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- Title
Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model.
- Authors
Mohammad Mirzaei, Navid; Changizi, Navid; Asadpoure, Alireza; Su, Sumeyye; Sofia, Dilruba; Tatarova, Zuzana; Zervantonakis, Ioannis K.; Chang, Young Hwan; Shahriyari, Leili
- Abstract
The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies. Author summary: One of the outstanding challenges of the mathematical modeling of cancer progression is the existence of many unknown parameters. In this work, we develop a data-driven mathematical model of breast cancer progression by deriving a system of ordinary differential equations for the interaction networks of key cell types and molecules in breast tumors. To overcome the limitations of unknown parameters, we utilize a time course data of a PyMT mice model of breast cancer and estimate parameters using an optimization method. Although the predicted dynamics of cancer and necrotic cells using the obtained values of parameters are in good agreement with the data, the predicted values for a few other variables do not match the data. This might indicate that there are some other key interactions that have not been modeled, and/or there is a noise in the data. The sensitivity and bifurcation analyses show that the most important parameters in controlling the cancer cells population are the proliferation and death rates of cancer cells and adipocytes. These results are in agreement with some biological and clinical studies of breast cancer, which have reported a link between adipocytes and breast cancer progression.
- Subjects
BREAST cancer; ANIMAL disease models; MATHEMATICAL models; LABORATORY mice; GENE expression profiling; CANCER prognosis; BREAST
- Publication
PLoS Computational Biology, 2022, Vol 18, Issue 3, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1009953