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

doi:10.3389/fmolb.2022.972146

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

Front Mol Biosci. 2022 Sep 12:9:972146. doi: 10.3389/fmolb.2022.972146. eCollection 2022.

Authors

Emily Y Yang¹, Grant R Howard², Amy Brock^{2

3

4}, Thomas E Yankeelov^{1

2

3

5

6

7}, Guillermo Lorenzo^{1

8}

Affiliations

¹ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States.
² Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States.
³ Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.
⁴ Interdisciplinary Life Sciences Program, The University of Texas at Austin, Austin, TX, United States.
⁵ Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States.
⁶ Department of Oncology, The University of Texas at Austin, Austin, TX, United States.
⁷ Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
⁸ Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.

Abstract

The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that treatment with doxorubicin induces a compartmentalization of the breast cancer cell population into surviving cells, which continue proliferating after treatment, and irreversibly damaged cells, which gradually transition from proliferating to treatment-induced death. The model is fit to experimental data including variations in drug concentration, inter-treatment interval, and number of doses. Our model recapitulates tumor cell dynamics in all these scenarios (as quantified by the concordance correlation coefficient, CCC > 0.95). In particular, superior tumor control is observed with higher doxorubicin concentrations, shorter inter-treatment intervals, and a higher number of doses (p < 0.05). Longer inter-treatment intervals require adapting the model parameterization after each doxorubicin dose, suggesting the promotion of chemoresistance. Additionally, we propose promising empirical formulas to describe the variation of model parameters as functions of doxorubicin concentration (CCC > 0.78). Thus, we conclude that our mathematical model could deepen our understanding of the cytotoxic effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

Keywords: breast cancer; chemotherapy; drug resistance; mathematical modeling; mathematical oncology; population dynamics; time-resolved microscopy.