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In this review we summarize our recent efforts in trying to understand therole of heterogeneity in cancer progression by using neural networks tocharacterise different aspects of the mapping from a cancer cells genotype andenvironment to its phenotype. Our central premise is that cancer is an evolvingsystem subject to mutation and selection, and the primary conduit for theseprocesses to occur is the cancer cell whose behaviour is regulated on multiplebiological scales. The selection pressure is mainly driven by themicroenvironment that the tumour is growing in and this acts directly upon thecell phenotype. In turn, the phenotype is driven by the intracellular pathwaysthat are regulated by the genotype. Integrating all of these processes is amassive undertaking and requires bridging many biological scales (i.e.genotype, pathway, phenotype and environment) that we will only scratch thesurface of in this review. We will focus on models that use neural networks asa means of connecting these different biological scales, since they allow us toeasily create heterogeneity for selection to act upon and importantly thisheterogeneity can be implemented at different biological scales. Morespecifically, we consider three different neural networks that bridge differentaspects of these scales and the dialogue with the micro-environment, (i) theimpact of the micro-environment on evolutionary dynamics, (ii) the mapping fromgenotype to phenotype under drug-induced perturbations and (iii) pathwayactivity in both normal and cancer cells under different micro-environmentalconditions.

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks