TBIO-05. GENOME-SCALE NUCLEOTIDE-SPECIFIC CHARACTERIZATION OF 5-HYDROXYMETHYLCYTOSINE IN PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS
Bibliographic record
Abstract
Abstract Though aberrant cytosine modifications are prevalent in cancer, nucleotide-specific 5-hydroxymethylcytosine (5hmC) modifications remain understudied, including in pediatric CNS tumors. Brain 5-hydroxymethylation is linked with development and differentiation. We measured genome-scale nucleotide-specific 5hmC in patients with diagnoses of glioma, ependymoma, and embryonal tumors under age 18 (n=36), and in non-tumor pediatric brain tissues (n=3). DNA was processed with tandem oxidative (OxBS) and bisulfite (BS) treatments followed by hybridization to the Illumina Methylation EPIC Array that interrogates over 860,000 CpG sites. We used the OxyBS R package to determine levels of 5hmC and 5mC. Mean 5hmC levels were lower in tumors (gliomas 4.1%, ependymomas 3.9%, and embryonal tumors 3.4%) compared to nontumor tissues (5.3%). We subset to the CpGs with the 5% highest 5hmC content for downstream analyses (37,173 CpGs). These sites were enriched among regulatory elements, including TFBS (Odds Ratio 1.14 p-value 3.57E-20) and super-enhancers (OR 1.93, p-value 1.14E-126). Linear mixed-effects models adjusted for age, sex, and cell type proportions tested the CpG-specific differences in 5hmC between tumor and nontumor samples, as well as between tumor subtypes. 5hmC levels were depleted across tumors compared with nontumor brain tissues, including at CpG islands. Model-based clustering (RPMM) results indicated that patients with low 5hmC patterns have poorer overall survival and increased risk of recurrence. Our results indicate that 5hmC localizes to sites in the DNA critical to gene regulation and is associated with patient outcomes. This study offers an opportunity to potentially contribute to classification markers for childhood brain tumors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".