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Record W4388570298 · doi:10.1016/j.omtm.2023.101156

Case report of selumetinib as a novel therapy in a neurofibromatosis type 2-associated ependymoma

2023· article· en· W4388570298 on OpenAlexfundno aff
Nigel Blackwood, Christopher Zetzmann, Christopher Trevino

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsnot available
FundersOntario Ministry of Research, Innovation and Science
KeywordsNeurofibromatosis type 2SelumetinibMedicineSMARCB1SchwannomaEverolimusGermline mutationInternal medicineCancer researchPathologyMutationBiologyHistoneCancer

Abstract

fetched live from OpenAlex

We report partial response (PR) to novel therapy with selumetinib in a patient with neurofibromatosis type 2 (NF2). A 25-year-old male presented with bilateral vestibular schwannomas, spinal cord intramedullary ependymomas, cranial and spinal meningiomas, spinal nerve root mixed schwannoma-neurofibromas, and peripheral nerve sheath tumors. He tested negative for germline NF2 , SWItch/sucrose non-fermentable-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 ( SMARCB1) , and leucine zipper-like transcription regulator 1 ( LZTR1) mutations. Molecular analysis of a resected cervical spine schwannoma-neurofibroma demonstrated an isolated somatic SMARCB1 mutation. Due to progression of all tumors, he was treated medically with both everolimus (10 mg/day) and selumetinib (25 mg/kg twice a day), but he rapidly transitioned to selumetinib monotherapy due to everolimus toxicity. 3 months of treatment resulted in PR in one spinal ependymoma and stable disease in other tumors. This PR was quantified by the differences in units of intensity in pre- and post-treatment magnetic resonance image. To the best of our knowledge, this is the first reported case for using selumetinib in NF2-associated tumors or ependymomas.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.157
GPT teacher head0.453
Teacher spread0.295 · 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.

Study designBench or experimental
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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