EPCO-08. CONVERGENCE OF NON-CODING SOMATIC MUTATIONS ON CANCER DRIVER PLEXUSES IN PEDIATRIC HIGH-GRADE GLIOMA
Bibliographic record
Abstract
Abstract Central nervous system tumors are the most common pediatric malignancies after leukemia, and the most common cause of cancer death among persons ages 0 – 14 [1,2]. Despite its low incidence rate, pediatric high-grade gliomas (pHGGs) account for over 40% of all childhood brain tumor deaths [3, 4]. As such, it remains the focus of many research teams across the globe. The discovery of recurrent mutations in genes encoding histone H3.3 [5] was a breakthrough in the genetic basis of pHGGs and highlighted the relevance of epigenetic reconfiguration for oncogene activation. However, much work is still needed to uncover driver alterations responsible for tumor initiation or progression. pHGGs have frequent epigenetic dysregulation [6,7] and abundant somatic mutations in the noncoding regions of the genome [8], but the functional relevance of the noncoding somatic mutations has not been evaluated systematically. Here, we assess correlations between noncoding somatic mutations in pediatric brain-specific cis-regulatory elements (CREs) and changes in gene expression. We demonstrate the sparsity of recurrent mutations in CREs. Then illustrate the power of graph database in consolidating multi-omics datasets for efficient queries. Our network approach for charting the genomic, epigenetic, and transcriptomic landscape of pHGG allow us to 1) identify clusters of CREs associated with dysregulated gene expression, 2) nominate transcription factors whose binding affinities are recurrently impacted by somatic mutations found in pHGGs, and 3) establish regulatory networks potentially guiding pHGG tumor growth and progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".