A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer

Abstract

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.

Figure 1.

Association between debulking status and patient survival. A, hierarchical clustering of all 185 specimens using 1-correlation with average linkage for probe sets possessing a variance in the top two thirds. B, overall survival was analyzed relative to debulking status for all 185 tumors using a Kaplan-Meier survival curve.

Figure 2.

Identification and validation of gene signatures correlating with survival in papillary serous ovarian cancer patients. A, for optimally debulked tumors, the predictive classifier unsuccessfully classified tumors according to survival (P_permutation = 0.144). B, in contrast, significant association was found for suboptimally, debulked patients (P_permutation = 0.0179).

Figure 3.

Validation of select survival-associated genes identified in suboptimally debulked patients. Microarray signal intensity values were confirmed using qRT-PCR for a subset of 30 patients by Pearson correlation for (A) PDE8A, (B) RAB2, (C) ZBTB16, and (D) RUNX1T1.

Figure 4.

Clustering of highly correlated survival-associated probe sets in suboptimally debulked patients. A, median-centered expression data for the top 57 probe sets (y axis) associated with survival by Cox regression analysis (P < 0.001; red, overexpression; green, underexpression). B, the majority of living patients (12 versus 5; P < 0.05, χ²) clustered with deceased patients possessing extended survival times (y) in group A (x axis; P = 0.005, Student’s t test).

Figure 5.

Assessment of putative signaling events contributing to patient survival in suboptimally debulked patients. A, 1-correlation clustering with average linkage of the 57 survival-associated genes identified several patient subgroups with discrete sets of coregulated genes. B, pathway analysis of the prognostic signature identified signaling events implicated in cell proliferation, motility, apoptosis, chemoresistance, secretion, and chromatin maintenance.