Abstract P2-02-25: Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis

Field: Medicine (Health Sciences)

Abstract Introduction: Neoadjuvant systemic therapy (NAT) is the standard of care for the treatment of locally advanced triple-negative breast cancer (TNBC) [1]. Unfortunately, only 50-65% of TNBC patients who receive NAT attain a pathological complete response [2]. Our goal is to improve methods for predicting treatment response early during NAT to allow for better patient outcomes. Biology-based mathematical models calibrated to magnetic resonance imaging (MRI) data have proven accurate in predicting NAT response, but they require hundreds of locally calibrated parameters to achieve highly accurate predictions of voxel-wise tumor cellularity [3]. Fitting so many parameters is computationally expensive and may suffer from overfitting of the data. Here, we combine biology-based mathematical models with habitat analysis to dramatically reduce the number of parameters to be calibrated within our biology-based model, and then test the accuracy of the subsequent predictions. Methods: Data acquisition – This study used 138 patients from the ARTEMIS trail (NCT02276443) who received Adriamycin/Cytoxan (A/C) followed by Paclitaxel. Each patient received MRI pre- (V1), post two cycles (V2), and post four cycles A/C (V3). Defining habitats –We applied habitat analysis on the apparent diffusion coefficient (ADC) map obtained from diffusion weighted MRI and the positive enhancement integral (PEI) map obtained from dynamic contrast enhanced MRI. To do so, we constructed a vector of ADC and PEI at V1 and V2 for every tumor bearing voxel in every patient. We then pooled these vectors across the cohort and applied k-means clustering to identify habitats. Mathematical model - Our biology-based mathematical model captures the spatio-development of tumor cellularity at each voxel location within the breast as the sum of tumor cell diffusion (controlled by a fixed diffusivity), proliferation (controlled by a calibrated proliferation rate), and death due to NAT (controlled by a calibrated drug efficacy rate). These parameters can be calibrated globally (one value per tumor) or locally (one value per tumor voxel). We calibrated the models to V1 and V2 data, after which the calibrated model is used to predict tumor status at V3. Habitat-informed model calibration – Habitats allow us to calibrate one drug efficacy value per habitat, as opposed to one value per tumor voxel. We first identified and calibrated for two habitats, and then gradually increased the number of habitats up to 16. We report preliminary results for three models, all of which calibrated for a global proliferation rate, but differ in how they handled drug efficacy: (A) global drug efficacy, (B) an efficacy value for each of ten habitats, and (C) local drug efficacy. Results: To evaluate the quality of the model calibrations we compare the median (interquartile range) of the absolute difference between the modeled and measured percent change in total tumor cellularity (%ΔTTC) across the cohort for each calibration. When calibrating from V1 to V2, we obtained %ΔTTC’s of 19 (5.0-25) %, 12 (7.7-15) %, and 2.0 (0.42-4.1) % for models A, B, and C, respectively. We repeat the analysis for predicting %ΔTTC at V3 and obtained 6.5 (0.90-20) %, 5.5 (2.8-12) %, and 15 (5.8-23) %, for models A, B, and C, respectively. Summary and Discussion: The accuracy of the calibrated V1 to V2 %ΔTTC increased as we increased the number of calibrated drug efficacy parameters due to the larger number of free parameters. However, the accuracy of models A and B for predicting %ΔTTC at V3 is superior to model C, which is also more computationally expensive. The more accurate and efficient models could support personalized treatment optimization, in which the calibrated model is used to predict patient response to a variety of treatment schedules. References[1] Liedtke et al., J of Clin Oncol, 2008. [2] Schmid et al., NEJM, 2020. [3] Jarrett et al., Nature Protocols, 2021. [4] Kazerouni et al., AACR Annual Meeting, 2022. Citation Format: Casey Stowers, Chengyue Wu, Zhan Xu, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-02-25.