A Compendium of Syngeneic, Transplantable Pediatric High-Grade Glioma Models Reveals Subtype-Specific Therapeutic Vulnerabilities
Bibliographic record
Abstract
Pediatric high-grade gliomas (pHGG) are lethal, incurable brain tumors frequently driven by clonal mutations in histone genes. They often harbor a range of additional genetic alterations that correlate with different ages, anatomic locations, and tumor subtypes. We developed models representing 16 pHGG subtypes driven by different combinations of alterations targeted to specific brain regions. Tumors developed with varying latencies and cell lines derived from these models engrafted in syngeneic, immunocompetent mice with high penetrance. Targeted drug screening revealed unexpected selective vulnerabilities-H3.3G34R/PDGFRAC235Y to FGFR inhibition, H3.3K27M/PDGFRAWT to PDGFRA inhibition, and H3.3K27M/PDGFRAWT and H3.3K27M/PPM1DΔC/PIK3CAE545K to combined inhibition of MEK and PIK3CA. Moreover, H3.3K27M tumors with PIK3CA, NF1, and FGFR1 mutations were more invasive and harbored distinct additional phenotypes, such as exophytic spread, cranial nerve invasion, and spinal dissemination. Collectively, these models reveal that different partner alterations produce distinct effects on pHGG cellular composition, latency, invasiveness, and treatment sensitivity. SIGNIFICANCE: Histone-mutant pediatric gliomas are a highly heterogeneous tumor entity. Different histone mutations correlate with different ages of onset, survival outcomes, brain regions, and partner alterations. We have developed models of histone-mutant gliomas that reflect this anatomic and genetic heterogeneity and provide evidence of subtype-specific biology and therapeutic targeting. See related commentary by Lubanszky and Hawkins, p. 1516. This article is highlighted in the In This Issue feature, p. 1501.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".