Disruption of ataxia telangiectasia–mutated kinase enhances radiation therapy efficacy in spatially directed diffuse midline glioma models
Bibliographic record
Abstract
Diffuse midline gliomas (DMGs) are lethal brain tumors characterized by p53-inactivating mutations and oncohistone H3.3K27M mutations that rewire the cellular response to genotoxic stress. We used RCAS/tv-a retroviruses and Cre recombinase to inactivate p53 and induce native H3.3K27M mutations in a lineage- and spatially directed manner. We generated primary mouse tumors that recapitulated human DMG. Disrupting ataxia-telangiectasia mutated (ATM) kinase enhanced the efficacy of radiation therapy (RT) in murine and patient-derived DMG models and increased survival. Microscopy-based in situ sequencing was used to spatially resolve transcriptional profiles in more than 750,000 single cells with or without ATM disruption and RT, revealing altered immune-neoplastic and endothelial cell interactions after treatment. An allelic series of primary murine DMG models with different p53 mutations confirmed that transactivation-independent p53 activity was a key mediator of radiosensitivity after ATM disruption. We generated primary DMG mouse models and performed deep profiling that revealed mechanisms of response to ATM disruption and RT that can be utilized as a therapeutic strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".