Cumulative Burden of Colorectal Cancer-Associated Genetic Variants is More Strongly Associated With Early-onset vs Late-onset Cancer

Alexi N Archambault; Yu-Ru Su; Jihyoun Jeon; Minta Thomas; Yi Lin; David V Conti; Aung Ko Win; Lori C Sakoda; Iris Lansdorp-Vogelaar; Elisabeth FP Peterse; Ann G Zauber; David Duggan; Andreana N Holowatyj; Jeroen R Huyghe; Hermann Brenner; Michelle Cotterchio; Stéphane Bézieau; Stephanie L Schmit; Christopher K Edlund; Melissa C Southey; Robert J MacInnis; Peter T Campbell; Jenny Chang-Claude; Martha L Slattery; Andrew T Chan; Amit D Joshi; Mingyang Song; Yin Cao; Michael O Woods; Emily White; Stephanie J Weinstein; Cornelia M Ulrich; Michael Hoffmeister; Stephanie A Bien; Tabitha A Harrison; Jochen Hampe; Christopher I Li; Clemens Schafmayer; Kenneth Offit; Paul D Pharoah; Victor Moreno; Annika Lindblom; Alicja Wolk; Anna H Wu; Li Li; Marc J Gunter; Andrea Gsur; Temitope O Keku; Rachel Pearlman; D Timothy Bishop; Sergi Castellví-Bel; Leticia Moreira; Pavel Vodicka; Ellen Kampman; Graham G Giles; Demetrius Albanes; John A Baron; Sonja I Berndt; Stefanie Brezina; Stephan Buch; Daniel D Buchanan; Antonia Trichopoulou; Gianluca Severi; María-Dolores Chirlaque; Maria-José Sánchez; Domenico Palli; Tilman Kühn; Neil Murphy; Amanda J Cross; Andrea N Burnett-Hartman; Stephen J Chanock; Albert de la Chapelle; Douglas F Easton; Faye Elliott; Dallas R English; Edith JM Feskens; Liesel M FitzGerald; Phyllis J Goodman; John L Hopper; Thomas J Hudson; David J Hunter; Eric J Jacobs; Corinne E Joshu; Sébastien Küry; Sanford D Markowitz; Roger L Milne; Elizabeth A Platz; Gad Rennert; Hedy S Rennert; Fredrick R Schumacher; Robert S Sandler; Daniela Seminara; Catherine M Tangen; Stephen N Thibodeau; Amanda E Toland; Franzel JB van Duijnhoven; Kala Visvanathan; Ludmila Vodickova; John D Potter; Satu Männistö

doi:10.1053/j.gastro.2019.12.012

. Author manuscript; available in PMC: 2021 Apr 1.

Published in final edited form as: Gastroenterology. 2019 Dec 19;158(5):1274–1286.e12. doi: 10.1053/j.gastro.2019.12.012

Cumulative Burden of Colorectal Cancer-Associated Genetic Variants is More Strongly Associated With Early-onset vs Late-onset Cancer

Alexi N Archambault ¹, Yu-Ru Su ², Jihyoun Jeon ³, Minta Thomas ², Yi Lin ², David V Conti ⁴, Aung Ko Win ⁵, Lori C Sakoda ^2,⁶, Iris Lansdorp-Vogelaar ⁷, Elisabeth FP Peterse ⁷, Ann G Zauber ⁸, David Duggan ⁹, Andreana N Holowatyj ¹⁰, Jeroen R Huyghe ², Hermann Brenner ^11,^12,¹³, Michelle Cotterchio ¹⁴, Stéphane Bézieau ¹⁵, Stephanie L Schmit ^4,¹⁶, Christopher K Edlund ⁴, Melissa C Southey ¹⁷, Robert J MacInnis ^5,¹⁸, Peter T Campbell ¹⁹, Jenny Chang-Claude ^20,²¹, Martha L Slattery ²², Andrew T Chan ^23,^24,^25,^26,^27,²⁸, Amit D Joshi ^25,²⁷, Mingyang Song ²⁹, Yin Cao ^25,³⁰, Michael O Woods ³¹, Emily White ^2,³², Stephanie J Weinstein ³³, Cornelia M Ulrich ³⁴, Michael Hoffmeister ¹¹, Stephanie A Bien ², Tabitha A Harrison ², Jochen Hampe ³⁵, Christopher I Li ², Clemens Schafmayer ³⁶, Kenneth Offit ^37,³⁸, Paul D Pharoah ³⁹, Victor Moreno ^40,^41,⁴², Annika Lindblom ^43,⁴⁴, Alicja Wolk ⁴⁵, Anna H Wu ⁴, Li Li ⁴⁶, Marc J Gunter ⁴⁷, Andrea Gsur ⁴⁸, Temitope O Keku ⁴⁹, Rachel Pearlman ⁵⁰, D Timothy Bishop ⁵¹, Sergi Castellví-Bel ⁵², Leticia Moreira ⁵², Pavel Vodicka ^53,^54,⁵⁵, Ellen Kampman ⁵⁶, Graham G Giles ^5,¹⁶, Demetrius Albanes ³², John A Baron ⁵⁷, Sonja I Berndt ³², Stefanie Brezina ⁴⁸, Stephan Buch ³⁴, Daniel D Buchanan ^5,^58,^59,⁶⁰, Antonia Trichopoulou ⁶¹, Gianluca Severi ⁶², María-Dolores Chirlaque ^41,⁶³, Maria-José Sánchez ⁶⁴, Domenico Palli ⁶⁵, Tilman Kühn ²⁰, Neil Murphy ⁶⁶, Amanda J Cross ⁶⁷, Andrea N Burnett-Hartman ⁶⁸, Stephen J Chanock ³², Albert de la Chapelle ⁶⁹, Douglas F Easton ³⁸, Faye Elliott ⁵¹, Dallas R English ^5,¹⁶, Edith JM Feskens ⁵⁶, Liesel M FitzGerald ^16,⁷⁰, Phyllis J Goodman ⁷¹, John L Hopper ^5,⁷², Thomas J Hudson ⁷³, David J Hunter ^27,⁷⁴, Eric J Jacobs ¹⁹, Corinne E Joshu ⁷⁵, Sébastien Küry ¹⁸, Sanford D Markowitz ⁴⁵, Roger L Milne ^5,¹⁶, Elizabeth A Platz ⁷⁵, Gad Rennert ^76,^77,⁷⁸, Hedy S Rennert ^76,^77,⁷⁸, Fredrick R Schumacher ⁷⁹, Robert S Sandler ⁴⁹, Daniela Seminara ⁸⁰, Catherine M Tangen ⁷¹, Stephen N Thibodeau ⁸¹, Amanda E Toland ⁶⁹, Franzel JB van Duijnhoven ⁵⁶, Kala Visvanathan ⁷⁵, Ludmila Vodickova ^53,^54,⁵⁵, John D Potter ², Satu Männistö ⁸², Korbinian Weigl ^11,⁸³, Jane Figueiredo ^4,⁸⁴, Vicente Martín ^41,⁸⁵, Susanna C Larsson ⁴⁴, Patrick S Parfrey ⁸⁶, Wen-Yi Huang ³³, Heinz-Josef Lenz ⁸⁷, Jose E Castelao ⁸⁸, Manuela Gago-Dominguez ^89,⁹⁰, Victor Muñoz-Garzón ⁹¹, Christoph Mancao ⁹², Christopher A Haiman ⁴, Lynne R Wilkens ⁹³, Erin Siegel ¹⁶, Elizabeth Barry ⁹⁴, Ban Younghusband ³⁰, Bethany Van Guelpen ^95,⁹⁶, Sophia Harlid ⁹⁶, Anne Zeleniuch-Jacquotte ¹, Peter S Liang ⁹⁷, Mengmeng Du ⁸, Graham Casey ⁹⁸, Noralane M Lindor ⁹⁹, Loic Le Marchand ⁹³, Steven J Gallinger ¹⁰⁰, Mark A Jenkins ⁵, Polly A Newcomb ^2,¹⁰¹, Stephen B Gruber ⁴, Robert E Schoen ¹⁰², Heather Hampel ⁵⁰, Douglas A Corley ^6,^§, Li Hsu ^2,^103,^§, Ulrike Peters ^2,^31,^§, Richard B Hayes ^1,^§

¹Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York, USA.

²Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

³Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA.

⁴Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

⁵Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.

⁶Division of Research, Kaiser Permanente Northern California, Oakland, California, USA.

⁷Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.

⁸Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

⁹Translational Genomics Research Institute - An Affiliate of City of Hope, Phoenix, Arizona, USA.

