Background: One of the hallmarks of cancer is metabolic dysregulation that has been therapeutically targeted with metabolism-based therapies, an important class of chemotherapeutics. However, there has previously been no systematic way to identify key targets for metabolism-based therapeutic interventions. Targeting tumor metabolism has been shown to increase the efficacy of standard chemotherapy in pre-clinical studies. Thus, more efforts need to be put in the discovery of novel targets especially for tumors with poor prognosis and response to chemotherapy, like pancreatic adenocarcinoma.
Method: We designed a systems medicine-based technique to understand tumor metabolism in individual patients at a level of detail not previously achievable. 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). The result is a mathematical description, or model, of a patient-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 177 pancreatic adenocarcinoma tumor RNA-seq profiles downloaded from The Cancer Genome Atlas to characterize metabolic differences across patients.
Results: We found that pancreatic adenocarcinoma tumors tended to be slow growing (median specific growth rate = 0.57 %/day, range = 0-1.4 %/day). This low growth rate helps explain why pancreatic adenocarcinoma tumors are resistant to chemotherapy – there is simply not enough cellular growth for chemo to be effective. However, this result suggests that a small subset of tumors with high growth rate will respond to chemo.
In addition, we found that pancreatic adenocarcinoma tumors were separated into three metabolic subtypes: classical, glutamate-addicted, and oxidative. The classical metabolic phenotype tended to have better overall survival than glutamate-addicted or oxidative. Whereas high levels of glutamate metabolism (characterizing the glutamate-addicted tumors) or high levels of oxidative phosphorylation (characterizing the oxidative tumors) were associated with poor overall survival prognosis.
Conclusion: Taken together, our systems medicine approach to characterizing patient specific tumor metabolism indicates that a one size fits all approach to chemotherapy in pancreatic adenocarcinoma is likely to fail; and different subsets of patients will likely respond to differently targeted therapies.
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