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Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways

机译:多尺度造型揭示血管生成诱导的脑肿瘤耐药性,并预测靶向EGFR和VEGFR途径的协同药物组合

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摘要

Abstract Background Experimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels. Results In this study, we developed a single cell-based multiscale spatiotemporal model to simulate vascular tumor growth and the drug response based on the vascular endothelial growth factor receptor (VEGFR) signaling pathway, the EGFR signaling pathway and the cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. By incorporating the EGFRI treatment effect, the model showed an interesting phenomenon in which the survival rate of tumor cells decreased in the early stage but rebounded in a later stage, revealing the emergence of drug resistance. Moreover, we revealed the critical role of angiogenesis in acquired drug resistance, since inhibiting blood vessel growth using a VEGFR inhibitor prevented the recovery of the survival rate of tumor cells in the later stage. We further investigated the optimal timing of combining VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both the EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. In addition, the combination of EGFR and VEGFR genes showed clinical relevance in glioma patients. Conclusions The developed multiscale model revealed angiogenesis-induced drug resistance mechanisms of brain tumors to EGFRI treatment and predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study explored the mechanistic and functional mechanisms of the angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.
机译:抽象背景实验研究已经证明,无论是细胞外脉管系统或微环境和细胞内分子网络(例如,表皮生长因子受体(EGFR)信号传导途径)是用于脑肿瘤生长是重要的。此外,一些药物已经发展到抑制EGFR信号通路。然而,如何影响血管生成肿瘤细胞对药物治疗的反应很少被机械地研究。因此,多尺度模型必须调查包含多个层次之间的互动和反馈,复杂的生物系统。结果在本研究中,我们开发了一种基于单细胞的多尺度时空模型模拟血管肿瘤的生长和基于所述血管内皮生长因子受体(VEGFR)信号通路,该EGFR信号传导途径和细胞周期的药物反应,以及几个决定细胞命运微环境因素切换在时间和空间上下文。通过将治疗EGFRI效果,模型显示一种有趣的现象,其中肿瘤细胞的存活率在早期降低,但反弹在稍后的阶段,露出药物耐药性的出现。此外,我们揭示了获得性耐药的血管生成中的关键作用,因为使用防止肿瘤细胞在稍后阶段的存活率的恢复VEGFR抑制剂抑制血管生长。我们进一步研究相结合的最佳定时VEGFR抑制与EGFR抑制和预测,药物组合靶向EGFR途径和VEGFR途径都具有协同作用。实验数据证实药物协同作用的预测,证实了我们的模型的有效性。此外,EGFR和VEGFR基因组合显示在胶质瘤患者的临床意义。结论发达多尺度模型显示脑肿瘤至治疗EGFRI血管生成诱导的药物抗性机制和预测的协同药物组合靶向EGFR和VEGFR途径具有最佳组合的定时。本研究探讨血管生成肿瘤的生长和药物抗性底层,这增强了我们的药物抗性的新机制的理解以及设计更有效的癌症疗法提供影响的机械和功能的机制。

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