PD-L1 Expression in High-Risk Non-Muscle-Invasive Bladder Cancer Is Influenced by Intravesical Bacillus Calmette–Guérin (BCG) Therapy
Bibliographic record
Abstract
In the expanding landscape of immune checkpoint inhibitors (CPI) in high-risk (HR) non-muscle-invasive bladder cancer (NMIBC), the role of programmed death ligand 1 (PD-L1) as prognostic and predictive is increasingly significant. However, data evaluating its variability and susceptibility to Bacillus Calmette–Guérin (BCG) therapy in HR NMIBC patients is scarce. This retrospective study analyzed 126 HR NMIBC tissue samples from 63 patients (38× BCG-treated, 25× BCG-naïve) at two time points to assess PD-L1 expression using the ‘combined positivity score’ (CPS) with the 22C3 DAKO antibody method and correlated it with clinicopathological parameters. A CPS > 10 defined PD-L1 positivity. The impact of initial PD-L1 status and its change over time on time-to-recurrence, progression-free survival, and overall survival (TTR, PFS, OS) was analyzed using Kaplan–Meier and Cox proportional hazard models. BCG treatment significantly increased PD-L1 expression (5.31 vs. 0.22, p = 0.0423), with PD-L1 positive cases rising post-treatment in the BCG group and remaining unchanged in BCG-naïve patients. Multivariate analysis including T-stage, CIS, grading, tumor size, multifocality, age, and sex revealed a significant correlation between PD-L1 status change to positivity and improved TTR (p = 0.03). Our findings demonstrate a potential modulation of the PD-L1 status by an intravesical BCG therapy. However, its prognostic value appears limited.
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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 | 0.001 |
| 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".