Upfront BRAF/MEK inhibitors for treatment of high-grade glioma: A case report and review of the literature
Bibliographic record
Abstract
Abstract Background High-grade gliomas (HGG) with BRAFV600E mutation represent a unique subset of central nervous system tumors. Targeted therapies including BRAF and MEK inhibitors are now being explored as possible new treatment options. Methods We report an 18-year-old female with a grade 3 pleomorphic xanthoastrocytoma treated upfront with dabrafenib and trametinib. We also conducted a systematic literature review of patients with HGG and BRAFV600E mutations treated with BRAF inhibitors. Results Despite local recurrences resected surgically, the patient has been on dabrafenib and trametinib for more than 54 months. Thirty-two patients with HGG and BRAFV600E mutations treated with BRAF inhibitors were retrieved through our systematic review of the literature. Only 1 young patient with an anaplastic ganglioglioma was treated upfront with a BRAF inhibitor with a curative intent. Best response reported with radiation therapy and systemic therapy was a stable disease (SD) for 18 patients (56.3%) and progressive disease (PD) for 9 patients (28.1%). Responses to treatment regimens that included BRAF inhibitors were reported in 31 patients and included 4 complete responses (12.9%), 23 partial responses (74.2%), 2 SDs (6.5%), and 2 PDs (6.5%). Conclusions Our patient had durable disease control with dabrafenib and trametinib. Given favorable responses reported in patients with HGG treated with BRAF inhibitors, we believe that upfront targeted therapy is a possible treatment approach that should be studied in the context of a clinical trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".