Analysis of chemotherapeutic response in ovarian cancers using publically available high-throughput data
Menée à l'aide de la base de données du projet "The Cancer Genome Atlas", cette étude identifie un ensemble d'anomalies génomiques et épigénomiques associées à la réponse thérapeutique chez les patientes atteintes d'un cancer épithélial de l'ovaire
A third of patients with epithelial ovarian cancer (OVCA) will not respond to standard treatment. The determination of a robust signature that predicts chemo-response 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 biological processes affecting candidate pathways associated with chemo-response and to create a robust gene signature for follow-up studies. After identifying common pathways associated with chemo-response in serous OVCA in 3 independent gene expression experiments, we assessed the biological processes associated with them using The Cancer Genome Atlas (TCGA) dataset for serous OVCA. We identified 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 to gene expression to create a signature of 422 genes associated with chemo-response. A consensus clustering of this signature identified 2 differentiated clusters with unique molecular patterns: cluster #1 was significant 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 5 independent OVCA gene expression experiments. Genes were located in the same cluster with consistent agreement in all 5 studies (Kappa coefficient ≥ 0.6 in 4). Integrating high throughput biological data has created a robust molecular signature that predicts chemo-response in OVCA.
Cancer Research , résumé, 2014