Effectiveness of PD1/PD-L1 combined with anti-angiogenic drugs in patients with advanced nonsmall cell lung cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Protein-1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1) therapy have become an important treatment approach for patients with advanced nonsmall cell lung cancer (NSCLC), but primary or secondary resistance remains a challenge for some patients. PD-1/PD-L1 combined with anti-angiogenic drugs (AAs) in NSCLC patients have potential synergistic effects, and the survival benefit may vary based on a treatment order. To investigate the efficacy of PD-1/PD-L1 combined with AAs as the treatment for patients with advanced NSCLC. Materials and Methods: We comprehensively searched EMBASE, PubMed, Web of Science, CNKI, VIP, and Wanfang databases from January 2017 to September 2022. The Cochrane risk bias tool evaluated the quality of included randomized clinical trials. Newcastle-Ottawa-Scale score was used to evaluate the quality of retrospective studies. Publication bias was evaluated by funnel plot, Begg’s test, and Egger’s test. Results: Seventeen articles were finally selected, involving 5182 patients. Meta-analysis results showed that PD1/PD-L1 combined with AAs therapy significantly improved progression-free survival (PFS) (hazard ratio [HR] = 0.61, 95% confidence interval [CI]: 0.50–0.75, P < 0.00001), overall survival (OS) (HR = 0.79, 95% CI: 0.71–0.88, P < 0.00001), and objective response rate (ORR) (risk ratio = 0.88, 95% CI: 0.81–0.96, P = 0.004), with the statistically significant difference. The sensitivity analysis demonstrated the robustness of the PFS, ORR, and OS. Conclusion: The combination of PD-1/PD-L1 inhibitors with AAs in treating advanced patients has exhibited notable therapeutic advantages when contrasted with monotherapy. Specifically, the administration of PD-1/PD-L1 inhibitors in conjunction with AAs, or sequential treatment involving PD-1/PD-L1 followed by AAs, has shown enhanced therapeutic efficacy in this patient population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".