Chromatin Remodeling Defects in Pediatric and Young Adult Glioblastoma: A Tale of a Variant <scp>H</scp>istone 3 Tail
Bibliographic record
Abstract
Primary brain tumors occur in 8 out of 100 000 people and are the leading cause of cancer-related death in children. Among brain tumors, high-grade astrocytomas (HGAs) including glioblastoma multiforme (GBM) are aggressive and are lethal human cancers. Despite decades of concerted therapeutic efforts, HGAs remain essentially incurable in adults and children. Recent discoveries have revolutionized our understanding of these tumors in children and young adults. Recurrent somatic driver mutations in the tail of histone 3 variant 3 (H3.3), leading to amino acid substitutions at key residues, namely lysine (K) 27 (K27M) and glycine 34 (G34R/G34V), were identified as a new molecular mechanism in pediatric GBM. These mutations represent the pediatric counterpart of the recurrent mutations in isocitrate dehydrogenases (IDH) identified in young adult gliomas and provide a much-needed new pathway that can be targeted for therapeutic development. This review will provide an overview of the potential role of these mutations in altering chromatin structure and affecting specific molecular pathways ultimately leading to gliomagenesis. The distinct changes in chromatin structure and the specific downstream events induced by each mutation need characterizing independently if progress is to be made in tackling this devastating cancer.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".