Paper Info
Title
Data-driven mathematical modeling and quantitative analysis of cell dynamics in the tumor microenvironment
Abstract
The tumor microenvironment (TME) exerts key effects on tumor development, progression and treatment. Therefore, a quantitative understanding of various cellular and molecular interactions in the TME is very important. In this study, we combined a dynamic model and data analysis to quantitatively explore how microenvironmental factors influence tumor growth and three phases of cancer immunoediting. First, we presented a model system of partial differential equations (PDEs) using four main types of cells within the microenvironment of solid tumors and used two sets of published experimental data to validate the model. Accordingly, the partial rank correlation coefficient (PRCC) was calculated to identify the sensitive parameters related to significant biological processes. Furthermore, numerical simulations indicated that the power of tumor proliferation exerts a substantial effect on the state of malignancy, but tumor control is achieved by adjusting sensitive microenvironmental factors, such as immune intensity and the proliferation of cancer-associated fibroblasts (CAFs). Moreover, we used two indicators to quantify three states, i.e., elimination, equilibrium and escape from cancer immunoediting. The quantitative analysis of the TME revealed that immune cells and CAFs dynamically guide the transition of the three states of immunoediting, namely, how these related factors affect the capacity of the immune system to eliminate developing tumor cells, hold them in an equilibrium state, or facilitate their expanded growth. These quantitative results provide new insights into how various microenvironmental changes mediate both natural and therapeutically induced cancer immunoediting responses.
Year
DOI
Venue
2022
10.1016/j.camwa.2022.03.012
Computers & Mathematics with Applications
Keywords
DocType
Volume
Tumor microenvironment,Cancer immunoediting,The model of partial differential equations,Partial rank correlation coefficient (PRCC),Dynamic transition
Journal
113
ISSN
Citations 
PageRank 
0898-1221
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sicheng Li100.34
Shun Wang200.34
Xiufen Zou327225.44