PD-1 inhibitors versus PD-L1 inhibitors in PD-L1–negative advanced non-small cell lung cancer: A meta-analysis of survival outcomes.
Bibliographic record
Abstract
e20585 Background: Immune checkpoint inhibitors (ICIs) which target programmed cell death protein 1 receptor (PD-1) or its ligand (PD-L1) are used extensively in non-small cell lung cancer (NSCLC). In this study, we aimed to compare the relative efficacy of PD-1 inhibitors and PD-L1 inhibitors in PD-L1-negative advanced NSCLC. Methods: A systematic search was conducted in MEDLINE (via PubMed, Scopus, and Google Scholar) to identify randomized trials of advanced NSCLC that evaluated ICIs (anti-PD-1 or anti-PD-L1) and reported outcomes stratified by PD-L1 status, including data specific to PD-L1-negative patients. Hazard ratios (HRs) with 95% confidence intervals (CIs) and p-values for progression-free survival (PFS) and overall survival (OS) were extracted for the PD-L1-negative subgroup of each trial. Data were pooled using a random-effects meta-analysis, and comparisons between anti-PD-1 and anti-PD-L1 agents were performed. Subgroup analyses were conducted to explore variations in effect size. Results: The meta-analysis included 23 trials comprising 4,548 PD-L1-negative patients, representing 33% of all participants. PD-L1 testing was conducted in all studies. Anti-PD-1 agents significantly improved OS in PD-L1-negative patients with advanced NSCLC (HR 0.75, 95% CI 0.67–0.83, p < 0.01). In contrast, anti-PD-L1 agents did not show a statistically significant improvement in OS (HR 0.90, 95% CI 0.78–1.05, p = 0.18). Direct comparisons revealed that anti-PD-1 agents were associated with better OS compared to anti-PD-L1 agents (HR 0.83, 95% CI 0.67–0.99, p = 0.01). The benefit of anti-PD-1 agents was more pronounced in the first-line setting (pairwise HR 0.79, 95% CI 0.66–0.93, p = 0.01), whereas no significant difference was observed in later lines of therapy (pairwise HR 1.13, 95% CI 0.82–1.55, p = 0.45). These differences in OS were not reflected in PFS outcomes. 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 setting. This should be validated using real-world studies.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.060 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".