Abstract
Simple SummaryOsteosarcoma is the most common primary bone tumor and has a poor prognosis. Therefore, it is important to understand the mechanism of the development of osteosarcoma to overcome therapy resistance. Several mathematical models have been developed to study the initiation and progression of many cancer types. However, there are currently no mathematical models for the progression of osteosarcoma, to the best of our knowledge. In this work, we develop a data-driven mathematical model to analyze the impact of the immune cell interactions on the growth of osteosarcoma tumors that have distinct immune patterns. Our model provides a foundation for investigating the effect of various treatments on the dynamics of key players in the primary tumor, including immune cells and cytokines, and ultimately the whole tumor.As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.
Highlights
Osteosarcoma is the most common form of bone malignancy, which is a rare type of cancer with about 1000 new cases diagnosed each year in the United States [1]
We develop a data-driven mathematical model of osteosarcoma based on the network given in Figure 1 and use a system of ordinary differential equations (ODEs) to represent the interactions
IL-12 and IL-23 are both secreted by M1 macrophages and dendritic cells [29,32,65,66,67,78]; we model the concentration of these cytokines as:
Summary
Osteosarcoma is the most common form of bone malignancy, which is a rare type of cancer with about 1000 new cases diagnosed each year in the United States [1]. Immunotherapy and targeted therapy have recently demonstrated significant results in the treatment of certain cancer types [8,9]. These are popular alternative treatments for osteosarcoma, they are still ineffective for many patients [10]. Osteosarcoma tumors have been reported to be resistant to the radiotherapy [11,12]. For this reason, a novel technique, hyperthermia, has been developed to increase the effectiveness of radiation [13,14,15,16]. It is important to investigate the osteosarcoma tumor microenvironment to understand the variability in response to these treatments to overcome therapy resistance [19]
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