OBJECTIVE: Rapamycin analogs have reproducible but modest efficacy in endometrial cancer (EC). Identification of molecular biomarkers that predict benefit could guide clinical development. METHODS: Fixed primary tissue and whole blood were collected prospectively from patients enrolled on GOG 248. DNA was isolated from macro-dissected tumors and blood; next-generation sequence analysis was performed on a panel of cancer related genes. Associations between clinical outcomes [response rate (RR) 20%; progression-free survival (PFS) median 4.9months] and mutations (PTEN, PIK3CA, PIK3R1, KRAS, CTNNB1, AKT1, TSC1, TSC2, NF1, FBXW7) were explored. RESULTS: Sequencing data was obtained from tumors of 55 of the 73 enrolled pts. Mutation rates were consistent with published reports: mutations in PTEN (45%), PIK3CA (29%), PIK3R1 (24%), K-RAS (16%), CTNNB1 (18%) were common and mutations in AKT1 (4%), TSC1 (2%), TSC2 (2%), NF1 (9%) and FBXW7 (4%) were less common. Increased PFS (HR 0.16; 95% CI 0.01-0.78) and RR (response difference 0.83; 95% CI 0.03-0.99) were noted for AKT1 mutation. An increase in PFS (HR 0.46; 95% CI 0.20-0.97) but not RR (response difference 0.00, 95% CI -0.34-0.34) was identified for CTNNB1 mutation. Both patients with TSC mutations had an objective response. There were no statistically significant associations between mutations in PIK3CA, PTEN, PIK3R1, or KRAS and PFS or RR. CONCLUSIONS: Mutations in AKT1, TSC1 and TSC2 are rare, but may predict clinical benefit from temsirolimus. CTNNB1 mutations were associated with longer PFS on temsirolimus.

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.