Abstract A028: Harnessing patient derived models to understand tumor evolution in metastatic breast cancer

Abstract In the United States, more than 280,000 women are diagnosed with breast cancer every year, with more than 90% of breast cancer deaths caused by metastatic disease. 20-30% of breast cancer patients will develop metastasis, despite receiving state-of-the-art therapies. Once metastases are detected, the disease becomes incurable. Developing therapeutic interventions that eliminate metastasis represents a significant challenge in the field, as breast tumors evolve and develop resistance, ultimately undermining a tumor’s response to therapy. Underlying this resistance is the complex heterogeneity of tumors, which includes different disease subtypes, diverse patient biology, and unpredictable therapeutic responses. To date, there is very limited use of models for breast cancer research that accurately reflect the dynamic way tumors evolve in patients and how heterogeneity contributes to this process. Though cell lines have been useful to elucidate several mechanisms of drug resistance and response, their findings have failed to translate to accurate predictions of efficacy in clinical settings. The development of patient-derived models of cancer (PDMC), including patient-derived xenografts (PDX) have advanced the field by providing model systems with high fidelity to the tumor of origin, thereby providing opportunities to identify fundamental characteristics that underlie tumor evolution, patient drug response and resistance. This study hypothesizes that PDXs can model lethal disease and recapitulate tumor evolution in response to therapy, allowing us to leverage this data to predict patient responses, and improve patient outcomes. Longitudinal samples from metastatic breast cancer patients, representing various disease stages and subtypes will be used to uncover mutational, transcriptomic and epigenetic alterations that may contribute to metastatic disease progression. This study will also lead to a better understanding of mechanisms that contribute to resistance, such as cellular plasticity, by examining the evolutionary trajectories of cell populations during tumor evolution. These pioneering studies aim to establish PDMCs as more accurate representations of metastatic breast cancer evolution, while unravelling links between heterogeneity and therapy response in metastatic disease, with the hope of ultimately improving breast cancer patient outcomes. Citation Format: Zannel Blanchard, Sandra D Scherer, Zhengtao Chu, Chieh-Hsiang Yang, Emilio Cortes-Sanchez, Bryan E Welm, Alana L Welm. Harnessing patient derived models to understand tumor evolution in metastatic breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A028.