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The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.

Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients