31P Accurate detection of HRD status in multiple cancer types using somatic mutation pattern

Abstract

Homologous Recombination Deficiency (HRD) is caused by various molecular alterations in genes that participate in HR repair. However, HR is a complicated biological process and it is unlikely that all HRD causing alterations have been uncovered so far. We hypothesized that HRD detection based on mutation pattern can accurately detect HRD status with better patient stratification. We retrieved mutations of ovarian cancer from TCGA repository. Samples treated with platinum chemotherapy were selected for further analysis. Mutation pattern were profiled using negative matrix factorization (NMF) method. Samples were tiered based on weight of HRD-related mutation signature. Cox model was applied to compare survival of samples of each tier against non-HRD samples separately. To test the feasibility of our method on targeted sequencing data, mutation profile was shrunk to selected genomic region composed of 825 genes (∼ 2.2Mb). We retrieved 192 in-house clinical samples and labeled HRD status based on known HRD causing variants. We applied our method on these samples to inspect the consistency. 375 samples were profiled for HRD status and were divided into four tiers based on the weight of HRD-related mutation pattern. Cox model showed significant survival difference (p-value = 0.001) proportional to the weight of HRD mutation pattern with higher weight being the longest survival and vice versa. 223 and 152 samples were categorized as HRD and non-HRD based on optimized threshold and survival difference remains significant (p-value = 0.0017). Similar survival difference was observed (p-value=0.0013) suggesting the validity of the method on targeted sequencing. Out of 124, 49 and 19 breast, ovarian and prostate cancer samples, 13, 11 and 4 of them were labeled as HRD positive based on known HRD causing variants. All but one HRD positive samples were consistently classified by our method. Our results showed that NMF method is able to faithfully detect HRD samples. The method can be applied to WES as well as targeted sequencing and achieve equally significant survival difference between samples with different HR status. Upon detecting HRD, this method can identify additional HRD patients and therefore make better patient stratification.