioeconomic status and undernutrition must be resolved before this variable can be considered as being on the causal pathway to risk (Hashibe et al., 2000).

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There is some epidemiologic evidence to support an etiologic role for human papillomavirus (HPV) infection in oral cancers, but the overall picture is less coherent than the role these viruses play in anogenital cancer (Herrero et al., 2003). Two comprehensive literature overviews found that HPV DNA is present in biopsy specimens from 15%–20% of patients with leukoplakia, a figure that is intermediate between levels observed for normal oral mucosa and that affected by squamous carcinoma (Franceschi et al., 1996; Miller and White, 1996). Potentially precancerous oral lesions are also found more frequently in human immunodeficiency virus (HIV)-positive individuals. Hairy leukoplakia is a common finding in immunosuppressed HIV patients and is associated with abundant replication of the Epstein-Barr virus (Grbic and Lamster, 1997; Ikediobi and Tyring, 2002). Oral warts in these individuals are caused by oral HPV infection, a condition that has increased in frequency in the era of highly active antiretroviral therapy (Hille et al., 2002). Despite these clinical associations, there is no evidence that the incidence of oral cancer is increased in patients with long-term follow-up following HIV infection.

Progression to Cancer The propensity for progression to invasive carcinoma is relatively high for lesions showing leukoplakia, but rates vary widely as a function of study design, patient population, diagnostic criteria, follow-up time, and case selection for follow-up. Malignant transformation occurs in 0.1%–18.0% of precursor lesions after periods of 1–40 years (Silverman, 2003). An actuarial analysis of cohort studies from high risk areas indicated annual rates of progression of 0.1%–17.0%, depending on the prevalence of smoking and the study site. Hospital-based series tend to yield higher rates of progression than community-based cohorts (Sankaranarayanan and Somanathan, 2002). Lesion regression has been measured at 20%–30% over periods ranging from 1 to 30 years (Pindborgh et al., 1968; Silverman et al., 1976; Banoczy, 1977). Erythroplakias and erythroleukoplakias have the highest potential to become invasive because they contain more extensive areas of dysplasia and carcinoma in situ (Scully et al., 2003). Invasive carcinomas are associated 17 times more frequently with these lesions than with leukoplakias (Shafer and Waldron, 1975). Recently, there has been considerable interest in identifying molecular markers for the risk of progression in oral precursor lesions (Sudbø et al., 2001; Epstein et al., 2002). Accumulation of genomic deletions or amplification at specific sites in chromosomes 3 and 9 has been found to predict the subsequent risk of malignancy (Mao et al., 1996). Loss of heterozygosity in the latter sites and in chromosomes 4, 8, 11, 13, and 17 has been equated with progression to invasive carcinoma in 50% of cases within 5 years versus only 2% in those lacking such molecular changes (Partridge et al., 2000; Zhang et al., 2001).

Esophagus There are two main histologic types of cancer of the esophagus: squamous cell carcinoma and adenocarcinoma. Precursors of the former are not particularly well defined, although there is some suggestion that there is an ordered sequence of events leading from esophagitis (which is thought to develop as a result of chronic gastroesophageal reflux disease (GERD) (Sonnenberg and El-Serag, 1999) ), to low and high grade dysplasia, and then to squamous cell carcinoma (Vaughan, 2002). Esophagitis is generally thought to be a relatively early lesion in the natural history of adenocarcinoma despite some evidence to the contrary (Cameron and Arora, 2002). According to one model of the natural history of adenocarcinoma, individuals with esophagitis may develop specialized intestinal metaplasia (Barrett’s esophagus) as a metaplastic reaction to GERD and then progress through stages of low and high grade dysplasia before developing esophageal adenocarcinoma (Vaughan, 2002). As with cancer precursors at other anatomic sites (e.g., those of the cervix (Holowaty et al., 1999) ), low and high grade dysplasia appear to be capable of regressing to earlier stages (Miros et al., 1991; O’Connor et al., 1999). There has been little etiologic study of putative precursors of squamous cell carcinoma. The remainder of this section is devoted to a discussion of the epidemiology of Barrett’s esophagus.

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A

Figure 3–1. Barrett’s esophagus. A. Villiform structures are lined by tall columnar cells with mucin-containing cytoplasm. B. This villiform structure contains goblet cells intermixed with the tall columnar cells. No dysplasia is seen. C. High-grade dysplasia. The surface epithelium and the glands contain highly atypical cells with large hyperchromatic and overlapping nuclei.

B

C

Pathology

Descriptive Epidemiology

Histologically, Barrett’s esophagus consists of two cell types: columnar mucin-producing cells and goblet cells that line villiform structures. Glands in the lamina propria are also lined by intestinalized columnar and goblet cells. Paneth and endocrine cells may also be present. With low grade dysplasia, the glands present in the lamina propria are lined by pseudostratified columnar or elongated cells with large hyperchromatic nuclei. These abnormal glands often resemble those of tubular adenomas of the colon with mild dysplasia. The dysplastic cells usually extend to the surface epithelium. With high grade dysplasia, there is greater cytologic atypia in both the glands and the surface epithelium, as well as increased mitotic activity (Fig. 3–1). Pathological evaluation is the basis for patient stratification and management. The histologic classification of Barrett’s dysplasia is identical to the classification of dysplasia in inflammatory bowel disease (Table 3–2). Standard definitions for grades of dysplasia have been published (Reid et al., 1988).

