Speaker
Description
In this talk, I will introduce four systems of ODEs to identify the most plausible model and the key reactions governing the dynamics of the tumor microenvironment (TME) in estrogen receptor–positive (ER+) breast cancer. All models quantitatively fit the experimental data for radiation therapy (RT) and endocrine therapy fulvestrant (Fulv) in TC11 ER+ cells, as well as RT and immune checkpoint inhibitor (anti-PD-1/anti-PD-L1) treatments in 4T1-HA and MCF-7 breast tumor cells.
The Akaike information criterion analysis suggests that interactions among tumor cells, CD8$^+$ T cells, and estrogen are the primary drivers of the dynamics in the TC11 and MCF-7 cell lines, whereas interactions involving dendritic cells (DCs) and M2 macrophages are required to capture the dynamics in the 4T1-HA cell line. Global sensitivity analysis and identifiability analysis are then conducted to evaluate the uniqueness of parameter values in model calibration and to suggest additional data points that may improve model reliability. Furthermore, numerical simulations and model comparisons indicate the optimal treatment protocols for different cell lines and provide guidance for additional experimental designs to identify the most plausible model.
