Ovarian Cancer-Specific <i>BRCA</i>-like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial.

Abstract

Previously, we developed breast cancer <i>BRCA1-</i>like and <i>BRCA2</i>-like copy-number profile shrunken centroid classifiers predictive for mutation status and response to therapy, targeting homologous recombination deficiency (HRD). Therefore, we investigated <i>BRCA1-</i> and <i>BRCA2-</i>like classification in ovarian cancer, aiming to acquire classifiers with similar properties as those in breast cancer.<b>Experimental Design:</b> We analyzed DNA copy-number profiles of germline <i>BRCA1</i>- and <i>BRCA2</i>-mutant ovarian cancers and control tumors and observed that existing breast cancer classifiers did not sufficiently predict mutation status. Hence, we trained new shrunken centroid classifiers on this set and validated them in the independent The Cancer Genome Atlas dataset. Subsequently, we assessed <i>BRCA1/2</i>-like classification and obtained germline and tumor mutation and methylation status of cancer predisposition genes, among them several involved in HR repair, of 300 ovarian cancer samples derived from the consecutive cohort trial AGO-TR1 (NCT02222883). The detection rate of the <i>BRCA1</i>-like classifier for <i>BRCA1</i> mutations and promoter hypermethylation was 95.6%. The <i>BRCA2</i>-like classifier performed less accurately, likely due to a smaller training set. Furthermore, three quarters of the <i>BRCA1/2</i>-like tumors could be explained by (epi)genetic alterations in <i>BRCA1/2</i>, germline <i>RAD51C</i> mutations and alterations in other genes involved in HR. Around half of the non-<i>BRCA</i>-mutated ovarian cancer cases displayed a <i>BRCA</i>-like phenotype. The newly trained classifiers detected most <i>BRCA</i>-mutated and methylated cancers and all tumors harboring a <i>RAD51C</i> germline mutations. Beyond that, we found an additional substantial proportion of ovarian cancers to be <i>BRCA</i>-like.