Abstract 6245: Predicting and optimizing the response of breast cancer patients to neoadjuvant therapy using a multiscale computational model

Abstract The high risk for metastasis, poor prognosis, and low overall survival rates in triple-negative breast cancer (TNBC) underline the crucial need for more accurate prediction and optimization of the therapeutic response. The goal of this work is to combine patient-specific imaging with a multi-scale mathematical model to predict and optimize the treatment response of TNBC on an individual basis. This study employed a cohort of 50 TNBC patients from the ARTEMIS trial (NCT02276443) who received standard-of-care neoadjuvant chemotherapy that consisted of four cycles of Adriamycin/cyclophosphamide (A/C) and 12 cycles of paclitaxel. We established a cell-scale model that describes drug transport via vascular delivery, tissue distribution, and cellular uptake and coupled it to a tissue-scale model of tumor size and cellularity to predict treatment response. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data were used to inform patient-specific pharmacokinetic parameters to estimate drug concentration in tissue, and in-vitro drug uptake data were used to model cellular drug concentration. The tissue-scale model incorporates cell movement, proliferation, and treatment-induced death and was calibrated using DCE-MRI and diffusion-weighted MRI data from pre- and post-two cycles of A/C treatment. Once calibrated on a patient, the model ran forward in time to predict tumor status at the end of A/C. Over the cohort, the concordance correlation coefficient between the predicted and measured changes in total tumor cellularity and volume were 0.96 and 0.91, respectively.We also evaluated hypothetically alternative treatment protocols for each patient by varying the total dose from 180-300 mg/m2 and the number of cycles from four to eight within a 56-day A/C treatment period. For the standard-of-care total dose of 240 mg/m2, eight cycles of A/C treatment resulted in a median (range) of 10% (7.12%-14.99%) reduction in total tumor cell count compared to the standard-of-care four cycles. Further, increasing the total dose improved the patients' response. Within the cohort, a median (range) of 36.83% (29.33%-47.41%) reduction of total tumor cellularity was observed for the 300 mg/m2 total dose over eight treatment cycles.To the best of our knowledge, this is the first multi-scale model calibrated with patient data to generate a testable prediction of treatment response. The methods in this study accurately captured the spatio-temporal dynamics of drug distribution and the response of TNBC to neoadjuvant A/C. Our ongoing work focuses on using this approach to assess the effects of increasing or decreasing the intensity of the treatment on non-responding and responding patients, respectively. Integrating MRI data with pharmacokinetic and mechanistic models has the potential to accurately predict treatment response in TNBC and bring tangible benefits to the patients. Citation Format: Mahesh Nandyala, Chengyue Wu, Casey E. Stowers, Reshmi Patel, Clinton Yam, Jingfei Ma, David Fuentes, Gaiane M. Rauch, Thomas E. Yankeelov. Predicting and optimizing the response of breast cancer patients to neoadjuvant therapy using a multiscale computational model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6245.