Agent-Based Modeling and Analysis of Cancer Evolution

Abstract

Before the development of the next-generation sequencing (NGS) technology, carcinogenesis was regarded as a linear evolutionary process, driven by repeated acquisition of multiple driver mutations and Darwinian selection. However, recent cancer genome analyses employing NGS revealed the heterogeneity of mutations in the tumor, which is known as intratumor heterogeneity (ITH) and generated by branching evolution of cancer cells. In this chapter, we introduce a simulation modeling approach useful for understanding cancer evolution and ITH. We first describe agent-based modeling for simulating branching evolution of cancer cells. We next demonstrate how to fit an agent-based model to observational data from cancer genome analyses, employing approximate Bayesian computation (ABC). Finally, we explain how to characterize the dynamics of the simulation model through sensitivity analysis. We not only explain the methodologies, but also introduce exemplifying applications. For example, simulation modeling of cancer evolution demonstrated that ITH in colorectal cancer is generated by neutral evolution, which is caused by a high mutation rate and stem cell hierarchy. For cancer genome analyses, new experimental technologies are actively being developed; these will unveil various aspects of cancer evolution when combined with the simulation modeling approach.

Unravelling the factors of evolutionary repeatability: insights and perspectives on predictability in evolutionary biology

“…By simulating the behaviour of these individuals over time, ABM can provide insights into the dynamics of evolutionary processes and make predictions about the future of evolutionary trajectories (Crooks and Heppenstall, 2012;Nemiche et al, 2013;DeAngelis and Diaz, 2019). For example, agent-based simulation modelling of colorectal cancer has demonstrated that intratumor heterogeneity is due to a high mutation rate and the presence of a stem cell hierarchy (Niida and Iwasaki, 2022). Varying parameters to simulate specific conditions provides a framework to test hypotheses and make informative predictions about evolutionary outcomes.…”