Gene signature for predicting homologous recombination deficiency in triple-negative breast cancer

Triple-negative breast cancers (TNBCs) are a subset of breast cancers that has remained difficult to treat. One of the more efficient treatments for TNBCs is by targeting the homologous recombination (HR) pathway, for example using PARP inhibitors, that has benefited TNBC patients carrying BRCA1/2 alterations. We developed a method to identify TNBC patients, regardless of their BRCA1/2 status, who may benefit from therapies targeting the HR pathway. Using an RNA-seq gene expression dataset of 100 genes (HRD100) from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a nearest centroid classifier for homologous recombination deficiency (HRD) in TNBCs. The HRD100 classifier identified samples with strong HRD mutational signature at an AUROC of 0.892 in the MyBrCa training dataset, as well as 0.783 and 0.713 in MyBrCa and TCGA validation datasets, respectively. Analysis of the 100 genes in the HRD100 classifier using the NanoString nCounter platform showed a concordance rate of 98% (CI: 95-100%) with RNA-seq gene expression analyses, and a concordance rate of 87% (CI: 73-100%) between FFPE and fresh frozen tissue. Taken together, gene expression using these 100 selected genes may identify triple-negative breast cancer patients with homologous recombination deficiency who may benefit from treatment with PARP inhibitors or platinum chemotherapy.