Leveraging Systematic Chemical-Genetic Profiling as a Path to Expand Precision Medicine in Breast Cancer

Large-scale efforts have now sequenced the DNA of thousands of breast tumors generating a massive catalogue of genes that are lost, gained, and mutated in tumors compared to normal tissue. While this has undoubtedly catalyzed research and clinical efforts, the majority of these genetic alterations lack definitive experimental evidence for an active role in breast cancer growth. Moreover, the manner in which these alterations modify susceptibility and resistance to treatment options are largely unknown. This interplay between DNA changes and treatment response is termed a “chemical-genetic” interaction. When loss of a gene results in drug sensitivity that kills cancer cells, this interaction is called “synthetic lethal”. Searching for such synthetic lethal relationships is a powerful, emerging strategy for the treatment of cancer, best exemplified by the FDA-approved use of PARP inhibitors to target tumors with defects in DNA repair pathways due to BRCA mutations. Thus, there is an urgent need to experimentally characterize these catalogs of tumor mutations to identify how to uniquely pair drugs to mutations at scale, particularly for breast cancer patients with metastatic disease which currently has limited treatment options. There have been several recent efforts to systematically assess the tumorigenicity of cancer mutations using cell line and mouse experiments, however, efforts to identify how these alterations contribute to treatment response have been limited to either low-throughput (i.e. the detailed study of an individual or handful of mutations) or correlative approaches such as computational prediction of synthetic lethal interactions. A key limitation of previous studies is their inability to characterize the breadth of mutations observed in patient tumors in a time or cost-effective manner. To address these clinical and technical challenges, we propose to leverage and further optimize a quantitative and high-throughput chemical-genetic profiling