<i>Ataxia-telangiectasia mutated</i> ( <i>Atm</i> ) disruption sensitizes spatially-directed H3.3K27M/TP53 diffuse midline gliomas to radiation therapy
Bibliographic record
Abstract
Summary 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, which presents therapeutic opportunities. We used RCAS/tv-a retroviruses and Cre recombinase to inactivate p53 and induce K27M in the native H3f3a allele in a lineage- and spatially-directed manner, yielding primary mouse DMGs. Genetic or pharmacologic disruption of the DNA damage response kinase Ataxia-telangiectasia mutated (ATM) enhanced the efficacy of focal brain irradiation, extending mouse survival. This finding suggests that targeting ATM will enhance the efficacy of radiation therapy for p53-mutant DMG but not p53-wildtype DMG. We used spatial in situ transcriptomics and an allelic series of primary murine DMG models with different p53 mutations to identify transactivation-independent p53 activity as a key mediator of such radiosensitivity. These studies deeply profile a genetically faithful and versatile model of a lethal brain tumor to identify resistance mechanisms for a therapeutic strategy currently in clinical trials.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".