Consortium trial demonstrates efficacy of selumetinib in pediatric low-grade glioma
Bibliographic record
Abstract
Unresectable pediatric low-grade glioma (pLGG) is a chronic disease that can be debilitating due to the need for multiple lines of therapies throughout childhood following serial progressions, each with additive morbidity, despite the excellent overall survival of patients.1 Molecular profiling over the past 2 decades has demonstrated that majority of pLGG are driven by aberrations in the Ras-Raf-MEK-ERK (or simply MAPK) pathway, allowing opportunity for therapeutic targeting.2 This is an important advancement, given the long-term toxicity and general reluctance to use radiotherapy in pLGG, and the limited, as well as lack of durable responses to chemotherapy.1 In this issue of Neuro-Oncology, Fangusaro et al.3 report on a decade-long, multi-centric, phase 2 trial conducted by the Pediatric Brain Tumor Consortium (PBTC-029) using the oral MEK-1/2 inhibitor selumetinib for a duration of 2 years in children older than 3 years with recurrent/progressive pLGG (PBTC029; NCT01089101). This supplements their previous work, by providing results for previously unreported arms, and a longer follow-up for those reported previously.4,5 The primary endpoint was objective response rate (ORR, including complete and partial responses) using T2/T2-FLAIR MRI sequences. Progression-free survival (PFS), associations with molecular features, and pharmacokinetic measurements were the planned secondary objectives. Stable disease (SD) was highlighted as a surrogate for efficacy, given that the enrolled patients already had progression following their previous therapy that was now halted on selumetinib.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| 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".