Evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics -- mathematical model framework, applications, and open problems

TL;DR

This paper introduces a comprehensive mathematical framework for modeling cancer evolution, incorporating cell heterogeneity, plasticity, and epigenetic state transitions, with applications to tumor development and relapse.

Contribution

It develops a novel differential-integral equation model capturing cancer cell heterogeneity and plasticity, extending existing models to include epigenetic dynamics and tumor progression.

Findings

Model highlights cell heterogeneity and plasticity effects.

Applied to inflammation-induced tumorigenesis and CAR-T therapy relapse.

Identifies key mathematical problems for understanding cancer evolution.

Abstract

Predictive modeling of the evolutionary dynamics of cancer is a challenge issue in computational cancer biology. In this paper, we propose a general mathematical model framework for the evolutionary dynamics of cancer with plasticity and heterogeneity in cancer cells. Cancer is a group of diseases involving abnormal cell growth, during which abnormal regulations in stem cell regeneration are essential for the dynamics of cancer development. In general, the dynamics of stem cell regeneration can be simplified as a $\mathrm{G_0}$ phase cell cycle model, which lead to a delay differentiation equation. When cell heterogeneity and plasticity are considered, we establish a differential-integral equation based on the random transition of epigenetic states of stem cells during cell division. The proposed model highlights cell heterogeneity and plasticity, and connects the heterogeneity with cell-to-cell variance in cellular behaviors, e.g. proliferation, apoptosis, and differentiation/senescence, and can be extended to include gene mutation-induced tumor development. Hybrid computations models are developed based on the mathematical model framework, and are applied to the process of inflammation-induced tumorigenesis and tumor relapse after CAR-T therapy. Finally, we give rise to several mathematical problems related to the proposed differential-integral equation. Answers to these problems are crucial for the understanding of the evolutionary dynamics of cancer.