¹⁰Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.

¹¹Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.

¹²Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.

¹³German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

¹⁴Population Health and Prevention, Cancer Care Ontario, Toronto, Ontario, Canada.

¹⁵Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.

¹⁶Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.

¹⁷Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia.

¹⁸Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

¹⁹Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA.

²⁰Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

²¹University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany.

²²Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.

²³Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

²⁴Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA.

²⁵Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

²⁶Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

²⁷Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.

²⁸Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.

²⁹Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.

³⁰Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, Missouri, USA.

³¹Memorial University of Newfoundland, Discipline of Genetics, St. John’s, Canada.

³²Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, USA.

³³Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

³⁴Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.

³⁵Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany.

³⁶Department of General and Thoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.

³⁷Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

³⁸Department of Medicine, Weill Cornell Medical College, New York, New York, USA.

³⁹Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

⁴⁰Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain.

⁴¹CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain

⁴²Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.

⁴³Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.

⁴⁴Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.

⁴⁵Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.

⁴⁶Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA.

⁴⁷Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France.

⁴⁸Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, Vienna, Austria.

⁴⁹Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, USA.

⁵⁰Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.

⁵¹Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.

⁵²Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain.

⁵³Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.

⁵⁴Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic.

⁵⁵Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic.

⁵⁶Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands.

⁵⁷Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.

⁵⁸Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia.

⁵⁹Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia.

⁶⁰University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.

⁶¹Hellenic Health Foundation, Athens, Greece.

⁶²Centre de Recherche en Épidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, Gustave Roussy, Villejuif, France.

⁶³Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain.

⁶⁴Escuela Andaluza de Salud Pública, CIBER de Epidemiología y Salud Pública, Granada, Spain.

⁶⁵Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy.

⁶⁶Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France.

⁶⁷School of Public Health, Imperial College London, London, UK.

⁶⁸Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado, USA.

⁶⁹Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA.

⁷⁰Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.

⁷¹SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

⁷²Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea.

⁷³Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

⁷⁴Nuffield Department of Population Health, University of Oxford, Oxford, UK.

⁷⁵Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

⁷⁶Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel.

⁷⁷Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

⁷⁸Clalit National Cancer Control Center, Haifa, Israel.

⁷⁹Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA.

⁸⁰Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.

⁸¹Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.

⁸²Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.

⁸³Medical Faculty, University of Heidelberg, Heidelberg, Germany.

⁸⁴Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

⁸⁵Biomedicine Institute (IBIOMED), University of León, León, Spain.

⁸⁶The Clinical Epidemiology Unit, Memorial University Medical School, St. John’s, Newfoundland, Canada.

⁸⁷Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

⁸⁸Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Oncology and Genetics Unit, Vigo, Spain.

⁸⁹Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain.

⁹⁰Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.

⁹¹Radiotherapy Department, Complejo Hospitalario Universitario de Vigo, SERGAS, Vigo, Spain.

⁹²Genentech, Inc., Basel, Switzerland.

⁹³Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA.

⁹⁴Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.

⁹⁵Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.

⁹⁶Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden.

⁹⁷Department of Medicine, New York University School of Medicine, New York, New York, USA.

⁹⁸Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA.

⁹⁹Department of Health Science Research, Mayo Clinic, Scottsdale, Arizona, USA.

¹⁰⁰Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto,Toronto, Ontario, Canada.

¹⁰¹School of Public Health, University of Washington, Seattle, Washington, USA.

¹⁰²Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.

¹⁰³Department of Biostatistics, University of Washington, Seattle, Washington, USA.

^§

These authors jointly supervised this work.

Author Contributions

Alexi N Archambault - Study concept and design, Analysis and interpretation of data, Drafting of the manuscript, Critical revision of the manuscript for important intellectual content, Statistical analysis

Yu-Ru Su - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content, Statistical analysis

Jihyoun Jeon - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content, Statistical analysis

Minta Thomas - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content, Statistical analysis

Yi Lin - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content, Statistical analysis

David V Conti - Critical revision of the manuscript for important intellectual content

Aung Ko Win - Critical revision of the manuscript for important intellectual content

Lori C Sakoda - Critical revision of the manuscript for important intellectual content

Iris Lansdorp-Vogelaar- Critical revision of the manuscript for important intellectual content

Elisabeth FP Peterse - Critical revision of the manuscript for important intellectual content

Ann G Zauber - Critical revision of the manuscript for important intellectual content

David Duggan- Critical revision of the manuscript for important intellectual content

Andreana N Holowatyj - Critical revision of the manuscript for important intellectual content

Jeroen R Huyghe - Critical revision of the manuscript for important intellectual content

Hermann Brenner - Critical revision of the manuscript for important intellectual content

Michelle Cotterchio - Critical revision of the manuscript for important intellectual content

Stéphane Bézieau - Critical revision of the manuscript for important intellectual content

Stephanie L Schmit - Critical revision of the manuscript for important intellectual content

Christopher K Edlund - Critical revision of the manuscript for important intellectual content

Melissa C Southey - Critical revision of the manuscript for important intellectual content

Robert J MacInnis - Critical revision of the manuscript for important intellectual content

Peter T Campbell - Critical revision of the manuscript for important intellectual content

Jenny Chang-Claude - Critical revision of the manuscript for important intellectual content

Martha L Slattery - Critical revision of the manuscript for important intellectual content

Andrew T Chan - Critical revision of the manuscript for important intellectual content

Amit D Joshi - Critical revision of the manuscript for important intellectual content

Mingyang Song - Critical revision of the manuscript for important intellectual content

Yin Cao - Critical revision of the manuscript for important intellectual content

Michael O Woods - Critical revision of the manuscript for important intellectual content

Emily White - Critical revision of the manuscript for important intellectual content

Stephanie J Weinstein - Critical revision of the manuscript for important intellectual content

Cornelia M Ulrich - Critical revision of the manuscript for important intellectual content

Michael Hoffmeister - Critical revision of the manuscript for important intellectual content

Stephanie A Bien - Critical revision of the manuscript for important intellectual content

Tabitha A Harrison - Critical revision of the manuscript for important intellectual content

Jochen Hampe - Critical revision of the manuscript for important intellectual content

Christopher I Li - Critical revision of the manuscript for important intellectual content

Clemens Schafmayer - Critical revision of the manuscript for important intellectual content

Kenneth Offit - Critical revision of the manuscript for important intellectual content

Paul D Pharoah - Critical revision of the manuscript for important intellectual content

Victor Moreno - Critical revision of the manuscript for important intellectual content

Annika Lindblom - Critical revision of the manuscript for important intellectual content

Alicja Wolk - Critical revision of the manuscript for important intellectual content

Anna H Wu - Critical revision of the manuscript for important intellectual content

Li Li - Critical revision of the manuscript for important intellectual content

Marc J Gunter - Critical revision of the manuscript for important intellectual content

Andrea Gsur - Critical revision of the manuscript for important intellectual content

Temitope O Keku - Critical revision of the manuscript for important intellectual content

Rachel Pearlman - Critical revision of the manuscript for important intellectual content

D Timothy Bishop - Critical revision of the manuscript for important intellectual content

Sergi Castellví-Bel - Critical revision of the manuscript for important intellectual content

Leticia Moreira - Critical revision of the manuscript for important intellectual content

Pavel Vodicka - Critical revision of the manuscript for important intellectual content

Ellen Kampman - Critical revision of the manuscript for important intellectual content

Graham G Giles - Critical revision of the manuscript for important intellectual content

Demetrius Albanes - Critical revision of the manuscript for important intellectual content

John A Baron - Critical revision of the manuscript for important intellectual content

Sonja I Berndt - Critical revision of the manuscript for important intellectual content

Stefanie Brezina - Critical revision of the manuscript for important intellectual content

Stephan Buch - Critical revision of the manuscript for important intellectual content

Daniel D Buchanan - Critical revision of the manuscript for important intellectual content

Antonia Trichopoulou - Critical revision of the manuscript for important intellectual content

Gianluca Severi - Critical revision of the manuscript for important intellectual content

María-Dolores Chirlaque - Critical revision of the manuscript for important intellectual content

Maria José Sánchez - Critical revision of the manuscript for important intellectual content

Domenico Palli - Critical revision of the manuscript for important intellectual content

Tilman Kühn - Critical revision of the manuscript for important intellectual content

Neil Murphy - Critical revision of the manuscript for important intellectual content

Amanda J Cross - Critical revision of the manuscript for important intellectual content

Andrea N Burnett-Hartman - Critical revision of the manuscript for important intellectual content

Stephen J Chanock - Critical revision of the manuscript for important intellectual content

Albert de la Chapelle - Critical revision of the manuscript for important intellectual content

