Gaddy Thomas D, Wu Qianhui, Arnheim Alyssa D, Finley Stacey D
Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California, United States of America.
Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
PLoS Comput Biol. 2017 Dec 21;13(12):e1005874. doi: 10.1371/journal.pcbi.1005874. eCollection 2017 Dec.
Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the "angiogenic signal" produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor's response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.
肿瘤利用血管生成（即从已有的脉管系统形成新的血管）来获取持续生长和增殖所需的营养物质。因此，靶向调控血管生成的因子，包括强效促进因子血管内皮生长因子（VEGF），是抑制肿瘤生长的一种有吸引力的策略。计算建模可用于识别影响抗血管生成策略反应的肿瘤特异性特性。在此，我们基于之前关于荷瘤小鼠中VEGF运输和动力学的系统生物学模型，纳入一个肿瘤区室，其体积取决于VEGF与其在肿瘤内皮细胞上的受体结合时产生的“血管生成信号”。我们使用已发表的异种移植肿瘤体积的体内测量数据对模型进行训练和验证，生成了一个能准确预测肿瘤对抗血管生成治疗反应的模型。我们应用该模型来研究肿瘤生长动力学如何影响针对VEGF的抗血管生成治疗的反应。基于多变量回归分析，我们发现某些表征肿瘤生长的内在动力学参数能够成功预测对抗VEGF治疗的反应，即肿瘤体积的减小。最后，我们使用训练好的模型来预测表达不同水平VEGF受体的肿瘤对抗VEGF治疗的反应。该模型预测某些肿瘤对治疗比其他肿瘤更敏感，并且治疗反应对VEGF受体表达呈非线性依赖。总体而言，该模型是预测肿瘤将如何对抗VEGF治疗反应的有用工具，并且它补充了临床前体内小鼠研究。
