Abstract
Triple-negative breast cancer (TNBC) is a heterogenous disease that is defined by its lack of targetable receptors, thus limiting treatment options and resulting in higher rates of metastasis and recurrence. Combination chemotherapy treatments, which inhibit tumor cell proliferation and regeneration, are a major component of standard-of-care treatment of TNBC. In this manuscript, we build a coupled ordinary differential equation model of TNBC with compartments that represent tumor proliferation, necrosis, apoptosis, and immune response to computationally describe the biological tumor affect to a combination of chemotherapies, doxorubicin (DRB) and paclitaxel (PTX). This model is parameterized using longitudinal [18F]-fluorothymidine positron emission tomography (FLT-PET) imaging data which allows for a noninvasive molecular imaging approach to quantify the tumor proliferation and tumor volume measurements for two murine models of TNBC. Animal models include a human cell line xenograft model, MDA-MB-231, and a syngeneic 4T1 mammary carcinoma model. The mathematical models are parameterized and the percent necrosis at the end time point is predicted and validated using histological hematoxylin and eosin (H&E) data. Global Sobol’ sensitivity analysis is conducted to further understand the role each parameter plays in the model’s goodness of fit to the data. In both the MDA-MB-231 and the 4T1 tumor models, the designed mathematical model can accurately describe both tumor volume changes and final necrosis volume. This can give insight into the ordering, dosing, and timing of DRB and PTX treatment. More importantly, this model can also give insight into future novel combinations of therapies and how the immune system plays a role in therapeutic response to TNBC, due to its calibration to two types of TNBC murine models.
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Data availability
The datasets analyzed throughout this study are available from the corresponding author upon reasonable request.
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Acknowledgements
We thank the American Cancer Society for funding through RSG-18-006-01-CCE. We also thank the UAB O’Neal Comprehensive Cancer Center’s Preclinical imaging shared facility, P30CAO13148-48.
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Davenport, A.A., Lu, Y., Gallegos, C.A. et al. Mathematical Model of Triple-Negative Breast Cancer in Response to Combination Chemotherapies. Bull Math Biol 85, 7 (2023). https://doi.org/10.1007/s11538-022-01108-1
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DOI: https://doi.org/10.1007/s11538-022-01108-1