Barrett’s esophagus is usually diagnosed by biopsy during the course of esophagogastroduodenoscopy (EGD) for investigation of GERD, the symptoms of which include heartburn, regurgitation, and dysphagia. Estimates of the prevalence of Barrett’s esophagus among those with GERD have ranged from 6% to 14% (Gruppo Operativo per lo Studio delle Precancerosi delle’Esofago, 1991; Lieberman et al., 1997; Bersentes et al., 1998; DeVault and Castell, 1999; Voutilainen et al., Table 3–2. Classification of Epithelial Dysplasia in Barrett’s Esophagus Negative Indefinite for dysplasia Positive Low grade (mild/moderate dysplasia) High grade (severe dysplasia and carcinoma in situ)

Cancer Precursors 2000; Conio et al., 2001; Eloubeidi and Provenzale, 2001; Gerson et al., 2001; Shaheen and Ransohoff, 2002; van Sandick et al., 2002). The prevalence of Barrett’s esophagus increases with age (Cameron and Lomboy, 1992), and it is higher among males than females (DeMas et al., 1999; Campos et al., 2001; Gerson et al., 2001) and among whites than other ethnic groups (Kim et al., 1997). On a population basis, the prevalence of diagnosed Barrett’s esophagus appears to have risen substantially during the past few years; for example, in Olmsted County, Minnesota it increased from 22.6/100,000 in 1987 to 82.6/100,000 in 1998 (Conio et al., 2001). Although some of this increase probably reflects increased detection because of greater use of upper gastrointestinal endoscopy in more recent years (Conio et al., 2001), the sharp rise in the esophageal adenocarcinoma incidence rates over a similar period (Blot and McLaughlin, 1999) suggests that at least some of the increase in Barrett’s esophagus diagnoses may be real (Prach et al., 1997). However, it seems that many cases of Barrett’s esophagus remain undiagnosed because in the same Minnesota population in which an increase in the prevalence of diagnosed Barrett’s esophagus was demonstrated the prevalence of Barrett’s esophagus at autopsy was shown to be 376/100,000 during the mid-1980s, which is much higher than the prevalence of clinically diagnosed Barrett’s esophagus (Cameron et al., 1990).

Etiology There is now considerable evidence that Barrett’s esophagus has an acquired rather than a congenital etiology (Kim et al., 1997). Much of the emphasis in etiologic studies of Barrett’s esophagus has been on factors that have been shown to be associated with a risk of esophageal adenocarcinoma. Among the latter, major risk factors include GERD (Lagergren et al., 1999b) and a relatively high body mass index (BMI) (Lagergren et al., 1999a). Other risk factors for adenocarcinoma include cigarette smoking and alcohol consumption, both of which are associated with moderately increased risk (Vaughan, 2002), and infection with the cagA+ strain of Helicobacter pylori, which is associated with reduced risk (Chow et al., 1998). A relatively high BMI [weight (kg)/height (m2)] is associated with a substantial increase in the risk of adenocarcinoma (Vaughan, 2002). Moreover, although the relation between the BMI and Barrett’s esophagus has received little attention to date, several studies have presented evidence in support of a positive association between obesity and GERD (Fisher et al., 1999; Locke et al., 1999; Wajed et al., 2001; Vaughan, 2002), although one did not (Lagergren et al., 2000). To date, most of the emphasis in etiologic studies of Barrett’s esophagus has been on the role of GERD (Kim et al., 1997). Avidan and colleagues (2001, 2002) studied consecutive patients undergoing endoscopy to compare subjects with Barrett’s esophagus with those found not to have this condition. They observed that hiatal hernia and the duration or severity of acid reflux predisposed to Barrett’s esophagus (Avidan et al., 2002), whereas gastric surgery did not (Avidan et al., 2001). Others have also shown that the duration of GERD and the severity of symptoms are factors that predispose to the development of Barrett’s esophagus. For example, Eloubeidi and Provenzale (2001) observed an increased risk with increased frequency of heartburn, and Conio et al. (2002) showed that GERD symptom frequency and the presence of hiatal hernia increased the risk of Barrett’s esophagus. Gerson et al. (2001) showed that heartburn, nocturnal pain, and odynophagia were associated with increased risk. Campos et al. (2001) found that increased frequency of reflux episodes that lasted more than 5 minutes was associated with a twofold increase in the risk of Barrett’s esophagus, as did GERD symptoms of more than 5 years’ duration. Eisen et al. (1997) compared 79 patients with Barrett’s esophagus to 180 age-, gender-, and race-matched controls. This study had two control groups: one had undergone endoscopy for GERD, and the other had undergone endoscopy for other indications. Earlier age at reflux symptom onset, longer duration of symptoms, esophagitis, stricture, and ulceration were all associated with increased risk of Barrett’s esophagus, regardless of the control group. The GORGE consortium is a community-based study of 2641 consecutive patients undergoing elective endoscopy for GERD. In a report based on 701 of these patients, 77 of whom had Barrett’s esophagus, Lieberman et

