Abstract
The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. While mathematical models describing the dynamics of resistant cancer cell subpopulations have been proposed, experimental validation has been difficult due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently identify the drug resistant subpopulations. We address this problem with a coupled experimental/modeling approach to reveal the composition of drug resistant subpopulations changing in time following drug exposure. We calibrate time-resolved drug sensitivity assays to three mathematical models to interrogate the models’ ability to capture drug response dynamics. The Akaike information criterion was employed to evaluate the three models, and it identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity as the optimal model. To validate the model’s ability to identify subpopulation composition, we mixed different proportions of wild-type MCF-7 and MCF-7/ADR resistant cells and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell types with an R-squared value of 0.857. To the best of our knowledge, this is the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.
Highlights
We aim to investigate how the therapeutic sensitivity of a breast cancer cell population changes over time following exposure to a pulse of chemotherapy
Resistance to chemotherapy is a major cause of failure in breast cancer treatment, we do not currently have a mathematical model describing the development of resistance in the context of a dynamic heterogeneous cancer cell population
MCF-7/ADR human breast cancer cells were obtained from Robert Clarke[33] and maintained in MEM (Gibco) supplemented with 10% fetal bovine serum (Gibco), 1% PenicillinStreptomycin (Gibco), and 500 nM doxorubicin (Sigma-Aldrich)
Summary
We aim to investigate how the therapeutic sensitivity of a breast cancer cell population changes over time following exposure to a pulse of chemotherapy. We hypothesize that intratumoral heterogeneity and cellular plasticity play a direct role in the progression of resistance This hypothesis is based on previous work demonstrating that exposure to chemotherapy induces gene expression changes, metabolic state transitions, and increased drug resistance in subsets of cancer cells[1,2,3,4,5,6,7,8,9,10]. We test this hypothesis of the direct role of the changing composition of subpopulations of differing drug resistance in the observed resistance response using mathematical modeling to estimate the relative frequencies of cells in different drug sensitivity states over time. Resistance observed in these cell lines may not be physiologically relevant to the clinical onset of chemoresistance, in which transient drug resistance may be induced in response to periodic treatment
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