Leveraging Systematic Chemical-Genetic Profiling as a Path to Expand Precision Medicine in Breast Cancer
Abstract
Large-scale consortia 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 anti-cancer therapies is 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 an exciting emerging strategy for the treatment of cancer, as it provides a therapeutic window because the genetic alteration -- and thus the accompanying sensitivity of cells to the drug -- is limited to cancer cells and not normal cells. Indeed, this strategy has found recent success in the clinic, resulting in 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 and 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. Thus, 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 technology we recently developed to systematically determine how individual tumor mutations impact response to therapy in breast cancers. Our initial application of this technology will be to identify novel and immediately actionable chemical-genetic, synthetic lethal interactions. In Aim 1, we will develop and optimize our quantitative and highly scalable technology for the testing of hundreds of drugs against dozens of patient mutations in parallel. Such an assay has the power to reveal biological vulnerabilities unique to particular cancer mutations that sensitize these tumors to specific treatments. This aim will center on determining optimal parameters and scale of our technology. In Aim 2, we will test this platform in an experiment to identify synthetic lethal events between a diversity of common mutations in DNA damage response (DDR) genes and a DDR-focused collection of potential drugs. Consistent with the scope of the Breakthrough Award Level 1 mechanism, this is a high-risk but high-reward project at an early stage of development and thus our anticipated outcome is a first-draft drug-breast cancer mutation interaction map, and proof of concept that this drug-mutation map provides novel therapeutic strategies designed to target individual mutations found in difficult-to-treat breast cancers that can ultimately guide patient therapy. More broadly, successful implementation of our strategy (demonstrated by validation of the two most promising chemical-genetic synthetic lethal interactions in a mouse model study), provides a pipeline that can be utilized to determine the function and drug sensitivity of tumor mutations beyond DDR in future studies. In addition, our drug-mutation map provides a resource to help th
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210018
Entities
People
- Marc Mendillo
Organizations
- Northwestern University
- United States Army