Analysis of chemotherapeutic response in ovarian cancers using publicly available high-throughput data

A third of patients with epithelial ovarian cancer (OVCA) will not respond to standard treatment. The determination of a robust signature that predicts chemoresponse could lead to the identification of molecular markers for response as well as possible clinical implementation in the future to identify patients at risk of failing therapy. This pilot study was designed to identify biologic processes affecting candidate pathways associated with chemoresponse and to create a robust gene signature for follow-up studies. After identifying common pathways associated with chemoresponse in serous OVCA in three independent gene-expression experiments, we assessed the biologic processes associated with them using The Cancer Genome Atlas (TCGA) dataset for serous OVCA. We identi fied differential copy-number alterations (CNA), mutations, DNA methylation, and miRNA expression between patients that responded to standard treatment and those who did not or recurred prematurely. We correlated these significant parameters with gene expression to create a signature of 422 genes associated with chemoresponse. A consensus clustering of this signature identified two differentiated clusters with unique molecular patterns: cluster 1 was signi ficant for cellular signaling and immune response (mainly cell-mediated); and cluster 2 was significant for pathways involving DNA-damage repair and replication, cell cycle, and apoptosis. Validation through consensus clustering was performed in five independent OVCA gene-expression experiments. Genes were located in the same cluster with consistent agreement in all five studies (κ coefficient ≥ 0.6 in 4). Integrating high-throughput biologic data have created a robust molecular signature that predicts chemoresponse in OVCA.