Exploring the dynamic tumor-immune ecosystem of breast cancer in heterogeneous tumor microenvironments

ABSTRACT / PROJECT SUMMARY In breast cancer, interactions between suppressive components of the immune system contribute to the lack of efficacy of immunotherapies. This project will use mechanistic computational modeling to understand the tumor-immune ecosystem of breast tumors and guide strategies to improve immunotherapy. The main objective of this proposal is to quantitatively study and model the tumor-immune microenvironment in breast cancer. We will use an integrated approach combining computational modeling of the spatiotemporal organization of the breast tumors with quantitative experiments. The overarching hypothesis is that constructing a computational model whose long-term states match tumor characteristics identified in vivo can successfully predict the spatiotemporal behaviors and effects of immunotherapies for breast tumors. The outcomes of this project are a validated computational model of the tumor-immune ecosystem and new quantitative insights into how immunotherapies can be more strategically employed in breast cancer. This work builds on the PIs’ extensive experience in modeling and analysis of cancer cell behavior and tumor growth (Finley) and clinical oncology and immunotherapy (Roussos Torres). To establish a predictive framework of the breast tumor microenvironment (TME), we will construct an agent- based computational model that captures the tumor-immune ecosystem and cell-cell interactions (AIM 1). In parallel with development of the computational model, we will quantify MDSC immunosuppressive function, measure tumor growth dynamics, and profile the composition and spatial organization of advanced breast tumors using an established in vivo mouse model of breast-to-lung metastasis (AIM 2). These data will be used to calibrate the computational model using a novel approach to leverage tumor imaging data. We will apply the calibrated model to determine how individual cell-cell interactions and cellular properties affect tumor composition, spatial organization, and response to immunotherapies (AIM 3). We will investigate how cell-cell interactions affect tumor growth and simulate a range of immunotherapies to determine the cell properties and interactions that influence response. The effective immunotherapeutic strategies will be tested in vivo and validated using human tumor samples. This project leverages a physical sciences perspective to drive cancer research, leveraging experiment-based mechanistic modeling of the tumor-immune ecosystem. Upon completion of this project, we will: (1) quantitatively illuminate the impact of cell-cell interactions on metastatic breast tumor growth, (2) predict the effects of immunotherapy strategies for metastatic breast tumors, and (3) test the predicted strategies in vivo. We will pursue in silico, in vivo, and ex vivo studies, ultimately paving the way for clinical translation. Our work will provide quantitative understanding of the tumor-immune ecosystem in breast tumors and aid in the development of effective immunotherapy for this devastating form of cancer.