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Record W2148291345 · doi:10.1111/pcmr.12424

Extrinsic factors can mediate resistance to <scp>BRAF</scp> inhibition in central nervous system melanoma metastases

2015· article· en· W2148291345 on OpenAlexfundno aff
Heike Seifert, Eishu Hirata, Martin Gore, Komel Khabra, Christina Messiou, James Larkin, Erik Sahai

Bibliographic record

VenuePigment Cell & Melanoma Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsnot available
FundersInstitute of Cancer ResearchCancer Research UKNational Institute for Health and Care ResearchNovartisNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchPfizerRoyal Marsden NHS Foundation TrustFrancis Crick Institute
KeywordsVemurafenibDabrafenibMelanomaMedicineCancer researchCentral nervous systemCerebrospinal fluidMAPK/ERK pathwayMEK inhibitorPathologyKinaseInternal medicineBiologyMetastatic melanoma

Abstract

fetched live from OpenAlex

Here, we retrospectively review imaging of 68 consecutive unselected patients with BRAF V600-mutant metastatic melanoma for organ-specific response and progression on vemurafenib. Complete or partial responses were less often seen in the central nervous system (CNS) (36%) and bone (16%) compared to lung (89%), subcutaneous (83%), spleen (71%), liver (85%) and lymph nodes/soft tissue (83%), P < 0.001. CNS was also the most common site of progression. Based on this, we tested in vitro the efficacy of the BRAF inhibitors PLX4720 and dabrafenib in the presence of cerebrospinal fluid (CSF). Exogenous CSF dramatically reduced cell death in response to both BRAF inhibitors. Effective cell killing was restored by co-administration of a PI-3 kinase inhibitor. We conclude that the efficacy of vemurafenib is variable in different organs with CNS being particularly prone to resistance. Extrinsic factors, such as ERK- and PI3K-activating factors in CSF, may mediate BRAF inhibitor resistance in the CNS.

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)0.002
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.288
Teacher spread0.238 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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