Using systems medicine for comprehensive metabolic profiling of tumors: how tumor metabolism shapes prognosis and response to chemotherapy


Background: Metabolic disfunction is one of the hallmarks of cancer, and metabolism-based therapies are a key class of chemotherapeutics. Despite this, there has previously been no systematic way to identify metabolism-based targets for therapeutic intervention. This is especially concerning when considering that targeting tumor metabolism has been shown to increase the efficacy of standard chemotherapy in pre-clinical studies. We therefore developed a systems medicine approach to interrogate tumor metabolism systematically, identify the effects of metabolic heterogeneity on overall survival prognosis, and gain insights into new metabolism-associated targets or therapies to complement standard of care for cancer patients.

Method: Our systems medicine framework statistically integrates 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).

Results: We applied this systems medicine approach across a range of tumor types, including lung adenocarcinoma, pancreatic adenocarcinoma, metastatic melanoma, clear cell renal cell carcinoma, and salivary cystic adenoid carcinoma. Within each cancer type, we found significant patient-to-patient metabolic heterogeneity. Across cancer types, a consistent pattern that emerged was that the metabolic systems with the most patient-to-patient heterogeneity were also the systems most associated with patient overall survival. This result suggests that metabolic variability is a key driver of tumor response to therapy and overall patient survival. Furthermore, targeting metabolic pathways in a patient-specific manner could enhance chemotherapy in tumors with metabolic profiles associated with poor prognosis.

Conclusion: This study shows that the specific metabolic profiles of tumors are key drivers of overall survival. We therefore believe that metabolic tumor characterization and intervention could be a useful strategy to enhance chemotherapy efficacy and overall survival in patients across tumor types.

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