Impact of Programmed Death-ligand 1 Expression on Oncological Outcomes in Patients with Muscle-invasive Bladder Cancer Treated with Radiation-based Therapy
Bibliographic record
Abstract
No biomarkers are recommended for patients undergoing radiation-based therapy (RT) for muscle-invasive bladder cancer (MIBC). We aim to evaluate the predictive role of programmed death-ligand 1 (PD-L1) expression on the oncological outcomes of patients treated with RT for MIBC. A single-center retrospective analysis of tumor specimens collected through transurethral resection (TURBT) from 104 MIBC patients, implemented in a tissue microarray and stained with the SP263 PD-L1 clone (Ventana Medical Systems, Tucson, AZ, USA), was conducted. Two reviewers measured the PD-L1 H-score for tumor and immune cells. RT (maximal TURBT followed by radiation and concurrent chemotherapy when eligible). Logistic and Cox regression models were used to predict 3-mo complete response (CR) and overall survival (OS) after RT, respectively. A total of 88 (85%) patients had cT2 disease and 39 (37.5%) had high immune cell PD-L1 expression. A CR was achieved in 68 (65%) patients. On the multivariable analysis (MVA), a higher clinical stage (p = 0.02) and a low immune cell PD-L1 H-score (p = 0.02) were associated with a decreased CR after RT. The median time to death was 43 mo (95% confidence interval 20–66). On Cox MVA, a high immune cell PD-L1 H-score (p = 0.0017) was associated with better OS, independently of performance status (p = 0.0005) or tumor stage (p = 0.0013). A high tumor cell PD-L1 H-score was not an independent predictor of CR or OS. Limitations of the study include the retrospective design. MIBC patients with high PD-L1 expression on immune cells appear to have better oncological outcomes following RT. Our results may aid in patient stratification for future clinical trial design. In this report, we evaluated the role of programmed death-ligand 1 (PD-L1) expressed on tumor and immune cells in the tumor microenvironment for patients treated with a bladder-sparing regimen. We found that PD-L1 overexpression on immune cells is able to predict a better response to radiation-based 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".