LGG-16. PREDICTORS OF OUTCOME IN BRAF-V600E PEDIATRIC GLIOMAS TREATED WITH BRAF INHIBITORS: A REPORT FROM THE PLGG TASKFORCE
Bibliographic record
Abstract
The BRAF-V600E mutation is found in 15–20% of pediatric low grade gliomas (PLGG) and result in worse outcome and higher risk of transformation to high grade gliomas (PHGG). Although ongoing trials are assessing the role of BRAF inhibitors (BRAFi) in these children, data are still limited. We aimed to report overall response rates and predictors of outcome in childhood BRAF-V600E gliomas. We collected clinical, imaging and molecular information of patients treated with BRAFi outside trials from centers participating in the PLGG taskforce. Response was calculated by RANO criteria and follow up data were collected for all patients. Sixty-six patients were treated with BRAFi (55 PLGG and 11 PHGG); median follow-up time was 1.5 years (0.1-5y). In PLGG, objective response (tumor reduction of >25%) was observed in 77% compared to 15% in a cohort treated with conventional chemotherapy (pCDKN2A deletion was not associated with lack of response, while specific enhancing patterns correlated strongly with response to BRAFi. Two-year PFS for the BRAF-V600E PLGG was 74% vs 47% for BRAFi vs chemotherapy, respectively (p=0.02). Our data reveal rapid, dramatic and sustained response of BRAF-V600E PLGG to BRAFi. These are in contrast to BRAF-V600E PHGG and non-enhancing PLGG. Additional molecular analyses are being performed to identify poor responders and emerging mechanisms of resistance in these tumors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".