An agent-based model of avascular tumor growth: Immune response tendency to prevent cancer development

Abstract

Mathematical and computational models are of great help to study and predict phenomena associated with cancer growth and development. These models may lead to introduce new therapies or improve current treatments by discovering facts that may not be easily discovered in clinical experiments. Here, a new two-dimensional (2D) stochastic agent-based model is presented for the spatiotemporal study of avascular tumor growth based on the effect of the immune system. The simple decision-making rules of updating the states of each agent depend not only on its intrinsic properties but also on its environment. Tumor cells can interact with both normal and immune cells in their Moore neighborhood. The effect of hypoxia has been checked off by considering non-mutant proliferative tumor cells beside mutant ones. The recruitment of immune cells after facing a mass of tumor is also considered. Results of the simulations are presented before and after the appearance of immune cells in the studied tissue. The growth fraction and necrotic fraction are used as output parameters along with a 2D graphical growth presentation. Finally, the effect of input parameters on the output parameters generated by the model is discussed. The model is then validated by an in vivo study published in medical articles. The results show a multi-spherical tumor growth before the immune system strongly involved in competition with tumor cells. Besides, considering the immune system in the model shows more compatibility with biological facts. The effect of the microenvironment on the proliferation of cancer and immune cells is also studied.

Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer

“…These models capture system scale behavior in cancer patients and are capable of population level predictions of disease trajectories in response to intervention. On tissuecellular scale, ABMs have been employed and used for spatially explicit simulations to investigate emergent behavior arising from interactions between cancer and immune cells, such as spatial and spatio-temporal variations in tumor morphology and immuno-architecture (Kim et al, 2009;Shi et al, 2014;Wells et al, 2015;Gong et al, 2017;Norton et al, 2017Norton et al, , 2019Pourhasanzade et al, 2017;Hoehme et al, 2018;Ji et al, 2019). When combining QSP models with ABM, cancer models can be further enhanced by taking advantage of both model types: while the QSP module captures whole-body temporal dynamics including lymph nodes, blood, peripheral compartment, and tumor, ABM simulation accounts for crucial aspects of highgranularity features such as cancer cell clonal evolution and TME heterogeneity.…”

In Silico Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer

“…Furthermore, they are increasingly used to optimize therapies, for example radiation therapy of solid tumors (18). Also, some models of immune-cell interactions with (19)(20)(21)(22)(23)(24) or without tumor cells (25) have been proposed. Although these studies gave important insight into parts of the tumor-immune interaction, they did not accurately reproduce the diverse spatial patterns in human tumors and did not investigate therapeutic strategies.…”

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

“…A 2D agent-based model was used to study the interactions between an avascular tumor and immune cells (NK cells and cytotoxic T-cells) [137]. They examined the effects of cancer cell proliferation on overall tumor growth under two conditions: the first, where cancer cells do not consider the microenvironment when deciding when to proliferate, and the second, where they proliferate based on the number of healthy cells surrounding them.…”