27

al. (1997) found that the presence of GERD symptoms for 1–5 years was associated with an odds ratio of 3.0 for risk of Barrett’s esophagus; the ratio increased to 6.4 for those who had had symptoms for more than 10 years. The length of the esophagus showing the specialized metaplastic changes of Barrett’s esophagus seems to be related to the risk of esophageal adenocarcinoma. Individuals with long-segment Barrett’s esophagus (LSBE) (≥3 cm in length) are at higher risk than those with the more common short-segment Barrett’s esophagus (SSBE) (Hirota et al., 1999). In relation to this, Avidan et al. (2002) studied 502 consecutive patients with GERD determined by pH monitoring, 174 of whom had Barrett’s esophagus (67 SSBE, 107 LSBE). They showed that hiatal hernia and frequent reflux episodes had positive associations with the Barrett’s esophagus segment length (Avidan et al., 2002). The length of the longest reflux episode was also associated with a much higher risk of LSBE than SSBE. Cameron (1999) compared 46 patients with SSBE to 103 controls and found that 96% of the patients and 42% of the controls had a hiatal hernia. Fass et al. (2001) found a correlation between the amount of esophageal acid exposure and the length of Barrett’s esophagus. In general, although the severity of GERD symptoms and the presumed extent of acid exposure also seem to correlate with increased length of Barrett’s esophagus, this has not been a consistent finding across studies. Although the relation between BMI and Barrett’s esophagus has received little attention to date, several studies have presented evidence to support a positive association between obesity and GERD (Fisher et al., 1999; Locke et al., 1999; Wajed et al., 2001), although one did not (Lagergren et al., 2000). Vaughan (2002) reported data from a cross-sectional study that showed a twofold increase in the risk of developing Barrett’s esophagus in patients in the highest BMI quartile compared to those in the lowest quartile. The study by Avidan et al. (2002) found that smoking and high alcohol intake were associated with increased risk of Barrett’s esophagus, especially LSBE; in the same study, the use of nonsteroidal antiinflammatory drugs (NSAIDs) was not associated with Barrett’s esophagus. Vaughan (2002) has also reported an increased risk of Barrett’s esophagus among cigarette smokers. There is some evidence that CagA+ strains of Helicobacter pylori might be protective against GERD and Barrett’s esophagus. This finding perhaps reflects reduced acid production as a consequence of gastritis and gastric atrophy (Vicari et al., 1998; Loffeld et al., 2000; Vaezi et al., 2000).

Progression to Cancer Individuals with Barrett’s esophagus are at high risk of developing esophageal adenocarcinoma (Vaughan, 2002). Indeed, the risk of patients with Barrett’s esophagus developing adenocarcinoma has been estimated to be about 0.5%–1.0% per year, a 30- to 125-fold higher risk than that of the general population (Kim et al., 1997; Heath et al., 2000). The estimates of risk are somewhat imprecise because most studies have followed relatively small numbers of study subjects for short periods of time and therefore have observed only a few incident cancers each. In addition, some cohorts have included patients referred to specialist centers, as a result of which individuals with high grade dysplasia may have been overrepresented (O’Connor et al., 1999). As indicated earlier, the risk of progression to adenocarcinoma appears to be directly related to the length of esophagus showing the specialized metaplastic changes of Barrett’s esophagus (Hirota et al., 1999). It is also directly related to the extent of high grade dysplasia found in Barrett’s esophagus (Buttar et al., 2001). Progression from Barrett’s esophagus to high grade dysplasia has been estimated to take 9–13 years and from high-grade dysplasia to adenocarcinoma 3–4 years (O’Shaughnessy et al., 2002).

Colorectum Most colorectal cancers are thought to arise from adenomatous tissue, a progression referred to as the adenoma–carcinoma sequence (Morson, 1974; Hill et al., 1978). Histologically, the latter reflects the

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progression of normal epithelium through a stage of epithelial dysplasia—the distinguishing histologic feature of colorectal adenomas (Compton, 2000)—and then on to invasive cancer. Recent data suggest that the adenomas themselves might be preceded by lesions called aberrant crypt foci (Bird, 1987; Takayama et al., 1998). There is little in the way of direct evidence for an adenoma–carcinoma sequence, but the designation of colorectal adenomas as cancer precursors is supported by the frequent presence of carcinomas within adenomas (Morson, 1974; Muto, 1989) and by the fact that the spectrum of somatic genetic changes observed in adenomas places them in an intermediate position in the progression from normal mucosa to invasive carcinoma (Baron, 2002). Furthermore, the distribution of large (>1 cm) adenomas in the bowel is similar to that of colorectal cancer (Konishi and Morson, 1982; Matek et al., 1986). Other potential colorectal cancer precursor lesions include hyperplastic polyps (Hamilton, 2001), the commonest type of polyp detected in the colorectum (Jass, 1991), and the morphologically similar serrated adenomas (Jass, 1999) as well as chronic inflammatory bowel disease, hamartomatous polyps, mixed adenomatoushyperplastic polyps, flat adenomas, and dysplastic aberrant crypt foci (Takayama et al., 1998; Hamilton, 2001). Epidemiologically, however, these lesions have been less well studied than adenomatous polyps, on which the remainder of this section focuses.

A

Pathology The formation of aberrant crypt foci (ACF) is considered one of the earliest histologic events in the development of colorectal cancer (Takayama et al., 1998; Fenglio-Preiser and Noffsinger, 1999). ACF are larger than normal crypts, stain more intensely with methylene blue, and are seen to have either a bulging or concave surface on endoscopy. ACF are found throughout the colon and rectum, although they are more common in the distal colon. Adenomas, which are thought to arise from ACF, are benign glandular neoplasms that arise from the intestinal mucosa and contain dysplastic epithelium. They may be solitary or multiple and sporadic or hereditary. Histologically, adenomas are classified as tubular (most common type), villous, or mixed tubulovillous (Fig. 3–2). Adenomas can grow to be more than 5 cm in diameter.

B

Descriptive Epidemiology Given that colorectal adenomas are usually asymptomatic, it is difficult to obtain unbiased estimates of their frequency of occurrence (Peipins and Sandler, 1994). Indeed, most of the available estimates have come from autopsy and screening studies, both of which usually involve selected groups of individuals. Autopsy studies have generally shown positive associations between the prevalence of adenomas and the risk of colorectal cancer in the corresponding underlying population (Correa, 1978; Clark et al., 1985), with prevalence estimates ranging from zero among the Bantu in South Africa (Bremner and Ackerman, 1970), a population at low risk of colorectal cancer, to between 40% (Blatt, 1961) and 60% (Stemmermann and Yatani, 1973) in the United States, a population at relatively high risk. The prevalence of adenomas increases with age, and in high risk populations (e.g., the United States) it has been observed to exceed 50% after age 65 (Neugut et al., 1993). Also, the prevalence of adenomas is generally higher in males than in females, particularly among the middleaged (Neugut et al., 1993). There appear to be no estimates of the incidence of adenomas (Peipins and Sandler, 1994).

