LGG-20. LANDSCAPE OF FGFR ALTERATIONS IN PEDIATRIC AND AYA GLIOMAS
Bibliographic record
Abstract
Abstract FGFR alterations including single nucleotide variants (SNV) and rearrangements represent common oncogenic alterations found in pediatric low-grade gliomas. From a population-based cohort of over 1800 pediatric and AYA (adolescent and young adult) gliomas, FGFR mutations were found in 6% of pediatric low-grade gliomas (LGG) and 16 % of IDH-WT AYA gliomas. Given the frequency of FGFR driver alterations, we assembled a large cohort of 370 FGFR mutated glioma across all ages (6 months to 87 years) 53% of which were LGG. In the pediatric patients, most (87%) of FGFR altered gliomas were low-grade; while in AYA patients only 67%. Fusions were more frequent in pediatric LGG (60%), while SNVs were more common in AYA LGG (57%). The rearranged genes also differed; pediatric LGG: 26% FGFR1 ITD, 20% FGFR1 fusions, 15% FGFR2 fusions. AYA LGG had equal frequency of FGFR1 and FGFR2 fusions (30%). In adults, high grade gliomas were more common (73%), and frequently had additional oncogenic drivers. In general, additional oncogenic drivers were more common with increasing age and grade. In contrast to pediatric and AYA LGG, FGFR3-TACC3 fusions represented 40% of adult cases. Interestingly, this fusion was also found in 10% of FGFR LGGs overall. Although analysis of clinical outcomes are still ongoing, in our preliminary data 18 patients with FGFR altered low grade gliomas were treated with targeted agents; 3 stopped due to toxicity and were not evaluated for response. From patients on MAPKi 1/6 had a minor response and 3/6 stable disease; while 6/9 on FGFRi had partial or minor responses with additional 3 patients recently initiated on the drug. FGFR alterations are frequently encountered in AYA and pediatric population, molecular characterization is important in guiding prognosis and therapy. Although encouraging, further studies are needed to assess the benefit of FGFR inhibition in these patients.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".