Integrating multiscale data and mechanistic models to predict breast cancer response to neoadjuvant therapies

Neoadjuvant therapy is a standard treatment for locally advanced breast cancer before surgery. In this therapeutic option, pathological complete response is defined as the absence of tumor after completion of the prescribed drug regimen and it has been linked to superior cancer control and survival.

The early assessment of a patient's breast tumor response to  neoadjuvant therapy would enable the treating oncologist to adapt the treatment plan of a non-responding patient (e.g., by adapting regimen schedule, dosage, and drugs).  As a result, this early adjustment would contribute to improve therapeutic outcomes and limit the treatment toxicities. However, the current methods to assess the tumor response to neoadjuvant therapy are unfit for this purpose because they either rely on changes in tumor size (which are only measurable after several drug cycles) or tissue biomarkers (which require an invasive biopsy and are subject to sampling errors due to tumor heterogeneity).

To address this challenge, this work aims at leveraging patient-specific in silico forecasts of breast cancer response to neoadjuvant therapeutic regimens, which are obtained via computer simulation of mathematical models describing the action of the usual drug combinations on the patient's tumor growth. To personalize these predictions, patient-specific longitudinal anatomic and quantitative magnetic resonance data acquired early in the course of the treatment are used to calibrate the model. Experimental data on combined drug effects and synergies further inform about expected parameter values and ideal model formulations to describe these key phenomena underlying treatment outcomes. 
Figure. The mechanistic models of breast cancer response in this research are personalized by using longitudinal anatomic and quantitative imaging data from each patient, which enable the estimation of tumor cell density, perfusion maps, and the segmentation of the tumor (top). These models can also include the pharmacokinetics of the usual drugs prescribed in neoadjuvant therapy (top), as well as their combined effect on tumor proliferation (top), which can be assessed with the MuSyC equation (DOX: doxorubicin, CYC: cyclophosphamide). Then, the personalized models can be used to make forecasts of treatment response in terms of tumor volume and tumor cell density maps during the course of neoadjuvant therapy (bottom). Adapted from Lorenzo et al. Arxiv, arXiv:2212.04270.


A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Hugo JM Miniere, Ernesto ABF Lima, Guillermo Lorenzo, David A Hormuth, Sophia Ty, Amy Brock, Thomas E Yankeelov

Cancer Biology & Therapy, vol. 25, 2024, p. 2321769

Designing clinical trials for patients who are not average

Thomas E Yankeelov, David A Hormuth, Ernesto ABF Lima, Guillermo Lorenzo, Chengyue Wu, Lois C Okereke, Gaiane M Rauch, Aradhana M Venkatesan, Caroline Chung

Iscience, vol. 27, 2024, p. 108589

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

Guillermo Lorenzo, Angela M Jarrett, Christian T Meyer, Julie C DiCarlo, John Virostko, Vito Quaranta, Darren R Tyson, Thomas E Yankeelov

Engineering with Computers, 2023, pp. 1--31

Chapter 25. Emerging Techniques in breast MRI

Anum S. Kazerouni, Adrienne N. Dula, Angela M. Jarrett, Guillermo Lorenzo, Jared A. Weis, James A. Bankson, Eduard Y. Chekmenev, Federico Pineda, Gregory S. Karczmar, Thomas E. Yankeelov

Advances in Magnetic Resonance Technology and Applications, In: K. Pinker, R. Mann, S. Partridge, Breast MRI: State of the Art and Future Directions, vol. 5, Elsevier, 2022, pp. 503-531

Integrating mechanism-based modeling with biomedical imaging to build digital twins for clinical oncology

Chengyue Wu, Guillermo Lorenzo, David A. Hormuth II, Ernesto A.B.F. Lima, Kalina P. Slavkova, Julie C. DiCarlo, John Virotsko, Caleb M. Phillips, Debra Patt, Caroline Chung, Thomas E. Yankeelov

Biophysics Review, vol. 3(2), 2022, p. 021304

Mathematical characterization of population dynamics in breast cancer cells treated with doxorubicin

Emily Y. Yang, Grant R. Howard, Amy Brock, Thomas E. Yankeelov, Guillermo Lorenzo

Frontiers in Molecular Biosciences, vol. 9, 2022, p. 972146

Math, magnets, and medicine: enabling personalized oncology

David A. Hormuth, Angela M. Jarrett, Guillermo Lorenzo, Ernesto A. B. F. Lima, Chengyue Wu, Caroline Chung, Debra Patt, Thomas E. Yankeelov

Expert Review of Precision Medicine and Drug Development, vol. 6(2), 2021, pp. 79-81

Patient-specific characterization of breast cancer hemodynamics using image-guided computational fluid dynamics

Chengyue Wu, David A. Hormuth, Todd A. Oliver, Federico Pineda, Guillermo Lorenzo, Gregory S. Karczmar, Robert D. Moser, Thomas E. Yankeelov

IEEE Transactions on Medical Imaging, vol. 39(9), 2020, pp. 2760-2771


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