HGG-60. Structural variants shape driver combinations and outcomes in pediatric high-grade glioma
Bibliographic record
Abstract
Abstract Pediatric high-grade gliomas (pHGGs), encompassing hemispheric and diffuse midline gliomas (DMGs), remain a devastating disease. The last decade has revealed oncogenic drivers including single nucleotide variants (SNVs) in histones. However, the contribution of structural variants (SVs) to gliomagenesis has not been systematically explored due to limitations in early SV analysis approaches. Using SV algorithms, we recently created, we analyzed SVs in whole-genome sequences of 179 pHGGs including a novel cohort of treatment naïve samples–the largest WGS cohort assembled in adult or pediatric glioma. The most recurrent SVs targeted MYC isoforms and receptor tyrosine kinases, including a novel SV amplifying a MYC enhancer in the lncRNA CCDC26 in 12% of DMGs and revealing a more central role for MYC in these cancers than previously known. Applying de novo SV signature discovery, we identified five signatures including three (SVsig1-3) involving primarily simple SVs, and two (SVsig4-5) involving complex, clustered SVs. These SV signatures associated with genetic variants that differed from what was observed for SV signatures in other cancers, suggesting different links to underlying biology. Tumors with simple SV signatures were TP53 wild-type but were enriched with alterations in TP53 pathway members PPM1D and MDM4. Complex signatures were associated with direct aberrations in TP53, CDKN2A, and RB1 early in tumor evolution, and with extrachromosomal amplicons that likely occurred later. All pHGGs exhibited at least one simple SV signature but complex SV signatures were primarily restricted to subsets of H3.3K27M DMGs and hemispheric pHGGs. Importantly, DMGs with the complex SV signatures SVsig4-5 were associated with shorter overall survival independent of histone type and TP53 status. These data inform the role and impact of SVs in gliomagenesis and mechanisms that shape them.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".