Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named "Classification of Ovarian Cancer" (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens. IntroductionHigh-grade serous ovarian carcinoma (HGS-OvCa) accounts for 60%-80% of the approximately 26,000 women diagnosed with epithelial ovarian carcinoma in the US annually (1, 2). Known risk determinants for the development of ovarian carcinoma include BRCA1/BRCA2 mutations, family history, nulliparity, oral contraceptive use, tubal ligation, pregnancy, and lactation (1, 3). A common treatment regimen consists of tumor debulking, followed by administration of platinum and taxane-based chemotherapy (4). The advanced stage at which most patients present, combined

Summary Phosphoenolpyruvate carboxykinase (PEPCK) is well known for its role in gluconeogenesis. However, PEPCK is also a key regulator of TCA cycle flux. The TCA cycle integrates glucose, amino acid and lipid metabolism depending on cellular needs. In addition, biosynthetic pathways crucial to tumor growth require the TCA cycle for the processing of glucose and glutamine derived carbons. We show here an unexpected role for PEPCK in promoting cancer cell proliferation in vitro and in vivo by increasing glucose and glutamine utilization toward anabolic metabolism. Unexpectedly, PEPCK also increased the synthesis of ribose from non-carbohydrate sources, such as glutamine, a phenomenon not previously described. Finally, we show that the effects of PEPCK on glucose metabolism and cell proliferation are in part mediated via activation of mTORC1. Taken together, these data demonstrate a role for PEPCK that links metabolic flux and anabolic pathways to cancer cell proliferation.

The MUC1 heterodimeric transmembrane glycoprotein is aberrantly overexpressed by diverse human carcinomas. Galectin-3 is a beta-galactoside binding protein that has also been associated with the development of human cancers. The present results demonstrate that MUC1 induces galectin-3 expression by a posttranscriptional mechanism. We show that the MUC1 C-terminal subunit is glycosylated on Asn-36 and that this modification is necessary for upregulation of galectin-3. N-glycosylated MUC1-C increases galectin-3 mRNA levels by suppressing expression of the microRNA miR-322 and thereby stabilizing galectin-3 transcripts. The results show that, in turn, galectin-3 binds to MUC1-C at the glycosylated Asn-36 site. The significance of the MUC1-C-galectin-3 interaction is supported by the demonstration that galectin-3 forms a bridge between MUC1 and the epidermal growth factor receptor (EGFR) and that galectin-3 is essential for EGF-mediated interactions between MUC1 and EGFR. These findings indicate that MUC1 and galectin-3 function as part of a miR-322-dependent regulatory loop.