Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups.

BACKGROUND: The malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification 'gold-standard', typically delivered 3-4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS). METHODS: Metabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival. FINDINGS: Group-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4-8.1, p = 0.025). INTERPRETATION: Tissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis. FUNDING: Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.

The Plasma Membrane Calcium Pump in Pancreatic Cancer Cells Exhibiting the Warburg Effect Relies on Glycolytic ATP.

Evidence suggests that the plasma membrane Ca(2+)-ATPase (PMCA), which is critical for maintaining a low intracellular Ca(2+) concentration ([Ca(2+)]i), utilizes glycolytically derived ATP in pancreatic ductal adenocarcinoma (PDAC) and that inhibition of glycolysis in PDAC cell lines results in ATP depletion, PMCA inhibition, and an irreversible [Ca(2+)]i overload. We explored whether this is a specific weakness of highly glycolytic PDAC by shifting PDAC cell (MIA PaCa-2 and PANC-1) metabolism from a highly glycolytic phenotype toward mitochondrial metabolism and assessing the effects of mitochondrial versus glycolytic inhibitors on ATP depletion, PMCA inhibition, and [Ca(2+)]i overload. The highly glycolytic phenotype of these cells was first reversed by depriving MIA PaCa-2 and PANC-1 cells of glucose and supplementing with α-ketoisocaproate or galactose. These culture conditions resulted in a significant decrease in both glycolytic flux and proliferation rate, and conferred resistance to ATP depletion by glycolytic inhibition while sensitizing cells to mitochondrial inhibition. Moreover, in direct contrast to cells exhibiting a high glycolytic rate, glycolytic inhibition had no effect on PMCA activity and resting [Ca(2+)]i in α-ketoisocaproate- and galactose-cultured cells, suggesting that the glycolytic dependence of the PMCA is a specific vulnerability of PDAC cells exhibiting the Warburg phenotype.