A diverse landscape of FGFR alterations and co-mutations suggests potential therapeutic strategies in pediatric low-grade gliomas
Bibliographic record
Abstract
Oncogenic alterations in fibroblast growth factor receptor (FGFR)-family proteins occur across cancers, including pediatric gliomas. Our genomic analysis of 11,635 gliomas across ages finds that 5.3% of all gliomas harbor FGFR alterations, with an incidence of almost 9% in pediatric gliomas. Alterations in FGFR proteins are differentially enriched by age, tumor grade, and histology, with FGFR1 alterations associated with glioneuronal histologies. Leveraging isogenic systems, we confirm FGFR1 alterations to induce downstream Mitogen Activated Protein Kinase (MAPK) and mTOR signaling pathways, drive gliomagenesis, activate neuronal transcriptional programs and exhibit sensitivity to MAPK pathway and pan-FGFR inhibitors. Finally, we perform a retrospective analysis of clinical responses in children diagnosed with FGFR-altered gliomas and find that treatment with currently available inhibitors is largely associated with stability of disease. This study provides key insights into the biology of FGFR1-altered gliomas, therapeutic strategies to target them and associated challenges that still need to be overcome. A subset of pediatric gliomas harbour alterations in fibroblast growth factor receptor (FGFR)-family proteins. Here, the authors characterise the genomic landscape of 11,635 gliomas across ages and use isogenic model systems to explore the underlying biology of FGFR1-altered gliomas and potential therapeutic vulnerabilities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".