Targeting synthetic lethality between non-homologous end joining and radiation in very high-risk SHH medulloblastoma
Bibliographic record
Abstract
Summary Specific and biologically informed treatments for medulloblastoma, especially the highly lethal TP53 mutant SHH subgroup, remain elusive, where radiotherapy is the primary treatment option. Applying genome-wide CRISPR-Cas9 screening in combination with lethal doses of radiotherapy, we identified the main driver of radiation resistance in SHH medulloblastoma is loss of p53. A negative selection CRISPR-Cas9 screen across multiple models of Trp53 -deficient SHH medulloblastoma revealed a strong synthetic lethal interaction between components of the non-homologous end-joining pathway and radiation, particularly DNA protein kinase (DNA-PK) and its binding partners. Both genetic and pharmacological perturbation of DNA-PK enhanced radiosensitivity in TP53 -deficient SHH medulloblastoma, leading to cell death. In vivo treatment of somatic and germline TP53 -mutant SHH medulloblastoma models with peposertib, a small-molecule inhibitor of DNA-PK, significantly improved survival when combined with radiotherapy, strongly supporting further clinical investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".