耐药性是影响各种癌症类型（包括胶质母细胞瘤（GBM））靶向治疗疗效的一个突出障碍。然而，理解耐药性背后复杂的细胞内和细胞外机制仍然难以捉摸。实证研究表明，基因畸变（例如基因突变）以及微环境适应（尤其是血管生成）是肿瘤进展和耐药性的关键驱动因素。尽管如此，数学模型经常将这些因素分开划分。在这项研究中，我们提出了一种基于多尺度药物的 GBM 模型，包括细胞动力学、复杂的信号通路、基因突变、血管生成和治疗干预。这种综合框架有助于探索基因突变与血管微环境之间的相互作用，以塑造酪氨酸激酶抑制剂治疗期间肿瘤的动态进化。我们的模拟揭示了影响肿瘤细胞迁移和增殖的突变加速了表型异质性的出现，从而加剧了治疗和未治疗条件下的肿瘤侵袭。此外，肿瘤附近的血管生成培育了促肿瘤环境，通过提高肿瘤细胞的存活率来增强突变诱导的耐药性。总的来说，我们的研究结果强调了内在基因突变和外在微环境适应在控制肿瘤生长和耐药性方面的双重作用。最后，我们通过整合单细胞 RNA-seq、空间转录组学、批量 RNA-seq 和 GBM 患者的临床数据，证实了关于基因突变和血管生成对靶向治疗反应性影响的模型预测。多维方法增强了我们对神经胶质瘤耐药性控制复杂性的理解，并提供了对潜在治疗策略的见解。© 2023 作者。

Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.© 2023 The Authors.