University of Cambridge > > CMS Special Lectures > Mathematical modeling of heterogeneity in solid tumors: Emergent patterns of metabolism in colon cancer

Mathematical modeling of heterogeneity in solid tumors: Emergent patterns of metabolism in colon cancer

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If you have a question about this talk, please contact June Rix.

Refreshments will be available after the Seminar in Central Core at 16.00 hrs

Many solid tumors exhibit a striking phenotypic diversity that helps the tumor survive harsh circumstances such as the limited availability of nutrients, an attacking immune system or chemotherapy treatment. This can arise over time from genetic mutations or more rapidly from non-genetic changes such as self-organization processes, where signals produced by the tumor cells themselves drive the tumor population to bifurcate into multiple cell states. The idea that heterogeneity could help tumors survive and grow is well accepted, but actual mechanisms are poorly understood. In this talk, we use multiscale mathematical modeling to investigate the emergence and consequences of non-genetic heterogeneity, focusing on colon cancer as an example. In particular, we report on a self-organizing pattern of metabolism in xenograft colon tumors where clusters of highly glycolytic cells are arranged in a regular, spotted array. To explore the basis for this pattern, we develop Turing-like reaction-diffusion equations describing the interactions between different metabolic cell types, nutrients, and growth factors. A key component of the model is Wnt signaling, a pathway known to upregulate glycolysis (Warburg metabolism), which is highly active in colon cancer. Diffusive instability analysis and nonlinear simulations characterize the dependence of the patterning on the signalling processes. The model predicts that partial inhibition of Wnt signaling alters the patterning and the expression of factors that increase the range of Wnt ligand diffusion. These predictions are validated in xenograft tumors and are consistent with expression data in primary human colon cancer. The model also predicts that inhibitors that target glycolysis and/or Wnt signaling are not so effective as single therapies for cancer as they are in combination for synergistic reduction of tumor growth. We validated this prediction in experiments in vitro using 3D colon tumor spheroids and a novel microfluidic device containing vascularized colon micro-tumors.

This talk is part of the CMS Special Lectures series.

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