Evaluation of lymphocyte‐specific programmed cell death protein 1 receptor expression and cytokines in blood and urine in canine urothelial carcinoma patients
Bibliographic record
Abstract
Abstract Urothelial carcinoma (UC) is the most common urinary tumour in dogs. Despite a range of treatment options, prognosis remains poor in dogs. In people, breakthroughs with checkpoint inhibitors have established new standards of care for muscle‐invasive bladder cancer patients and elevated levels of programmed cell death protein 1 (PD‐1) suggest immune checkpoint blockade may be a novel target for therapy. The goal of this study was to determine if canine UC patients express elevated levels of lymphocyte‐specific PD‐1 and/or urinary cytokine biomarkers compared to healthy dogs. Paired blood and urine were evaluated in 10 canine UC patients, five cystitis patients and 10 control dogs for lymphocyte‐specific PD‐1 expression via flow cytometry and relative cytokine expression. In UC patients, PD‐1 expression was significantly elevated on CD8+ lymphocytes in urine samples. UC patients had a higher CD4:CD8 ratio in their urine compared to healthy dogs, however, there was no significant variation in the CD8:Treg ratio between any group. Cystitis patients had significantly elevated levels of CD4+ T cells, CD8+ T cells and Tregs in their blood samples compared to UC patients and healthy dogs. Cytokine analysis demonstrated significant elevations in urinary cytokines (granulocyte‐macrophage colony‐stimulating factor, interferon‐gamma [IFN‐γ], interleukin (IL)‐2, IL‐6 IL‐7, IL‐8 and IL‐15, IP‐10, KC‐like, IL‐18, monocyte chemoattractant protein‐1 and tumour necrosis factor‐alpha). Several of these cytokines have been previously correlated with both lymphocyte‐specific PD‐1 expression (IFN‐γ, IL‐2, IL‐7 and IL‐15) in muscle‐invasive urothelial carcinoma in humans. Our results provide evidence of urinary lymphocyte PD‐1 expression and future studies could elucidate whether veterinary UC patients will respond favourably to anti‐PD‐1 immune checkpoint inhibitor therapy.
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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".