Douglas F Easton - Critical revision of the manuscript for important intellectual content

Faye Elliott - Critical revision of the manuscript for important intellectual content

Dallas R English - Critical revision of the manuscript for important intellectual content

Edith JM Feskens - Critical revision of the manuscript for important intellectual content

Liesel M FitzGerald - Critical revision of the manuscript for important intellectual content

Phyllis J Goodman - Critical revision of the manuscript for important intellectual content

John L Hopper - Critical revision of the manuscript for important intellectual content

Thomas J Hudson - Critical revision of the manuscript for important intellectual content

David J Hunter - Critical revision of the manuscript for important intellectual content

Eric J Jacobs - Critical revision of the manuscript for important intellectual content

Corinne E Joshu - Critical revision of the manuscript for important intellectual content

Sébastien Küry - Critical revision of the manuscript for important intellectual content

Sanford D Markowitz - Critical revision of the manuscript for important intellectual content

Roger L Milne - Critical revision of the manuscript for important intellectual content

Elizabeth A Platz - Critical revision of the manuscript for important intellectual content

Gad Rennert - Critical revision of the manuscript for important intellectual content

Hedy S Rennert - Critical revision of the manuscript for important intellectual content

Fredrick R Schumacher - Critical revision of the manuscript for important intellectual content

Robert S Sandler - Critical revision of the manuscript for important intellectual content

Daniela Seminara- Critical revision of the manuscript for important intellectual content

Catherine M Tangen - Critical revision of the manuscript for important intellectual content

Stephen N Thibodeau - Critical revision of the manuscript for important intellectual content

Amanda E Toland - Critical revision of the manuscript for important intellectual content

Franzel JB van Duijnhoven - Critical revision of the manuscript for important intellectual content

Kala Visvanathan - Critical revision of the manuscript for important intellectual content

Ludmila Vodickova - Critical revision of the manuscript for important intellectual content

John D Potter - Critical revision of the manuscript for important intellectual content

Satu Männistö - Critical revision of the manuscript for important intellectual content

Korbinian Weigl - Critical revision of the manuscript for important intellectual content

Jane Figueiredo - Critical revision of the manuscript for important intellectual content

Vicente Martín - Critical revision of the manuscript for important intellectual content

Susanna C Larsson - Critical revision of the manuscript for important intellectual content

Patrick S Parfrey - Critical revision of the manuscript for important intellectual content

Wen-Yi Huang - Critical revision of the manuscript for important intellectual content

Heinz-Josef Lenz - Critical revision of the manuscript for important intellectual content

Jose E Castelao - Critical revision of the manuscript for important intellectual content

Manuela Gago-Dominguez - Critical revision of the manuscript for important intellectual content

Victor Muñoz-Garzón - Critical revision of the manuscript for important intellectual content

Christoph Mancao - Critical revision of the manuscript for important intellectual content

Christopher A Haiman - Critical revision of the manuscript for important intellectual content

Lynne R Wilkens - Critical revision of the manuscript for important intellectual content

Erin Siegel - Critical revision of the manuscript for important intellectual content

Elizabeth Barry - Critical revision of the manuscript for important intellectual content

Ban Younghusband - Critical revision of the manuscript for important intellectual content

Bethany Van Guelpen - Critical revision of the manuscript for important intellectual content

Sophia Harlid - Critical revision of the manuscript for important intellectual content

Anne Zeleniuch-Jacquotte - Critical revision of the manuscript for important intellectual content

Peter S Liang - Critical revision of the manuscript for important intellectual content

Mengmeng Du - Critical revision of the manuscript for important intellectual content

Graham Casey - Critical revision of the manuscript for important intellectual content

Noralane M Lindor - Critical revision of the manuscript for important intellectual content

Loic Le Marchand - Critical revision of the manuscript for important intellectual content

Steven J Gallinger - Critical revision of the manuscript for important intellectual content

Mark A Jenkins - Critical revision of the manuscript for important intellectual content

Polly A Newcomb - Critical revision of the manuscript for important intellectual content

Stephen B Gruber - Critical revision of the manuscript for important intellectual content

Robert E Schoen - Critical revision of the manuscript for important intellectual content

Heather Hampel - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content

Douglas A Corley - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content

Li Hsu - Study concept and design, Analysis and interpretation of data, Critical revision of the manuscript for important intellectual content

Ulrike Peters - Study concept and design, Acquisition of data, Analysis and interpretation of data, Drafting of the manuscript, Critical revision of the manuscript for important intellectual content, Obtained funding, Study supervision

Richard B Hayes - Study concept and design, Acquisition of data, Analysis and interpretation of data, Drafting of the manuscript, Critical revision of the manuscript for important intellectual content, Obtained funding, Study supervision

^✉

Shared corresponding authors: Richard B. Hayes, NYU Langone Health, 180 Madison Ave, Room 415, New York, NY 10016, Ulrike Peters, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M4-B402, Seattle, WA 98109-1024

PMCID: PMC7103489 NIHMSID: NIHMS1547252 PMID: 31866242

Abstract

Background & Aims

Early-onset colorectal cancer (CRC, in persons younger than 50 years old) is increasing in incidence; yet, in the absence of a family history of CRC, this population lacks harmonized recommendations for prevention. We aimed to determine whether a polygenic risk score (PRS) developed from 95 CRC-associated common genetic risk variants was associated with risk for early-onset CRC.

Methods

We studied risk for CRC associated with a weighted PRS in 12,197 participants younger than 50 years old vs 95,865 participants 50 years or older. PRS was calculated based on single-nucleotide polymorphisms associated with CRC in a large-scale genome-wide association study as of January 2019. Participants were pooled from 3 large consortia that provided clinical and genotyping data: the Colon Cancer Family Registry, the Colorectal Transdisciplinary study, and the Genetics and Epidemiology of Colorectal Cancer Consortium and were all of genetically defined European descent. Findings were replicated in an independent cohort of 72,573 participants.

Results

Overall associations with CRC per standard deviation of PRS were significant for early-onset cancer, and were stronger compared with late-onset cancer (P for interaction=.01); when we compared the highest PRS quartile with the lowest, risk increased 3.7-fold for early-onset CRC (95% CI, 3.28–4.24) vs 2.9-fold for late-onset CRC (95% CI, 2.80–3.04). This association was strongest for participants without a first-degree family history of CRC (P for interaction=5.61×10⁻⁵). When we compared the highest with the lowest quartiles in this group, risk increased 4.3-fold for early-onset CRC (95% CI, 3.61–5.01) vs 2.9-fold for late-onset CRC (95% CI, 2.70–3.00). Sensitivity analyses were consistent with these findings.

Conclusions

In an analysis of associations with CRC per standard deviation of PRS, we found the cumulative burden of CRC-associated common genetic variants to associate with early-onset cancer, and to be more strongly associated with early-onset than late-onset cancer—particularly in the absence of CRC family history. Analyses of PRS, along with environmental and lifestyle risk factors, might identify younger individuals who would benefit from preventative measures.

Keywords: colon cancer, SNP, penetrance, EOCRC

Lay Summary

Genetic variants associated with risk of colorectal cancer are more strongly associated with development of early-onset compared with late-onset colorectal cancer.

Graphical Abstract

graphic file with name nihms-1547252-f0001.jpg

Introduction

Colorectal cancer (CRC) incidence and mortality have been declining in the U.S. over the last several decades.¹ These reductions are largely attributed to successes in CRC early detection, surveillance, and treatment for this disease.^{2, 3} In contrast to these overall trends, the incidence of CRC in individuals less than 50 years of age (early-onset disease) has been increasing in the U.S. and elsewhere:⁴ early-onset CRC incidence in the U.S. has increased by an average of 1.8% annually from 1992–2012, and is projected to account for 10% to 25% of newly-diagnosed CRC by 2030.^{1, 5–10} Furthermore, early-onset CRC tends to present with higher pathologic grade, distant disease, and a greater incidence of recurrence and metastatic disease.⁵ In response to this newly recognized disease burden, the US Preventative Services Task Force,¹¹ the American Cancer Society,¹² the U.S. Multi-Society Task Force on Colorectal Cancer¹³ and other professional bodies¹⁴ have initiated discussions on the merits of revising recent consensus CRC prevention guidelines to include early detection of average-risk individuals younger than 50 years of age. While the American Cancer Society recommends lowering the screening age to 45 years for individuals at average risk,¹² others recommend targeting only high-risk groups for early detection.^{13, 15}

Weighing against the potential benefits of CRC early detection and prevention programs targeted to those aged younger than 50 years are concerns about adverse side effects and associated costs.^{14, 16} New approaches to disease prevention in younger adults are warranted, and assessing germline genetic variants, along with other known risk factors, could facilitate tailored early detection of high risk individuals due to their genetic makeup and lifestyle. To date, genetic research on factors associated with early-onset CRC has been limited largely to the rare monogenic, high-penetrance genetic syndromes associated with this disease in high-risk families, while the frequently occurring low-penetrance polymorphisms have been understudied.

