Beclin-1 is a novel predictive biomarker for canine cutaneous and subcutaneous mast cell tumors
Bibliographic record
Abstract
Mast cell tumors (MCTs) are the most common skin tumor of the dog, and accurately predicting their clinical behavior is critical in directing patient therapy, as they range from benign lesions to a fatal systemic disease. Grading is useful for prognosis, but it cannot predict the behavior of all MCTs. We hypothesized that biomarker immunolabeling in tumor tissues would correlate with patient morbidity and mortality. A clinically annotated tissue microarray (TMA) of primary, recurrent, and metastatic (to lymph node) canine dermal and subcutaneous MCTs was created. Some dogs whose MCTs were included in the TMA did not receive adjunctive treatment after surgical excision of the MCT, whereas others were treated with one or a combination of chemotherapy, radiation, or oral toceranib. Immunohistochemistry for beclin-1, an autophagy protein, was performed followed by digital image analysis. Beclin-1 immunolabeling was higher in recurrent tumors (mean H-score 110.8) than primary MCTs (mean H-score 73.5), and highest in lymph node metastases (mean H-score 138.5) with a significant difference in means ( P < .001). While beclin-1 level was not prognostic, it was strongly predictive for survival after adjunctive treatment; dogs with high beclin-1-expressing tumors showed poorer survival compared to those with low beclin-1-expressing tumors (HR = 5.7, P = .02), especially in Kiupel high-grade tumors (HR = 16.3, P = .01). Beclin-1 immunolabeling was the only significant predictive factor by multivariable analysis ( P = .04). These findings may improve our ability to predict the response to adjunctive therapy. Importantly, these data suggest that autophagy inhibitors may be useful in improving response to treatment for dogs with high-grade MCTs.
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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 | 0.001 | 0.001 |
| 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".