Expression of the Hippo signalling effectors YAP and TAZ in canine mammary gland hyperplasia and malignant transformation of mammary tumours
Bibliographic record
Abstract
Canine mammary tumours (CMTs) are common neoplasms in dogs that feature many of the clinical, genetic and molecular characteristics of human breast cancer. Despite their high metastatic potential, few adjuvant chemotherapeutic treatment options exist for malignant CMTs, and the development of novel, targeted pharmacological approaches will require a better understanding of their pathogenesis. As recent evidence suggests that dysregulated Hippo signalling is involved in the development and progression of breast cancer, we sought to determine if this pathway could also play a role in CMT. The expression of the Hippo signalling effectors YAP and TAZ was analysed by immunoblotting and immunohistochemistry in samples including normal mammary gland, lobular hyperplasia, benign tumours and malignant tumours of all grades. We found a significant increase in TAZ (but not YAP) expression occurred in lobular hyperplasia relative to normal mammary gland, suggesting a role for TAZ in non-neoplastic epithelial proliferation. Nuclear expression of both TAZ and YAP were significantly higher in malignant tumours than in benign ones, suggesting that Hippo dysregulation could play a role in CMT malignant transformation. No differences in YAP or TAZ expression were detected between grades of malignant tumours. Together, our results indicate that alterations in Hippo signalling may play a role in the pathogenesis of CMT, in a manner similar to breast cancer. Hippo pathway components may therefore represent targets for the development of novel chemotherapeutic agents that could be useful for the treatment of both the human and canine diseases.
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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".