Here, we report on CRC risks for early (<50 years of age) and late-onset disease (≥50 years of age) associated with a polygenic risk score (PRS) developed from 95 common genetic risk variants identified in previous CRC genome-wide association studies (GWAS). Our research provides the first substantive evidence that early-onset CRC exhibits differential genetic risks, compared with late-onset disease, due to low-penetrance, common genetic polymorphisms. The findings of our research may contribute to the identification of individuals susceptible to early-onset CRC for tailored early detection or other preventive interventions.

Methods

Study Participants

We studied 108,062 participants in the discovery dataset, including 50,023 CRC cases and 58,039 controls. Participants for this study were pooled from three large consortia that provided clinical and genotyping data: the Colon Cancer Family Registry (CCFR), the Colorectal Transdisciplinary (CORECT) Study, and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) (Table 1 and Table S1) (for additional study information, see earlier publications^17–20). All analyses were restricted to participants of genetically defined European descent. Family history of CRC was ascertained through self-report or interviewer-administered questionnaire, and defined as having one or more first-degree relatives with CRC. Participant recruitment across all studies occurred between the 1990’s and the early 2010’s. All study participants provided written informed consent and studies were approved by their respective Institutional Review Boards (see Supplementary Information).

Table 1:

Baseline study characteristics of the discovery and replication datasets


	Discovery dataset				Replication dataset

	Cases (N=50,023)		Controls (N=58,039)		All participants		CRC Cases

	<50 Years-Old	≥50 Years-Old	<50 Years-Old	≥50 Years-Old	Eligible cohort	CRC cases	<50 Years-Old	≥50 Years-Old

N	5479	44544	6718	51321	72573	1093	25	1068
Age, Mean (SD)	43.1 (5.6)	66.5 (8.7)	41.3 (7.2)	65.3 (8.3)	71.5 (13.1)	73.1 (10.8)	45.2 (3.3)	73.7 (10.1)
Sex, N (%)
Male	2767 (50.5)	24145 (54.2)	3272 (48.7)	26886 (52.4)	30160 (41.6)	526 (48.1)	9 (36.0)	517 (48.4)
Female	2706 (49.4)	20336 (45.7)	3446 (51.3)	24435 (47.6)	42413 (58.4)	567 (51.9)	16 (64.0)	551 (51.6)
Missing	6 (0.1)	63 (0.1)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
Family History of CRC, N (%)
Yes	944 (17.2)	5558 (12.5)	578 (8.6)	5330 (10.4)	6956 (9.6)	204 (18.7)	7 (28.0)	197 (18.4)
No	3159 (57.7)	24028 (53.9)	4130 (61.5)	28317 (55.2)	65617 (90.4)	889 (81.3)	18 (72.0)	871 (81.6)
Missing	1376 (25.1)	14958 (33.6)	2010 (29.9)	17674 (34.4)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
Tumor Site, N (%)
Proximal Colon	1231 (22.5)	12978 (29.1)	--	--	--	--	--	--
Distal Colon	1442 (26.3)	12036 (27.0)	--	--	--	--	--	--
Rectum	1920 (35.0)	12918 (29.0)	--	--	--	--	--	--
Missing	886 (16.2)	6612 (14.8)	--	--	--	--	--	--
PRS, N (%)
Quartile 1	693 (12.6)	6227 (14.0)	1659 (24.7)	12863 (25.1)	18175 (25.0)	163 (14.9)	2 (8.0)	161 (15.1)
Quartile 2	1048 (19.1)	8824 (19.8)	1666 (24.8)	12848 (25.0)	18150 (25.0)	232 (21.2)	4 (16.0)	228 (21.3)
Quartile 3	1396 (25.5)	11877 (26.7)	1674 (24.9)	12824 (25.0)	18132 (25.0)	287 (26.3)	7 (28.0)	280 (26.2)
Quartile 4	2342 (42.7)	17616 (39.5)	1719 (25.6)	12786 (24.9)	18116 (25.0)	411 (37.6)	12 (48.0)	399 (37.4)

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Genotyping and SNP Selection

We included 95 CRC-risk-associated SNPs that reached genome-wide significance (p ≤ 5×10⁻⁸), in large-scale GWAS, as of January, 2019. No new discovery of CRC-related SNPs was carried out here. Individual participant and genotype data for the 95 SNPs were extracted from GWAS and imputed to the Haplotype Reference Consortium panel, which provides high-quality, accurate imputation for variants with a minor allele frequency as low as 0.1%.²¹ For details, see Huyghe et al.¹⁷ Additional information on SNPs can be located in Table S2.

Statistical Analysis

For cases and controls, we compared baseline participant characteristics between individuals who had a reference age of <50 years to those with a reference age of ≥50 years of age. For cases, reference age was defined as the age of diagnosis of first primary CRC. For controls, reference age was defined as the age at selection.

Genotyped SNPs were coded as 0, 1, or 2 copies of the risk allele. Imputed SNPs were coded for the expected number of copies of the risk allele, as imputed dosages. Potential population substructure within the GECCO, CCFR, and CORECT studies was accounted for through adjustment by principal components of genetic ancestry. To develop the weighted PRS, we used log-odds ratios derived from the literature for 55 of the SNPs, and for the remaining 40 SNPs that were first identified within this discovery dataset, we computed log-odds ratios from a regression model fit with CRC as the outcome (1 vs. 0) and the following independent variables: 95 SNPs, age (in years), sex, principal components, and genotype platform. For the 40 SNPs identified within this discovery dataset, we then implemented a conservative winner’s curse adjustment of the log-odds ratios from the risk model, using Zhong and Prentice’s approach.²² We then weighted the PRS for individuals, by multiplying the number of risk alleles for each SNP by their adjusted log-odds ratios, summing and recoding as a percentile based on the distribution in the controls. The final PRS was modelled as a continuous variable per 1 standard deviation (SD), transformed to the standard normal distribution. Odds ratios and 95% confidence intervals were also estimated comparing quartiles of PRS.

We used unconditional logistic regression to assess the association between the PRS and CRC for those with a reference age <50 years and for those with a reference age ≥50 years. All models additionally included sex, reference age in years, principal components, and genotype platform. Further adjustment by study was not warranted as extensive genome-wide analyses with and without adjusting for study have been conducted, with the results being consistent.¹⁷ To test for differences in associations across age, an interaction term was included for age category (<50, ≥50) and PRS (continuous). Models were also examined separately by first-degree family history of CRC. We evaluated the discriminatory accuracy of the risk prediction models by calculating the area under the receiver operating characteristic curve (AUC) for 5-year diagnostic age groups, adjusting for sex, PCs, and genotype platform, using the adjusted.ROC function from the R Package ROCt.

For the larger group with no first-degree family history of CRC, additional sub-group analyses were performed including estimation of CRC risk within specific reference-age groups (15–39, 40–49, 50–59, 60–69, and 70–79 years) and by disease site (proximal colon, distal colon, and rectum). The interaction term used to assess differences in associations across age categories consisted of age as a continuous variable and PRS (continuous). Multinomial logistic regression was used to assess risk differentials by disease site within age strata. Analyses were completed using the R statistical software program version 3.5.1.

Sensitivity Analyses

Replication accounting for cases with Lynch syndrome

Screening of colorectal cancer cases for the presence of Lynch syndrome was systematically carried out for CRC cases recruited through the Ohio State University Medical Center (OSUMC) (Table S1: HNPCC, OCCPI, and OSUMC) as described in detail elsewhere^23–25. All cases were screened for MMR deficiency using immunohistochemical analysis. Cases with probable characteristics of Lynch syndrome were subjected to additional genetic testing for conclusively determining a diagnosis of Lynch syndrome based on the presence of one or more germline high penetrance mutations in DNA mismatch repair genes (MLH1, MSH2, MSH6, PMS2) or the EPCAM gene.

Using unconditional logistic regression in these studies, we evaluated the association between the PRS and CRC for those aged <50 years and for those ≥50 years of age, with consideration of Lynch syndrome status among cases. All models additionally included sex, reference age in years, and principal components. To test for differences in associations across age, an interaction term was included for age category (<50, ≥50) and PRS (continuous).

