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

Specific patterns of genomic allelic imbalances (AIs) have been associated with Homologous recombination DNA-repair deficiency (HRD). We performed a systematic pan-cancer characterization of AIs across tumor types, revealing unique patterns in ovarian cancer. Using machine learning on a multi-omics dataset, we generated an optimized algorithm to detect HRD in ovarian cancer (ovaHRDscar). ovaHRDscar improved the prediction of clinical outcomes in three independent validation cohorts (PCAWG, HERCULES, TERVA). Characterization of 98 spatiotemporally distinct tumor samples indicated ovary/adnex as the preferred site to assess HRD. In conclusion, ovaHRDscar improves the detection of HRD in ovarian cancer with the premise to improve patient selection for HR-targeted therapies.