Kinase-driven metabolic signalling as a predictor of response to carboplatin-paclitaxel adjuvant treatment in advanced ovarian cancers.

BACKGROUND: The biological mechanisms underlying early- and advanced-stage epithelial ovarian cancers (EOCs) are still poorly understood. This study explored kinase-driven metabolic signalling in early and advanced EOCs, and its role in tumour progression and response to carboplatin-paclitaxel treatment. METHODS: Tumour epithelia were isolated from two independent sets of primary EOC (n=72 and 30 for the discovery and the validation sets, respectively) via laser capture microdissection. Reverse phase protein microarrays were used to broadly profile the kinase-driven metabolic signalling of EOC with particular emphasis on the LBK1-AMPK and AKT-mTOR axes. Signalling activation was compared between early and advanced lesions, and carboplatin-paclitaxel-sensitive and -resistant tumours. RESULTS: Advanced EOCs were characterised by a heterogeneous kinase-driven metabolic signature and decreased phosphorylation of the AMPK-AKT-mTOR axis compared to early EOC (P<0.05 for AMPKα T172, AMPKα1 S485, AMPKß1 S108, AKT S473 and T308, mTOR S2448, p70S6 S371, 4EBP1 S65, GSK-3 α/ß S21/9, FOXO1 T24/FOXO3 T32, and FOXO1 S256). Advanced tumours with low relative activation of the metabolic signature and increased FOXO1 T24/FOXO3 T32 phosphorylation (P=0.041) were associated with carboplatin-paclitaxel resistance. CONCLUSIONS: If validated in a larger cohort of patients, the decreased AMPK-AKT-mTOR activation and phosphorylation of FOXO1 T24/FOXO3 T32 may help identify carboplatin-paclitaxel-resistant EOC patients.

Functional characterization of epithelial ovarian cancer histotypes by drug target based protein signaling activation mapping: implications for personalized cancer therapy.

Epithelial ovarian carcinoma (EOC) is a deadly disease, with a 5-year survival of 30%. The aim of the study was to perform broad-scale protein signaling activation mapping to evaluate if EOC can be redefined based on activated protein signaling network architecture rather than histology. Tumor cells were isolated using laser capture microdissection (LCM) from 72 EOCs. Tumors were classified as serous (n = 38), endometrioid (n = 13), mixed (n = 8), clear cell (CCC; n = 7), and others (n = 6). LCM tumor cells were lysed and subjected to reverse-phase protein microarray to measure the expression/activation level of 117 protein drug targets. Unsupervised hierarchical clustering analysis was utilized to explore the overall signaling network. ANOVA was used to detect significant differences among the groups (p < 0.05). Regardless of histology, unsupervised analysis revealed five pathway-driven clusters. When the EOC histotypes were compared by ANOVA, only CCC showed a distinct signaling network, with activation of EGFR, Syk, HER2/ErbB2, and SHP2 (p = 0.0007, p = 0.0021, p < 0.0001, and p = 0.0410, respectively). The histological classification of EOC fails to adequately describe the underpinning protein signaling network. Nevertheless, CCC presents unique signaling characteristics compared to the other histotypes. EOC may need to be characterized by functional signaling activation mapping rather than pure histology.