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Inhibition of melanoma brain metastasis by targeting melanotransferrin at the cell surface

2008· article· en· W2154851272 on OpenAlexafffund
Yannève Rolland, Michel Demeule, Laurence Fénart, Richard Béliveau

Bibliographic record

VenuePigment Cell & Melanoma Research · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsAngiochem (Canada)Université du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMelanomaMetastasisCancer researchBrain metastasisMonoclonal antibodyCellIn vitroPathologyMedicineIn vivoAntibodyBiologyCancerImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Brain metastases are a common feature of malignant melanoma and are associated with poor prognosis. Melanotransferrin (MTf), one of several antigens associated with the surface of melanoma cells, has been demonstrated to promote cell invasion. In this study, we investigated the role of membrane-bound MTf in several of the steps leading to the development of melanoma brain metastasis. Our results indicated that MTf-positive cells were detected in the brains of nude mice injected intravenously with human melanoma SK-Mel 28 cells. Moreover, administration of a single dose of a monoclonal antibody (L235) directed against human MTf significantly reduced the development of human melanoma brain metastases in nude mice. The ability of melanoma cells to cross the blood-brain barrier (BBB) in vitro is correlated with their MTf expression levels at the cell surface. Overall, our results indicated that membrane-bound MTf is a key element in melanoma cell transmigration across the BBB and subsequent brain metastasis. Thus, these data suggest MTf as an attractive target and demonstrate the therapeutic potential of an anti-MTf mAb for preventing metastatic melanoma.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.034
GPT teacher head0.284
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

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

Citations79
Published2008
Admission routes2
Has abstractyes

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