Replication in an independent cohort

To independently replicate the association of this PRS with younger and older-onset CRC, we studied all 72,573 participants of European ancestry who were genotyped in the Research Program on Genes, Environment and Health (RPGEH), a cohort comprised of Kaiser Permanente Northern California (KPNC) health plan members.^{26, 27} This cohort was not included in the discovery of any of the 95 CRC genetic risk variants. Cancer history was determined from initiation of health plan membership by linkage to the KPNC Cancer Registry, which adheres to the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program standards.

Family history of CRC, defined as having one or more first-degree relatives with CRC, was ascertained through a baseline study questionnaire, electronic family history data in the medical records, and International Classification of Disease codes Z80.0 (Family history of malignant neoplasm of digestive organs) and V16.0 (Cancer family history, gastrointestinal tract). Analyses were restricted to participants of genetically defined European descent. All study participants provided written informed consent, and the study was approved by the Kaiser Permanente Northern California Institutional Review Board.

RPGEH biospecimens were genotyped using the Affymetrix Axiom platform. Details on the calling and quality control can be found elsewhere.²⁸ Consistent with genetic data in the discovery set, we imputed the genotyped data to the Haplotype Reference Consortium. To develop the PRS for this replication, we used 94 SNPs from the discovery dataset, as described above, and, for 1 unmatched SNP (rs755229494), we included the best available surrogate (rs112334046, R²=0.40, MAF=0.0026).

For the longitudinal replication cohort, we employed Cox proportional hazards models to assess the association of PRS with CRC, which was not feasible for the discovery dataset since it included case-control data. The coefficients from the model fit with 95 SNPs in the discovery dataset were used to fit the PRS in the replication analysis, thereby reducing potential for overfitting. The observed time was defined from the age of initial KPNC enrollment to the earliest of age at CRC diagnosis, death or end of follow-up (the RPGEH cohort was followed until December 31, 2016). The replication models also included sex and principal components to account for potential population substructure. Estimates of absolute risk are inferred using Kaplan-Meier plots produced using RPGEH data.

Results

Early-onset CRC cases (N=5,479) had a mean age at diagnosis of 43.1 years, while the older-onset cases (N=44,544) had a mean age at diagnosis of 66.5 years (Table 1). Men and women were approximately equally represented across cases and controls. A first-degree family history of CRC, among those ascertained for family history, was reported for 17.2% of early-onset and 12.5% for late-onset CRC cases, and, respectively, for 8.6% of younger and 10.4% for older controls. Family history information was missing for >25% of participants; all of whom were from 9 studies that did not query participants on family history and therefore were not included in our family history-specific analyses. Younger onset cases tended to have fewer proximal colon tumors and a greater preponderance of tumors in the rectum. Both early-onset and late-onset CRC cases showed marked skewing toward higher PRS values compared with controls, when represented as quartiles (Table 1) and as a continuous score (Figure S1).

We found that associations with risk for CRC per SD of PRS were significant among participants <50 years of age, and were stronger compared with participants aged ≥50 years (P for interaction = 0.01). Contrasting the highest PRS quartile with the lowest, risks were 3.7-fold higher (OR: 3.73; 95% CI: 3.28, 4.24) for early-onset CRC and 2.9-fold higher (OR: 2.92; 95% CI: 2.80, 3.04) for late-onset disease (Table 2 and Figure 1A). For the larger group of participants who reported a negative first-degree family history of CRC, PRS-associated risks for CRC among participants aged <50 years were also stronger than those for individuals aged ≥50 years (P for interaction = 5.61×10⁻⁵); risks comparing the highest with the lowest quartile of PRS were 4.3-fold (OR: 4.26; 95% CI: 3.61, 5.01) for early-onset CRC and 2.9-fold (OR: 2.85; 95% CI: 2.70, 3.00) for late-onset disease (Table 2 and Figure 1B). In contrast, for the smaller group of participants who reported a positive first-degree family history of CRC, risks per SD of PRS tended to be greater for older individuals (P for interaction = 0.003); risks in the highest quartile for PRS were 1.7-fold (OR: 1.70; 95% CI: 1.17, 2.47) for early-onset CRC, and 2.5-fold (OR: 2.47; 95% CI: 2.18, 2.79) for late-onset disease (Table 2 and Figure 1C). The discriminatory capabilities for prediction (i.e., AUC) of these models across the entire age spectrum tended to be highest for early-onset individuals without a family history of CRC, ranging from 0.64 to 0.65 (Table S3).

Table 2:

Risk estimates for early-onset versus late-onset CRC associated with a 95-SNP PRS in the discovery dataset^a

PRS	N (cases)	N (controls)	OR (95% CI)	P value	P value for interaction^b
All Subjects					0.0137

<50 Years-Old per 1 SD	5479	6718	1.64 (1.57, 1.72)	6.00E-107
Quartile 1 (ref)	693	1659	1.00
Quartile 2	1048	1666	1.64 (1.43, 1.89)	2.07E-12
Quartile 3	1396	1674	2.19 (1.91, 2.50)	2.17E-30
Quartile 4	2342	1719	3.73 (3.28, 4.24)	1.13E-89
≥50 Years-Old per 1 SD	44544	51321	1.52 (1.50, 1.54)	< 2.23E-308
Quartile 1 (ref)	6227	12863	1.00
Quartile 2	8824	12848	1.45 (1.39, 1.51)	8.55E-62
Quartile 3	11877	12824	1.95 (1.87, 2.03)	1.37E-208
Quartile 4	17616	12786	2.92 (2.80, 3.04)	< 2.23E-308

Negative Family History					5.61E-05

<50 Years-Old per 1 SD	3159	4130	1.74 (1.65, 1.84)	1.33E-81
Quartile 1 (ref)	388	1085	1.00
Quartile 2	601	1025	1.66 (1.39, 1.98)	1.58E-08
Quartile 3	820	1001	2.46 (2.07, 2.92)	3.37E-25
Quartile 4	1350	1019	4.26 (3.61, 5.01)	3.65E-67
≥50 Years-Old per 1 SD	24028	28317	1.50 (1.47, 1.53)	< 2.23E-308
Quartile 1 (ref)	3529	7341	1.00
Quartile 2	4869	7083	1.44 (1.36, 1.53)	1.85E-36
Quartile 3	6494	7058	1.92 (1.82, 2.03)	6.17E-119
Quartile 4	9136	6835	2.85 (2.70, 3.00)	< 2.23E-308

Positive Family History					0.0028

<50 Years-Old per 1 SD	944	578	1.19 (1.05, 1.35)	0.0063
Quartile 1 (ref)	133	105	1.00
Quartile 2	203	133	1.58 (1.05, 2.36)	0.0265
Quartile 3	208	152	1.22 (0.82, 1.83)	0.3277
Quartile 4	400	188	1.70 (1.17, 2.47)	0.0052
≥50 Years-Old per 1 SD	5558	5330	1.42 (1.36, 1.48)	7.02E-57
Quartile 1 (ref)	690	1134	1.00
Quartile 2	1037	1264	1.42 (1.24, 1.63)	5.85E-07
Quartile 3	1478	1343	1.81 (1.59, 2.07)	8.44E-19
Quartile 4	2353	1589	2.47 (2.18, 2.79)	2.70E-45

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The logistic regression models include age, sex, principal components, genotype platform, and polygenic risk score.

P value produced from interaction term with continuous PRS (per SD) and age (<50 versus ≥50 years).

Figure 1: — Risk estimates for early-onset versus late-onset CRC associated with a 95-SNP PRS in the discovery dataset. (A) Model includes all study participants regardless of first-degree family history of CRC. (B) Model includes study participants without a first-degree family history of CRC. (C) Model includes study participants with a first-degree family history of CRC. Models were adjusted for age, sex, principal components, genotype platform, and polygenic risk score. The interaction p-value reported was produced from a model including an interaction term with a continuous PRS (per SD) and age (<50 years versus ≥50 years).

