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Updated: Jun 6, 2025

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
Functional profiling of murine glioma models highlights targetable immune evasion phenotypes
Nicholas Mikolajewicz1, Nazanin Tatari2,3,4, Jiarun Wei1,5
1Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada.
Abstract:
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.
Insights
This study identifies key cancer immune evasion mechanisms in glioma models, revealing genetic programs that predict immunotherapy response in human glioblastoma patients.
Area of Science:
- Immunology
- Oncology
- Genetics
Background:
- Cancer-intrinsic immune evasion hinders immunotherapy effectiveness, particularly in aggressive cancers like glioblastoma (GBM).
- Preclinical models are crucial for understanding GBM and developing new treatments.
Purpose of the Study:
- To evaluate murine glioma models (GL261, CT2A) as preclinical tools for human GBM.
- To identify cancer-intrinsic immune evasion mechanisms and genetic dependencies.
- To correlate findings with human GBM and predict immunotherapy response.
Main Methods:
- Utilized CRISPR genome-wide co-culture killing screens with immune cells.
- Performed functional genetic screens and single-cell transcriptomics.
- Applied machine learning approaches to analyze data and identify gene programs.
Main Results:
- Identified NFκB signaling, autophagy/endosome machinery, and chromatin remodeling as key evasion mechanisms.
- Murine models recapitulated human GBM dependencies (e.g., UFMylation) and immune gene programs (hypoxia, interferon, TNF signaling).
- Discovered shared transcription factors and identified a clinically relevant immune evasion gene program associated with stem-like glioma cells predicting immunotherapy response.
Conclusions:
- Murine glioma models provide valuable insights into human GBM biology and immune evasion.
- Characterization of immune evasion mechanisms and genetic programs can guide therapeutic development.
- Identified a specific glioma cell subpopulation that predicts response to immune checkpoint inhibition.
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