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.
Highlights
Cancer is a clump of abnormal cells that originates from normal cells
We introduced agent-based modeling of cancer evolution along with methodologies for data fitting and sensitivity analysis
There is a long history of theoretical science in the field of cancer research, this approach has been overshadowed by experimental science until recently
Summary
Cancer is a clump of abnormal cells that originates from normal cells. Normal cells proliferate or stop proliferating depending on their surrounding environment. Tumor suppressor genes act as brakes to stop cell proliferation, and inhibiting the function of the brakes is necessary for malignant transformation. Normal cells are transformed into cancer cell when two to 10 driver mutations are acquired. Because these mutations are not induced simultaneously, but rather gradually over a long period of time, this process is known as “multi-stage carcinogenesis” [1]. This process is regarded as a linear evolutionary process, driven by repeated acquisition of multiple driver mutations and Darwinian selection. Nextgeneration sequencing (NGS) technology, which raised around 2010, enabled cancer genome analysis to comprehensively detect mutations in cancer cells. We explain how to characterize the dynamics of the simulation models through sensitivity analysis
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