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.