Data-Driven Mathematical Model of Osteosarcoma.
Data-Driven Mathematical Model of Osteosarcoma.
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DOI：
10.3390/cancers13102367
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发表时间：
2021-05-14
期刊：
影响因子：
5.2
通讯作者：
Shahriyari L
中科院分区：
文献类型：
--
作者：
Le T;Su S;Kirshtein A;Shahriyari L
骨肉瘤是最常见的原发性骨肿瘤，预后差。因此，了解骨肉瘤发生发展的机制，对克服骨肉瘤的治疗耐药性具有重要意义。已经开发了几种数学模型来研究许多癌症类型的发生和发展。然而，据我们所知，目前还没有骨肉瘤进展的数学模型。在这项工作中，我们开发了一个数据驱动的数学模型来分析免疫细胞相互作用对具有不同免疫模式的骨肉瘤肿瘤生长的影响。我们的模型为研究各种治疗对原发性肿瘤中关键参与者（包括免疫细胞和细胞因子）以及最终整个肿瘤的动态影响提供了基础。由于免疫系统在肿瘤进展中起着重要作用，在本文中，我们开发了一个数据驱动的数学模型来研究免疫细胞与骨肉瘤微环境之间的相互作用。骨肉瘤肿瘤根据其基因表达谱估计的免疫细胞的相对丰度分为三个簇。然后，我们分析了每个集群中的肿瘤进展和免疫系统对癌症生长的影响。具有大约相同数量的初始和M2巨噬细胞的簇3具有最慢的肿瘤生长，并且具有最高初始巨噬细胞群体的簇2在稳态下具有最高的癌症群体。我们还发现，当抗肿瘤免疫细胞和细胞因子（包括树突状细胞、辅助性T细胞、细胞毒性细胞和IFN-γ）从增加转为减少时，癌症的增长最快，而调节性T细胞的动态从减少转为增加。重要的是，对癌症和总细胞数量影响最大的免疫参数是所有簇的巨噬细胞和调节性T细胞的活化和衰减率。这项工作提出了第一个骨肉瘤进展模型，以后可以扩展到调查各种骨肉瘤治疗的有效性。
Osteosarcoma 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.
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影响因子：
8.8
作者：
Aras S;Zaidi MR
通讯作者：
Zaidi MR
影响因子：
--
作者：
De Pillis, LG;Radunskaya, A
通讯作者：
Radunskaya, A
DOI：
10.1002/wsbm.1461
发表时间：
2019-07-17
影响因子：
7.9
作者：
Chamseddine, Ibrahim M.;Rejniak, Katarzyna A.
通讯作者：
Rejniak, Katarzyna A.
影响因子：
5.8
作者：
CRUM, ED
通讯作者：
CRUM, ED
DOI：
10.1080/17486700802216301
发表时间：
2009-01-01
影响因子：
--
作者：
de Pillis, Lisette;Fister, K. Renee;Preskill, Benjamin
通讯作者：
Preskill, Benjamin