Figure 3–2. Tubular adenoma of the colon. A. Tiny tubular adenoma in a patient with familial adenomatous polyposis. Although the normal architecture of the mucosa is maintained, some superficial crypts show adenomatous changes that are easily distinguished from the adjacent normal glands. B. Tubular adenoma with intramucosal carcinoma. A cribriform structure is seen in the lamina propria underneath a tubular adenoma. C. Tubular adenoma and infiltrating adenocarcinoma. The adenocarcinoma is composed of closely packed neoplastic glands different from those of the overlying tubulovillous adenoma.

C

Cancer Precursors

Etiology Much of the emphasis in etiologic studies of colorectal adenomas has been on diet, although other factors have been studied including alcohol consumption, cigarette smoking, BMI, physical activity, and use of hormone replacement therapy. Most of the evidence concerning the roles of these factors has accrued from case-control studies. Although recall bias may not be a major issue in case-control studies of adenoma, given that adenomas are often asymptomatic, selecting an appropriate comparison group can be problematic, given the need to enroll adenoma-free subjects from the same source population as the cases (Potter, 1996). Failure to accomplish this can lead to misclassification of the controls and as a consequence can bias estimates of association conservatively. Therefore, in many etiologic studies of polyps, cases have been subjects whose polyps were identified through sigmoidoscopy or colonoscopy, and controls have been subjects found on screening to be polyp-free. Essentially, therefore, many of the etiologic studies to date have focused on polyp prevalence rather than polyp incidence. Despite these caveats, many of the risk factors for colorectal adenomas appear to be similar to those for colorectal cancer (Baron, 2002). The risk of developing colorectal adenomas has been shown to be associated inversely with physical activity (Kato et al., 1990; Giovannucci et al., 1995, 1996; Sandler et al., 1995; Enger et al., 1997; Kono et al., 1999; Terry et al., 2002) and positively with the BMI and waist-to-hip ratio (Neugut et al., 1991; Shinchi et al., 1994; Giovannucci et al., 1995; Giovannucci et al., 1996; Bird et al., 1998; Kono et al., 1999); the associations were independent of each other in one study (Giovannucci et al., 1995) but not in another (Giovannucci et al., 1996). The association with obesity might result from hyperinsulinemia or insulin resistance (Kono et al., 1999), whereas the association with physical activity might result from a decrease in secondary bile acids and/or bowel transit time or from altered prostaglandin levels, with consequent inhibitory effects on colonic cell proliferation (Enger et al., 1997). Hormone replacement therapy, which can also affect bile acid profiles, has been associated with a decreased risk of colorectal adenoma (Potter et al., 1996; Peipins et al., 1997; Chen et al., 1998; Grodstein et al., 1998). Cigarette smoking has been found consistently to be associated with increased risk of colorectal adenomas (Martinez et al., 1995; Giovannucci and Martinez, 1996; Nagata et al., 1999; Almendingen et al., 2000; BreuerKatschinski et al., 2000; Inoue et al., 2000; Ulrich et al., 2001; Erhardt et al., 2002). The relation may be causal, given that it is strong, dosedependent, and generally persists after controlling for potential confounding variables such as diet and alcohol consumption (Giovannucci and Martinez, 1996). The dietary factors studied most extensively in relation to adenoma risk include fat and fiber intake, as well as intake of fruits and vegetables. To date, the findings have been considerably inconsistent. Specifically, although some case-control studies (Giovannucci et al., 1992; Sandler et al., 1993; Martinez et al., 1996; Almendingen et al., 2002) and cohort studies (Giovannucci et al., 1992) have suggested that risk is increased in association with total fat intake, other casecontrol studies have not found an association (Macquart-Moulin et al., 1987; Benito et al., 1993; Little et al., 1993; Olsen et al., 1994; BreuerKatschinski et al., 2001; Nagata et al., 2001; Voskuil et al., 2002). Several case-control and prevalence studies have shown inverse associations between total fiber intake or intake of cereal, vegetable, and/or fruit fiber and the risk of developing colorectal adenoma (Benito et al., 1993; Martinez et al., 1996; Breuer-Katschinski et al., 2001; Almendingen et al., 2002; Peters et al., 2003), whereas others, both case-control studies (Little et al., 1993; Platz et al., 1997) and cohort studies (Fuchs et al., 1999), have shown either equivocal findings or no association. Findings for fruit and vegetable intake have also been variable, with some studies showing inverse associations with either or both factors (Kato et al., 1990; Benito et al., 1993; Sandler et al., 1993; Platz et al., 1997; Almendingen et al., 2001) or no association (Nagata et al., 2001; Senesse et al., 2002; Smith-Warner et al., 2002). Other dietary factors that have been examined in relation to adenoma risk include red meat and fish/poultry intake, for which there

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is some evidence for positive and inverse associations, respectively (Yoon et al., 2000), and folate intake, for which there is some evidence for an inverse association, especially in conjunction with relatively high alcohol consumption (Giovannucci et al., 1993; Bird et al., 1995; Boutron-Ruault et al., 1996). Alcohol consumption itself has been associated with increased risk in some studies (Giovannucci et al., 1993; Martinez et al., 1995; Boutron-Ruault et al., 1996; Tiemersma et al., 2003), but other studies have shown null associations or only weak increases in the risk (Benito et al., 1993; Lubin et al., 1997; Nagata et al., 1999; Breuer-Katschinski et al., 2000). The potential mechanisms by which dietary factors might influence colorectal cancer risk have been discussed elsewhere (Potter, 1999).

