A Mechanistic Modeling Framework Reveals the Key Principles Underlying Tumor Metabolism

Abstract

While aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, utilizing glycolysis, oxidative phosphorylation, or both, and can switch between different metabolic phenotypes. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive metabolic heterogeneity. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. We report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer.SignificanceThis study presents an interpretable mathematical framework for analyzing the metabolic heterogeneity and plasticity exhibited by tumor cells.

Hybrid computational models of multicellular tumour growth considering glucose metabolism

“…Recently, researchers have developed several biochemical network models to analyse the Warburg effect and other aspects of metabolic reprogramming [92] , [93] , [94] , [95] , [96] , [97] . Nonetheless, only a few models have explicitly taken into account the effect of glucose availability and its metabolism at a larger scale [98] , [99] , [33] , [100] , [36] , [101] , [14] .…”