Abstract PS13-44: Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data

The early determination of response to neoadjuvant therapy (NAT) in triple-negative breast cancer would enable the treating oncologist to adapt the therapeutic regimen of a non-responding patient (e.g., by changing dosage, dose schedule, prescribed drugs), and thereby improve treatment outcomes while avoiding unnecessary toxicities. To address this challenge, we propose to use personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal multi-parametric magnetic resonance imaging (MRI) data acquired early in the course of NAT. Here, we extend our mechanistic model to include a new term describing the synergistic effects of NAT drug combinations and identify the driving parameters involved in its formulation by means of a sensitivity analysis. Our model describes tumor cell dynamics as a combination of proliferation, which is regulated by a logistic term, and mobility, which is described as a diffusion process constrained by the local tumor-induced mechanical stress. Tumor cell density is extracted from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented T1-weighted MRI data. We adjust the tumor proliferation rate in response to NAT drug combinations with a recent model of drug synergy, MuSyC, which accounts for distinct types of synergistic drug effects (synergy of potency vs. synergy of efficacy). We also consider the heterogeneous intratumoral delivery of drugs by means of perfusion maps estimated from dynamic contrast-enhanced MRI data. We use Sobol’s method for the sensitivity analysis of two different tumors - one well-perfused and one poorly-perfused. We simulate a four-cycle NAT protocol in which NAT drugs are delivered every 14 days, and assess the total effect (ST) of each parameter on the mean relative difference of tumor cell density with respect to a control simulation of tumor growth without NAT. Sensitivity analysis results directly depend on the definition of the parameter space, which we construct by combining two approaches. First, we experimentally constrain parameter ranges using time-resolved, high-throughput, automated microscopy assays to capture the changes in proliferation rates of various breast cancer lines (HCC1143, SUM149, MDAMB231, and MDAMB468) caused by two standard drug combinations: paclitaxel with carboplatin and doxorubicin with perfosfamide (metabolic derivative of the pro-drug cyclophosphamide), and fitting the MuSyC model to these data. Second, we scale the resulting in vitro parameter ranges to clinically-relevant in vivo ranges by running an in silico study with our mechanistic model of breast cancer growth and NAT response. Our results show that, out of the ten parameters involved in the synergy term, three have a dominant role in the dynamics of breast cancer during NAT (ST g 0.1): synergistic potency, the maximal change in tumor cell proliferation by the slowest decaying drug, and its concentration producing half of maximal effects. The other parameters have marginal (0.02 l ST l 0.1) to negligible effect (ST l 0.02). Ongoing studies are assessing the ability of our mechanistic model to forecast NAT response over a small patient cohort after patient-specific calibration of the driving parameters identified in the present study. Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-44.