Abstract 2587: Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention

Abstract

Abstract Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States, despite the fact that it is one of the most preventable and treatable cancers when detected early via screening. The current screening guidelines for CRC are based on age, family history of CRC, and previous screening results. However, multiple environmental and lifestyle risk factors have been established or suspected for CRC, as have many common genetic susceptibility loci. It is critical to utilize this information to better stratify individuals into low- and high-risk groups for optimized and personalized screening and intervention recommendations. Using data from two large consortia (8421 CRC cases and 9767 controls): the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Transdisciplinary study (CORECT), we developed risk prediction models for men and women based on family history, environmental and lifestyle risk factors, and known CRC susceptibility loci identified through genome-wide association studies. We constructed an environmental risk score (E-score) as a weighted sum of 19 established or potential environmental and lifestyle risk factors for CRC with weights obtained from a multivariate logistic regression analysis. Compared to the model that includes only family history, the E-score significantly improves the discriminatory accuracy for both men (AUC = 0.62 vs. 0.53, p-value < 1e-5) and women (AUC = 0.60 vs. 0.52, p-value < 1e-5). Similarly, we also constructed a genetic risk score (G-score) using 50 common variants associated with CRC risk, and the G-score also significantly improves the discriminatory accuracy for both men (AUC = 0.60 vs. 0.53, p-value < 1e-5) and women (AUC = 0.59 vs. 0.52, p-value < 1e-5) over the family history-only model. Compared to the model with family history and E-score, the inclusion of the G-score in the model further improves the discriminatory accuracy for both men (AUC = 0.65 vs. 0.62, p-value = 0.0152) and women (AUC = 0.63 vs. 0.60, p-value = 0.0005). Our risk prediction models are the first to incorporate both comprehensive environmental and lifestyle risk factors, and known CRC common genetic variants. The E- and G-scores are independent risk predictors for CRC, and models that incorporate both scores improve the discriminatory accuracy significantly compared to family history-only models. Using risk-factor distributions available from nationally representative data (e.g., NHANES), we will provide absolute-risk estimates of CRC using both the E- and G-scores. We expect our comprehensive models incorporating both environmental and genetic risk factors to provide more accurate estimation of CRC, which will be useful for recommending individually tailored screening and intervention strategies to prevent this common cancer. Citation Format: Jihyoun Jeon, Sonja I. Berndt, Hermann Brenner, Peter T. Campbell, Andrew T. Chan, Jenny Chang-Claude, Mengmeng Du, Graham Giles, Jian Gong, Stephen B. Gruber, Tabitha A. Harrison, Michael Hoffmeister, Loic LeMarchand, Li Li, John D. Potter, Gad Rennert, Robert E. Schoen, Martha L. Slattery, Emily White, Michael O. Woods, Ulrike Peters, Li Hsu. Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2587.