Speaker
Description
Neoadjuvant therapy (NAT) is a standard initial treatment for triple-negative breast cancer (TNBC), yet early prediction of treatment response remains challenging. This capability would enable adjustment of therapeutic plans to optimize outcomes and minimize toxicities. To this end, I will present a mechanistic, multiscale mathematical model of TNBC response to NAT that integrates in vivo longitudinal MRI with time-resolved in vitro drug-response data to identify key mechanisms driving tumor dynamics. The model describes tumor cell density evolution through mechanically-constrained mobility and net proliferation, coupled to a pharmacokinetic/pharmacodynamic (PK/PD) representation of NAT drug regimens. To facilitate clinical applicability, a global sensitivity analysis (GSA) is carried out by using Sobol’s method on 3D MRI-based tissue domains from well and poorly-perfused tumors. The parameter space combines prior in silico estimates informed by in vivo imaging with in vitro measurements capturing drug-induced proliferation changes across TNBC cell lines. The GSA reveals a small subset of dominant parameters governing treatment response, enabling construction of a parsimonious surrogate model that preserves the dynamics of the full formulation. Building on these results, I will briefly outline a personalized tumor forecasting pipeline in which the reduced model is calibrated to early-treatment MRI data and used to obtain patient-specific forecasts of tumor response to NAT.
Bibliography
@article{lorenzo2024global,
title={A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data},
author={Lorenzo, Guillermo and Jarrett, Angela M and Meyer, Christian T and DiCarlo, Julie C and Virostko, John and Quaranta, Vito and Tyson, Darren R and Yankeelov, Thomas E},
journal={Engineering with computers},
volume={40},
number={3},
pages={1469--1499},
year={2024},
publisher={Springer}
}
@article{meyer2019quantifying,
title={Quantifying drug combination synergy along potency and efficacy axes},
author={Meyer, Christian T and Wooten, David J and Paudel, B Bishal and Bauer, Joshua and Hardeman, Keisha N and Westover, David and Lovly, Christine M and Harris, Leonard A and Tyson, Darren R and Quaranta, Vito},
journal={Cell systems},
volume={8},
number={2},
pages={97--108},
year={2019},
publisher={Elsevier}
}
@incollection{lorenzo2022quantitative,
title={Quantitative in vivo imaging to enable tumour forecasting and treatment optimization},
author={Lorenzo, Guillermo and Hormuth II, David A and Jarrett, Angela M and Lima, Ernesto ABF and Subramanian, Shashank and Biros, George and Oden, J Tinsley and Hughes, Thomas JR and Yankeelov, Thomas E},
booktitle={Cancer, complexity, computation},
pages={55--97},
year={2022},
publisher={Springer}
}
@article{patel2025mri,
title={MRI-Based Mathematical Modeling to Predict the Response of I-SPY 2 Patients with Breast Cancer to Neoadjuvant Therapy},
author={Patel, Reshmi JS and Wu, Chengyue and Stowers, Casey E and Mohamed, Rania M and Ma, Jingfei and Rauch, Gaiane M and Yankeelov, Thomas E},
journal={Clinical Cancer Research},
volume={31},
number={22},
pages={4846--4856},
year={2025},
publisher={American Association for Cancer Research}
}
