A subset of pediatric-type thalamic gliomas share a distinct DNA methylation profile, H3K27me3 loss and frequent alteration of <i>EGFR</i>
Bibliographic record
Abstract
BACKGROUND: Malignant astrocytic gliomas in children show a remarkable biological and clinical diversity. Small in-frame insertions or missense mutations in the epidermal growth factor receptor gene (EGFR) have recently been identified in a distinct subset of pediatric-type bithalamic gliomas with a unique DNA methylation pattern. METHODS: Here, we investigated an epigenetically homogeneous cohort of malignant gliomas (n = 58) distinct from other subtypes and enriched for pediatric cases and thalamic location, in comparison with this recently identified subtype of pediatric bithalamic gliomas. RESULTS: EGFR gene amplification was detected in 16/58 (27%) tumors, and missense mutations or small in-frame insertions in EGFR were found in 20/30 tumors with available sequencing data (67%; 5 of them co-occurring with EGFR amplification). Additionally, 8 of the 30 tumors (27%) harbored an H3.1 or H3.3 K27M mutation (6 of them with a concomitant EGFR alteration). All tumors tested showed loss of H3K27me3 staining, with evidence of overexpression of the EZH inhibitory protein (EZHIP) in the H3 wildtype cases. Although some tumors indeed showed a bithalamic growth pattern, a significant proportion of tumors occurred in the unilateral thalamus or in other (predominantly midline) locations. CONCLUSIONS: Our findings present a distinct molecular class of pediatric-type malignant gliomas largely overlapping with the recently reported bithalamic gliomas characterized by EGFR alteration, but additionally showing a broader spectrum of EGFR alterations and tumor localization. Global H3K27me3 loss in this group appears to be mediated by either H3 K27 mutation or EZHIP overexpression. EGFR inhibition may represent a potential therapeutic strategy in these highly aggressive gliomas.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".