Anti-Programmed Cell Death-1 Versus Anti-Programmed Death-Ligand 1 (PD-L1) in PD-L1-Negative Advanced Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) which target programmed cell death-1 (PD-1) receptor or its ligand (PD-L1) are used extensively in non-small cell lung cancer (NSCLC). In this article, we compared the relative efficacy of PD-1 inhibitors and PD-L1 inhibitors in PD-L1-negative advanced NSCLC. Methods: We searched MEDLINE (host: PubMed, Scopus, and Google Scholar) for randomized trials for advanced NSCLC in which ICIs (anti-PD-1 or anti-PD-L1) were used where outcome data were reported based on PD-L1 testing, including the subset of PD-L1-negative patients. We extracted hazard ratios (HRs) and related 95% confidence intervals (CIs) and/or P values for progression-free survival (PFS) and overall survival (OS) for the PD-L1-negative subgroup of each included trial. We then pooled data using a random effects meta-analysis and compared anti-PD-1 to anti-PD-L1 inhibitors. Variations in effect size were examined using subgroup analyses. Results: Twenty-three trials were included in the meta-analysis. PD-L1 testing was performed in all participants. A total of 4,548 PD-L1-negative patients were included in the analysis, representing 33% of all participants in the included clinical trials. Overall, the addition of anti-PD-1 was associated with better OS in PD-L1-negative advanced NSCLC patients (HR: 0.75, 95% CI: 0.67 - 0.83, P < 0.01), while the addition of anti-PD-L1 inhibitors showed no improvement in OS (HR: 0.90, 95% CI: 0.78 - 1.05, P = 0.18). Compared to anti-PD-L1 agents, anti-PD-1 resulted in better OS in PD-L1-negative patients (HR: 0.83, 95% CI: 0.67 - 0.99, P = 0.01). The differential benefit of anti-PD-1 over anti-PD-L1 was of larger magnitude when checkpoint inhibitors were used in the first-line setting (pairwise comparison HR: 0.79, 95% CI: 0.66 - 0.93, P = 0.01), while there was no difference for later lines of therapy (pairwise comparison 1.13; 95% CI: 0.82 - 1.55, P = 0.45). These differences in OS were not observed when pooling PFS data. Conclusions: Compared to checkpoint inhibitors targeting PD-L1, those targeting PD-1 are associated with better OS in PD-L1-negative advanced NSCLC, a finding influenced by trials performed in the first-line sitting. These data should be validated using real-world studies.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".