Modeling tumor progression in heterogeneous microenvironments: A cellular automata approach

TL;DR

This study introduces a cellular automata model to simulate how microenvironmental heterogeneity and genetic mutation rates influence tumor progression, revealing the critical impact of microenvironmental conditions on tumor dynamics and potential therapeutic strategies.

Contribution

The paper develops a novel CA model integrating microenvironmental heterogeneity and genetic mutations to better understand tumor evolution.

Findings

Lower mutation rates reduce tumor growth.

Microenvironmental conditions critically influence tumor dynamics.

Initial tumor burden has limited impact.

Abstract

Understanding how microenvironmental heterogeneity influences tumor progression is essential for advancing both cancer biology and therapeutic strategies. In this study, we develop a cellular automata (CA) model to simulate tumor growth under varying microenvironmental conditions and genetic mutation rates, addressing a gap in existing studies that rarely integrate these two factors to explain tumor dynamics. The model explicitly incorporates the cellular heterogeneity of stem and non-stem cells, dynamic cell-cell interactions, and tumor-microenvironment crosstalk. Using computational simulations, we examine the synergistic effects of gene mutation rate, initial tumor burden, and microenvironmental state on tumor progression. Our results demonstrate that lowering the mutation rate significantly mitigates tumor expansion and preserves microenvironmental integrity. Interestingly, the initial tumor burden has a limited impact, whereas the initial condition of the microenvironment critically shapes tumor dynamics. A supportive microenvironment promotes proliferation and spatial invasion, while inhibitory conditions suppress tumor growth. These findings highlight the key role of microenvironmental modulation in tumor evolution and provide computational insights that may inform more effective cancer therapies.