A Mathematical Framework for Modelling the Metastatic Spread of Cancer.

Cancer is a complex disease that starts with mutations of key genes in one cell or a small group of cells at a primary site in the body. If these cancer cells continue to grow successfully and, at some later stage, invade the surrounding tissue and acquire a vascular network, they can spread to distant secondary sites in the body. This process, known as metastatic spread, is responsible for around 90% of deaths from cancer and is one of the so-called hallmarks of cancer. To shed light on the metastatic process, we present a mathematical modelling framework that captures for the first time the interconnected processes of invasion and metastatic spread of individual cancer cells in a spatially explicit manner-a multigrid, hybrid, individual-based approach. This framework accounts for the spatiotemporal evolution of mesenchymal- and epithelial-like cancer cells, membrane-type-1 matrix metalloproteinase (MT1-MMP) and the diffusible matrix metalloproteinase-2 (MMP-2), and for their interactions with the extracellular matrix. Using computational simulations, we demonstrate that our model captures all the key steps of the invasion-metastasis cascade, i.e. invasion by both heterogeneous cancer cell clusters and by single mesenchymal-like cancer cells; intravasation of these clusters and single cells both via active mechanisms mediated by matrix-degrading enzymes (MDEs) and via passive shedding; circulation of cancer cell clusters and single cancer cells in the vasculature with the associated risk of cell death and disaggregation of clusters; extravasation of clusters and single cells; and metastatic growth at distant secondary sites in the body. By faithfully reproducing experimental results, our simulations support the evidence-based hypothesis that the membrane-bound MT1-MMP is the main driver of invasive spread rather than diffusible MDEs such as MMP-2.

Examining the role of individual movement in promoting coexistence in a spatially explicit prisoner's dilemma.

The emergence of cooperation is a major conundrum of evolutionary biology. To unravel this evolutionary riddle, several models have been developed within the theoretical framework of spatial game theory, focussing on the interactions between two general classes of player, "cooperators" and "defectors". Generally, explicit movement in the spatial domain is not considered in these models, with strategies moving via imitation or through colonisation of neighbouring sites. We present here a spatially explicit stochastic individual-based model in which pure cooperators and defectors undergo random motion via diffusion and also chemotaxis guided by the gradient of a semiochemical. Individual movement rules are derived from an underlying system of reaction-diffusion-taxis partial differential equations which describes the dynamics of the local number of individuals and the concentration of the semiochemical. Local interactions are governed by the payoff matrix of the classical prisoner's dilemma, and accumulated payoffs are translated into offspring. We investigate the cases of both synchronous and non-synchronous generations. Focussing on an ecological scenario where defectors are parasitic on cooperators, we find that random motion and semiochemical sensing bring about self-generated patterns in which resident cooperators and parasitic defectors can coexist in proportions that fluctuate about non-zero values. Remarkably, coexistence emerges as a genuine consequence of the natural tendency of cooperators to aggregate into clusters, without the need for them to find physical shelter or outrun the parasitic defectors. This provides further evidence that spatial clustering enhances the benefits of mutual cooperation and plays a crucial role in preserving cooperative behaviours.