LGG-50. POOR RESPONSE TO MEKI RE-INITIATION IN PEDIATRIC LOW-GRADE GLIOMA AFTER TREATMENT DISCONTINUATION. A PLGG TASKFORCE STUDY
Bibliographic record
Abstract
Abstract BACKGROUND Pediatric low-grade gliomas (pLGG) exhibit dramatic objective response to treatment with MEKi (Mitogen-Activated Protein Kinase inhibitors). Although the initial response to MEKi is promising, it is unknown whether it is safe to stop MEKi therapy and if the response to re-initiating MEKi would have the same impact. The study objective was to compare the first and second response to MEKi in patients with pLGG who stopped therapy and had further tumor progression. METHODS PLGG identified at neurooncology centers in Canada, Australia, and US, which were treated twice with MEKi were included. Second therapy was defined as MEKi upon progression for tumors where treatment was stopped. Response to MEKi between 2 treatments (MEKi1, MEKi2) were analyzed by using RANO criteria. RESULTS Mature data on 19 patients reveals a median patient age of 13 years (range 7–24), and the most common tumor location is hypothalamic/chiasmatic (89%). PLGG alterations include KIAA1549:BRAF fusion in 69%, NF1 in 21%, and other alterations in 10%. The overall response (minor and partial) to MEKi1 was 63% compared to 26% in MEKi2 (p=0.04). Furthermore, upon restarting MEKi2, 11% had further growth on therapy in contrast to none at MEKi1. MEKi1 resulted in average of 37% size decrease (range, 3%-98%), including 6 PLGG with over 50% response. In contrast, the average decrease to MEKi2 was 10% (range, 6%-40%, p=0.005), and none had >50% size reduction. Comparing each patient MEKi1 to MEKi2 revealed that initial response was superior to the second one in all PLGG. CONCLUSION These data suggest that the second response to MEKi is inferior when compared to initial therapy. Expansion of this cohort and biomarker correlates are ongoing. Biological insights are required for this observation, and careful consideration should be made for stopping MEKi, especially in strategic locations such as the optic pathway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".