UC Irvine

The objective of this work is to develop data-driven mechanistic mathematical modeling to understand and make predictions on how metabolic and tumor heterogeneity impact tumor growth and resistance to treatment. Cell-intrinsic metabolic reprogramming is a hallmark of cancer that provides anabolic support to cell proliferation. How reprogramming influences tumor heterogeneity or drug sensitivities is not well understood. Here we report a self-organizing spatial pattern of glycolysis in xenograft colon tumors where pyruvate dehydrogenase kinase (PDK1), a negative regulator of oxidative phosphorylation, is highly active in clusters of cells arranged in a spotted array. To understand this pattern, we developed a reaction-diffusion model that incorporates Wnt signaling, a pathway known to upregulate PDK1 and Warburg metabolism. Partial interference with Wnt alters the size and intensity of the spotted pattern in tumors and in the model. The model predicts that Wnt inhibition should trigger an increase in proteins that enhance the range of Wnt ligand diffusion. Not only was this prediction validated in xenograft tumors, but similar patterns also emerge in radiochemotherapy-treated colorectal cancer. Finally, to investigate how self-organized, patterned communities of colon cancer cells emerge, and how heterogeneity influences growth we performed single cell RNA sequencing (scRNAseq) on xenograft tumors. Single cell RNA sequencing also indicates the interactions between cell types. We then developed a multispecies mathematical model that incorporates cellular interactions informed by scRNAseq analysis. This multispecies model recapitulates the spotted patterns, stromal content, morphologies and their spatial variations in the tumor. The model predicts that blocking positive feedback signaling will alter population heterogeneity and the spatial patterning of Wnt signaling, resulting in significant reduction of a cell type. However, the tumor retains the capacity to grow indicating that there is a possible link between heterogeneity and drug resistance.