Department of Bioinformatics and Computational Biology

Publications:23945238

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Predicting time to ovarian carcinoma recurrence using protein markers.

J. Clin. Invest. 123 (9):3740-50
Sep 2013

Yang J, Yoshihara K, Tanaka K, Hatae M, Masuzaki H, Itamochi H, Cancer Genome Atlas (TCGA) Research Network, Takano M, Ushijima K, Tanyi J, Coukos G, Lu Y, Mills G, Verhaak R

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230-1402, USA.

Abstract

Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.