LGG-25. A PHASE 2 STUDY OF TRAMETINIB FOR PATIENTS WITH PEDIATRIC GLIOMA WITH ACTIVATION OF THE MAPK/ERK PATHWAY. TRAM-01
Bibliographic record
Abstract
Abstract BACKGROUND Pediatric low-grade gliomas (PLGG) are the most frequent brain tumors in children. It is now known that the majority of PLGG have activation of the MAPK/ERK pathway. We hypothesize that we will observe responses in recurrent/refractory PLGG treated with trametinib. METHODS This is a multicenter phase II including three progressing/refractory PLGG groups: NF1 patients, KIAA1549-BRAF fusion patients and patients with other activation of the MAPK/ERK pathway (excluding V600E). Patients will receive daily oral trametinib for a total of 18 cycles of 28 days. A total of 104 patients will be enrolled in seven Canadian centers. Secondary objectives include the assessment of progression-free survival, tolerability of trametinib, serum levels of trametinib and evaluation of quality of life during treatment. RESULTS As of January 7 2020, 28 patients have been enrolled (NF1: 6 patients, KIAA1549-BRAF fusion: 17, other: 5 including 3 patients FGFR1 alteration). Median age is 8.5 years (range 2.5–25.4 years). Median follow-up is currently 4.6 months (range 0.16–14.7 months). Twenty patients are currently evaluable. Best response includes: 1 complete response (5%), 3 partial response (15%), 4 minor response (20%), 8 stable disease (40%), 4 progressive disease (20%). 8 patients (28,5%) discontinued treatment: 4 for progressive disease, 3 adverse event (alanine aminotransferase increase), 1 withdrew. CONCLUSION Trametinib is potential effective targeted therapy for patients with recurrent/refractory PLGG. Overall treatment is well tolerated. This ongoing trial will continue to gather data on response rate, duration of response and safety of trametinib for PLGG.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".