The majority of patients with ovarian cancer ultimately develop recurrent chemotherapy-resistant disease. Treatment stratification is mainly based on histological subtype and stage, prior response to platinum-based chemotherapy, and time to recurrent disease. Here, we integrated clinical treatment, treatment response, and survival data with whole-genome sequencing profiles of 132 solid tumor biopsies of metastatic epithelial ovarian cancer to explore genome-informed stratification opportunities. Samples from primary and recurrent disease harbored comparable numbers of single nucleotide variants and structural variants. Mutational signatures represented platinum exposure, homologous recombination deficiency, and aging. Unsupervised hierarchical clustering based on genomic input data identified specific ovarian cancer subgroups, characterized by homologous recombination deficiency, genome stability, and duplications. The clusters exhibited distinct response rates and survival probabilities which could thus potentially be used for genome-informed therapy stratification for more personalized ovarian cancer treatment.
Keywords: ovarian cancer; patient stratification; personalized treatment; treatment response; whole-genome sequencing.