Liang Wang1,2, Xiaosheng He3, Tomotaka Ugai2,4, Koichiro Haruki4, Chun-Han Lo2,5, Dong Hang6, Naohiko Akimoto4, Kenji Fujiyoshi4, Molin Wang2,7, Charles S Fuchs8,9,10, Jeffrey A Meyerhardt11, Xuehong Zhang12,13, Kana Wu13, Andrew T Chan5,13,14,15,16, Edward L Giovannucci2,12,13, Shuji Ogino2,4,14,17, Mingyang Song2,5,13,15. 1. Center of Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, P.R. China. 2. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 3. Department of Colorectal Surgery, the Six Affiliated Hospital, Sun Yat-sen University, Guangzhou, P.R. China. 4. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Program in MPE Molecular Pathological Epidemiology, Boston, MA, USA. 5. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 6. Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China. 7. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 8. Department of Medicine, Yale Cancer Center, New Haven, CT, USA. 9. Department of Medicine, Yale School of Medicine, New Haven, CT, USA. 10. Department of Medicine, Smilow Cancer Hospital, New Haven, CT, USA. 11. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. 12. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 13. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 14. Broad Institute of MIT and Harvard, Cambridge, MA, USA. 15. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 16. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 17. Cancer Immunology and Cancer Epidemiology Programs, Dana-Farber Harvard Cancer Center, Boston, MA, USA.
Abstract
Background: Colorectal cancer (CRC) is a heterogeneous disease that can develop via 3 major pathways: conventional, serrated, and alternate. We aimed to examine whether the risk factor profiles differ according to pathway-related molecular subtypes. Methods: We examined the association of 24 risk factors with 4 CRC molecular subtypes based on a combinatorial status of microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and BRAF and KRAS mutations by collecting data from 2 large US cohorts. We used inverse probability weighted duplication-method Cox proportional hazards regression to evaluate differential associations across subtypes. Results: We documented 1175 CRC patients with molecular subtype data: subtype 1 (n = 498; conventional pathway; non-MSI-high, CIMP-low or negative, BRAF-wild-type, KRAS-wild-type), subtype 2 (n = 138; serrated pathway; any MSI status, CIMP-high, BRAF-mutated, KRAS-wild-type), subtype 3 (n = 367; alternate pathway; non-MSI-high, CIMP-low or negative, BRAF-wild-type, KRAS-mutated), and subtype 4 (n = 172; other marker combinations). Statistically significant heterogeneity in associations with CRC subtypes was found for age, sex, and smoking, with a higher hazard ratio (HR) observed for the subtype 2 (HR per 10 years of age = 2.64, 95% CI = 2.13 to 3.26; HR for female = 2.65, 95% CI = 1.60 to 4.39; HR per 20-pack-year of smoking = 1.29, 95% CI = 1.14 to 1.45) than other CRC subtypes (all P heterogeneity < .005). A stronger association was found for adiposity measures with subtype 1 CRC in men and subtype 3 CRC in women and for several dietary factors with subtype 1 CRC, although these differences did not achieve statistical significance at α level of .005. Conclusions: Risk factor profiles may differ for CRC arising from different molecular pathways.
Background: Colorectal cancer (CRC) is a heterogeneous disease that can develop via 3 major pathways: conventional, serrated, and alternate. We aimed to examine whether the risk factor profiles differ according to pathway-related molecular subtypes. Methods: We examined the association of 24 risk factors with 4 CRC molecular subtypes based on a combinatorial status of microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and BRAF and KRAS mutations by collecting data from 2 large US cohorts. We used inverse probability weighted duplication-method Cox proportional hazards regression to evaluate differential associations across subtypes. Results: We documented 1175 CRC patients with molecular subtype data: subtype 1 (n = 498; conventional pathway; non-MSI-high, CIMP-low or negative, BRAF-wild-type, KRAS-wild-type), subtype 2 (n = 138; serrated pathway; any MSI status, CIMP-high, BRAF-mutated, KRAS-wild-type), subtype 3 (n = 367; alternate pathway; non-MSI-high, CIMP-low or negative, BRAF-wild-type, KRAS-mutated), and subtype 4 (n = 172; other marker combinations). Statistically significant heterogeneity in associations with CRC subtypes was found for age, sex, and smoking, with a higher hazard ratio (HR) observed for the subtype 2 (HR per 10 years of age = 2.64, 95% CI = 2.13 to 3.26; HR for female = 2.65, 95% CI = 1.60 to 4.39; HR per 20-pack-year of smoking = 1.29, 95% CI = 1.14 to 1.45) than other CRC subtypes (all P heterogeneity < .005). A stronger association was found for adiposity measures with subtype 1 CRC in men and subtype 3 CRC in women and for several dietary factors with subtype 1 CRC, although these differences did not achieve statistical significance at α level of .005. Conclusions: Risk factor profiles may differ for CRC arising from different molecular pathways.
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