Progression to Cancer Colorectal cancer is generally thought to develop over several decades (Giovannucci and Martinez, 1996). Although the time required for an adenoma to develop following an initiating event is unknown (Giovannucci and Martinez, 1996), it has been suggested that it is of the order of 5–20 years (O’Shaughnessy et al., 2002). With respect to the time required for progression from adenoma to cancer, the average age at diagnosis of colorectal adenoma patients is about 7–8 years less than that of colorectal cancer patients (Enterline, 1976). However, this is likely to underestimate the interval between the occurrence of a polyp and the subsequent occurrence of cancer given the inevitable uncertainty that exists regarding the time of onset of polyps (Muto et al., 1975). Indeed, evidence from studies of metachronous cancer rates and age distribution curves suggests that progression from adenoma to carcinoma requires 10–15 years on average (Muto et al., 1975; Day and Morson, 1978). Approximately 50% of patients with an adenomatous polyp develop a subsequent (recurrent) adenoma within 7.6 years (Yood et al., 2003). Furthermore, data collected prior to the routine use of sigmoidoscopy, colonoscopy, and polypectomy showed that the cumulative risk of colon cancer at intervals of 5, 10, and 20 years after discovery of an index polyp was 4%, 14%, and 35%, respectively (Stryker et al., 1987). The risk of patients with polyps developing colorectal cancer is two- to fourfold higher than that for the general population (Lotfi et al., 1986; Atkin et al., 1992; Otchy et al., 1996). The risk of progression is related to the size, histologic type, and degree of dysplasia in the index adenoma. It is higher for those with polyps that are large, have villous architecture, and exhibit severe dysplasia (Peipins and Sandler, 1994). Polypectomy is associated with a reduction in the incidence of colorectal carcinoma (Winawer et al., 1993; Zheng et al., 2002).

Breast Benign breast disease (BBD) is a heterogeneous condition consisting of many histologic entities (see below). The prevailing hypothesis concerning the natural history of breast cancer is that nonatypical proliferative forms of BBD, proliferative disease with atypia, and in situ cancer represent successive steps preceding the development of invasive breast carcinoma (Lakhani, 1999). This model is supported by experimental and epidemiologic evidence. Experimentally, xenografts of MCF10AneoT cells have been shown to progress from intraductal proliferative changes to lesions resembling atypical hyperplasia of the human breast and ultimately to lesions resembling carcinoma in situ (Miller et al., 1993). Also, a transgenic rat model (Davies et al., 1999) and a mouse model (Li et al., 2000) have demonstrated the stepwise development of breast cancer. In epidemiologic studies, the risk of subsequent breast cancer has been observed to be increased in women with proliferative epithelial disorders affecting the small ducts and the terminal ductal lobular units of the breast, particularly when epithelial proliferation is accompanied by evidence of atypia (Rohan and Kandel, 2002; Schnitt, 2003). The higher risk associated with atypia is consistent with the notion that it is more proximal to carcinoma than is proliferative disease without atypia. As a result of such findings, benign proliferative epithelial disorders (BPED) of the breast are thought to have malignant potential

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(Wang and Fentiman, 1985). Our focus in the remainder of this section is on BPED of the breast.

Pathology Although many histologic entities are included in the rubric “benign breast disease,” the relevant lesions with respect to the risk of subsequent breast cancer are those of epithelial origin. In addition to hyperplasia with or without atypia, these lesions include sclerosing adenosis, solitary papilloma, and fibroadenoma (Bodian, 1993; Fitzgibbons et al., 1998). Ductal epithelial hyperplasias display a spectrum of changes ranging from mild to florid. They are classified further as proliferative disease without atypia or atypical ductal hyperplasia depending on the architectural patterns and the cytologic appearance of the cells. Atypical ductal hyperplasia (ADH) is considered to be the precursor of ductal carcinoma in situ (DCIS) (a spectrum of diseases characterized by noninvasive epithelial proliferation), and it has some morphologic features of the cribriform and micropapillary types of in situ carcinoma. It involves only a portion of a single duct or several ducts, of which the aggregate sectional diameter does not exceed 2 mm.

Descriptive Epidemiology An unknown proportion of women with benign breast disease come to clinical attention and proceed to biopsy (Rohan et al., 1998b). Therefore, it is difficult to measure the population prevalence of benign breast lesions overall or by histologic subtype. Nevertheless, estimates of the frequency of occurrence of BPEDs can be obtained from autopsy and epidemiologic studies. Autopsy studies of nonfatal conditions in which the included subjects represent an unselected series can provide estimates of the prevalence of the condition at death (Cook and Rohan, 1985). Data on the prevalence of BPEDs of the breast are available from several autopsy studies (Frantz et al., 1951; Sloss et al., 1957; Sandison, 1962; Humphrey and Swerdlow, 1966; Kramer and Rush, 1973; Sasano et al., 1978; Nielsen et al., 1984; Alpers and Wellings, 1985; Bhathal et al., 1985; Nielsen et al., 1987; Sarnelli et al., 1991). Although there were some differences between the studies in their use of histopathologic terminology, they do indicate that BPEDs of the breast are relatively common at death, with prevalence estimates ranging from around 5%–15% (Frantz et al., 1951; Sandison, 1962) to as high as 64% (Nielsen et al., 1987). Furthermore, in most of these series, the prevalence of proliferative epithelial disorders substantially exceeded that of occult carcinoma of the breast, suggesting that even if BPEDs of the breast are precursors of breast cancer they do not necessarily progress to cancer. There are no published estimates of the incidence rates of BPEDs of the breast. However, incidence rates of broader groupings of benign breast disease (e.g., fibrocystic disease or benign mammary dysplasia) have been shown with considerable consistency to increase rapidly with age until about 40–44 years, with peak incidence rates being somewhere between 200 and 400/100,000/annum, and to decrease rapidly thereafter (Ory et al., 1976; Cole et al., 1978; Brinton et al., 1981; Soini et al., 1981; Fleming et al, 1982). Nevertheless, the disease remains relatively common after menopause, with estimates of the annual incidence rate ranging from about 100/100,000 women during the early postmenopausal years to 20–30/100,000 women during the later postmenopausal years (Cook and Rohan, 1985).

