Comprehensive annotation of BRCA1 and BRCA2 missense variants by functionally validated sequence-based computational prediction models.

PURPOSE: To improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function.

METHODS: Functional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 and BRCA2 were developed to increase predictive accuracy.

RESULTS: Optimized thresholds for in silico prediction models significantly improved the accuracy of predicted functional effects for BRCA1 and BRCA2 variants. In addition, new BRCA1-RF and BRCA2-RF metapredictors showed area under the curve (AUC) values of 0.92 (95% confidence interval [CI]: 0.88-0.96) and 0.90 (95% CI: 0.84-0.95), respectively. Similarly, the BRCA1-NVM and BRCA2-NVM models had AUCs of 0.93 and 0.90. The RF and NVM models were used to predict the pathogenicity of all possible missense variants in BRCA1 and BRCA2.

CONCLUSION: The recalibrated algorithms and new metapredictors significantly improved upon current models for predicting the impact of variants in cancer risk-associated domains of BRCA1 and BRCA2. Prediction of the functional impact of all possible variants in BRCA1 and BRCA2 provides important information about the clinical relevance of variants in these genes.