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Record W4385635813 · doi:10.33590/emjoncol/10305035

Acquired PTEN Loss May Mediate Dabrafenib and Trametinib Resistance in BRAF V600E-Mutated Epithelioid Glioblastoma: A Case Report and Literature Review

2023· article· en· W4385635813 on OpenAlexaff
Abrar Ahmed, Liana Nobre, Warren Mason, Julie Bennett, Uri Tabori, Cynthia Hawkins, Seth Climans

Bibliographic record

VenueEMJ Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick ChildrenSickKids FoundationWestern University
Fundersnot available
KeywordsTrametinibDabrafenibTemozolomidePTENMedicineCancer researchMEK inhibitorVemurafenibMelanomaOncologyGliomaMAPK/ERK pathwayInternal medicineKinasePI3K/AKT/mTOR pathwayBiologySignal transductionMetastatic melanoma

Abstract

fetched live from OpenAlex

Epithelioid glioblastoma is a rare and aggressive variant of glioblastoma that is common in children and young adults. This variant frequently has a BRAF V600E mutation, and in recent years, this is often treated with BRAF and mitogen-activated protein kinase kinase inhibitors. An 18-year-old female initially presented with headaches and vomiting. They were diagnosed with an epithelioid glioblastoma and treated with temozolomide chemoradiotherapy. Upon progression, they had to redo surgery and then received dabrafenib and trametinib. They had one last surgery shortly before fatal tumour progression. Retrospective molecular analysis of three tumour specimens showed a PTEN mutation that arose upon first progression, but was not there initially. There were no new tumour mutations after initiation of dabrafenib and trametinib. The acquired PTEN mutation may have conferred resistance to dabrafenib and trametinib. This case highlights the potential importance of early treatment with BRAF and mitogen-activated protein kinase kinase inhibitors in high-grade BRAF V600E-mutated gliomas, ideally before the tumour develops resistance to targeted therapy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.294
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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