Etiology Studies of the etiology of putative breast cancer precursors have focused largely on factors suspected to be involved in the etiology of breast cancer itself (e.g., menstrual and reproductive history, use of oral contraceptives and hormone replacement therapy, cigarette smoking, obesity, and more recently diet). This follows from the premise that if specific types of benign breast lesions are precursors of breast cancer, then factors related to the etiology of the former should be a subset of those related to the latter. There have now been several case-control studies (Lance, 1981; Soini et al., 1981; Parazzini et al., 1984, 1991; Berkowitz et al., 1985; Pastides et al., 1985; Bright et al., 1989; Ingram et al., 1989, 1991;

Rohan and Cook, 1989; Rohan et al., 1989a,; London et al., 1992; Minami et al., 1998) and cohort studies (Hsieh et al., 1984; Rohan et al., 1998b; Rohan, 1999; Friedenreich et al., 2000) on the etiology of BPEDs of the breast. These studies have reported on the risk of such lesions overall (Lance 1981; Soini et al., 1981; Parazzini et al., 1984, 1991; Pastides et al., 1985; Bright et al., 1989; Ingram et al., 1989, 1991; Rohan et al., 1989; London et al., 1992; Minami et al., 1998) or on the risk by the degree of epithelial proliferation or cytologic atypia displayed in the benign lesions (Hsieh et al., 1984; Berkowitz et al., 1985; Pastides et al., 1985; Rohan and Cook, 1989; Rohan et al., 1989). Of these studies, none of those that examined age at menarche showed an association with risk (Soini, 1981; Parazzini et al., 1984; Berkowitz et al., 1985; Pastides et al., 1985; Ingram et al., 1991; London et al., 1992; Minami et al., 1998; Rohan et al., 1998b); two (Lance 1981; Parazzini et al., 1984) observed positive associations with age at first pregnancy, whereas in the remainder there was no association (Soini et al., 1981; Hsieh et al., 1984; Berkowitz et al., 1985; Pastides et al., 1985; London et al., 1992); one (Minami et al., 1998) showed an inverse association with parity, whereas the reminder showed no association (Soini et al., 1981; Hsieh et al., 1984; Parazzini et al., 1984; Berkowitz et al., 1985; Pastides et al., 1985; Bright et al., 1989; Ingram et al., 1991; London et al., 1992; Rohan et al., 1998b; Minami et al., 1998); several (Rohan and Cook, 1989; Ingram et al., 1991; Rohan et al., 1998b) showed increased risk for those with a family history of breast cancer, whereas others (Berkowitz et al., 1985; Pastides et al., 1985; Bright et al., 1989; London et al., 1992) observed no association; three (Ingram et al., 1989; Rohan and Cook, 1989; Rohan et al., 1998b) yielded findings suggesting an inverse association with the BMI, whereas three others (Berkowitz et al., 1985; Pastides et al., 1985; Bright et al., 1989) did not observe such an association; and to date, no study has found as association with cigarette smoking (Berkowitz et al., 1985; Pastides et al., 1985; Rohan et al., 1989; Parazzini et al., 1991; Rohan, 1999). Other variables that have been studied include use of oral contraceptives (OCs), use of hormone replacement therapy (HRT), and diet. Two studies, one cohort study (Rohan and Miller, 1999b) and one case-control study (Rohan et al., 1992), have presented results for the association between OC use and risk of BPED, and several other studies (all case-control) have reported on the association between OC use and the risk of BBD by degree of histologic atypia (Li Volsi et al., 1978; Kampert et al., 1983; Pastides et al., 1983; Berkowitz et al., 1984; Hsieh et al., 1984; Rohan et al., 1992). With respect to the former, one study (Rohan and Miller, 1999a) showed that the risk of BPED was reduced in association with OC use; the other (Rohan et al., 1992) showed no association. Findings for the latter association have varied from those showing reduced risk of all grades of atypia (Kampert et al., 1983; Hsieh et al., 1984) to those showing no reduction in risk with any grade of atypia (Rohan et al., 1992). With respect to HRT use, one recent prospective study (Rohan and Miller, 1999b) observed increased risk of BPED of the breast in association with HRT use of more than 8 years, but an earlier case-control study, in which the association between HRT use and risk of BBD was examined by the degree of cytologic atypia, found no evidence for a linear relation between the degree of atypia and the risk (Berkowitz et al., 1984). The association between diet and risk of BPED has been examined in several case-control studies (Lubin et al., 1989; Hislop et al., 1990; Rohan et al., 1990; Ingram et al., 1991; London et al., 1993) and cohort studies (Rohan et al., 1998c). In two studies (Lubin et al., 1989; Hislop et al., 1990), there were positive associations between saturated fat intake (or indices thereof) and risk of atypical (Lubin et al., 1989) or proliferative (Hislop et al., 1990) forms of benign breast disease, whereas in the remainder (Rohan et al., 1990; Ingram et al., 1991; London et al., 1993; Rohan et al., 1998c) there was little support for an association with dietary fat. With respect to other nutrients, one study provided some evidence for inverse associations between retinol and b-carotene intake and risk (Rohan et al., 1990) and showed strong inverse associations with dietary fiber and its constituents (soluble and insoluble nonstarch polysaccharides and cellulose) (Baghurst and Rohan, 1995). Although these findings were supported to some extent

