P.051 5-hydroxymethylcytosine profiling identifies differential targeting in IDH1 mutant versus IDH1 wild-type high-grade gliomas
Bibliographic record
Abstract
Background: Gliomas demonstrate epigenetic dysregulation highlighted by the Glioma CpG-Island Methylator Phenotype (G-CIMP) seen in IDH1 mutant tumors. IDH1 mutation perturbs the balance between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) by inhibiting TET-mediated active demethylation. The role 5hmC plays in IDH1 mutant tumors remains poorly understood. Methods: We profiled 5hmC in high grade IDH1 mutant (n = 12) and wild-type (n = 9) tumors on the Illumina MethylationEPIC Beadchip. We examined regions with high 5hmC abundance (top 1% probes), and differentially hydroxymethylated regions (DHMR). 5hmC profiles were correlated with gene expression. Results: Mean 5hmC b-values were 4.6%% and 3.8% for IDH1 mutant and wild-type tumors, respectively. Top 1% and DHMR probes demonstrated increased 5hmC among IDH1 mutants. 5hmC enriched for enhancer and super-enhancers. Among G-CIMP target genes, 22/50 were hydroxymethylated in our IDH1 mutant cohort, suggesting that 5hmC contributes to their overall methylation. Gene expression was associated with gene body 5hmC. 48 genes differentially expressed between IDH1 cohorts showed a positive Spearman correlation between 5hmC and gene expression, in particular for genes upregulated in IDH1 mutants. Conclusions: Locus-specific gain of 5hmC, targeting regulatory regions and associated with over-expressed genes, suggests a significant role for 5hmC in IDH1 mutant HGG.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".