The metabolic requirements of cancer and proliferating cells are different from that of normal differential tissue and may have diverse applications in the treatment of cancers. However, many of the molecular mechanisms that reorganize metabolism to support cell proliferation are unknown. To study cancer cell metabolism, we implemented a mass spectrometry based platform to quantitatively profile endogenous metabolites from proliferating cell lines, tumor tissues and formalin fixed paraffin embedded (FFPE) tissue. Cell lines are derived from several cancers including lung, multiple myeloma, prostate, and gliobastoma (GBM). In some cases, these were compared to a drosophila reference cell line. Patients were also profiled for disease classification from their cerebrospinal fluid (CSF). We also were successful in extracting polar metabolites from FFPE tissue more than five years old stored at room temperature. For FFPE tissue samples, we show that we can observe differences in disease states involving PI3K-TSC-TOR pathway and we compared different extraction methods for acquiring metabolomics data from FFPE blocks. We show that we can cluster GBM versus normal patients from analyzing their CSF. We target more than 255 unique metabolites using selected reaction monitoring (SRM) based analyses with an AB/Sciex 5500 QTRAP mass spectrometer coupled to a Shimadzu UFLC using normal phase chromatography. For a single 18 min run, 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 2–3 million cells from tissue samples. We find that amide XBridge columns (Waters) at 275 uL/min perform well in both negative and positive ion switching mode and that the sampling rate of the instrument is sufficiently fast (cycle time of 1.6 sec with 3 msec dwell times) to effectively capture up to 300 metabolite targets without scheduled SRM runs. Peak areas of metabolites are integrated using MultiQuant 1.1 software (Applied Biosystems). Peak areas from triplicate runs are hierarchically clustered and statistical analyses are applied to generate P values for metabolite changes over different biological conditions. We also probed metabolic flux in pathways by targeting a set of 13C glucose labeled metabolites. In addition, we have also analyzed a model cell line after stimulation with Insulin and EGF to examine if growth factor induced metabolic changes are evolutionarily conserved. Using metabolic inhibitors, such as Iodoacetic acid, KCN, etc., we have been able to characterize the consequences of inhibiting glycolysis and oxidative phosphorylation, respectively. Finally, we considered kinase inhibitors and measured their effects on metabolism in proliferating cancer cell lines.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-255. doi:10.1158/1538-7445.AM2011-LB-255