Cancer Precursors by those of another study (Ingram et al., 1991) in which risk of benign epithelial hyperplasia was reduced in association with consumption of fruit and leafy orange-red vegetables, in yet another study (London et al., 1992) carotene and retinol intake were not associated with the risk of atypical or nonatypical forms of BPED. One study (Rohan et al., 1998c) showed no association between dietary calcium intake and risk of BPED. Between-study differences in the results described above may have arisen from the fact that studies of biopsy-confirmed BBD (as with studies of other potentially premalignant conditions) are prone to selection bias because (as indicated earlier) an unknown proportion of women with BBD come to clinical attention. The differences, however, may also have resulted from differences in the distribution of case groups by histologic subcategory of BBD and also in the method of classifying BBD (Cook and Rohan, 1985). For these reasons, there is a need for studies in which the histologic classification is based on a standardized classification scheme and in which the problem of selection bias is minimized. The latter might be addressed by conducting studies in screened populations (Dubin and Pasternack, 1984).

Progression to Cancer Women with a history of BBD are at increased risk of developing breast cancer, and risk differs according to the histologic characteristics of BBD (Rohan and Kandel, 2002; Schnitt, 2003). Depending on the reference group used, the relative risk for subsequent development of breast cancer in women with proliferative disease without atypia have ranged from 1.3 (Palli et al., 1991; Dupont et al., 1993) to 7.3 (Minami et al., 1999) but mostly have been of the order of 2.0 (Rohan and Kandel, 2002). The relative risk for women with proliferative disease with atypia has ranged from 2.5 (Carter et al., 1988) to 16.0 (Minami et al., 1999), but most authors have reported values between 3.0 and 5.0 (Rohan and Kandel, 2002; Schnitt, 2003). Although there appears to be no information on the time required for the development of the early morphologic stages preceding breast cancer development (i.e., proliferative disease without or with atypia), estimates of the time interval between a diagnosis of proliferative disease with atypia and the development of invasive carcinoma are available. In a follow-up study of 150 women with atypical ductal hyperplasia (Page et al., 1985), 18 women developed invasive breast cancer during follow-up; the mean interval between the diagnosis of hyperplasia and diagnosis of the subsequent invasive breast cancer was 8.2 years (range 1.4–24.3 years), and the annual incidence rate of carcinoma in women with atypical ductal hyperplasia was 7.5/1000 women. The mean interval between DCIS and subsequent invasive breast cancer ranges from 6 to 10 years (Frykberg and Bland, 1993). However, as with other anatomic sites, there is some uncertainty in the foregoing estimates given the difficulty of pinpointing the time of onset of the various lesions (Frykberg and Bland, 1993).

Uterine Cervix Cervical cancer precursors have by far been the most studied of all preinvasive neoplastic conditions, thanks to the work of Papanicolaou more than 50 years ago that led to the widespread acceptance of cervical cytology, or the Pap test, as the most established medical screening test (Papanicolaou, 1954). Indeed, the designation “cancer precursors” evokes the notion of cervical cancer prevention, and in many respects the study of precursor lesions of the uterine cervix has been a paradigm for studying the etiology and natural history of cancer. Cervical cancer ultimately arises from two main histologic lineages depending on whether its precursors originate in squamous or glandular cervical epithelium. Squamous and glandular precursors are described separately.

Squamous Lesions of the Uterine Cervix About 80% of cervical cancers are squamous cell in origin (Platz and Benda, 1995). The natural history of cervical cancer begins as a slow

31

process of disruption of the normal maturation of the transformation zone epithelium of the uterine cervix. Historically, this preinvasive phase has been known variably as dysplasia or dyskariosis under the traditional Pap cytology nomenclature (Papanicolaou, 1954), as cervical intraepithelial neoplasia (CIN) according to the classification scheme of the World Health Organization (WHO) (Richart, 1968), or as squamous intraepithelial lesion (SIL) by the more recent Bethesda classification system (Solomon et al., 1989; Kurman et al., 1991). The latter underwent further revisions in 2001 and is essentially a twotiered system consisting of low grade SIL (LSIL) and high grade SIL (HSIL), supplemented by a dichotomous equivocal atypia category of atypical squamous cells (ASCs), which is qualified either as “of undetermined significance” (ASC-US) or “cannot exclude HSIL” (ASC-H) (Solomon et al., 2002). The Bethesda system has brought the old classification nomenclatures in line with the new knowledge concerning the role of HPV infection as the central causal agent in the genesis of cervical cancer. HPV-associated changes in the absence of other squamous abnormalities are classified as LSIL (which includes CIN 1). More advanced degrees of dysplasia (originally graded as moderate and severe, corresponding to CIN 2 and 3, respectively) and carcinoma in situ (CIS) (originally defined as a lesion encompassing the full thickness of the epithelium or, equivalently, CIN 3 in its most severe form) are included in the HSIL category as a single lesion grade. The Bethesda classification was developed exclusively for cytopathology use to serve as an analogue to the histopathologic classification based on CIN grades. However, owing to its widespread use in North America, the Bethesda classification has occasionally been used to denote histologically ascertained lesions. In Europe, many laboratories still favor the dysplasia/dyskariosis/CIS classification (i.e., mild, moderate, and severe dysplasia and CIS), which makes it difficult to compare the results of studies conducted in different geographic locales.

