Functional profiling of murine glioma models highlights targetable immune evasion phenotypes
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".