Phase II Trial of Dabrafenib Plus Trametinib in Relapsed/Refractory <i>BRAF</i> V600–Mutant Pediatric High-Grade Glioma
Bibliographic record
Abstract
PURPOSE BRAF V600 mutation is detected in 5%-10% of pediatric high-grade gliomas (pHGGs), and effective treatments are limited. In previous trials, dabrafenib as monotherapy or in combination with trametinib demonstrated activity in children and adults with relapsed/refractory BRAF V600–mutant HGG. METHODS This phase II study evaluated dabrafenib plus trametinib in patients with relapsed/refractory BRAF V600–mutant pHGG. The primary objective was overall response rate (ORR) by independent review by Response Assessment in Neuro-Oncology criteria. Secondary objectives included ORR by investigator determination, duration of response (DOR), progression-free survival, overall survival (OS), and safety. RESULTS A total of 41 pediatric patients with previously treated BRAF V600–mutant HGG were enrolled. At primary analysis, median follow-up was 25.1 months, and 51% of patients remained on treatment. Sixteen of 20 discontinuations were due to progressive disease in this relapsed/refractory pHGG population. Independently assessed ORR was 56% (95% CI, 40 to 72). Median DOR was 22.2 months (95% CI, 7.6 months to not reached [NR]). Fourteen deaths were reported. Median OS was 32.8 months (95% CI, 19.2 months to NR). The most common all-cause adverse events (AEs) were pyrexia (51%), headache (34%), and dry skin (32%). Two patients (5%) had AEs (both rash) leading to discontinuation. CONCLUSION In relapsed/refractory BRAF V600–mutant pHGG, dabrafenib plus trametinib improved ORR versus previous trials of chemotherapy in molecularly unselected patients with pHGG and was associated with durable responses and encouraging survival. These findings suggest that dabrafenib plus trametinib is a promising targeted therapy option for children and adolescents with relapsed/refractory BRAF V600–mutant HGG.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".