A Mass Spectrometry Platform to Quantitatively Profile Cancer Cell Metabolism from Cell Lines to Tissues. (2010)

RP-60 The metabolic requirements of cancer and proliferating cells are different from that of normal differential tissue (the Warburg effect) and may have diverse applications in the treatment of cancers and other neoplastic diseases. However, many of the molecular mechanisms that conspire to reorganize metabolism to support cell proliferation are unknown. To study the mechanisms of cancer cell metabolism, we have implemented a mass spectrometry based platform to robustly quantitatively profile endogenous metabolites from proliferating cell lines and tumor tissues to extensively study cancer cell metabolism. Cell lines are derived from several cancers including lung, multiple myeloma and prostate, as well as from a fast proliferating Drosophila cell line. In addition, endogenous and Xenograft tumor tissue from tumors such as prostate are profiled before and after drug treatments. We routinely target nearly 250 metabolites using multiple reaction monitoring (MRM) based analyses with an AB/Sciex 5500 QTRAP mass spectrometer coupled to a Shimadzu UFLC using normal phase Hydrophilic interaction chromatography (HILIC) at pH=9.0 with positive/negative switching within the same experimental 25 minute LC/MS/MS run. For a single experiment, our platform allows for unprecedented sensitivity, quantitation and coverage of metabolites that comprise of diverse metabolic pathways from as little as a single 6 cm tissue culture dish of cells or approximately 3 million cells from tissue samples. We find that 2.00mm id x 10cm Luna NH2 HILIC columns (Phenomenex) at 250uL/min perform well in both negative and positive ion mode and that the sampling rate of the instrument is sufficiently fast to effectively capture up to 300 metabolite targets within approximately a 20-25 minute gradient without the need for scheduled MRM runs resulting in a cycle time of approximately 2 seconds with 5ms dwell times. Peak areas of metabolites are integrated post run using MultiQuant 1.1 software (Applied Biosystems). Peak areas from triplicate runs are then clustered using hierarchical clustering and statistical analyses are applied in order to generate P values for metabolite changes over different cellular conditions. We have also carried out preliminary studies to probe flux in pathways by targeting a set of 13C labeled metabolites from experiments where 13C labeled glucose is added to cells. Using this platform, we have observed the metabolic effects of growth factor signaling by analyzing metabolites from serum-starved versus serum-fed cells derived from several cancers. We have also analyzed the metabolism of a Drosophila model cell line after stimulation with Insulin and EGF (Spitz) to examine if growth factor induced metabolic changes are evolutionarily conserved. Using metabolic inhibitors, such as Iodoacetic acid and KCN, we have also been able to characterize the consequences of inhibiting glycolysis and oxidative phosphorylation, respectively. Finally, we considered a dual-pan PI3K/mTOR catalytic site inhibitor and measured its effects on metabolism in a proliferating breast epithelial cell line. In addition, we have profiled cerebral spinal fluid (CSF) from gliobastoma (GBM) patients and noticed several metabolioc profiles that are unique to GBM patients with mutated genes.