胶质母细胞瘤是成人中最常见和最致命的脑肿瘤，在目前的护理标准下，中位生存期为 15 个月。免疫检查点抑制剂和溶瘤病毒等免疫疗法已被广泛研究以改善这一终点。然而，迄今为止大多数都失败了。为了提高免疫疗法治疗胶质母细胞瘤的疗效，可以利用新的单细胞成像方式（例如成像质谱流式细胞仪）并将其与计算模型集成。这使得我们能够更好地了解肿瘤微环境及其在这种难以治疗的肿瘤治疗成功或失败中的作用。在这里，我们实现了一个基于代理的模型，可以对联合化疗、溶瘤病毒和针对胶质母细胞瘤的免疫检查点抑制剂进行空间预测。我们用患者成像质谱流式细胞仪数据初始化我们的模型，以预测患者特异性反应，并发现溶瘤病毒驱动由瘤内细胞密度决定的联合治疗反应。我们发现肿瘤细胞密度较高的肿瘤对治疗的反应更好。当固定癌细胞数量时，治疗效果被证明是 CD4  T 细胞的函数，并且在较小程度上是巨噬细胞计数的函数。至关重要的是，我们的模拟表明，必须注意空间数据和基于代理的模型的集成，以有效捕获肿瘤内动态。总之，这项研究强调使用预测空间模型来更好地了解癌症免疫疗法的治疗动态，同时强调模型设计和实施过程中需要考虑的关键因素。© 2024。作者。

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.© 2024. The Author(s).