Integrating breast tumor homologous recombination deficiency status to aid germline BRCA1 and BRCA2 variant classification

Abstract

Pathogenic germline variants in certain genes are associated with somatic tumor mutation signatures. The use of somatic tumor mutation data has the potential to improve the identification of true pathogenic variants but remains underexplored. We investigated the integration of tumor homologous recombination (HR) deficiency status as a predictor of pathogenicity for germline BRCA1 and BRCA2 variants, building on the established link between HR deficiency and germline pathogenic variants in these genes. We analyzed breast tumor whole-genome sequence and matching germline data from 350 patients across four datasets: Familial Breast Cancer (N=77), The Cancer Genome Atlas (TCGA-BRCA, N=96), the MAGIC study (N=136), and Q-IMPROvE (N=41). A total of 13,364 germline variants (including structural variations) in BRCA1, BRCA2, and other DNA repair genes (ATM, BARD1, BRIP1, CHEK2, PALB2, PTEN, RAD51C, RAD51D, TP53) underwent variant curation. Patients were categorized based on germline classification as BRCA1 positive (N=25), BRCA2 positive (N=21), and BRCA1/2 negative (N=149), excluding those with BRCA1/2 variants of uncertain significance (N=21) and pathogenic variants in other DNA repair genes (N=134). Somatic HR status (deficient or proficient) was predicted using three algorithms: HRDetect, CHORD, and HRDSum. HR-deficient and HR-proficient status were significant predictors of germline BRCA1/2 pathogenic variant status (positive and negative directions). The CHORD algorithm, which estimates BRCA1 and BRCA2 subtype specifically, added precision contributing evidence towards pathogenicity for the corresponding gene. Finally, we assessed CHORD HR predictions for variants of uncertain significance in BRCA1 and BRCA2, and reported their tumor HR status for potential use as additional evidence in variant curation. Analysis across multiple tumor whole-genome sequencing datasets has shown that HR status prediction algorithms can separate profiles for BRCA1 and BRCA2 pathogenic variants and provide further evidence at increased weight to aid in the classification of germline BRCA1 and BRCA2 variants. Tumor sequencing offers a promising strategy for reducing the uncertainty in germline variant interpretation.