DIPG-37. Exploring the role of the epigenetic factor H2A.Z acetylation in DIPG
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine glioma (DIPG) is the most aggressive brain tumor found in children with a peak incidence of 5-7 years of age, with median survival after diagnosis less than one year. In more than 60% of DIPG cases, a recurring somatic mutation in the H3F3A gene, that causes a lysine 27 to methionine substitution is seen in the histone variant H3.3 (H3.3K27M). Wildtype histone variants H2A.Z and H3.3, are frequently found in the same nucleosome and cooperate to regulate transcription. We and others have identified that acetylation of H2A.Z (H2A.Zac) can be an oncogenic driver in adult cancer types through mislocalization of H2A.Z at promoters and enhancers of cancer-associated genes loci. However, the role of H2A.Z in H3.3K27M+ DIPG has never been studied. Thus, we hypothesized that H2A.Zac cooperates with H3.3K27M to drive DIPG oncogenesis. Here we aim to unravel the molecular relationship between H2A.Zac and H3.3K27M in DIPG and their link to oncogenesis. First, using a histone mass spectrometry dataset, we found that the level of H2A.Zac is significantly higher in samples with H3K27M compared to H3.3WT. In addition, the comparison between H2A.Zac with H3.3K27M ChIP-seq data in several DIPG cell lines showed that around 30% of H3.3K27M peaks overlapped with H2A.Zac marked regions, a similar proportion found between H3.3 and H2A.Z under physiological conditions. Interestingly, active enhancers are the most enriched regulatory regions for H3.3K27M/H2A.Zac overlapping regions and those enhancers are associated with genes involved in pathways commonly altered in H3.3K27M gliomas. These data suggest H2A.Zac levels are altered in DIPG and H2A.Zac may be involved in aberrant enhancer activation in DIPG and thus constitute a novel therapeutic target for H3.3K27M+ DIPG.
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 | 0.001 | 0.001 |
| 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".