The tumor microenvironment (TME) is a critical factor in cancer initiation, progression, and treatment resistance. However, a lack of tools to study its dynamic nature has hindered our understanding of these key interactions, which likely explains important aspects of cancer progression and provides potential therapeutic targets. Our work introduces an integrated approach combining preclinical data, spatial statistics, mathematical modeling, and evolutionary learning to study TME dynamics. Our goal is to fill these knowledge gaps and design new treatment schedules that can prevent or delay the emergence of treatment resistance in cancer.