Biology subjects

Howard, G. R.

Publications and source records attributed to Howard, G. R..

3 recordsLinked to original sources

bioRxiv2021

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

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 effects of the standard chemotherapeutic drug doxorubicin on the MCF-7 breast cancer cell line. We assume that the tumor is composed of two subpopulations: drug-resistant cells, which continue proliferating after treatment, and drug-sensitive 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 growth 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 effects of doxorubicin and could be used to explore practical drug regimens achieving optimal tumor control.

cancer biology↗

bioRxiv2020

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

cancer biology↗

bioRxiv2017

A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

The development of resistance to chemotherapy is a major cause of treatment failure in breast cancer. Although several molecular mechanisms of chemotherapeutic resistance are well studied, a quantitative understanding of the dynamics of resistant subpopulations within a heterogeneous tumor cell population remains elusive. While mathematical models describing the dynamics of heterogeneous cancer cell populations have been proposed, few have been experimentally validated due to the complex nature of resistance that limits the ability of a single phenotypic marker to sufficiently isolate drug resistant subpopulations. In this work, we address this problem with a combined experimental and modeling system that uses drug sensitivity data to reveal the composition of multiple subpopulations differing in their level of drug resistance. We calibrate time-resolved dose-response data to three mathematical models to interrogate the models ability to capture the dynamics of drug. All three models demonstrated an increase in population level resistance following drug exposure. The candidate models were compared by Akaike information criterion and the model selection criteria identified a multi-state model incorporating the role of population heterogeneity and cellular plasticity. To validate the ability of this model to identify the composition of subpopulations, we mixed wild-type MCF-7 and MCF-7/ADR resistant cells at various proportions and evaluated the corresponding model output. Our blinded two-state model was able to estimate the proportions of cell subtypes, with the measured proportions falling within the 95 percent confidence intervals on the parameter estimations and at an R-squared value of 0.986. To the best of our knowledge, this contribution represents the first work to combine experimental time-resolved drug sensitivity data with a mathematical model of resistance development.

systems biology↗