Pathology According to the extent of the cytologic atypia, cervical squamous dysplasia is categorized as mild (CIN I), moderate (CIN II), or severe (CIN III) (Fig. 3–3). Applying the Bethesda system to these lesions (Solomon et al., 1989; Kurman et al., 1991), mild dysplasia (CIN I) is classified as a low grade squamous intraepithelial lesion (LSIL), and moderate to severe dysplasia and carcinoma in situ (CIN II and CIN III) are classified as high grade squamous intraepithelial lesions (HSIL). The category of atypical cells of undetermined significance (ASCUS) is reserved for borderline cytologic changes. As indicated above, the Bethesda system is now widely used for reporting the results of screening. In biopsy specimens, increased mitotic activity, immature cell proliferation, incomplete or lack of maturation, nuclear pleomorphism, and clumping of the chromatin characterize dysplasia. The excess proliferation is first seen in the basal or reserve cells located along the basement membrane. Dysplasia is considered present when the abnormal cells occupy less than the full thickness of the cervical epithelium. Full-thickness involvement is considered to be carcinoma in situ (Kurman et al., 1992). Koilocytotic atypia, manifested by large cells with prominent cytoplasmic vacuoles and enlarged nuclei, is often a prevalent feature of dysplasia and usually indicates a cytopathic effect of HPV. As with other sites, squamous cell dysplasia is preceded by squamous metaplasia.

Descriptive Epidemiology In the United States, for each new case of invasive cancer found by Pap cytology screening there are approximately 50–100 other cases of smears consistent with ASC or SIL abnormalities (Franco and Ferenczy, 2002b). Triaging these women to the most appropriate management option has become a great problem, given the growing concerns with widespread malpractice litigation in the United States today. Epidemiologic surveillance of precursor lesions of cervical cancer has traditionally been based on monitoring incidence trends of histologically ascertained CIS using tumor registry data (Chow et al., 1986; Morrison et al., 1996; Bergstrom et al., 1999), which are routinely collected in some jurisdictions, and/or on prevalence data from

32

A


B

Figure 3–3. Cervical squamous dysplasia. A. Mild squamous dysplasia (CIN I) of the cervix. Some cells show large nuclei. B. Severe squamous dysplasia (CIN III) with koilocytotic cells. C. Squamous cell carcinoma in situ (CIN III).

cytopathology series (Bjorge et al., 1994; CDC, 1994; Lawson et al., 1998). The former has the advantage of being population-based and thus reflects time trends for an entire region, whereas the latter simply reflects the particular profile of the clientele attending a single screening facility or network of sites. Changes in lesion nomenclature over the years and the gradual loss of emphasis on CIS as the sentinel precursor stage have hampered surveillance activities. In the United States, the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program routinely collected data on CIS and invasive cancer from 1973 onwards for at least nine population-based tumor registration jurisdictions in the states of Connecticut, Hawaii, Iowa, New Mexico, and Utah and in the metropolitan areas of Detroit, Atlanta, San Francisco-Oakland, and Seattle-Puget Sound. However,

C

as was expected, the SEER program stopped collecting data on CIS in 1996 because this histologically based definition was quickly abandoned following adoption of the Bethesda classification. Concerning cytopathology surveillance, the U.S. Centers for Disease Control and Prevention’s (CDC) National Breast and Cervical Cancer Early Detection Program (NBCCEDP), albeit not population-based, represents the largest cytopathology database in the United States. It is based entirely on the Bethesda classification system and meets high quality control standards (CDC, 1994). Given the variety of health care delivery settings providing information nationwide, the NBCCEDP provides a comprehensive portrait of the situation with respect to cervical cytology screening and has been used for surveillance activities (Lawson et al., 1998; Sawaya et al., 2000).

33

Cancer Precursors

tion plays a necessary causal role in cervical neoplasia has gained ground and has led to rethinking of the role of other risk factors as simply cofactors determining the risk of acquisition and persistence of HPV infection or in mediating lesion risk among HPV-infected women (Castelsague and Munoz, 2003; Wacholder, 2003; Tortolero-Luna and Franco, 2004). Environmental and life style characteristics currently supported by epidemiologic data as potential cofactors in HPV-related cervical carcinogenesis include smoking, high parity, use of OCs, dietary factors, and infection with other sexually transmitted agents such as Chlamydia and herpesvirus type 2 (Potischman and Brinton, 1996; Ho et al., 1998; Castellsague et al., 2002; Franco and Ferenczy, 2002b; Munoz et al., 2002; Castelsague and Munoz, 2003; Castle and Giuliano, 2003; Green et al., 2003). Host-related factors that seem to influence the early phases of cervical carcinogenesis include endogenous hormones, immunosuppressive conditions, and genetic susceptibility traits that affect antigen recognition and processing and DNA repair, such as specific HLA alleles and haplotypes and polymorphisms in the p53 tumor suppressor gene, respectively (Makni et al., 2000; Hildesheim and Wang, 2002; Wang and Hildesheim, 2003; Koushik et al., 2004). Factors related to HPV, such as HPV type and variant, viral load, and viral integration, have also been found to affect the natural history of precursor lesions (Xi et al., 1997; Villa et al., 2000; Ylitalo et al., 2000; Lorincz et al., 2002; Schlecht et al., 2003).

Cervical cytologic abnormalities are a common finding during opportunistic screening. Prevalence data from the NBCCEDP for the mid to late 1990s showed the following distribution of results: 5.2% ASC, 2.9% LSIL, 0.8% HSIL, and

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