Correlation of mismatch repair protein deficiency, PD-L1 and CD8 expression in high-grade urothelial carcinoma of the bladder
Bibliographic record
Abstract
Mismatch repair-deficient (d-MMR) tumours have been reported to show susceptibility to immune checkpoint inhibitors targeting programmed death-1/PD ligand-1 (PD-1/PD-L1). In this study, we sought to correlate the association of d-MMR, PD-L1 and CD8 expression in muscle invasive, high-grade urothelial carcinoma (HGUC) of bladder. A tissue microarray (TMA) was constructed from 201 cases and sequentially stained with PD-L1, CD8, MSH2, MSH6, MLH1 and PMS2. PD-L1 was assessed in tumour and immune cells. CD8 was assessed in a hotspot fashion with results averaged across cores. Loss of nuclear MMR expression on TMA sections was further assessed using corresponding whole tissue sections. d-MMR was identified in four cases (2%). The mean CD8 count was significantly higher in d-MMR tumours (10 vs 35, p=0.007) as was the proportion of PD-L1 positivity (75% vs 20%, p=0.031). d-MMR is uncommon in HGUC of bladder but shows strong correlation with cytotoxic T lymphocyte infiltration and PD-L1 tissue expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".