Tumor relapse during therapies is thought to reflect the ability of tumor cells to escape treatment via cell-intrinsic genetic and epigenetic changes, either preexistent or evolved de novo under the drug imposed cytostatic and cytotoxic selective pressures. Multiple published and unpublished reports, show that at least some of the otherwise drug-sensitive tumor cells might be able to avoid elimination due to microenvironmental factors, such as pro-survival paracrine signals, capable of providing alternative means of survival despite the shutdown of oncogenic signaling. Concurrently, clinical studies indicated that higher stroma-to-tumor content have poorer prognosis and increased risk of relapse in various cancers. However, despite our advances in deciphering the molecular mechanisms behind tumor progression, our understanding of how stroma contributes to the evolution of tumor resistance and relapse remain limited. Our objective is to build stochastic mathematical models to help understand the stroma-tumor eco-evolutionary interplay so that we can design new therapeutic strategies for a tumor elimination or treatments that provide long-term control of tumor growth. The models integrate the spatio-temporal of stroma-tumor cells interactions measured using a novel purposely-designed spatial analysis pipeline, and in vivo and in vitro tumor growth dynamics data. We focus on two distinct stromal effects studied in our lab, namely drug-sheltering effects against targeted therapies in Non-Small Cell Lung Cancer (NSCLC) and enhanced tumor cells’ proliferation, indiscriminately of treatment presence, in Triple Negative Breast Cancer (TNBC). The preliminary results show that enhanced proliferation in the vicinity of stroma in TNBC could be sufficient to drive tumor relapse after four cycles of chemotherapy and greatly accelerates relapse in tumors where the treatment has a low killing rate. In the context of NSCLC, the drug-sheltering effects of stroma could lead to tumor relapse in absence of hard-wired resistance and disrupting the drug-sheltering stromal effects result in a higher rate of tumor eradication. Our results show that using carefully designed mathematical models, we can gain a deeper understanding of the ecological mechanisms that lead to tumor relapse as well as uncover new therapeutic strategies that account for stromal effects and are successful at eradicating tumors.