HGG-06. CHARACTERIZING TEMPORAL GENOMIC HETEROGENEITY IN PEDIATRIC HIGH-GRADE GLIOMAS
Bibliographic record
Abstract
Pediatric high-grade gliomas (pHGG) are aggressive malignant neoplasms representing approximately 20% of all pediatric central nervous system tumors. Current therapies offer limited disease control, and patients with recurrent disease have a particularly poor prognosis. While the empiric use of targeted therapy, especially at progression, is being increasingly practiced, there is a paucity of data regarding temporal and therapy-driven genomic evolution in pHGGs. To better understand the genetic landscape of pHGGs at recurrence, we performed whole exome, transcriptome and methylation analyses on pHGG matched primary and recurrent tumors from 16 patients, median age of 15 years (range: 4–29). Matched normal tissue, where available, was evaluated to determine mutation somatic status. Genetic mutational results suggested that our cohort could be segregated into 3 groups. Group 1 tumor pairs (n=6) carry driver mutations in histone 3 (H3F3A, HIST1H3B) or IDH1 that were retained between the primary tumor and recurrence. In tumors with H3F3A and IDH1 mutations, their obligate partner TP53 or ATRX mutations were also conserved. Group 2 tumor pairs (n=8) were histone 3 or IDH1 wild-type, but most harbored mutations in genes that are modifiers of histone or chromatin (KMT2D, ZMYND11, EP300, BCOR). TP53 and ATRX driver mutations were present in 50% (n=4) of tumors, and 25% (n=2) had a BRAF V600E mutation. Mutations in other putative drug targets (EGFR, ERBB2, PI3-kinase mutations) were not always shared between primary and recurrent tumors, indicating evolution during tumor progression. Group 3 samples (n=2) carried germline mutations in NF1. Mutational burden, gene ontology analysis and correlation with treatment exposure are ongoing. The finding that key driver mutations, some of which are targetable (e.g. IDH1, BRAF V600E) are conserved at recurrence, while other targetable mutations are acquired, indicates that re-biopsy at recurrence may provide better guidance for effective treatment of pHGG with targeted therapy.
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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 | 0.001 |
| 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) | 0.003 | 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".