A phase 2 study of trametinib for patients with pediatric glioma or plexiform neurofibroma with refractory tumor and activation of the MAPK/ERK pathway: TRAM-01
Bibliographic record
Abstract
BACKGROUND: Pediatric low-grade gliomas (PLGG) are the most frequent brain tumors in children. Up to 50% will be refractory to conventional chemotherapy. It is now known that the majority of PLGG have activation of the MAPK/ERK pathway. The same pathway is also activated in plexiform neurofibromas (PNs) which are low-grade tumors involving peripheral nerves in patients with neurofibromatosis type 1 (NF1). These lesions are known to be refractory to chemotherapy. Specific MEK inhibitors such as trametinib are now available and have been approved for other cancers harboring mutations in the MAPK/ERK pathway such as melanoma. We have observed significant responses to trametinib in patients with refractory PLGG in our institutions and results from the phase I study are promising. The treatment appears not only efficacious but is also usually well tolerated. We hypothesize that we will observe responses in the majority of refractory PLGG and PN treated with trametinib in this phase 2 study. METHODS: The primary objective is to determine the objective response rate of trametinib as a single agent for treatment of progressing/refractory tumors with MAPK/ERK pathway activation. The TRAM-01 study is a phase II multicentric open-label basket trial including four groups. Group 1 includes NF1 patients with progressing/refractory glioma. Group 2 includes NF1 patients with plexiform neurofibroma. Group 3 includes patients with progressing/refractory glioma with KIAA1549-BRAF fusion. Group 4 includes other patients with progressing/refractory glioma with activation of the MAPK/ERK pathway. Eligible patients for a given study group will receive daily oral trametinib at full dose for a total of 18 cycles of 28 days. A total of 150 patients will be enrolled in seven Canadian centers. Secondary objectives include the assessment of progression-free survival, overall survival, safety and tolerability of trametinib, serum levels of trametinib and evaluation of quality of life during treatment. DISCUSSION: Trametinib will allow us to target directly and specifically the MAPK/ERK pathway. We expect to observe a significant response in most patients. Following our study, trametinib could be integrated into standard treatment of PLGG and PN. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03363217 December 6, 2017.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".