Project 3: Systematic characterization of factors controlling breast cancer progression and resistance

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Abstract/Project Summary Metastatic breast cancer and relapse following therapy are dependent on (1) resistance to recognition and destruction of cancer cells by the immune system, and (2) development of intrinsic resistance to targeted and endocrine therapies. The study of these processes using in vitro cancer models have been limited in scale and often lack key properties of the tumor microenvironment. We recently developed a scalable cancer spheroid system that enabled the first genome-wide CRISPR screens in 3D culture; phenotypes in this system much better reflect in vivo tumors (Nature, 2020). In addition, we developed a magnetic separation strategy to rapidly identify regulators of phagocytosis by macrophages (Nature Genetics, 2018) and have successfully extended this strategy to study macrophage-tumor cell interactions. Here we will use these systems to identify regulators of therapeutic relapse and immune evasion in metastatic breast cancer. To investigate mechanisms of relapse after therapy, we will focus on four ER+ breast cancer subgroups with high relapse risk previously identified by the Curtis Lab (Project 1). This has formed the basis of a biomarker- driven clinical trial targeting the presumed candidate drivers in these high-risk subgroups. Since the amplicons defining these subgroups each contain multiple genes, we will use functional CRISPR perturbations to test which genes (or combinations thereof) are the true drivers. Further, we will build on the comprehensive characterization of these tumors from transcriptomics (Project 1) and spatial proteomics (Project 2), adding functional measurements of the requirement for each altered factor in growth and resistance to therapy using high- throughput CRISPR screens. Together these studies will dramatically enhance our understanding of which genes are critical targets for improved therapies in high-relapse risk breast cancers. To investigate how metastatic tumors evade the immune system, we will focus on macrophage-tumor interactions. Surprisingly, although macrophages comprise 50% of the cell mass of some tumors, breast cancer cells appear resistant to macrophage killing. This is largely due to anti-phagocytic signals expressed by cancer cells, including CD47; however, accumulating evidence points to the existence of additional, unidentified anti-phagocytic signals in breast cancer. In addition, tumor-associated macrophages (TAM) are re-wired to support tumor development and have reduced phagocytosis. It remains unclear, however, which genes mediate resistance to phagocytosis in high-risk IC subtypes, and which macrophage genes underlie immunosuppression by metastatic breast cancers. Here, we will systematically identify genes limiting anti-cancer activity by macrophages by conducting CRISPR screens in both macrophages and cancer cells, making use of sophisticated ALI patient-derived organoid models to validate hits. These complementary approaches will functionally define breast cancer driver genes and therapeutic targets that control therapeutic response and immune evasion, informing the next generation of clinical trials.