Functional Identification of High-Dimensional Driver Combinations in Basal-Like Breast Cancer
Abstract
Basal-like breast cancer (BLBC) is one of the most aggressive subtypes of breast cancer that does not harbor currently known druggable mutations. As a result, precise and efficient molecular targeted therapy is still not available for this disease, leaving chemotherapy as the main treatment option. Although chemotherapy can efficiently halt BLBC growth in 30% of patients, for the rest, rapid relapses will occur within 3 years of treatment. One key to solve these major challenges facing survival of BLBC patients is to identify mutations that act as drivers of BLBC initiation, maintenance, and recurrence from a myriad of mutations found in patients. Identification of BLBC drivers can lead to two potential strategies for the successful treatment of BLBC: (1) develop molecular targeted therapies to block driver lesions that are required and essential to program basal-like fate and (2) improve the efficiency of existing chemotherapies by targeting recurrence drivers. The mouse mammary gland is a powerful system to identify breast cancer drivers because it resembles major properties of human counterparts. However, modeling large amounts of patient-derived genetic lesions in mouse mammary glands and identifying cancer drivers from them is not easy. First, most traditional genomic engineered mouse models can only test one genetic condition in one animal, and the process of generating mouse models can take years. Moreover, in BLBC evidence from our previous tumor screens and published large-scale tumor-sequencing projects suggests that basal-like breast cancer requires a combination of multiple drivers to form. These are not modeled adequately in current mouse models – nowhere near to the degree of complexity and diversity required to understand what genetic lesions drive BLBC formation, drug resistance, and recurrence locally and at different metastatic sites. To solve these problems, in the past few years, we have developed a novel strategy that allows us to deliver and test up to ~1200 patient-derived mutations in the mammary glands of a single mouse. In the current project, we will model and test highly complicated combinations of BLBC mutations in mouse mammary glands. Using these cutting-edge approaches, we will identify driver combinations that are required and essential to induce primary BLBCs, drive treatment resistance, and ultimately cause recurrence. Successful completion of the current project will point out which drivers are the most efficient candidates for developing molecular targeted therapy of BLBC, will identify which drivers are associated with resistance to specific chemotherapy agents, and will determine whether inhibition of these drivers in combination with chemotherapy can achieve prevention of BLBC recurrence, as a temporary/alternative plan before molecular targeted therapy fully replace chemotherapy.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252310038
Entities
People
- Zhe Ying
Organizations
- Icahn School of Medicine at Mount Sinai
- United States Army