Abstract A027: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit

Abstract Triple-negative breast cancer is a highly aggressive disease subtype with limited treatment options. Despite the approval of targeted therapies such as PARP inhibitors, metastatic progression remains inevitable. Additionally, spatial arrangements, including immune exclusion zones and immune–stromal crosstalk, can profoundly influence therapeutic response. These observations underscore the need for more effective, individualized treatments that both target malignant cells and promote anti-tumor microenvironments. Toward that goal, we have developed a coordinated experimental-computational approach designed to enable identification of effective treatment strategies that consider the functional relationships between different cell types. First, to identify adaptive responses that may serve as therapeutic vulnerabilities, we treated C3TAg genetically engineered mouse model with a panel of clinically approved therapies then subjected these samples to single-cell RNAseq and multiplex tissue imaging. We observed that relatively short treatment durations lead to changes in the composition and spatial organization of tumors, suggesting adaptive responses that may be leveraged in combination treatment strategies. Notably, we observed changes in several macrophage populations, including changes to co-localization of M2-like macrophage and cancer-associated fibroblasts, highlighting stromal responses to therapeutic pressure. Second, we are using the resultant data to guide development and parameterization of an agent-based model that considers the functional relationships between different tumor components and can be used to predict effective therapeutic treatment strategies. We have developed analytical approaches to leverage our single-cell RNAseq data to estimate key model parameters operable in prioritized cell populations, including those related to proliferation, motility, antigen presentation, and cell-cell signaling. Initial model simulations suggest that spatial cell patterns are often not steady: immune cells dynamically aggregate, disperse, migrate, and reaggregate at new locations; macrophages infiltrated hypoxic and necrotic regions, where they polarized from an anti-tumor M1-like state to a pro-tumor M2-like state. These observations lay the groundwork for leveraging agent-based models for in silico testing of therapeutic combinations that can be prioritized for experimental validation. In total, our approach has the potential to rationally identify effective therapeutic strategies that lead to long-term durable control of tumors. Citation Format: Laura M. Heiser, Young Hwan Chang, Paul Macklin. Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A027.

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