Cancer is a complex ecosystem where genetic and phenotypic diversity drive tumor evolution, progression, and treatment resistance. Understanding how microscopic cellular processes, such as mutation, proliferation, and migration, shape macroscopic tumor dynamics remains a challenge. In this talk, I present a mathematical modeling framework to study tumor heterogeneity [1]. By linking discrete stochastic models to population genetics and evolutionary game theory, I explore the evolution of phenotypic plasticity in tumor growth. The findings highlight how spatial dynamics influence cancer evolution and how tumor heterogeneity can predict treatment outcomes [2]. This work provides new insights into cancer progression and potential strategies for therapeutic intervention. REFERENCES [1] S. Syga, J.M. Nava-Sedeno and A. Deutsch. A novel cellular automaton approach for modeling genotypic and phenotypic heterogeneity in cell systems. European Physical Journal Special Topics, in print. [2] S. Syga, et al. Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy. PLOS Computational Biology, 20(8), pp. e1012003, 2024.