DNMTs as potential therapeutic targets in high-risk pediatric embryonal brain tumors
Bibliographic record
Abstract
Malignant brain tumors, which are the leading cause of cancer-related morbidity and mortality in children, span a wide spectrum of diseases with distinct clinical phenotypes but may share remarkably similar morphologic features. Until recently, few molecular markers of childhood brain tumors have been identified, which has limited therapeutic advances. Recent global genomic studies have enabled robust molecular classification of childhood brain tumors and the identification and consolidation of rare, seemingly disparate clinical entities. It is now increasingly evident that deregulation of epigenetic processes contributes substantially to heterogeneity in tumor phenotypes and comprise significant drivers of cancer initiation and progression. Specifically, DNA hypermethylation and silencing of critical tumor suppressor genes by DNA methyltransferases (DNMT) has emerged as an important and fundamental mechanism in brain tumor pathogenesis. These observations have been underscored by the recent discovery of TTYH1-C19MC gene fusions in an aggressive pediatric embryonal brain tumor, which results in deregulation and increased expression of a neural-specific DNMT3B isoform in C19MC-associated brain tumors. Our observations that pharmacological inhibitors of DNMTs and histone deacetylases significantly inhibit growth of cells derived from C19MC-associated tumors indicate targeting of epigenomic modifiers as a novel therapeutic approach for these highly treatment-resistant 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".