Functional and topographic effects on DNA methylation in IDH1/2 mutant cancers
Bibliographic record
Abstract
IDH1/2 mutations are early drivers present in diverse human cancer types arising in various tissue sites. IDH1/2 mutation is known to induce a global hypermethylator phenotype. However, the effects on DNA methylation across IDH mutant cancers and functionally different genome regions, remain unknown. We analyzed DNA methylation data from IDH1/2 mutant acute myeloid leukemia, oligodendroglioma, astrocytoma, solid papillary breast carcinoma with reverse polarity, sinonasal undifferentiated carcinoma and cholangiocarcinoma, which clustered by their embryonal origin. Hypermethylated common probes affect predominantly gene bodies while promoters in IDH1/2 mutant cancers remain unmethylated. Enhancers showed global hypermethylation, however commonly hypomethylated enhancers were associated with tissue differentiation and cell fate determination. We demonstrate that some chromosomes, chromosomal arms and chromosomal regions are more affected by IDH1/2 mutations while others remain resistant to IDH1/2 mutation induced methylation changes. Therefore IDH1/2 mutations have different methylation effect on different parts of the genome, which may be regulated by different mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".