PDCT-08. TRAMETINIB AND DABRAFENIB FOR REFRACTORY/INOPERABLE PEDIATRIC LOW GRADE GLIOMAS
Bibliographic record
Abstract
Low grade gliomas (LGG) are the most common pediatric brain tumors and frequently harbor BRAF mutations or fusions. In cases of refractory and progressing LGG, BRAF and MEK inhibitors now offer an interesting therapeutic approach. However, there are only few cases report on the use BRAF/MEK inhibitors in pediatric LGG. We here describe six children with LGG treated with a BRAF or MEK inhibitor. Charts from all patients that received dabrafenib or trametinib were retrieved and retrospectively reviewed. Demographic data, pathology, tumor location, toxicity and progression free survival were retrieved. The mean age at diagnosis was 3.2 years (range: 11mo-6 yo). There were 3 females and 3 males. Four children had a sporadic pilocytic astrocytoma of the optic pathway, one patient with NF1 had an optic pathway glioma and subsequently developed a glioblastoma and one had a ganglioglioma. KIAA1549-BRAF fusion was found in all four sporadic OPG tumors and BRAF v600e mutation was found in the ganglioglioma. Five patients received at least one previous line of chemotherapy (range 1-4) and for one patient it was a first line treatment. Five patients received trametinib (BRAF fusion patients and NF1 patient) and one patient received dabrafenib (BRAFv600e). The mean time on treatment was 13.1 months (range 4-21). Four patients had partial response, one had stable disease and one had progressive disease. Three patients are still on treatment. One patient stopped treatment due to progressive disease (after 10 months) and two patients stopped due to toxicity (1 severe skin toxicity, 1 for suspicion of retinitis pigmentosa, finally ruled out). The most frequent toxicities were minor skin and gastro-intestinal toxicities. In conclusion, BRAF/MEK inhibitors seem to be an interesting option for refractory pediatric LGG. More studies are warranted to better define their role in pediatric LGG and their toxicity profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".