Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

Abstract

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present work characterizing dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600-800 hours, and this data is used to calibrate a family of cancer growth models derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-106%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

“…The importance of choosing the optimal dosage as well as the drug holiday to achieve maximum drug benefit to the patient and treatment efficacy is shown in Figs. 2 and 3 , demonstrating good qualitative compatibility with the literature 57 – 59 . Based on computational-experimental study of Howard et al 57 , inter-treatment interval is a significant factor in determining the efficacy of successive dosing plans and identifying an optimal retreatment time.…”

“…2 and 3 , demonstrating good qualitative compatibility with the literature 57 – 59 . Based on computational-experimental study of Howard et al 57 , inter-treatment interval is a significant factor in determining the efficacy of successive dosing plans and identifying an optimal retreatment time. The effects of treatment scheduling—with varying treatment dose, duration, and length of drug holiday—on the drug resistance evolution in breast cancer have been investigated by Patwardhan et al 59 .…”

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

“…Doxorubicin is an anthracycline drug that is extensively used in chemotherapeutic regimens for breast cancer [30], [48], [49] and whose mechanism of action induces tumor cell death [50]- [52]. Our work continues the first efforts of Howard et al in studying doxorubicin resistance in breast cancer cell populations by leveraging several experimentally-informed mechanistic models [34], [53]. While Howard et al originally proposed multiple models to characterize this phenomenon and selected the best of them for each dataset, we have developed a single model that can be extended for multiple drug doses and is also amenable to an adaptive parametrization with each doxorubicin dose.…”

“…Individual cells were consistently resolved using standard image analysis techniques of background subtraction, followed by thresholding, edge detection, and minimum area filtering. The phase contrast images were consulted in parallel to aid the validation of image analysis [34], [53].…”

“…There are several mechanistic approaches to mathematically describe chemoresistance [31], with the original theoretical models dating back more than two decades [32], [33]. The standard strategy consists of defining a multicompartmental tumor cell population including one or multiple species of both drugresistant and drug-sensitive cells, which evolve and interact over time following a set of ordinary differential equations, or over both space and time according to a set of partial differential equations [34]- [38]. Alternatively, Sun et al utilized a stochastic, multiscale model that incorporated heterogeneous population dynamics with drug pharmacokinetics and microenvironment contributions to drug resistance in melanoma patients [39].…”