As the PRS displayed the strongest association for early-onset CRC without a first-degree family history, we investigated whether certain subgroups could account for these strong effects. When stratified further by age at diagnosis, CRC risks were 1.7-fold (OR per SD of PRS: 1.74; 95% CI: 1.55, 1.96) for those diagnosed aged 15–39 years and 1.8-fold (OR per SD of PRS: 1.75; 95% CI: 1.64, 1.87) for those diagnosed aged 40–49 years of age. For participants diagnosed at ≥50 years of age, the related CRC risks were 1.6-fold (OR per SD of PRS: 1.60; 95% CI: 1.54, 1.67) for participants aged 50–59 years, 1.5-fold (OR per SD of PRS: 1.52; 95% CI: 1.48, 1.57) for individuals 60–69 years old, and 1.4-fold (OR per SD of PRS: 1.44; 95% CI: 1.39, 1.49) for those diagnosed between 70–79 years, with age and PRS exhibiting statistical interaction across the entire study age range (Table S4, P for interaction = 3.44×10⁻¹⁰). Furthermore, as found for all cancer sites (Table 2 and Figure 1), the PRS was also more strongly associated with risks for early-onset, compared with late-onset, cancers of the proximal colon, distal colon and rectum (Table S5 and Figure S2), with the greatest risk differentials observed for cancers of the distal colon and rectum (Table S6).

Sensitivity Analyses

Replication accounting for cases with Lynch syndrome

A total of 37 Lynch cases <50 years of age (6.4%, among 574 cases) and 54 Lynch cases ≥50 years of age (2.1%, among 2525 cases) were identified in the Ohio-based studies. Removing Lynch cases from the analysis demonstrated that the relatively small number of these cases did not substantially impact the relationship of PRS with CRC (Table 3). After exclusion of Lynch cases, risks for early-onset CRC per SD of PRS remained similarly increased in participants <50 years of age (OR per SD of PRS: 1.82; 95% CI: 1.61, 2.06) and were greater compared with participants aged ≥50 years (OR per SD of PRS: 1.49; 95% CI: 1.39, 1.60; P for interaction = 0.01). These trends held particularly for participants who reported a negative first-degree family history of CRC (aged <50 years, OR per SD of PRS: 1.83; 95% CI: 1.60, 2.09; aged ≥50 years, OR per SD of PRS: 1.46; 95% CI: 1.35,1.57; P for interaction = 0.01).

Table 3:

Risk estimates for early-onset versus late-onset CRC associated with a 95-SNP PRS among participants with and without Lynch Syndrome, in the Ohio cohort^a

PRS per 1 SD	N (cases)	N (controls)	OR (95% CI)	P value	P value for interaction^b
Including Lynch and Non-Lynch Cases

All Subjects					0.0369
<50 Years-Old	574	979	1.73 (1.54, 1.95)	1.39E-19
≥50 Years-Old	2525	1463	1.47 (1.37, 1.58)	1.77E-28
Negative Family History					0.0106
<50 Years-Old	449	931	1.81 (1.59, 2.07)	9.64E-19
≥50 Years-Old	1885	1271	1.45 (1.34, 1.56)	1.16E-21
Positive Family History					0.1517
<50 Years-Old	106	48	1.28 (0.84, 1.97)	0.2530
≥50 Years-Old	565	192	1.55 (1.30, 1.84)	1.12E-06

Excluding Lynch Cases

All Subjects					0.0149
<50 Years-Old	537	979	1.82 (1.61, 2.06)	2.63E-21
≥50 Years-Old	2471	1463	1.49 (1.39, 1.60)	1.11E-29
Negative Family History					0.0107
<50 Years-Old	438	931	1.83 (1.60, 2.09)	7.50E-19
≥50 Years-Old	1856	1271	1.46 (1.35, 1.57)	4.30E-22
Positive Family History					0.5627
<50 Years-Old	80	48	1.53 (0.98, 2.41)	0.0635
≥50 Years-Old	540	192	1.61 (1.34, 1.92)	2.34E-07

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The logistic regression models include age, sex, principal components, and polygenic risk score.

P value produced from interaction term with continuous PRS (per SD) and age (<50 versus ≥50 years).

Replication in an independent cohort

In RPGEH, early-onset CRC cases (N=25) had a mean age of 45.2 years, while the older-onset cases (N=1,068) had a mean age of 73.7 years (Table 1). More women participated than men. A first-degree family history of CRC was reported for 28.0% of early-onset and 18.4% of late-onset CRC cases, compared to 9.6% for the cohort overall. Consistent with the discovery dataset, the distributions of PRS for both early and late-onset CRC cases were skewed towards higher PRS quartiles compared with controls. Right-censoring was due to either death (15%, N=11,165) or lost to follow-up (1%, N=735).

Hazard ratio estimates for PRS and CRC in the independent replication (Table 4) were consistent with findings from the discovery dataset (Table 2), overall (aged <50 years, HR per SD of PRS: 1.73; 95% CI: 1.17, 2.56; aged ≥50 years, HR per SD of PRS: 1.43; 95% CI: 1.34, 1.51) and for individuals who reported a negative first-degree family history of CRC (aged <50 years, HR per SD of PRS: 1.76; 95% CI: 1.11, 2.78; aged ≥50 years, HR per SD of PRS: 1.42; 95% CI: 1.33, 1.52). Although the effects seen for younger and older individuals were consistent with our primary analysis, the specific evaluation of whether these effects differ by age (<50 vs. age ≥50 years) was underpowered in RPGEH, due to the limited number of early-onset CRC cases in this cohort. Numbers of early-onset CRC among individuals with a first-degree family history of CRC in the replication dataset were too few for a meaningful interpretation of the analysis. Kaplan-Meier survival plots, stratified by family history, are displayed in Figure 2, consistent with the hypothesized PRS-related probability gradients across the full age range.

Table 4:

Risk estimates for early-onset versus late-onset CRC associated with a 95-SNP PRS in the RPGEH replication cohort^a

PRS	N in eligible cohort	N (cases)	HR (95% CI)	P value	P value for interaction^b
All Subjects					0.3291

<50 Years-Old per 1 SD	26983	25	1.73 (1.17, 2.56)	0.0056
≥50 Years-Old per 1 SD	67792	1068	1.43 (1.34, 1.51)	2.77E-31

Negative Family History					0.3681

<50 Years-Old per 1 SD	24472	18	1.76 (1.11, 2.78)	0.0161
≥50 Years-Old per 1 SD	61129	871	1.42 (1.33, 1.52)	2.85E-25

Positive Family History					0.6920

<50 Years-Old per 1 SD	2511	7	1.56 (0.75, 3.26)	0.2334
≥50 Years-Old per 1 SD	6668	202	1.34 (1.17, 1.54)	2.87E-05

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The Cox models include sex, principal components, and polygenic risk score.

P value produced from interaction term with continuous PRS (per SD) and age (<50 versus ≥50 years).

Figure 2: — Absolute risk estimates of being diagnosed with CRC across the age stratum by PRS percentile among individuals in the RPGEH cohort. (A) Among individuals with a first-degree relative with CRC. (B) Among individuals without a family history of CRC.

Discussion

Our study, including more than 50,000 CRC cases and 50,000 controls, demonstrated that a PRS, derived from common genetic variants, successfully identifies participants at increased risk for early-onset CRC, particularly among individuals without a family history of CRC; additionally, the PRS was more strongly associated with early-onset cancer compared with late-onset CRC. The PRS-associated risks were found for early-onset cancer of the proximal and distal colon, and the rectum, with a modest increased propensity for the non-proximal cancers. We confirmed the overall findings for early-onset CRC in a sub-study from Ohio, where Lynch syndrome cases were excluded from the analysis. The results from these case-control studies were also supported by a smaller, prospective study that showed increased PRS-associated risks for early-onset CRC, particularly in those negative for CRC family history. Our findings may have important clinical relevance, as they could contribute, along with other lifestyle and environmental risk factors, to tailored screening in people aged <50 years who are currently not targeted for early detection and for whom CRC rates have increased over the last decades.

The development of a PRS to evaluate the overall predictive power of common risk loci for CRC has previously been carried out;^29–31 however, few studies evaluated specifically for association of common polymorphisms with early-onset CRC.^32–36 These smaller studies, involving 10 to 33 SNPs, pointed to some individual loci differentially associated with early-onset CRC; however, our much larger study, which included 95 loci identified from GWAS (Table S2), showed that risks related to an individual’s cumulative genetic risk profile for at-risk alleles, as reflected in the PRS, were much greater than the contributions of individual SNPs. A caveat to using these 95 variants in a PRS intended for discriminating early-onset CRC risk is that they are produced from GWAS analyses not specific to early-onset disease; adequately powered GWAS analyses specific for early-onset CRC have yet to be performed. Therefore, although our PRS positively identifies those at heightened risk for early-onset CRC, there is still room for improving its discriminatory accuracy. Furthermore, combining a genetic PRS with lifestyle and environmental risk factors could potentially contribute to even greater precision in identification of individuals who may benefit from earlier onset CRC screening.³⁷

Given that early-onset CRC is increasing in incidence and is commonly diagnosed at later stages, which carries a poorer prognosis, recommendations have been made to lower the screening age to 45 for individuals at average-risk.¹² Consideration of early detection for early-onset cancer is dependent, however, on a number of factors, including differentials in CRC risk in absolute terms, projected benefits, potential harms such as colonic perforation, and costs; therefore, potentially tempering some enthusiasm for lowering the CRC screening age and calling for identification of high-risk groups for more targeted early detection.^{16, 38, 39} Our study highlights the potential utility of a PRS in CRC risk stratification for people <50 years of age, which might inform precision cancer screening in this population that currently lacks consistent early detection recommendations, particularly for those without a family history of CRC.

