Cancer-intrinsic immune evasion mechanisms and pleiotropy are a barrier to cancer immunotherapy. This is apparent in certain highly fatal cancers, including high-grade gliomas and glioblastomas (GBM). In this study, we evaluated two murine syngeneic glioma models (GL261 and CT2A) as preclinical models for human GBM using functional genetic screens, single-cell transcriptomics and machine learning approaches. Through CRISPR genome-wide co-culture killing screens with various immune cells (cytotoxic T cells, natural killer cells, and macrophages), we identified three key cancer-intrinsic evasion mechanisms: NFκB signaling, autophagy/endosome machinery, and chromatin remodeling. Additional fitness screens identified dependencies in murine gliomas that partially recapitulated those seen in human GBM (e.g., UFMylation). Our single-cell analyses showed that different glioma models exhibited distinct immune infiltration patterns and recapitulated key immune gene programs observed in human GBM, including hypoxia, interferon, and TNF signaling. Moreover, in vivo orthotopic tumor engraftment was associated with phenotypic shifts and changes in proliferative capacity, with murine tumors recapitulating the intratumoral heterogeneity observed in human GBM, exhibiting propensities for developmental- and mesenchymal-like phenotypes. Notably, we observed common transcription factors and cofactors shared with human GBM, including developmental (Nfia and Tcf4), mesenchymal (Prrx1 and Wwtr1), as well as cycling-associated genes (Bub3, Cenpa, Bard1, Brca1, and Mis18bp1). Perturbation of these genes led to reciprocal phenotypic shifts suggesting intrinsic feedback mechanisms that balance in vivo cellular states. Finally, we used a machine-learning approach to identify two distinct immune evasion gene programs, one of which represents a clinically-relevant phenotype and delineates a subpopulation of stem-like glioma cells that predict response to immune checkpoint inhibition in human patients. This comprehensive characterization helps bridge the gap between murine glioma models and human GBM, providing valuable insights for future therapeutic development.

癌症内在免疫逃避机制和多效性是癌症免疫治疗的障碍。这在某些高度致命的癌症中很明显，包括高级别胶质瘤和胶质母细胞瘤 （GBM）。在这项研究中，我们使用功能遗传筛选、单细胞转录组学和机器学习方法评估了两种小鼠同基因神经胶质瘤模型 （GL261 和 CT2A） 作为人类 GBM 的临床前模型。通过与各种免疫细胞 （细胞毒性 T 细胞、自然杀伤细胞和巨噬细胞） 的 CRISPR 全基因组共培养杀伤筛选，我们确定了三种关键的癌症内在逃避机制：NFκB 信号传导、自噬/内体机制和染色质重塑。额外的健康筛查确定了小鼠神经胶质瘤中的依赖关系，这些依赖关系部分概括了在人类 GBM 中观察到的依赖关系（例如，UFMylation）。我们的单细胞分析表明，不同的神经胶质瘤模型表现出不同的免疫浸润模式，并概括了在人类 GBM 中观察到的关键免疫基因程序，包括缺氧、干扰素和 TNF 信号传导。此外，体内原位肿瘤植入与表型变化和增殖能力的变化有关，小鼠肿瘤概括了在人类 GBM 中观察到的瘤内异质性，表现出发育和间充质样表型的倾向。值得注意的是，我们观察到与人类 GBM 共享的常见转录因子和辅因子，包括发育 （Nfia 和 Tcf4）、间充质 （Prrx1 和 Wwtr1） 以及循环相关基因 （Bub3 、 Cenpa 、 Bard1 、 Brca1 和 Mis18bp1）。 这些基因的扰动导致相互表型变化，表明平衡体内细胞状态的内在反馈机制。最后，我们使用机器学习方法鉴定了两个不同的免疫逃避基因程序，其中一个代表临床相关的表型，并描绘了预测人类患者对免疫检查点抑制反应的干细胞样神经胶质瘤细胞亚群。这种全面的表征有助于弥合小鼠神经胶质瘤模型和人类 GBM 之间的差距，为未来的治疗开发提供有价值的见解。