DNAR-08. BASE EXCISION REPAIR AS A THERAPEUTIC TARGET FOR DIFFUSE HEMISPHERIC GLIOMA, H3.3G34-MUTANT
Bibliographic record
Abstract
Abstract Diffuse hemispheric gliomas with H3.3G34R/V mutations are primarily found in the cerebral hemisphere in adolescents and young adults, with a median age of 16-19 years. H3.3G34R frequently co-occurs with mutations in ATRX and TP53 as well as PDGFRα. We performed mutational signature analysis on a cohort of 28 cases of Diffuse hemispheric glioma, H3G34-mutant (7 WGS and 21 WES). Mutational signatures of defects in mismatch repair (MMR), homologous recombination (HR), and nucleotide excision repair (NER) pathways were found in 86% cases whereas only two (7%) had signatures of impaired base excision repair (BER). Analysis of H3.3G34R interactome data showed loss of association with MMR, HR, and NER pathways, with gains in association with proteins in the BER pathway compared to wildtype Histone H3. We thus hypothesise the Diffuse hemispheric glioma, H3G34-mutant may be dependent on BER over other DNA damage repair pathways. To test if BER could be a therapeutic vulnerability, we knocked down integral members of the BER pathway (UNG, APEX1, MPG, and XRCC1) via shRNA in 4 patient-derived H3.3G34R-mutant cell lines (two primary and two matched recurrent), and found that knockdown of XRCC1 and MPG, led to 99% (p< 0.0001) and 96% (p< 0.0001) cell death, respectively, after 8 days, compared to scrambled control. We next tested Triptolide, a BER inhibitor, and it led to cell death in vitro with IC50s of 1.1 to 1.7 nM. Taken together, these results indicate that inhibition of the BER pathway, specifically of the scaffolding-protein XRCC1, could be used as a novel therapeutic approach for diffuse hemispheric glioma, H3.3G34-mutant.
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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) | 0.003 | 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".