Targeting synthetic lethality between non-homologous end joining and radiation in very-high-risk medulloblastoma
Bibliographic record
Abstract
Specific and biologically informed treatments for medulloblastoma, especially for the highly lethal TP53-mutant SHH subgroup, remain elusive, where radiotherapy is the primary treatment modality. Leveraging genome-wide CRISPR-Cas9 dropout screening in combination with lethal doses of radiotherapy, we identify loss of p53 as the main driver of radiation resistance in SHH medulloblastoma. A negative-selection CRISPR-Cas9 screen across multiple models of Trp53-deficient SHH medulloblastoma reveals a strong synthetic lethal interaction between components of the non-homologous end-joining pathway and radiation, particularly DNA-dependent protein kinase (DNA-PK) and its binding partners. Both genetic and pharmacological perturbation of DNA-PK enhance radiosensitivity in TP53-deficient SHH medulloblastoma, leading to cell death. In vivo treatment of both somatic and germline TP53-mutant SHH medulloblastoma models with peposertib, a small-molecule inhibitor of DNA-PK, significantly improves survival when combined with radiotherapy, strongly supporting further clinical investigation.
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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) | 0.002 | 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".