Trametinib therapy for children with neurofibromatosis type 1 and life‐threatening plexiform neurofibroma or treatment‐refractory low‐grade glioma
Bibliographic record
Abstract
PURPOSE: To describe a series of children with extensive PNF or treatment refractory PLGG treated on a compassionate basis with trametinib. METHODS: We report on six patients with NF-1 treated with trametinib on a compassionate basis at British Columbia Children's Hospital since 2017. Data were collected retrospectively from the patient record. RAPNO and volumetric criteria were used to evaluate the response of intracranial and extracranial lesions, respectively. RESULTS: Subjects were 21 months to 14 years old at the time of initiation of trametinib therapy and 3/6 subjects are male. Duration of therapy was 4-28 months at the time of this report. All patients had partial response or were stable on analysis. Two patients with life-threatening PNF had a partial radiographic response in tandem with significant clinical improvement and developmental catch up. One subject discontinued therapy after 6 months due to paronychia and inadequate response. The most common adverse effect (AE) was grade 1-2 paronychia or dermatitis in 5/6 patients. There were no grade 3 or 4 AEs. At the time of this report, five patients remain on therapy. CONCLUSION: Trametinib is an effective therapy for advanced PNF and refractory PLGG in patients with NF-1 and is well tolerated in children. Further data and clinical trials are required to assess tolerance, efficacy and durability of response, and length of treatment required in such patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".