ACQUIRED PTEN LOSS MAY MEDIATE DABRAFENIB AND TRAMETINIB RESISTANCE IN BRAF V600E–MUTATED EPITHELIOID GLIOBLASTOMA: A CASE REPORT
Bibliographic record
Abstract
Abstract Epithelioid glioblastoma is a rare variant of glioblastoma that primarily affects children and young adults. This commonly has a BRAF V600E mutation and can be treated with BRAF and MEK inhibitors. CASE DESCRIPTION: An 18-year-old woman was diagnosed with an epithelioid glioblastoma. She received temozolomide chemoradiotherapy. Upon progression she had redo surgery and received dabrafenib and trametinib. The patient developed a drop metastasis to the cervical spine, and consequently received focal radiotherapy, 20 Gy in five fractions. Treatments were halted six months after her second resection due to further disease progression. She developed uncal herniation, requiring her to have a third resection approximately six months after her second resection. She passed away after this, 12 months following her initial diagnosis. Retrospective genetic analysis of three tumor specimens showed a novel PTEN mutation that arose after her first surgery. We suspect that the acquired PTEN mutation conferred resistance to dabrafenib and trametinib. DISCUSSION: This case offers teaching points and research questions. Firstly, early treatment with BRAF and MEK inhibitors in high grade BRAF V600E–mutated gliomas may be optimal as the patient may have stabilized with earlier treatment on dabrafenib and trametinib. Secondly, further studies are required to investigate whether the addition of PTEN mutation to BRAF V600E leads to aggressive tumor behavior. Lastly, there is a need for investigation into potential benefits of treating similar patients by co-targeting the BRAF and PI3K/AKT pathways.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".