Cancer immunotherapy harnesses components of the immune system to fight against malignant cells and aims to inhibit mechanisms that suppress immune function and promote tumor-killing immune functions. Unfortunately, many immunotherapies do not live up to their predicted success due to the intertwining of many different immunosuppressive interactions. In breast cancer, and particularly HER2+ breast tumors, interactions between different components of the immune system are thought to influence the efficacy of immunotherapies. To improve immunotherapy for HER2+ breast tumors, we must quantitatively understand the tumor microenvironment (TME). The TME in breast cancer is a complex ecosystem that develops over a long time, making extensive in vitro and in vivo studies prohibitive. Fortunately, computational modeling is a useful approach for studying the TME, bridging in vitro and in vivo models, and predicting response to treatment. However, many computational models only focus on aspects of the TME in isolation, missing how cell-cell interactions affect tumor growth. This project will test the hypothesis that computational models whose long-term states match tumor characteristics identified in vivo can successfully predict tumor dynamics and the effects of immunotherapies for HER2+ breast cancer. We will construct a computational model that captures the interactions between tumor and immune cell types, informed by new data from HER2+ mouse tumors (Aim 1). We will apply the model to determine how variations in cell-cell interactions and cellular properties affect the tumor’s immune composition and response to immunotherapies (Aim 2). This work will produce new insights into how immunotherapies can be more strategically employed in HER2+ breast cancer.