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Record W93014500 · doi:10.2310/7750.2011.11064

KIT Gene Mutations and Patterns of Protein Expression in Mucosal and Acral Melanoma

2012· article· en· W93014500 on OpenAlexaffabout
Suzan Abu–Abed, Nancy Pennell, Teresa M. Petrella, Frances C. Wright, Arun Seth, Wedad Hanna

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsSunnybrook Health Science Centre
FundersAmerican College of Surgeons
KeywordsMedicineMucosal melanomaGene expressionGeneMelanomaCancer researchDermatologyPathologyGeneticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Recently characterized KIT (CD117) gene mutations have revealed new pathways involved in melanoma pathogenesis. In particular, certain subtypes harbor mutations similar to those observed in gastrointestinal stromal tumors, which are sensitive to treatment with tyrosine kinase inhibitors. OBJECTIVE: The purpose of this study was to characterize KIT gene mutations and patterns of protein expression in mucosal and acral melanoma. METHODS: Formalin-fixed, paraffin-embedded tissues were retrieved from our archives. Histologic assessment included routine hematoxylin-eosin stains and immunohistochemical staining for KIT. Genomic DNA was used for polymerase chain reaction-based amplification of exons 11 and 13. RESULTS: We identified 59 acral and mucosal melanoma cases, of which 78% showed variable levels of KIT expression. Sequencing of exons 11 and 13 was completed on all cases, and 4 (6.8%) mutant cases were isolated. CONCLUSION: We successfully optimized conditions for the detection of KIT mutations and showed that 8.6% of mucosal and 4.2% of acral melanoma cases at our institution harbor KIT mutations; all mutant cases showed strong, diffuse KIT protein expression. Our case series represents the first Canadian study to characterize KIT gene mutations and patterns of protein expression in acral and mucosal 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.264
Teacher spread0.242 · 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 designObservational
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

Citations31
Published2012
Admission routes2
Has abstractyes

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