BRAF/MEK inhibitors use for pediatric gliomas; real world experience from a resource-limited country
Bibliographic record
Abstract
Introduction Most pediatric low-grade-gliomas (LGG) and some high-grade-gliomas (HGG) have alterations in the RAS/MAPK pathway. Promising high tumor response rates were achieved using BRAF/MEK inhibitors, however data on their use in low-middle-income-countries (LMICs) are limited. Methods We retrospectively reviewed our Jordanian experience of using compassionate BRAF/MEK inhibitors in treating children with gliomas. We reviewed patients’ clinical characteristics, tumor response, and side effects. Results Twenty patients (13 males, 7 females) were identified. Median age at diagnosis was 8.3 years (0.3-18.9years). There were fifteen LGGs, three HGGs and two grade-2 pleomorphic xanthoastrocytoma (PXA-2). Fifteen tumors were supratentorial, three posterior fossa/brainstem, one diffuse-glioneuronal tumor (DLGNT) and one spinal. Five tumors were metastatic. Except for one patient with neurofibromatosis, ten patients underwent partial resection and nine had biopsy. All patients, except three, received BRAF/MEK inhibitors after initial standard chemo/radiotherapy. Seven LGGs had BRAF-mutation, six had BRAF-fusion, and two were empirically treated (one neurofibromatosis and one DLGNT). Fourteen LGGs were treated with 1-4 chemotherapy regimens before BRAF/MEK inhibitors’ use; all had partial/stable response on targeted therapy at a median of 1.9 years (0.5-5.4years). Two patients with BRAFv600E -mutated/ CDKN2A deleted PXA-2, had progression following resection, and experienced stable/partial response at 9 months of dabrafenib use. Two patients with HGGs had BRAFv600E -mutation, and one had an FGFR-mutation. All three patients with HGG had temporary stable/partial response, two with significant clinical improvement. At a median of 2.7 years (1.3-3.2years), all patients experienced tumor progression, and two died. Eight patients (40%) developed acneiform rash, three (15%) paronychia, and one had significant panniculitis and fatigue. Six patients (30%) needed dose-reduction. Nine patients had temporary drug interruptions [due to side effects (5) and drug shortage (4)]. Two patients who stopped trametinib due to side effects (significant acneiform rash/paronychia and intracranial bleeding) did not experience progression. Conclusions Our experience with BRAF/MEK inhibitors’ use was positive achieving response in all LGGs and provided sustained response with good quality of life for patients with HGG. Cost effectiveness analyses and patients’ satisfaction comparisons with chemotherapy are needed to evaluate the routine use of these drugs in LMICs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".