Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk.

BACKGROUND

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited.

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.