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A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer

¹ Cell and Cancer Biology Branch, ² Biometrics Research Branch, ³ Laboratory of Cellular Oncology, National Cancer Institute, NIH, Rockville, Maryland; and ⁴ Memorial Sloan-Kettering Cancer Center, New York, New York

Requests for reprints: Michael J. Birrer, National Cancer Institute, Building 37/Room 1130, 9000 Rockville Pike, Bethesda, MD 20892. Phone: 301-402-9573; Fax: 301-480-4756; E-mail: birrerm{at}mail.nih.gov.

Key Words: microarray • ovarian cancer • prognosis

Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185 (90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. [Cancer Res 2008;68(13):5478–86]