miRNA based signature for predicting epithelial ovarian cancer relapse-progression: a step forward to prime time clinical adoption?

Epithelial ovarian cancer (EOC) is an extremely genomically heterogeneous disease (1) with a unique clinical phenotype: high rate of chemosensitivity but also high risk of relapse, also after adequate primary treatment (2). Even so, the development of predictive tools to stratify relapse risk can be helpful in identifying high risk populations, which represent 70% of advanced stage disease patients, and in tailoring following treatment. The Cancer Genome Atlas (TCGA) project aimed to unravel this genomic complexity, providing gene expression profiles to discriminate prognosis among patients (3,4). More recently, a meta-analytic approach merging all the gene expression signatures for EOC increased the prognostic accuracy of the model, but no clinically relevant prognostic differences among groups were identified (5).