Recent advances in understanding the role of somatic evolution have led to a growing field of research aimed at preventing or hindering the emergence of resistance to existing cancer treatments. Tumor populations are subject to numerous selective forces, with treatment being one of the most significant but not the only one. A refined understanding of these selective pressures and their interplay is critical if we want to design better evolutionary-informed treatments, such as adaptive and extinction therapies. To illustrate the importance of environmentally mediated drug resistance in adaptive therapies, I present an evolutionary game theory model in this work. The model considers a stromal population and two tumor populations: sensitive and resistant phenotypes. Stromal cells, such as carcinoma-associated fibroblasts, are known to weaken the tumor’s response to lapatinib in breast cancer. By incorporating experimental data to support the mathematical model, we can optimize adaptive therapies in this context. Our findings indicate that adaptive therapies that use a combination of drugs can significantly reduce the development of drug resistance in the tumor population. Furthermore, we show that these adaptive therapies can be further optimized by adjusting the treatment frequency and dosage, taking into account the interactions between the tumor and stromal cells. These results highlight the importance of considering the evolutionary dynamics of cancer cells when designing treatments. By understanding how selective pressures affect tumor populations and how they respond to treatment, we can develop more effective therapies that can prolong patient survival and improve their quality of life. Moreover, the evolutionary game theory model presented here can be adapted to other cancer types, potentially leading to the development of more personalized and effective treatments.