EPCO-51. FUNCTIONAL ANALYSIS OF REGULATORY MUTATIONS IN PEDIATRIC BRAIN TUMORS
Bibliographic record
Abstract
Abstract Pediatric brain tumors are the primary cause of cancer related mortality in children and are characterized by low mutations burden. The known somatic drivers reside in the coding regions of the genome. The role of non-coding mutations in brain tumors is important yet underexplored. In this study we investigate the impact of regulatory region mutations in pediatric brain tumors. We studied 1.3 million open chromatin regions (OCRs) genome-wide from 9 studies including developing/adult brain, and cancer. Within these OCRs we identified recurring mutations in whole genome sequences from 1003 pediatric brain tumors from CBTN and PCAWG cohorts. A total of 567 OCRs with significantly recurring mutations were found, showing enrichment in brain-specific enhancers and in repressed polycomb regions in stem cells. The mutations in these OCRs resulted in motif disruption or creation of 217 transcription factor binding sites. Differential expression analysis of mutated vs nonmutated samples identified perturbations of gene or protein expression in 67 (RNA) and 16 (protein) OCR-gene pairs. Furthermore, 17 mutated OCRs were found to correlate with alterations in gene expression programs, indicating a potential regulatory role affecting multiple downstream genes. We further performed network analysis to explore clusters of OCR associated genes that share biological networks and pathways, and identified 24 clusters. Among these, OCR mutation-associated genes in 6 clusters showed a survival impact in patients. These 6 clusters mapped to pathways including mRNA decay and degradation, longevity regulating pathway, autophagy, p53 pathway, HIF2-alpha transcription factor network, pathways in cancer among others. In summary, we report functionally important regulatory elements in the genome that impact the biological processes in pediatric 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".