This study is unique in the large size of the study population, particularly for those <50 years of age, allowing for evaluation of PRS-related risks overall, and by family history, refined age groups, and tumor site. Major results for association of the PRS with early-onset cancer were also replicated in an independent community-based cohort, although the number of early-onset cases in that cohort was limited. Limitations of our study include the lack of CRC family history information on a substantial subset of study participants; however, missingness was defined by study and therefore unlikely to introduce bias. Also, our PRS was generated and validated in individuals of European ancestry, currently limiting its applicability for different ancestral groups, until a PRS is developed and validated in diverse populations. Another limitation is that we did not systematically take into account the genetic mutations related to Lynch and other rarer hereditary cancer syndromes;^{23, 34, 40–42} however, our sensitivity analysis, in the Ohio investigations where this information was systematically assessed, indicated that risks associated with PRS remained very similar after the removal of Lynch cases from the analysis.

Nevertheless, further research is needed on the combined utility for risk prediction of rare and common variants in those with or without a family history of CRC as it can be expected that accounting for both PRS and high penetrance genes will further improve risk stratification.^{43, 44} There remains more to be discovered about the genetics of CRC, particularly for early-onset disease, as substantial heritability for CRC remains unexplained and genetic effects are typically stronger for early-onset diesase.^{45, 46} As more risk loci will be discovered, the predictive power of the PRS is expected to further improve, and to be tested in clinical trials.

In conclusion, we demonstrated that a PRS, derived from common genetic variants, successfully stratifies individuals for early onset CRC based on genetic risk, particularly among individuals who report a negative first-degree family history of CRC. Furthermore, the associations between the PRS and CRC are greater for young-onset than for older-onset disease. The PRS may contribute, along with lifestyle and environmental risk profiling, toward prioritizing individuals at increased susceptibility to early-onset CRC for personalized screening regimens or other intervention strategies. Early-onset CRC is increasing in the US and elsewhere; by selecting high-risk individuals <50 years of age, we can reduce the burden on early detection programs and potentially provide more individualized prevention approaches.

Supplementary Material

NIHMS1547252-supplement-Supplemental_File_1.docx^{(56.1KB, docx)}

Figure S2

NIHMS1547252-supplement-Figure_S2.tiff^{(10.3MB, tiff)}

Figure S1

NIHMS1547252-supplement-Figure_S1.tiff^{(12.4MB, tiff)}

What you need to know.

BACKGROUND AND CONTEXT

Although early-onset colorectal cancer (CRC, in persons younger than 50 years old) is increasing in incidence; yet, in the absence of a family history of CRC, this population lacks harmonized recommendations for prevention.

NEW FINDINGS

In an analysis of associations with CRC per standard deviation of polygenic risk score (PRS), we found the cumulative burden of CRC-associated common genetic variants to associate with early-onset cancer, and to be more strongly associated with early-onset than late-onset cancer—particularly in the absence of CRC family history.

LIMITATIONS

Further studies are needed to identify genetic factors associated with early-onset CRC.

IMPACT

Analyses of PRS, along with environmental and lifestyle risk factors, might identify younger individuals who would benefit from preventative measures.

Acknowledgements

ASTERISK: We are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students.

COLON and NQplus: the authors would like to thank the COLON and NQplus investigators at Wageningen University & Research and the involved clinicians in the participating hospitals.

CORSA: We thank all those who agreed to participate in the CORSA study, including the patients and the control persons, as well as all the physicians and students.

CPS-II: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program.

Czech Republic CCS: We are thankful to all clinicians in major hospitals in the Czech Republic, without whom the study would not be practicable. We are also sincerely grateful to all patients participating in this study.

DACHS: We thank all participants and cooperating clinicians, and Ute Handte-Daub, Utz Benscheid, Muhabbet Celik and Ursula Eilber for excellent technical assistance.

EDRN: We acknowledge all the following contributors to the development of the resource: University of Pittsburgh School of Medicine, Department of Gastroenterology, Hepatology and Nutrition: Lynda Dzubinski; University of Pittsburgh School of Medicine, Department of Pathology: Michelle Bisceglia; and University of Pittsburgh School of Medicine, Department of Biomedical Informatics.

EPICOLON: We are sincerely grateful to all patients participating in this study who were recruited as part of the EPICOLON project. We acknowledge the Spanish National DNA Bank, Biobank of Hospital Clínic-IDIBAPS and Biobanco Vasco for the availability of the samples. The work was carried out (in part) at the Esther Koplowitz Centre, Barcelona.

Harvard cohorts: The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the HPFS, NHS, and PHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

LCCS: We acknowledge the contributions of Jennifer Barrett, Robin Waxman, Gillian Smith and Emma Northwood in conducting this study.

NCCCS I & II: We would like to thank the study participants, and the NC Colorectal Cancer Study staff.

NSHDS: We thank all participants in the NSHDS cohorts, the staff at the Department of Biobank Research, Umeå University, the staff at Biobanken norr, Västerbotten County Council, and the scientists managing the Northern Sweden Diet Database. We also thank Prof. Richard Palmqvist and Dr. Björn Gylling at the Department of Medical Biosciences, and Dr. Robin Myte, formerly at the Department of Radiation Sciences, all at Umeå University, Sweden, for their valuable contributions in defining the nested case-control study.

PLCO: The authors thank the PLCO Cancer Screening Trial screening center investigators and the staff from Information Management Services Inc and Westat Inc. Most importantly, we thank the study participants for their contributions that made this study possible.

PMH: The authors would like to thank the study participants and staff of the Hormones and Colon Cancer study.

SEARCH: SEARCH team.

UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number 8614.

WHI: The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf.

Funding:

ASTERISK: a Hospital Clinical Research Program (PHRC-BRD09/C) from the University Hospital Center of Nantes (CHU de Nantes) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC).

The ATBC Study was supported by the US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute.

COLO2&3: National Institutes of Health (R01 CA60987).

ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184 (Li/Ulrich), U01 CA206110 (Ulrich/Li/Siegel/Figueireido/Colditz, 2P30CA015704-40 (Gilliland), R01 CA207371 (Ulrich/Li)), NIH P30 CA42014 (Ulrich), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and by TRANSCAN (JTC2012-MetaboCCC, JTC2013-FOCUS).

The Colon Cancer Family Registry (CFR) Illumina GWAS was supported by funding from the National Cancer Institute, National Institutes of Health (grant numbers U01 CA122839, R01 CA143247). The Colon CFR/CORECT Affymetrix Axiom GWAS and OncoArray GWAS were supported by funding from National Cancer Institute, National Institutes of Health (grant number U19 CA148107 to S Gruber). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by the National Cancer Institute, National Institutes of Health (grant number U01 CA167551) and through cooperative agreements with the following Colon CFR centers: Australasian Colorectal Cancer Family Registry (NCI/NIH grant numbers U01 CA074778 and U01/U24 CA097735), USC Consortium Colorectal Cancer Family Registry (NCI/NIH grant numbers U01/U24 CA074799), Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (NCI/NIH grant number U01/U24 CA074800), Ontario Familial Colorectal Cancer Registry (NCI/NIH grant number U01/U24 CA074783), Seattle Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074794), and University of Hawaii Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074806), Additional support for case ascertainment was provided from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute to Fred Hutchinson Cancer Research Center (Control Nos. N01-CN-67009 and N01-PC-35142, and Contract No. HHSN2612013000121), the Hawai’i Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No. HHSN26120100037C, and the California Department of Public Health (contracts HHSN261201000035C awarded to the University of Southern California, and the following state cancer registries: AZ, CO, MN, NC, NH, and by the Victoria Cancer Registry and Ontario Cancer Registry.

