Background: Tumor cells are able to reprogram their metabolism in order to sustain continuous growth and proliferation. Although, this has led to the development of metabolism-based therapies, until now there are no systematic ways to identify key metabolic targets for therapy. This area of research deserves more attention considering that targeting tumor metabolism has been shown to increase the efficacy of standard chemotherapy in pre-clinical studies. For tumors with poor prognosis, like late-stage metastatic melanoma, this untapped area for therapeutic discovery could lead to the development of novel metabolism-based therapies.
Method: To address this challenge we designed a systems medicine-based technique to understand tumor metabolism in individual patients at a level of detail not previously achievable. In concrete, we statistically integrated metabolic network modeling with RNA-sequencing data (RNA-seq). This allows us to integrate molecular data about individual tumors (from RNA-seq) with a curated knowledge base of how these molecules interact within a patient’s tumor (using metabolic network models). This results in a mathematical description, or model, of a specific tumor’s metabolism that is able to be interrogated (i.e., it is high-dimensional like RNA-seq data) and able to be simulated (i.e., we can “drug” the model and investigate downstream effects). We applied this technique to 325 metastatic melanoma tumor RNA-seq profiles downloaded from The Cancer Genome Atlas to characterize metabolic differences across patients.
Results: We found considerable differences in metabolic function across metastatic melanoma patient tumors. One of the main drivers of patient-to-patient metabolic variability appeared to be BRAF mutations, with BRAF mutated tumors showing higher levels of glutathione metabolism, lower levels of glutamate metabolism, and lower levels of oxidative phosphorylation. In addition to these differences, we found that several metabolic pathways were associated with poor overall survival prognosis regardless of BRAF mutations status including high specific growth rate, high cholesterol metabolism, and high serotonin and melatonin biosynthesis. Furthermore, simulating knockouts of enzymes involved in these pathways resulted in a set of actionable targets that could enhance chemotherapy for metastatic melanoma patients.
Conclusion: Using systems medicine, we have characterized metabolism in metastatic melanoma at high resolution in 325 patient tumors. This characterization revealed the variability in metastatic melanoma across patients and how this variability is associated with clinical outcome. Furthermore, our approach suggests additional therapeutic targets to enhance patient care.
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