Abstract
Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Here, using transcriptomic data for 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we present a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. We confirm pathway dysregulation in separate patient populations, demonstrating the robustness of our approach. Bootstrapping simulations were then applied to assess the biological significance of these alterations. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest, and patterns of metabolic flux, in both common and rare disease sites. Further, we show that Master Metabolic Transcriptional Regulators explain why metabolic differences exist, can segregate patient populations, and predict responders to different metabolism-targeted therapeutics.
Highlights
Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic
We suggest elucidating expression and alteration of master metabolic transcriptional regulators (MMTRs) may provide novel understanding of why metabolism differs in varying tissues
Magnitude of metabolic dysregulation was calculated by determining DEGs, which includes log fold changes and adjusted p values, comparing tumor with normal matched samples and assigning scores based on 114 metabolic pathways from The Kyoto Encyclopedia of Genes and Genomes (KEGG)[35]
Summary
Understanding metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Scientific consortiums like The Cancer Genome Atlas (TCGA)[12] encourage comprehensive genomics approaches in large numbers of patients with many different cancer types, as well as their matched normal tissues This transcriptomic data has already been used to explore and explain a wide variety of important questions of cancer biology, those aimed at understanding immune response in different disease states[13] and oncogenic drivers[14,15]. Others have looked at known oncogenic molecular processes and pathways to better explain how genomic mutations can impact expression and signaling[14,15], highlighting combination therapy potential[15] These studies point to the utility of using transcriptomic data to exploit biologically relevant vulnerabilities, but do not focus on metabolic-targeted therapies. A recent study provided convincing evidence for the extrapolation of metabolite levels from transcriptomic data, based on high levels of significant correlation between the two in a detailed look at breast cancer RNA-sequencing and unbiased metabolomics[24]
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