An investigation of the association of genetic susceptibility risk with somatic mutation burden in breast cancer

BACKGROUND: Genome-wide association studies have reported nearly 100 common germline susceptibility loci associated with the risk for breast cancer. Tumour sequencing studies have characterised somatic mutation profiles in breast cancer patients. The relationship between breast cancer susceptibility loci and somatic mutation patterns in breast cancer remains largely unexplored. METHODS: We used single-nucleotide polymorphism (SNP) genotyping array data and tumour exome sequencing data available from 638 breast cancer patients of European ancestry from The Cancer Genome Atlas (TCGA) project. We analysed both genotype data and, when necessary, imputed genotypes for 90 known breast cancer susceptibility loci. We performed linear regression models to investigate possible associations between germline risk variants with total somatic mutation count (TSMC), as well as specific mutation types. We examined individual SNP genotypes, as well as a multi-SNP polygenic risk score (PRS). Models were statistically adjusted for age at diagnosis, stage, oestrogen-receptor (ER) and progesterone-receptor (PR) status of breast cancer. We also performed stratified analyses by ER and PR status. RESULTS: We observed a significant inverse association (P=8.75 × 10(−6); FDR=0.001) between the risk allele in rs2588809 of the gene RAD51B and TSMC across all breast cancer patients, for both ER(+) and ER(−) tumours. This association was also evident for different types of mutations. The PRS analysis for all patients, with or without rs2588809, showed a significant inverse association (P=0.01 and 0.04, respectively) with TSMC. This inverse association was significant in ER(+) patients with the ER(+)-specific PRS (P=0.02), but not among ER(−) patients for the ER(−)-specific PRS (P=0.39). CONCLUSIONS: We observed an inverse association between common germline risk variants and TSMC, which, if confirmed, could provide new insights into how germline variation informs our understanding of somatic mutation patterns in breast cancer.