SUMMARY Metabolic reprogramming is critical to oncogenesis, but the emergence and function of this profound reorganization remain poorly understood. Here we find that cooperating oncogenic mutations drive large-scale metabolic reprogramming, which is both intrinsic to cancer cells and obligatory for the transition to malignancy. This involves synergistic regulation of several genes encoding metabolic enzymes, including the lactate dehydrogenases LDHA and LDHB and mitochondrial glutamic pyruvate transaminase 2 (GPT2). Notably, GPT2 engages activated glycolysis to drive the utilization of glutamine as a carbon source for TCA cycle anaplerosis in colon cancer cells. Our data indicate that the Warburg effect supports oncogenesis via GPT2-mediated coupling of pyruvate production to glutamine catabolism. Although critical to the cancer phenotype, GPT2 activity is dispensable in cells that are not fully transformed, thus pinpointing a metabolic vulnerability specifically associated with cancer cell progression to malignancy.

Highlights d A network of non-mutated genes is critical to the malignant state d TopNet can accurately model cellular responses to genetic perturbations d TopNet is capable of pinpointing key architectural features of cancer cells

Malignant cell transformation and the underlying genomic scale reprogramming of gene expression require cooperation of multiple oncogenic mutations. Notably, this cooperation is reflected in the synergistic regulation of downstream genes, so-called cooperation response genes (CRGs). CRGs impact diverse hallmark features of cancer cells and are not known to be functionally connected. Yet, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency of >50%, as indicated by genetic perturbations. Here we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the robustness of the malignant state. Our approach, termed TopNet, utilizes attractor-based ternary network modeling that takes the novel approach of incorporating uncertainty in the underlying gene perturbation data and is capable of identifying non-linear gene interactions. TopNet reveals topological gene network architecture that effectively predicts previously unknown, functionally relevant epistatic gene interactions, and thus, among a broad range of applications, has utility for identification of non-mutant targets for cancer intervention.

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