Bridging Scales: a Hybrid Model to Simulate Vascular Tumor Growth and Treatment Response
Bridging Scales: a Hybrid Model to Simulate Vascular Tumor Growth and Treatment Response
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DOI：
10.1016/j.cma.2023.116566
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Tobias Duswald;E. Lima;J. Oden;B. Wohlmuth
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文献类型：
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作者：
Tobias Duswald;E. Lima;J. Oden;B. Wohlmuth
癌症是一种由随机DNA突变和许多复杂现象的相互作用驱动的疾病。为了提高理解并最终找到更有效的治疗方法，研究人员利用计算机模拟了肿瘤在硅片上的生长。这里的挑战是解释影响疾病进展和治疗方案的许多现象。本工作介绍了一个计算模型来模拟血管肿瘤的生长和对药物治疗的三维反应。它包括两个基于agent的肿瘤细胞和脉管系统模型。此外，偏微分方程控制着营养物、血管内皮生长因子和两种抗癌药物的扩散动力学。该模型明确关注过表达HER2受体的乳腺癌细胞，并结合标准化疗（阿霉素）和具有抗血管生成特性的单克隆抗体（曲妥珠单抗）进行治疗。然而，该模型的大部分可以推广到其他场景。我们通过将模拟结果与先前发表的临床前数据进行比较，表明该模型定性地捕获了联合治疗的效果。此外，我们通过使用总共9240万个代理来模拟一个体积为400 mm3的血管肿瘤，从而展示了模型的可扩展性和相关的c++代码。
Cancer is a disease driven by random DNA mutations and the interaction of many complex phenomena. To improve the understanding and ultimately find more effective treatments, researchers leverage computer simulations mimicking the tumor growthin silico. The challenge here is to account for the many phenomena influencing the disease progression and treatment protocols. This work introduces a computational model to simulate vascular tumor growth and the response to drug treatments in 3D. It consists of two agent-based models for the tumor cells and the vasculature. Moreover, partial differential equations govern the diffusive dynamics of the nutrients, the vascular endothelial growth factor, and two cancer drugs. The model focuses explicitly on breast cancer cells over-expressing HER2 receptors and a treatment combining standard chemotherapy (Doxorubicin) and monoclonal antibodies with anti-angiogenic properties (Trastuzumab). However, large parts of the model generalize to other scenarios. We show that the model qualitatively captures the effects of the combination therapy by comparing our simulation results with previously published pre-clinical data. Furthermore, we demonstrate the scalability of the model and the associated C++ code by simulating a vascular tumor occupying a volume of 400 mm3using a total of 92.4 million agents.