bioRxivCancer BiologyPreprint
Modeling VEGF and GLUT1 Expression as Coadapted Foraging Strategies in Cancer
Ranjini BhattacharyaiD, Robert A. GatenbyiD, Joel S. BrowniD
Abstract
Natural selection acting on cancer cells within their tumor microenvironment should favor cells with fast or efficient nutrient uptake strategies. Here, we develop and analyze a game-theoretic model focusing on the coadaptation between two foraging traits: vascular endothelial growth factor (VEGF) and glucose transporter 1 (GLUT1). Studies show that VEGF and GLUT1 are often co-expressed and are associated with more aggressive tumor phenotypes and poor clinical prognosis. VEGF is a diffusible paracrine factor that recruits blood vessels towards neighborhoods of cancer cells (angiogenesis). GLUT1 is a cell-surface transporter that enables the uptake of nutrients, especially glucose. We model these strategies operating at different scales: VEGF influences resource availability at the neighborhood level, while GLUT1 determines resource uptake at the cellular level. For VEGF, we introduce a resource-sharing continuum. With no resource sharing, cells access resources in proportion to their VEGF contribution. With uniform sharing, cells have equal access to resources, regardless of their VEGF contribution. The former leads to a tragedy of the commons and overproduction of VEGF. The latter yields a public goods game with moderate VEGF expression matching a group optimum. GLUT1 expression mediates uptake of resources recruited by VEGF and is largely independent of the degree of resource sharing. Therapeutically, both VEGF and GLUT1 inhibitors are more effective in high resource-sharing neighborhoods and less so as resource sharing declines. Overall, inhibition of GLUT1-mediated uptake emerges as more effective. The model, perhaps the first to consider VEGF and GLUT1 as coadaptations, emphasizes the need to consider cancer cell traits jointly.
- organism: human
- research focus: cancer & tumor microenvironment
- research focus: cell signaling & trafficking
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 0
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