HGG-20. DNA METHYLATION ANALYSIS OF HIGH-GRADE GLIOMA IN PATIENTS WITH MISMATCH REPAIR DEFICIENCIES
Bibliographic record
Abstract
Patients with constitutional mismatch repair deficiency (CMMRD) are prone to developing high-grade glioma (HGG). These tumours acquire DNA polymerase mutations and become ultra-hypermutant harbouring hundreds of mutations per megabase. The impact of these mutations on methylation profile and the ability of the tool to differentiate MMRD tumours from others is unknown. In order to answer these questions, we performed either 450k/850K methylation analysis on a cohort of 52 CMMRD-HGG and compared them to 148 non-CMMRD HGG and normal brain controls. CMMRD HGG harbouring classic mutations in histone 3 or IDH genes had a methylation profile which clustered closely with non-MMRD tumours harbouring these mutations. Tumours without these alterations exhibited a tendency to hypomethylation with some tumours being extremely hypomethylated in comparison to other HGG. Hypomethylation was unrelated to mutational burden and type of DNA polymerase mutation present. Gene set analysis of methylation patterns revealed enrichment of hypomethylation for cellular pathways involved in cellular metabolism, organelle maintenance, mitotic cell cycle and gene expression. This pattern persisted in subgroup analysis of IDH mutant tumours in patients with and without MMRD. Importantly, this pattern was present in MMRD HGG with mutational burdens <10 mutations/MB and shared between primary and recurrent tumours suggesting that hypomethylation is an early event. CMMR-HGG have unique pattern of hypomethylation which can distinguish them from other paediatric HGG. Several plausible explanations include that hypomethylation in specific pathways confer a survival advantage on the cells which acquire it, or that hypermutations in specific CpG affect methylation patterns in these genes.
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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".