Abstract
Because cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.
Highlights
Cancer is regarded as a disease of evolution; during tumorigenesis, a normal cell evolves to a malignant population by means of mutation accumulation and adaptive DarwinianHow to cite this article Niida A, Hasegawa T, Innan H, Shibata T, Mimori K, Miyano S. 2020
Each cell is subject to cell division with a probability g and cell death with a probability d.g depends on a base division rate g0, the increase in the cell division probability per driver mutation f, the number of driver mutations accumulated in the cell nd, population size p, and the carrying capacity pc : g = g0f nd (1 − p/pc ).d depends on the base death rate d0, the decrease in the cell death probability per driver mutation, and the number of driver mutations accumulated in the cell nd : d = d0e−nd
We examined the evolutionary dynamics of the driver-d models with different mutation rates by taking time-course snapshots of the simulations (Fig. S3)
Summary
Cancer is regarded as a disease of evolution; during tumorigenesis, a normal cell evolves to a malignant population by means of mutation accumulation and adaptive DarwinianHow to cite this article Niida A, Hasegawa T, Innan H, Shibata T, Mimori K, Miyano S. 2020. Cancer is regarded as a disease of evolution; during tumorigenesis, a normal cell evolves to a malignant population by means of mutation accumulation and adaptive Darwinian. A unified simulation model for understanding the diversity of cancer evolution. Evolution allows cancer cells to adapt to a new environment and acquire malignant phenotypes such as metastasis and therapeutic resistance. The view of cancer as an evolutionary system was established by Nowell (1976). By combining this view with a series of discoveries of onco- and tumor suppressor genes (hereinafter, collectively referred to as ‘‘driver genes’’), Fearon & Vogelstein (1990) proposed a multistep model for colorectal carcinogenesis. Cancer evolution has generally been described as ‘‘linear evolution,’’ where driver mutations are acquired linearly in a step-wise manner, generating a malignant clonal population
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