F.2 Comprehensively mapping transcriptionally relevant histone modifications in aggressive meningioma leads to novel biologic insights and therapeutic vulnerabilities
Bibliographic record
Abstract
Background: We recently identified four molecular subgroups of meningioma with distinct biology and outcomes. While two (MG3/MG4) are associated with poor outcome, they display divergent transcriptional profiles (enriched in metabolic and cell cycling pathways, respectively) and therapeutic vulnerabilities (MG3 has no clear treatment target). We sought to understand drivers of these key differences at a chromatin level. Methods: We profiled MG3/MG4 meningiomas for common histone marks H3K27me3, H3K27Ac, H3K4me1, H3K4me3, H3K9me3, and H3K36me3. Multiple computational approaches were used to compare MG3 and MG4 tumours including superenhancer ranking, differential binding analysis, and unsupervised clustering. Results: Our cohort includes 11-20 meningiomas per histone mark. Clustering revealed striking separation of subgroups based on multiple histone marks, particularly H3K36me3. FOXC1, a known driver of the epithelial to mesenchymal transition, was identified as a recurrent superenhancer in both groups, whereas MG3-specific superenhancers mapped to immune regulatory networks. Integrated differential binding analysis confirmed an immune-rich microenvironment in MG3 tumours driven by multiple histone marks, suggesting a role for targeting novel immune checkpoint genes CD84 and CD48. Conclusions: This study is the first to apply integrated analysis of multiple histone modifications to aggressive meningioma. We further characterize MG3 tumours by identifying an epigenetically-driven immune phenotype and propose novel treatment targets.
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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 | 0.001 |
| 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) | 0.002 | 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".