Epithelial ovarian cancer causes more deaths than any other female gynecologic cancer, with an estimated 21,550 new cases and 14,600 deaths in the United States. Better understanding of the molecular mechanisms in advanced ovarian cancer may improve patient treatment. Identification of molecular interactions that stratify prognosis may be the key for such novel treatments. Methylation, CNV and gene-expression are characterizing factors in malignancies. The-Cancer-Genome-Atlas, a multi center coordinated effort, has recently made available the molecular characteristics of more than 200 patients. Using this multi analyte study, two additional datasets from Duke University and a set of computational algorithms we have recently developed, we identify subnetworks that significantly stratify survival rates. The computational algorithms based on methods that 1) identify network alterations that may occur differently in different patients and 2) quantify network behavior through gene-expression. Interestingly, expression levels of single or sets of genes do not explain the prognostic stratification. Only when we apply network effects that phenotypically distinct groups emerge. The single most critical pathway we identify as enabling prognostic stratification is the PDGF pathway. Importantly, the pathway as a unit is the quantifiable unit that facilitates phenotypic divergence. None of the members of the pathway, by itself, facilitates phenotypic divergence. Only when we computationally quantify gene-expression co-respondence, we obtain this consistent prognostic metric for disease outcome. In uncovering the network mechanisms and the specific interactions that drive the phenotype, we catalyze targeted treatment. The work demonstrates possible clinical implementation of this computational approach and suggests specific mechanisms.