COLON: The COLON study is sponsored by Wereld Kanker Onderzoek Fonds, including funds from grant 2014/1179 as part of the World Cancer Research Fund International Regular Grant Programme, by Alpe d’Huzes and the Dutch Cancer Society (UM 2012–5653, UW 2013-5927, UW2015-7946), and by TRANSCAN (JTC2012-MetaboCCC, JTC2013-FOCUS). The NQplus study is sponsored by a ZonMW investment grant (98-10030); by PREVIEW, the project PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW) project which received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant no. 312057; by funds from TI Food and Nutrition (cardiovascular health theme), a public–private partnership on precompetitive research in food and nutrition; and by FOODBALL, the Food Biomarker Alliance, a project from JPI Healthy Diet for a Healthy Life.

Colorectal Cancer Transdisciplinary (CORECT) Study: The CORECT Study was supported by the National Cancer Institute, National Institutes of Health (NCI/NIH), U.S. Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350,; P01 CA196569; R01 CA201407) and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678).

CORSA: This study was funded by FFG BRIDGE (grant 829675, to Andrea Gsur), the “Herzfelder’sche Familienstiftung” (grant to Andrea Gsur) and was supported by COST Actions BM1206 and CA17118.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval.

CRCGEN: Colorectal Cancer Genetics & Genomics, Spanish study was supported by Instituto de Salud Carlos III, co-funded by FEDER funds –a way to build Europe– (grants PI14-613 and PI09-1286), Agency for Management of University and Research Grants (AGAUR) of the Catalan Government (grant 2017SGR723), and Junta de Castilla y León (grant LE22A10-2). Sample collection of this work was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncología de Catalunya (XBTC), Plataforma Biobancos PT13/0010/0013 and ICOBIOBANC, sponsored by the Catalan Institute of Oncology.

Czech Republic CCS: This work was supported by the Grant Agency of the Czech Republic (grants CZ GA CR: GAP304/10/1286 and 1585) and by the Grant Agency of the Ministry of Health of the Czech Republic (grants AZV 15-27580A and AZV 17-30920A).

DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1 and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B).

DALS: National Institutes of Health (R01 CA48998 to M. L. Slattery).

EDRN: This work is funded and supported by the NCI, EDRN Grant (U01 CA 84968-06).

EPIC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom).

EPICOLON: This work was supported by grants from Fondo de Investigación Sanitaria/FEDER (PI08/0024, PI08/1276, PS09/02368, P111/00219, PI11/00681, PI14/00173, PI14/00230, PI17/00509, 17/00878, Acción Transversal de Cáncer), Xunta de Galicia (PGIDIT07PXIB9101209PR), Ministerio de Economia y Competitividad (SAF07-64873, SAF 2010-19273, SAF2014-54453R), Fundación Científica de la Asociación Española contra el Cáncer (GCB13131592CAST), Beca Grupo de Trabajo “Oncología” AEG (Asociación Española de Gastroenterología), Fundación Privada Olga Torres, FP7 CHIBCHA Consortium, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya, 2014SGR135, 2014SGR255, 2017SGR21, 2017SGR653), Catalan Tumour Bank Network (Pla Director d’Oncologia, Generalitat de Catalunya), PERIS (SLT002/16/00398, Generalitat de Catalunya), CERCA Programme (Generalitat de Catalunya) and COST Action BM1206. CIBERehd is funded by the Instituto de Salud Carlos III.

ESTHER/VERDI. This work was supported by grants from the Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid.

Harvard cohorts (HPFS, NHS, PHS): HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, R35 CA197735, K07 CA190673, and P50 CA127003), NHS by the National Institutes of Health (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735, K07 CA190673, and P50 CA127003) and PHS by the National Institutes of Health (R01 CA042182).

Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U01 CA164930, R01 CA201407). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704.

Kentucky: This work was supported by the following grant support: 1) Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8) and 2) NCI R01CA136726; and, we would like to acknowledge the staff at the Kentucky Cancer Registry

Kiel: This work was supported by institutional funds from the Medical Faculties of the Christian-Albrechts University Kiel and the Technical University Dresden.

LCCS: The Leeds Colorectal Cancer Study was funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database.

MEC: National Institutes of Health (R37 CA54281, P01 CA033619, and R01 CA063464).

MECC: This work was supported by the National Institutes of Health, U.S. Department of Health and Human Services (R01 CA81488 to SBG and GR).

MSKCC: The work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the National Institutes of Health (grant numbers R01 CA189184, P30 CA076292), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (grant number P30 CA076292).

NCCCS I & II: We acknowledge funding support for this project from the National Institutes of Health, R01 CA66635 and P30 DK034987.

NFCCR: This work was supported by an Interdisciplinary Health Research Team award from the Canadian Institutes of Health Research (CRT 43821); the National Institutes of Health, U.S. Department of Health and Human Serivces (U01 CA74783); and National Cancer Institute of Canada grants (18223 and 18226). The authors wish to acknowledge the contribution of Alexandre Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to Michael O. Woods by the Canadian Cancer Society Research Institute.

NSHDS: Swedish Research Council; Swedish Cancer Society; Cutting-Edge Research Grant and other grants from the County Council of Västerbotten, Sweden; Wallenberg Centre for Molecular Medicine at Umeå University; Lion’s Cancer Research Foundation at Umeå University; the Cancer Research Foundation in Northern Sweden; and the Faculty of Medicine, Umeå University, Umeå, Sweden.

OFCCR: National Institutes of Health, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (U01 CA074783); see CCFR section above. Additional funding toward genetic analyses of OFCCR includes the Ontario Research Fund, the Canadian Institutes of Health Research, and the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation.

OSUMC: Funding was provided by Pelotonia and the NCI (CA16058 and CA67941).

PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Funding was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438.

PMH: National Institutes of Health (R01 CA076366 to P.A. Newcomb).

RPGEH: Data used in this study were provided by the Kaiser Permanente Research Bank (KPRB) from the KPRB collection, which includes the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the Genetic Epidemiology Research on Adult Health and Aging (GERA) data, funded by the National Institutes of Health [RC2 AG036607 (Schaefer and Risch)], The Ellison Medical Foundation, and the Kaiser Permanente Community Benefits Program. Access to data used in this study may be obtained by application to the KPRB via ResearchBankAccess@kp.org.

SEARCH: The University of Cambridge has received salary support in respect of PDPP from the NHS in the East of England through the Clinical Academic Reserve. Cancer Research UK (C490/A16561); the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge.

The Swedish Low-risk Colorectal Cancer Study: The study was supported by grants from the Swedish research council; K2015-55X-22674-01-4, K2008-55X-20157-03-3, K2006-72X-20157-01-2 and the Stockholm County Council (ALF project).

Swedish Mammography Cohort and Cohort of Swedish Men: This work is supported by the Swedish Research Council /Infrastructure grant, the Swedish Cancer Foundation, and the Karolinska Institutés Distinguished Professor Award to Alicja Wolk.

UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number 8614

VITAL: National Institutes of Health (K05 CA154337).

WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.

Grant Support: National Cancer Institute, National Institutes of Health (Grants: R03-CA215775-01A1, R01-CA206279-03).

Footnotes

Study descriptions:

Sample numbers of our discovery dataset study participants contributing to the whole-genome sequencing are provided in Table S1. The discovery dataset analyses comprised existing genotyping and clinical data from 42 studies that have been described in detail previously^13–16. Newly generated genotype data from the Research Program on Genes, Environment and Health (RPGEH) cohort formed our replication dataset.

Disclosures: No potential conflicts (financial, professional, or personal) relevant to the manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1547252-supplement-Supplemental_File_1.docx^{(56.1KB, docx)}

Figure S2

NIHMS1547252-supplement-Figure_S2.tiff^{(10.3MB, tiff)}

Figure S1

NIHMS1547252-supplement-Figure_S1.tiff^{(12.4MB, tiff)}

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PERMALINK

Cumulative Burden of Colorectal Cancer-Associated Genetic Variants is More Strongly Associated With Early-onset vs Late-onset Cancer

Alexi N Archambault

Yu-Ru Su

Jihyoun Jeon

Minta Thomas

Yi Lin

David V Conti

Aung Ko Win

Lori C Sakoda

Iris Lansdorp-Vogelaar

Elisabeth FP Peterse

Ann G Zauber

David Duggan

Andreana N Holowatyj

Jeroen R Huyghe

Hermann Brenner

Michelle Cotterchio

Stéphane Bézieau

Stephanie L Schmit

Christopher K Edlund

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Robert J MacInnis

Peter T Campbell

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Martha L Slattery

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Mingyang Song

Yin Cao

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Emily White

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Cornelia M Ulrich

Michael Hoffmeister

Stephanie A Bien

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Jochen Hampe

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Kenneth Offit

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María-Dolores Chirlaque

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