Jihyoun Jeon1, Mengmeng Du2, Robert E Schoen3, Michael Hoffmeister4, Polly A Newcomb5, Sonja I Berndt6, Bette Caan7, Peter T Campbell8, Andrew T Chan9, Jenny Chang-Claude4, Graham G Giles10, Jian Gong5, Tabitha A Harrison5, Jeroen R Huyghe5, Eric J Jacobs8, Li Li11, Yi Lin5, Loïc Le Marchand12, John D Potter5, Conghui Qu5, Stephanie A Bien5, Niha Zubair5, Robert J Macinnis10, Daniel D Buchanan13, John L Hopper14, Yin Cao15, Reiko Nishihara16, Gad Rennert17, Martha L Slattery18, Duncan C Thomas19, Michael O Woods20, Ross L Prentice5, Stephen B Gruber21, Yingye Zheng5, Hermann Brenner4, Richard B Hayes22, Emily White5, Ulrike Peters23, Li Hsu24. 1. Department of Epidemiology, University of Michigan, Ann Arbor, Michigan. Electronic address: jihjeon@umich.edu. 2. Memorial Sloan Kettering, New York, New York. 3. Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. 4. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center and National Center for Tumor Diseases, Heidelberg, Germany; German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany. 5. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 6. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland. 7. Division of Research, Kaiser Permanente Medical Care Program, Oakland, California. 8. Epidemiology Research Program, American Cancer Society, Atlanta, Georgia. 9. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. 10. Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, School of Global and Population Health, University of Melbourne, Melbourne, Australia. 11. Case Western Reserve University, Cleveland, Ohio. 12. Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii. 13. Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Genetic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia. 14. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia; Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea. 15. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. 16. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. 17. Carmel Medical Center, Haifa, Israel. 18. Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, Utah. 19. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California. 20. Memorial University of Newfoundland, St John's, Newfoundland, Canada. 21. USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California. 22. Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York. 23. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. Electronic address: upeters@fredhutch.org. 24. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. Electronic address: lih@fredhutch.org.
Abstract
BACKGROUND & AIMS: Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening. METHODS: We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry. RESULTS: In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk. CONCLUSIONS: We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.
BACKGROUND & AIMS: Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening. METHODS: We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry. RESULTS: In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, for individuals with the highest vs the lowest 10% of risk. CONCLUSIONS: We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, which is based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.
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