Functional profiling of murine glioma models highlights targetable immune evasion phenotypes

Cancer intrinsic immune evasion mechanisms and pleiotropy represent a barrier to effective translation of cancer immunotherapy. This is acutely apparent for certain highly fatal cancers such as high-grade gliomas and glioblastomas. In this study, we use functional genetic screens, single-cell transcriptomics and machine-learning approaches to deeply characterize murine syngeneic glioma models in vitro and in vivo , and compare-and-contrast their value as preclinical models for human glioblastoma (GBM). Systematic genome-wide co-culture killing screens with cytotoxic T cells, natural killer cells or macrophages established NFkB signaling, autophagy/endosome machinery, and chromatin remodeling as pan-immune cancer intrinsic evasion mechanisms. Additional fitness screens identified dependencies in murine gliomas that partially recapitulated those seen in human GBM (e.g., UFMylation). Different models associated with contrasting immune infiltrates including macrophages and microglia, and both models recapitulate hallmark immune gene programs seen in human GBM, including hypoxia, interferon and TNF signaling. Moreover, in vivo orthotopic tumor engraftment is associated with phenotypic shifts and changes in proliferative capacity, with models 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 , 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 evasion genes that revealed two 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 study offers relevant insights and serves to bridge the knowledge gap between murine glioma models and human GBM.