Modeling the chemotherapy-induced selection of drug-resistant traits during tumor growth
Modeling the chemotherapy-induced selection of drug-resistant traits during tumor growth
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DOI：
10.1016/j.jtbi.2017.10.005
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发表时间：
2018-01-07
影响因子：
2
通讯作者：
Levy, D.
中科院分区：
文献类型：
--
作者：
Cho, H.;Levy, D.
耐药性的出现是化疗面临的一大挑战。在这篇文章中，我们建立了一个数学模型来研究实体瘤的耐药性动力学。我们的模型遵循肿瘤的动态，假设癌细胞群依赖于一个表型变量，该变量对应于对细胞毒药物的耐药性水平。肿瘤密度的方程被写成带有依赖于局部细胞密度的压力项的反应-扩散方程。该模型结合了营养物质和两种不同类型药物的动态：一种是直接影响癌细胞死亡率的细胞毒性药物，另一种是降低癌细胞增殖率的细胞抑制药物。该模型成功地将表型结构化耐药方法与空间非对称肿瘤生长模型相结合。通过分析和模拟，我们研究了空间异质性和表型异质性对化疗下肿瘤生长的影响。我们证明，由于环境的选择动态，异质癌细胞可能会出现。我们的模型预测，在特定条件下，肿瘤内不同位置会出现多重耐药特征。我们表明，较高剂量的细胞毒药物可能会延缓复发，但当这种情况发生时，就会出现更具耐药性的特征。此外，我们根据耐药特性、营养水平和药物浓度来估计肿瘤边界的扩张率和复发时间。最后，我们提出了一个有效的药物时间表，旨在最小化最具抗性的性状的生长速度。通过结合细胞毒性药物和细胞抑制药物，我们证明了耐药细胞可以被消除。(C)2017爱思唯尔有限公司。保留所有权利。
The emergence of drug-resistance is a major challenge in chemotherapy. In this paper we develop a mathematical model to study the dynamics of drug-resistance in solid tumors. Our model follows the dynamics of the tumor, assuming that the cancer cell population depends on a phenotype variable that corresponds to the resistance level to a cytotoxic drug. The equation for the tumor density is written as a reaction-diffusion equation with a pressure term that depends on the local cell density. The model incorporates the dynamics of nutrients and two different types of drugs: a cytotoxic drug, which directly impacts the death rate of the cancer cells, and a cytostatic drug that reduces the proliferation rate. This model successfully integrates the phenotype structured drug-resistance approach with an asymmetric tumor growth model in space. Through analysis and simulations we study the impact of spatial and phenotypic heterogeneity on the tumor growth under chemotherapy. We demonstrate that heterogeneous cancer cells may emerge due to the selection dynamics of the environment. Our model predicts that under certain conditions, multiple resistant traits emerge at different locations within the tumor. We show that a higher dosage of the cytotoxic drug may delay a relapse, yet, when this happens, a more resistant trait emerges. Moreover, we estimate the expansion rate of the tumor boundary as well as the time of relapse, in terms of the resistance trait, the level of the nutrient, and the drug concentration. Finally, we propose an efficient drug schedule aiming at minimizing the growth rate of the most resistant trait. By combining the cytotoxic and cytostatic drugs, we demonstrate that the resistant cells can be eliminated. (C) 2017 Elsevier Ltd. All rights reserved.