Mutations in SETD2 and genes affecting histone H3K36 methylation target hemispheric high-grade gliomas
Bibliographic record
Abstract
Recurrent mutations affecting the histone H3.3 residues Lys27 or indirectly Lys36 are frequent drivers of pediatric high-grade gliomas (over 30% of HGGs). To identify additional driver mutations in HGGs, we investigated a cohort of 60 pediatric HGGs using whole-exome sequencing (WES) and compared them to 543 exomes from non-cancer control samples. We identified mutations in SETD2, a H3K36 trimethyltransferase, in 15% of pediatric HGGs, a result that was genome-wide significant (FDR = 0.029). Most SETD2 alterations were truncating mutations. Sequencing the gene in this cohort and another validation cohort (123 gliomas from all ages and grades) showed SETD2 mutations to be specific to high-grade tumors affecting 15% of pediatric HGGs (11/73) and 8% of adult HGGs (5/65) while no SETD2 mutations were identified in low-grade diffuse gliomas (0/45). Furthermore, SETD2 mutations were mutually exclusive with H3F3A mutations in HGGs (P = 0.0492) while they partly overlapped with IDH1 mutations (4/14), and SETD2-mutant tumors were found exclusively in the cerebral hemispheres (P = 0.0055). SETD2 is the only H3K36 trimethyltransferase in humans, and SETD2-mutant tumors showed a substantial decrease in H3K36me3 levels (P < 0.001), indicating that the mutations are loss-of-function. These data suggest that loss-of-function SETD2 mutations occur in older children and young adults and are specific to HGG of the cerebral cortex, similar to the H3.3 G34R/V and IDH mutations. Taken together, our results suggest that mutations disrupting the histone code at H3K36, including H3.3 G34R/V, IDH1 and/or SETD2 mutations, are central to the genesis of hemispheric HGGs in older children and young adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".