Abstract/Project Summary Metastatic breast cancer and relapse following therapy are dependent on (1) development of intrinsic resistance to targeted and endocrine therapies, and (2) resistance to recognition and destruction of cancer cells by the immune system. The Stanford Breast Metastasis Center (SBMC) is focused on (1) quantifying the timing of metastatic dissemination in breast cancer (2) functionally delineating the contribution of cellular and microenvironmental crosstalk on metastatic proclivity, and (3) characterizing the mechanisms of responses by metastatic cells to therapies. In order to achieve these goals, mechanistic computational models that capture dynamic and emergent tumor cell intrinsic and extrinsic properties are needed as are clinically annotated longitudinal tissue cohorts and experimental models that capture disease heterogeneity. The SBMC addresses each of these outstanding challenges. First, we have established an unparalleled collection of clinically annotated breast cancer cohorts sampled through treatment and metastasis, including both prospective and retrospective longitudinal cohorts, with multiple metastatic sites. We leverage a living biobank of breast cancer patient- derived organoids (PDOs) from primary tumors and metastases that recapitulate the heterogeneity of disease, high-risk of relapse subgroups and tumor-immune interactions and greatly facilitating the proposed functional studies. We characterize these vast tissue resources and model systems using state-of-the-art molecular profiling technologies to probe tumor tissue in situ at single cell and subcellular resolution. Specifically, with Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) and matrix-assisted laser desorption ionization imaging (MALDI) we simultaneously visualize the composition, lineage, function and spatial distribution of tumor and stromal cell populations and perform co-registered analysis of the glycome. We integrate these data within the genomic landscape of metastatic disease and analyze these data within robust machine learning and computational frameworks to uncover disease dynamics and features associated with clinical outcomes. Lastly, we conduct genome-scale CRISPR screens in 3D breast cancer models to systematically define oncogenic dependencies, therapeutic vulnerabilities and macrophage-tumor cell interactions. This integrated systems biology and functional genomics approach will contribute to a quantitative and mechanistic understanding of metastatic breast cancer and the dynamic relationship between tumor cells and the host, with implications for therapeutic targeting.