Evolutionary game theory in cancer
Cancer cells are treated as players whose success depends on what neighbouring cells do, which lets researchers predict how a tumour's mix of cell types shifts under treatment and design schedules that steer it.
Overview
Evolutionary game theory, developed for animal behaviour, models interactions between cell types whose fitness depends on the frequency of the others: producers and free-riders of growth factors, invasive and proliferative phenotypes, sensitive and resistant clones. David Basanta, Alexander Anderson and others applied it to glioma, prostate and lung cancer, and it provides the theoretical backbone for adaptive therapy and for 'evolutionary steering', in which a first treatment is chosen to make the tumour vulnerable to a second. Game assays that measure fitness interactions in co-culture have begun to supply real parameters.
How it works
Replicator dynamics: each cell type's frequency changes in proportion to its fitness relative to the population mean, with fitness set by a payoff matrix of pairwise interactions that treatment can alter.
- Captures cell-cell interactions other models ignore
- Basis of treatment sequencing ideas
- Parameters now measurable in game assays
- Payoff matrices are hard to estimate in patients
- Spatial structure often omitted
- Few clinical tests so far
Latest papers
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