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 a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize 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, each consisting of a system of ordinary differential equations, 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%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.
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.
Physiological processes rely on control of cell proliferation in time and space and dysregulation of cell growth underlies pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-lab studies may be integrated with different mathematical modeling approaches to aid interpretation of the results and to enable prediction of cell behaviors, specifically in the context of cancer.
Observing and quantifying the proliferation of subpopulations in cancer is key to understanding how heterogeneous groups of cells interact and respond to therapy, their environment, and each other. Prior research has demonstrated that cell-state properties such as metastatic potential and genotype perturbation are encoded in cellular morphology and can be identified with various machine-learning approaches. This encoding spans multiple imaging modalities such as brightfield, phase contrast, and stained whole-slide images, and phenotype prediction can be accomplished using both classical and deep machine learning methods. Here we show that not only do these prediction capabilities extend to transcriptomic subpopulations, but that they can be used to track these populations in high throughput longitudinal live-cell imaging experiments. Using single-cell RNA sequencing, we observed that, among untreated MDA-MB-231 triple negative breast cancer cells, there exist two transcriptomically distinct populations of cells. Using fluorescence-activated cell sorting based on the differentially expressed surface marker ESAM, we isolated these subpopulations and fluorescently labeled them with mCherry or GFP depending on their transcriptomic cluster. Cells were then grown in monoculture or coculture and imaged every 4 hours at 20x resolution. To identify the associated transcriptomic cluster, we trained an instance segmentation algorithm, Mask R-CNN, to both segment and classify cells. We find that, despite being derived from the same cell line, these phenotypes can be predicted using phase contrast images alone. These results demonstrate that cellular phenotypes manifested by distinctive RNA expression signatures can now be surveilled in a high-throughput manner across multiple samples and conditions without further RNA sequencing or biomarker labeling. We anticipate that this methodology of high throughput tracking will be applicable to other heterogeneous subpopulations, such as isolated therapy-resistant or sensitive cells. Tracking transcriptomically distinct populations using only high-throughput imaging will increase the granularity of population analysis and enable more rapid assessment of cancer cell evolutionary dynamics. Citation Format: Tyler Jost, Andrea Gardner, Amy Brock. Deep learning enables label-free tracking of heterogeneous subpopulations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5379.
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