Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival.

Highlights d We build the genomic and transcriptomic landscape of 465 primary TNBCs d Chinese TNBC cases demonstrate more PIK3CA mutations and LAR subtype d Transcriptomic data classify TNBCs into four subtypes d Multi-omics profiling identifies potential targets within specific TNBC subtypes

SUMMARY A handful of tumor-derived cell lines form the mainstay of cancer therapeutic development, yielding drugs with impact typically measured as months to disease progression. To develop more effective breast cancer therapeutics and more readily understand their clinical impact, we constructed a functional metabolic portrait of 46 independently-derived breast cell lines. Our analysis of glutamine uptake and dependence identified a subset of triple negative samples that are glutamine auxotrophs. Ambient glutamine indirectly supports environmental cystine acquisition via the xCT antiporter, which is expressed on 1/3 of triple negative tumors in vivo. xCT inhibition with the clinically approved anti-inflammatory Sulfasalazine decreases tumor growth revealing a therapeutic target in breast tumors of poorest prognosis, and a lead compound for rapid, effective drug development.

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Although targeting cancer metabolism is a promising therapeutic strategy, clinical success will depend on an accurate diagnostic identification of tumor subtypes with specific metabolic requirements. Through broad metabolite profiling, we successfully identified three highly distinct metabolic subtypes in pancreatic ductal adenocarcinoma (PDAC). One subtype was defined by reduced proliferative capacity, whereas the other two subtypes (glycolytic and lipogenic) showed distinct metabolite levels associated with glycolysis, lipogenesis, and redox pathways, confirmed at the transcriptional level. The glycolytic and lipogenic subtypes showed striking differences in glucose and glutamine utilization, as well as mitochondrial function, and corresponded to differences in cell sensitivity to inhibitors of glycolysis, glutamine metabolism, lipid synthesis, and redox balance. In PDAC clinical samples, the lipogenic subtype associated with the epithelial (classical) subtype, whereas the glycolytic subtype strongly associated with the mesenchymal (QM-PDA) subtype, suggesting functional relevance in disease progression. Pharmacogenomic screening of an additional ∼200 non-PDAC cell lines validated the association between mesenchymal status and metabolic drug response in other tumor indications. Our findings highlight the utility of broad metabolite profiling to predict sensitivity of tumors to a variety of metabolic inhibitors.metabolite profiling | metabolic subtypes in PDAC | glycolysis | lipid synthesis | biomarkers for metabolic inhibitors M etabolic reprogramming during tumorigenesis is an essential process in nearly all cancer cells. Tumors share a common phenotype of uncontrolled cell proliferation and must efficiently generate the energy and macromolecules required for cellular growth. The first example of metabolic reprogramming was discovered more than 80 y ago by Otto Warburg: tumor cells can shift from oxidative to fermentative metabolism in the course of oncogenesis (1). More recently, there has been a resurgence of interest in targeting cancer metabolism (2-4) because it may not only be effective in inhibiting tumor growth, but may also provide a therapeutic window (5, 6). For example, inactivation of lactate dehydrogenase-A (LDHA), an enzyme that catalyzes the final step of aerobic glycolysis, thereby reducing pyruvate to lactate, decreases tumorigenesis and induces regression of established tumors in mouse models of lung cancer driven by oncogenic KRAS or epidermal growth factor receptor (EGFR) while minimally affecting normal cell function (7). The finding that cancers have altered metabolism has prompted substantial investigation, both preclinically and in clinical trials, of several metabolically targeted agents, including those that elevate reactive oxygen species (ROS) or block glycolysis, lipid synthesis, mitochondrial function, and glutamine synthesis pathways (8).The identification of distinct metabolic reprogramming events or metabolic subtypes in cancer may inform patient selection for investigational...

Background:Recently, a Risk of Ovarian Malignancy Algorithm (ROMA) utilising human epididymis secretory protein 4 (HE4) and CA125 successfully classified patients as presenting a high or low risk for epithelial ovarian cancer (EOC). We validated this algorithm in an independent prospective study.Methods:Women with a pelvic mass, who were scheduled to have surgery, were enrolled in a prospective study. Preoperative serum levels of HE4 and CA125 were measured in 389 patients. The performance of each of the markers, as well as that of ROMA, was analysed.Results:When all malignant tumours were included, ROMA (receiver operator characteristic (ROC)-area under curve (AUC)=0.898) and HE4 (ROC-AUC)=0.857) did not perform significantly better than CA125 alone (ROC–AUC=0.877). Using a cutoff for ROMA of 12.5% for pre-menopausal patients, the test had a sensitivity of 67.5% and a specificity of 87.9%. With a cutoff of 14.4% for post-menopausal patients, the test had a sensitivity of 90.8% and a specificity of 66.3%. For EOC vs benign disease, the ROC–AUC of ROMA increased to 0.913 and for invasive EOC vs benign disease to 0.957.Conclusion:This independent validation study demonstrated similar performance indices to those recently published. However, in this study, HE4 and ROMA did not increase the detection of malignant disease compared with CA125 alone. Although the initial reports were promising, measurement of HE4 serum levels does not contribute to the diagnosis of ovarian cancer.

Metabolic reprogramming in tumors represents a potential therapeutic target. Herein we used shRNA depletion and a novel lactate dehydrogenase (LDHA) inhibitor, GNE-140, to probe the role of LDHA in tumor growth in vitro and in vivo. In MIA PaCa-2 human pancreatic cells, LDHA inhibition rapidly affected global metabolism, although cell death only occurred after 2 d of continuous LDHA inhibition. Pancreatic cell lines that utilize oxidative phosphorylation (OXPHOS) rather than glycolysis were inherently resistant to GNE-140, but could be resensitized to GNE-140 with the OXPHOS inhibitor phenformin. Acquired resistance to GNE-140 was driven by activation of the AMPK-mTOR-S6K signaling pathway, which led to increased OXPHOS, and inhibitors targeting this pathway could prevent resistance. Thus, combining an LDHA inhibitor with compounds targeting the mitochondrial or AMPK-S6K signaling axis may not only broaden the clinical utility of LDHA inhibitors beyond glycolytically dependent tumors but also reduce the emergence of resistance to LDHA inhibition.

Although glycolysis is substantially elevated in many tumors, therapeutic targeting of glycolysis in cancer patients has not yet been successful, potentially reflecting the metabolic plasticity of tumor cells. In various cancer cells exposed to a continuous glycolytic block, we identified a recurrent reprogramming mechanism involving sustained mTORC1 signaling that underlies escape from glycolytic addiction. Active mTORC1 directs increased glucose flux via the pentose phosphate pathway back into glycolysis, thereby circumventing a glycolysis block and ensuring adequate ATP and biomass production. Combined inhibition of glycolysis and mTORC1 signaling disrupted metabolic reprogramming in tumor cells and inhibited their growth in vitro and in vivo. These findings reveal novel combinatorial therapeutic strategies to realize the potential benefit from targeting the Warburg effect.