LGG-60. DISTRIBUTION AND CLINICAL SIGNIFICANCE OF FGFR ALTERATIONS IN PEDIATRIC AND AYA GLIOMAS
Bibliographic record
Abstract
Abstract Fibroblast growth factor receptor (FGFR) alterations are prevalent in human cancers. In a large Canadian population-based cohort, encompassing over 1,300 pediatric and Adolescent and young adult (AYA) gliomas, FGFR mutations occurred in 10% of pediatric low-grade gliomas (LGG) and 20% of IDH-WT AYA LGGs. Here we assembled a large multi-institutional cohort including more than 300 mutated gliomas, aged 6 months to 87 years, 53% of which were LGG. High-grade gliomas were more common with increasing age; over 90% of pediatric cases were LGGs, compared to 30% in the AYA group. Fusions, predominantly seen in the pediatric group (70% of LGGs), contrast with FGFR3-TACC3 fusions frequent found in adult high-grade gliomas. Interestingly, FGFR mutations sometimes existed as sole drivers or co-occurred with other alterations (~30%), it is unclear how this alters tumor behavior, although gain of additional alterations could be implied in malignant transformation and poor prognosis. Despite higher incidence of high-grade gliomas in AYAs, their progression-free survival was significantly better than that of pediatric LGGs (p=0.007), largely due to the prevalence of hemispheric tumors amenable to gross total resection. Approximately 30% of our LGG cohort underwent adjuvant therapy; with available data for 28 patients, objective responses were noted in 2/12 to chemotherapy, 2/9 to MEK inhibitors, and 3/7 to FGFR inhibitors, respectively. With the increasing promise of precision targeted therapy, molecular profiling and assessment for FGFR alterations is essential. 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 | 0.000 |
| 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".