Optimized detection of allelic imbalances specific for homologous recombination deficiency improves the prediction of clinical outcomes in cancer

Abstract

Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in human cancer. Using a systematic pan-cancer characterization, we revealed distinct patterns of somatic allelic imbalances (AIs) in high-grade serous ovarian cancer (HGSC). Using machine learning on a multi-omics dataset, we generated an optimized algorithm to detect HRD in HGSC (ovaHRDscar). ovaHRDscar improved the prediction of clinical outcomes in three independent validation cohorts. Characterization of 98 spatiotemporally distinct tumor samples indicated ovary/adnex as the preferred site to assess HRD. Cancer-specific optimization improved the prediction of clinical outcomes also in triple-negative breast cancer (tnbcHRDscar). In conclusion, our systematic approach based on multi-omics data improves the detection of HRD with the premise to improve patient selection for HR-targeted therapies.