Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk.
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Kalia Sarah S, Boddicker Nicholas J, Yadav Siddhartha, Huang Hongyan, Na Jie, Hu Chunling, Ambrosone Christine B, Yao Song, Haiman Christopher A, Chen Fei, John Esther M, Kurian Allison W, Guo Boya, Lindstrӧm Sara, Auer Paul, Lacey James V, Neuhausen Susan L, Martinez Maria Elena, Sandler Dale P, O'Brien Katie M, Taylor Jack A, Teras Lauren R, Hodge James M, Lori Adriana, Bodelon Clara, Trentham-Dietz Amy, Burnside Elizabeth S, Vachon Celine M, Winham Stacey J, Goldgar David E, Domchek Susan M, Nathanson Katherine L, Weitzel Jeffrey N, Couch Fergus J, Kraft Peter
Аннотация
METHODS: We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium.
RESULTS: The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population.
CONCLUSIONS: These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification.
IMPACT: Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.