RARE-11. EFFICACY AND SAFETY OF DABRAFENIB + TRAMETINIB IN PATIENTS WITH RECURRENT/REFRACTORY BRAF V600E–MUTATED LOW-GRADE GLIOMA (LGG)
Bibliographic record
Abstract
Approximately 9%-18% of LGGs possess BRAF V600E mutations. Combined BRAF and MEK inhibition is efficacious in BRAF V600–mutated melanoma, lung cancer, and anaplastic thyroid cancer. Dabrafenib (BRAF inhibitor) + trametinib (MEK inhibitor) was evaluated as treatment for patients with recurrent/refractory BRAF V600E–mutated LGG. In this phase 2, open-label trial (NCT02034110), patients with BRAF V600E mutations in 9 rare tumor types, including LGG, received continuous dabrafenib (150 mg BID) + trametinib (2 mg QD) until unacceptable toxicity, disease progression, or death. For the LGG cohort, eligible patients had histologically confirmed recurrent or progressive WHO grade 1 or 2 glioma that was refractory to standard-of-care therapies. The primary endpoint was investigator-assessed overall response rate (ORR) by RANO criteria. Secondary endpoints included duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety. Nine patients with LGG had enrolled at data cutoff (3 January 2018). Eight of 9 patients were evaluable for response. Median age was 33 years. Eight of 9 patients had received prior surgery. Investigator-assessed confirmed ORR was 50% (4/8; 95% CI, 16%-84%), with 3 of 4 responses ongoing at data cutoff. Two of 4 patients had a DOR of ≥ 18 months. The PFS and OS Kaplan-Meier estimates at 18 months were 50% (95% CI, 15%-78%) and 86% (95% CI, 33%-98%), respectively. Adverse events (AEs) in patients with LGG included fatigue (67%), headache (67%), arthralgia, nausea, and pyrexia (56% each). Grade 3/4 AEs included fatigue (22%), arthralgia, headache, and diarrhea (11% each). Biomarker analyses are ongoing and will be presented. CONCLUSIONS: Dabrafenib + trametinib demonstrated promising efficacy in patients with recurrent/refractory BRAF V600E-mutated LGG, with manageable AEs and no new safety signals
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".