Immunohistochemical detection of p53, PTEN, Rb, and p16 in canine osteosarcoma using tissue microarray
Bibliographic record
Abstract
Although inactivating mutations of tumor suppressor genes are well described in cell lines of canine osteosarcoma (OS), expression of tumor suppressor proteins in spontaneous disease is poorly characterized. We determined the immunohistochemical expression of p53, PTEN, Rb, and p16 in a large cohort of dogs with OS. Formalin-fixed, paraffin-embedded samples of canine OS were analyzed retrospectively. Primary tumor samples from 145 dogs, collected between 2003 and 2008, were evaluated by tissue microarray. Streptavidin-biotin complex immunohistochemistry was performed using monoclonal antibodies for Rb and PTEN and polyclonal antibodies for p16 and p53. The average age of dogs was 7.6 y, and 118 of 145 (81%) were purebred. Most commonly represented purebreds were Greyhound (23%), Rottweiler (11%), and Labrador Retriever (10%). Immunohistochemical detection of p53, PTEN, Rb, and p16 was 81%, 61%, 66%, and 66%, respectively. The staining pattern for p16 was primarily cytoplasmic; the predominant pattern for PTEN, Rb, and p53 was cytoplasmic and nuclear. Exclusively cytoplasmic staining was noted in 19% of samples positive for p53 and 8% of samples positive for Rb. Kaplan-Meier curves showed that protein expression was not associated with significant differences in overall survival ( p > 0.191). We documented heterogeneity in both immunostaining and subcellular localization of tumor suppressor proteins, providing further characterization of canine OS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".