Introduction: We show that the combination of quantitative magnetic resonance imaging (MRI) and mathematical modeling can accurately predict tumor response for individual patients, and we demonstrate the selection of personalized therapeutic regimens using our mathematical model to vary, in silico, a range of clinically feasible treatment plans to achieve the greatest tumor control.

Methods: Breast cancer patients (N = 18) were scanned by MRI at three time points during neoadjuvant therapy (NAT): 1) prior to NAT, 2) after one cycle of their initial NAT regimen, and 3) after the completion of their initial NAT regimen. Specifically, diffusion-weighted MRI data characterizing tumor cellularity was collected to estimate tumor cell burden, and dynamic contrast-enhanced MRI data was collected to estimate the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of the administered chemotherapies. To predict tumor response, we calibrated our tissue scale, 3D, biophysical mathematical model to each patient's MRI data set using their first two scans. Using the calibrated, patient-specific parameters, the model was projected forward to the third scan time. The predicted total tumor cellularity, volume, and longest axis were compared to the actual values measured from the patient's third scan. Following evaluation of the model's predictive ability, we then employed the model to identify potentially superior, clinically relevant, alternative dosing regimens for each patient. The alternative regimens were defined using the same total amount of drug each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Statistical analysis was completed with Pearson correlation and Wilcoxon signed rank test.

Results: The model's predictions of tumor response are significantly correlated to the measured tumor burden at the time of scan 3 (r2 > 0.88, p < 0.01) for total cellularity, total volume, and longest axis. For the alternative dosing regimens assessed, the model predicted that individual patients could have achieved, on average, an additional 21% (0-46%) reduction in total cellularity. The optimal dosing regimens chosen by the model were predicted to significantly outperform standard regimens for tumor control (p < 0.001).

Conclusions: These results suggest that the mathematical model can be predictive of tumor response by MRI in the clinical setting using data at the earliest times of therapy. With in silico studies, we illustrate how therapeutic regimens can be selected for individual patients for better tumor control, revealing that standard regimens may not be the most effective for every patient. These results represent a first step towards mathematically-based, personalized patient regimens. Future work aims to optimize therapy regimens via established optimal control theory methods.

Citation Format: Angela M. Jarrett, Ernesto A. Lima, David A. Hormuth, Chengyue Wu, John Virostko, Anna G. Sorace, Julie C. DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Patient-specific neoadjuvant regimens for breast cancer identified via image-driven mathematical modeling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5485.