Homologous recombination (HR) deficiency (HRD) is one of the key determinants of PARP inhibitor response in ovarian cancer, and its accurate detection in tumor biopsies is expected to improve the efficacy of this therapy. Because HRD induces a wide array of genomic aberrations, mutational signatures may serve as a companion diagnostic to identify PARP inhibitor–responsive cases.
From the The Cancer Genome Atlas (TCGA) whole-exome sequencing (WES) data, we extracted different types of mutational signature–based HRD measures, such as the HRD score, genome-wide LOH, and HRDetect trained on ovarian and breast cancer–specific sequencing data. We compared their performance to identify BRCA1/2-deficient cases in the TCGA ovarian cancer cohort and predict survival benefit in platinum-treated, BRCA1/2 wild-type ovarian cancer.
We found that the HRD score, which is based on large chromosomal alterations alone, performed similarly well to an ovarian cancer–specific HRDetect, which incorporates mutations on a finer scale as well (AUC = 0.823 vs. AUC = 0.837). In an independent cohort these two methods were equally accurate predicting long-term survival after platinum treatment (AUC = 0.787 vs. AUC = 0.823). We also found that HRDetect trained on ovarian cancer was more accurate than HRDetect trained on breast cancer data (AUC = 0.837 vs. AUC = 0.795; P = 0.0072).
When WES data are available, methods that quantify only large chromosomal alterations such as the HRD score and HRDetect that captures a wider array of HRD-induced genomic aberrations are equally efficient identifying HRD ovarian cancer cases.