Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies

Rotem Ben-Hamo, Sol Efroni

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Background: Ovarian cancer causes more deaths than any other gynecological cancer. Identifying the molecular mechanisms that drive disease progress in ovarian cancer is a critical step in providing therapeutics, improving diagnostics, and affiliating clinical behavior with disease etiology. Identification of molecular interactions that stratify prognosis is key in facilitating a clinical-molecular perspective.Results: The Cancer Genome Atlas has recently made available the molecular characteristics of more than 500 patients. We used the TCGA multi-analysis study, and two additional datasets and a set of computational algorithms that we developed. The computational algorithms are based on methods that identify network alterations and quantify network behavior through gene expression.We identify a network biomarker that significantly stratifies survival rates in ovarian cancer patients. Interestingly, expression levels of single or sets of genes do not explain the prognostic stratification. The discovered biomarker is composed of the network around the PDGF pathway. The biomarker enables prognosis stratification.Conclusion: The work presented here demonstrates, through the power of gene-expression networks, the criticality of the PDGF network in driving disease course. In uncovering the specific interactions within the network, that drive the phenotype, we catalyze targeted treatment, facilitate prognosis and offer a novel perspective into hidden disease heterogeneity.

Original languageEnglish
Article number3
JournalBMC Systems Biology
Volume6
DOIs
StatePublished - 11 Jan 2012

Bibliographical note

Funding Information:
Dr. Sol Efroni is supported by the European Union through the IRG program.

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