Receptor Tyrosine Kinase Expression Profiles in Canine Cutaneous and Subcutaneous Mast Cell Tumors
Bibliographic record
Abstract
The receptor tyrosine kinase (RTK) KIT is a major focus of current research into canine mast cell tumors (MCTs). Little is known about the role of other RTKs, such as vascular endothelial growth factor receptors (VEGFRs) and platelet-derived growth factor receptors (PDGFRs). These RTKs are dysregulated in many human and animal cancers and are key regulators of tumor angiogenesis. The aims of this study were to assess the expression and activation (phosphorylation) status of KIT, VEGFR2, and PDGFR (α and β) in canine MCTs and to examine associations with various clinical outcomes. c-KITmutational status and KIT cellular localization pattern were also evaluated for these tumors. Twenty-seven MCTs, consisting of 5 subcutaneous and 22 cutaneous tumors, from 25 dogs were evaluated. MCT biopsies, cultured mast cells, and skin from the surgical margin were analyzed through Western blotting. MCT biopsies were also used for KIT immunohistochemical labeling and polymerase chain reaction for c-KITmutational analysis. MCT had heterogeneous expression profiles for all 3 RTKs, which varied in intensity and activation status. Statistical analyses showed phosphorylated KIT, VEGFR2, and KIT cellular localization to be predictive of decreased survival time, disease-free interval, and increased metastatic rate. Expression of VEGFR2 and KIT diffuse cytoplasmic labeling were also significantly associated with increased rate of local recurrence. The results of the study show that phosphorylated KIT, KIT, VEGFR2, and PDGFRβ, in addition to KIT localization, may be valuable prognostic determinants in MCTs and should be further studied to improve diagnostic and therapeutic modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".