Characterizing temporal genomic heterogeneity in pediatric high-grade gliomas
Why this work is in the frame
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Bibliographic record
Abstract
Pediatric high-grade gliomas (pHGGs) are aggressive neoplasms representing approximately 20% of brain tumors in children. Current therapies offer limited disease control, and patients have a poor prognosis. Empiric use of targeted therapy, especially at progression, is increasingly practiced despite a paucity of data regarding temporal and therapy-driven genomic evolution in pHGGs. To study the genetic landscape of pHGGs at recurrence, we performed whole exome and methylation analyses on matched primary and recurrent pHGGs from 16 patients. Tumor mutational profiles identified three distinct subgroups. Group 1 (n = 7) harbored known hotspot mutations in Histone 3 (H3) (K27M or G34V) or IDH1 (H3/IDH1 mutants) and co-occurring TP53 or ACVR1 mutations in tumor pairs across the disease course. Group 2 (n = 7), H3/IDH1 wildtype tumor pairs, harbored novel mutations in chromatin modifiers (ZMYND11, EP300 n = 2), all associated with TP53 alterations, or had BRAF V600E mutations (n = 2) conserved across tumor pairs. Group 3 included 2 tumors with NF1 germline mutations. Pairs from primary and relapsed pHGG samples clustered within the same DNA methylation subgroup. ATRX mutations were clonal and retained in H3G34V and H3/IDH1 wildtype tumors, while different genetic alterations in this gene were observed at diagnosis and recurrence in IDH1 mutant tumors. Mutations in putative drug targets (EGFR, ERBB2, PDGFRA, PI3K) were not always shared between primary and recurrence samples, indicating evolution during progression. Our findings indicate that specific key driver mutations in pHGGs are conserved at recurrence and are prime targets for therapeutic development and clinical trials (e.g. H3 post-translational modifications, IDH1, BRAF V600E). Other actionable mutations are acquired or lost, indicating that re-biopsy at recurrence will provide better guidance for effective targeted therapy of pHGGs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it