Expression of PD-L1 for Predicting Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: We conducted this meta-analysis and systematic literature review to study the ability of PD-L1 to predict objective response in patients with urothelial cancer treated with PD-1/PD-L1 inhibitors. Methods: Relevant studies of PD-1 or PD-L1 inhibitors in urothelial cancer that reported objective response rate (orr) based on PD-L1 expression status in PubMed, embase, and the Cochrane Library were retrieved. Efficacy of PD-L1 expression status in predicting orr and the efficacy, safety of PD-1 and PD-L1 drugs were analyzed. Results: Studies were divided into ≥1%, ≥5%, and ≥25% based on PD-L1 positivity threshold, and the patients were grouped into PD-L1 positive and negative. In all 3 expression thresholds, patients with positive PD-L1 expression were more likely to experience an objective response [≥1% threshold odds ratio (or): 1.74; 95% confidence interval (ci): 1.20 to 2.53; ≥5% threshold or: 2.74; 95% ci: 2.01 to 3.724; ≥25% threshold or: 7.13; 95% ci: 2.38 to 21.40] in comparison with patients with negative PD-L1 expression. Of the 3 thresholds, the ≥25% threshold was better in predicting orr (1.74 vs. 2.93 vs. 7.13; p < 0.0001). The ≥1% PD-L1 threshold had a relatively high sensitivity in predicting orr; the ≥5% PD-L1 threshold was better for specificity. Sensitivity was higher at the ≥25% threshold than at the other two thresholds, but specificity was lower. Further, we found that there is no statistically significant difference in efficacy between PD-1 and PD-L1 drugs. Conclusions: Urothelial cancer patients with PD-L1 positive expression responded better than PD-L1 negative patients did, and a threshold of ≥5% or greater for PD-L1 expression might predict positive clinical response.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".