Evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics

Abstract

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Cancer development is a long-term process which remains mostly unknown; predictive modeling of the evolutionary dynamics of cancer is one of the major challenges in computational cancer biology. In this talk, I introduce a general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration, and the application of the model framework to study the evolutionary dynamics of cancer. The proposed model framework generalizes the classical G0 cell cycle model, incorporates the epigenetic states of stem cells that are represented by a continuous multidimensional variable, and the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, which are dependent on their epigenetic states. The random transition of epigenetic states is represented by an inheritance probability that can be described as a conditional beta distribution. Moreover, the model framework can be extended to investigate gene mutation-induced tumor development. The model equation further suggests a numerical scheme of multi-scale modeling for tissue growth where a multiple cell system is represented by a collection of epigenetic states in each cell. We applied the numerical scheme to model the two processes of inflammation-induced tumorigenesis and tumor relapse after CD19 chimeric antigen receptor(CAR) T cell therapy of acute B lymphoblastic leukemia (B-ALL). Model simulations reveal the multiple pathways of inflammation-induced tumorigenesis, and the a mechanism of tumor relapse due to leukemic cell plasticity induced by CAR-T therapy stress.