Abstract PS3-04-22: A mathematical framework to optimize combination therapy for triple-negative breast cancer in obese mice

Abstract Introduction. Triple-negative breast cancer (TNBC) disproportionately affects obese individuals, with emerging evidence suggesting that obesity may decrease or paradoxically enhance the efficacy of certain immunotherapies. However, obesity-driven intratumoral hypoxia presents additional challenges for treatment, motivating the use of evofosfamide (a hypoxia-activated prodrug) as a therapeutic adjunct. With this dual challenge in mind, we set out to build a clinically oriented ‘virtual trial’ platform that can forecast tumor response and pinpoint dosing schedules that maximize immunotherapy + evofosfamide benefit while minimizing toxicity in obesity-linked TNBC. Methods. We have developed a mathematical framework that allows us to conduct ‘virtual trials’ on the computer, screening thousands of possible dosing protocols before any animal or patient is treated. The framework is built on a nonlinear system of ordinary differential equations that simultaneously describe intrinsic tumor growth, immune response, angiogenesis and vessel regression, and the effects of evosfamide and immunotherapy—whose interaction is modulated by the well-vascularized fraction of the tumor. Model parameters are inferred with Markov Chain Monte Carlo, an iterative random-sampling algorithm that scans the parameter space and converges on the combinations that most closely replicate the observed data. Model calibration relies on 34 days of serial data from C57BL/6J mice (N=24). Prior to data acquisition, the obese model of TNBC was established with 13 weeks of a high-fat diet (HFD) and then orthotopically implanted into the mammary fat pad with E0771 TNBC cells 12 days before baseline (day 0). For each mouse, we tracked tumor volume by caliper and the well-vascularized fraction by [18F]-fluoromisonidazole (FMISO) PET across four study arms—control, evofosfamide alone, immunotherapy on normoxic mice, and the combination on hypoxic mice. Mice were grouped seven days before baseline. By varying dosing and scheduling, we optimize treatment for three objectives: 1) reducing final tumor volume, 2) minimizing total tumor burden (tumor volume integrated over the experimental interval), and 3) limiting therapy toxicity. Results. Our mathematical framework accurately reproduces (i.e., calibrate) tumor growth dynamics under control and individual treatment conditions, achieving an average concordance correlation coefficient (CCC) of 0.97 ± 0.005. The model is also able to accurately predict the response of tumors to combination therapy, yielding a CCC of 0.93. Through differential evolution optimization—a mathematical technique that mimics natural selection by iteratively combining and selecting the best treatment parameter sets—we identify candidate treatment protocols that we hypothesize will yield a 67.5% reduction in average final tumor volume, a 68.2% reduction in tumor burden, and an 18.7% reduction in total therapy dosage compared to the standard of care. Conclusion. The findings underscore the efficacy of reducing hypoxia to enhance immunotherapy in HFD mice, which can be significantly strengthened through optimized scheduling of evofosfamide and immunotherapy combination therapies. The proposed mathematical framework demonstrates a robust ability to predict tumor response and optimize treatment strategies, highlighting the potential for translating mathematical systems to guide combination therapies and develop digital twins for personalized medicine. The optimized schedules point to the possibility of achieving significantly better tumor control with less total drug dose, potentially translating into reduced toxicity while preserving benefit. These optimized regimens should be regarded as hypothesis-generating; their safety and therapeutic benefit must be experimentally confirmed. Citation Format: K. Vishwanath, C. I. Crawford, A. G. Sorace, T. E. Yankeelov, E. A. Lima. A mathematical framework to optimize combination therapy for triple-negative breast cancer in obese mice [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-04-22.