Correlation Between KIT Expression and <i>c-Kit</i> Mutations in 2 Subtypes of Canine Oral Melanocytic Neoplasms
Bibliographic record
Abstract
c-Kit mutations have been reported in 15% to 40% of certain human melanoma subtypes, including those histologically similar to canine oral malignant melanomas. Therapeutic response to tyrosine kinase inhibitors has been demonstrated in those human patients. As canine oral malignant melanomas tend to have a poor prognosis despite aggressive surgical removal, evaluation of KIT expression and identification of c-Kit mutations in canine oral melanocytic neoplasms was performed to determine if there is any indication that tyrosine kinase inhibitor drugs might effectively treat any of these cases. This study evaluated 27 canine oral malignant melanomas and 12 canine histologically well-differentiated oral melanocytic neoplasms for activating c-Kit mutations, determined differences in immunohistochemical expression of KIT and c-Kit mutation status, and determined if KIT expression could predict c-Kit mutation status. Among samples that contained intraepithelial nests of neoplastic melanocytes in the KIT-labeled sections, KIT was expressed within cells in these nests in 22/23 (96%) malignant melanomas and 5/7 histologically well-differentiated neoplasms. KIT was expressed in 10% to 30% of neoplastic melanocytes in the lamina propria in 3/24 (13%) malignant melanomas, but 0/9 (0%) histologically well-differentiated neoplasms. Next-generation sequencing identified 85 variants in c-Kit, including 9 nonsynonymous mutations that resulted in amino acid changes predicted to affect protein function. c-Kit mutations with predicted deleterious protein effects were more common in malignant melanomas (8/27 [30%] vs 1/12 [8%]). There was no apparent relationship between detected c-Kit mutations and KIT expression. These results do not support the use of therapies that target c-Kit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".