Prognostic Metabolic Signatures of Cancers Through Mass Spectrometry Imaging

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DESCRIPTION (provided by applicant): A hallmark feature of tumorigenesis is the global shift in metabolism. The ability to easily measure metabolism in situ could provide a powerful strategy for the early detection of cancers, as well as the ability to better diagnose and prognosticate cancers based on the detection of certain metabolic signatures related to specific oncogene expression and finally could uncover novel molecular underpinnings of cancer that could serve as better therapeutic targets. The MYC oncogene is one of the most commonly implicated causes of human tumorigenesis, in particular associated with the pathogenesis of hematopoietic tumors including lymphoma and epithelial tumors such as hepatocellular carcinoma. MYC contributes to tumorigenesis by functioning as a global regulator of transcription involving many cellular programs, in particular, cellular proliferation, growth and metabolism associated with the Warburg effect. MYC regulates key genes in glycolysis, glutaminolysis, tricarboxylic acid cycle, C1/folate, purine, pyrimidine and, notably, lipid metabolism. This suggests that in situ analysis of specific metabolic signatures could be a highly useful approach to detect, diagnose and prognosticate MYC-associated human tumors. However, using existing methods, it has not been readily possible to perform an in situ analysis of metabolism. Now, we have developed a highly sensitive approach that will enable us to use an innovative form of mass spectrometry to provide microscopic examination of the MYC-induced cancer metabolism. A key feature of this analysis is the ability to provide essentially rea time, in situ analysis. The method - called Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) - bombards cells and/or tissue sections with microdroplets containing acetonitrile and dimethylformamide that dissolve hundreds of lipids and metabolites. In this proposal, we will use DESI-MSI as a tool for identifying metabolic signatures causally associated with MYC expression in lymphomas. We will use this approach to decipher the biological mechanism by which MYC generates cancers thought the identified metabolites.