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Record W3010451274

KIT Mutations in Australian Canine Mast Cell Tumours and Correlations with Patient Prognostic Factors

2019· dissertation· en· W3010451274 on OpenAlexaboutno aff
Vanessa S. Tamlin

Bibliographic record

VenueAdelaide Research & Scholarship (AR&S) (University of Adelaide) · 2019
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsnot available
FundersAustralian Companion Animal Health Foundation
KeywordsMast cellMedicineMast (botany)OncologyInternal medicinePathologyCancer researchImmunology
DOInot available

Abstract

fetched live from OpenAlex

Mast cell tumour (MCT) is the most common skin neoplasm in dogs. Accurately predicting MCT behaviour is essential for proper tumour management. Histological tumour grading is useful for canine prognosis and can be supplemented with the mutational evaluation of the CD117 Proto-Oncogene Receptor Tyrosine Kinase gene, KIT. Dogs with MCTs harbouring an internal tandem duplication (ITD) within exon 11 of the extracellular regulatory domain of KIT are more likely to respond to treatment with tyrosine kinase inhibitors. Conversely, MCTs with mutation of the intracellular tyrosine kinase domain are resistant to this class of drugs. KIT exon 11 ITDs are common in almost 50% of histologically high-grade cutaneous MCTs, whereas the prevalence of enzymatic pocket-type mutations was unknown. Therefore, the prevalence of canine MCT KIT gene mutations and their correlation with prognostic influencers were determined herein. An exon 11 ITD mutation prevalence of 10% was determined in a cohort of 239 Australian dogs with cutaneous MCTs. An exon 11 ITD was detected in only one of 41 subcutaneous MCT. KIT mutation profiles were established using AmpliSeq™ Ion Torrent™ next-generation sequencing technology for 95 MCTs from 93 dogs. Non-synonymous KIT mutations and non-coding variants with a predicted gain-of-function effect on Kit protein activity were identified in 51.9% (n = 40/77) of cutaneous MCTs and 44.4% (n = 8/18) of subcutaneous MCTs. Enzymatic pocket-type mutations, predictably conferring tumour tyrosine kinase inhibitor resistance, were detected in 20.8% (n = 16/77) of the cutaneous MCTs. A novel finding of this research is that mutation of the KIT enzymatic pocket domain statistically significantly predicts 12-month canine MCT-related death in multivariable analysis, independent of tumour histological grade. This finding may help identify potentially aggressive MCT cases which would have been otherwise overlooked by evaluation of histological grade alone. Conversely, exon 11 ITD status did not add any prognostically useful information in the multivariable analyses. However, the analyses revealed that Labrador dogs were at risk of developing high-grade MCTs at an old age (≥ 7 years). In addition to dogs, over 30 other species of mammals, birds and reptiles have been documented with mast cell neoplasia. In a cohort of 20 domestic cats with cutaneous MCT, KIT mutations were detected in 60% of cases. KIT-mutant MCTs were not correlated with tumour increased histological grade or mitotic index and hence, KIT mutation identification was not prognostically useful for cats with MCT. KIT mutations were discovered in the neoplastic DNA from two of four related cheetahs diagnosed with mast cell neoplasia. One of the cheetahs with abnormal KIT was euthanised as a result of visceral mastocytosis. The contribution of mutant-KIT to mast cell oncogenesis and disease malignancy is unclear in this case. The implication of KIT in mast cell neoplasia in dogs, cats and other species is apparent. However, the mechanism of mutation and the contribution to mast cell pathogenesis and malignancy remains relatively obscure. Still, the findings herein will improve the use of KIT and genetic testing in canine MCT prognostication.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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