First-line immunotherapy efficacy in advanced squamous non-small cell lung cancer with PD-L1 expression ≥50%: a network meta-analysis of randomized controlled trials.

Abstract

The optimal first-line immunotherapy regimen for patients with PD-L1 expression ≥50% in squamous non-small cell lung cancer (Sq-NSCLC) remains uncertain. This study utilized net-work meta-analysis (NMA) to indirectly compare the efficacy of various first-line immuno-therapy regimens in this patient subset. Systematic searches were conducted across PubMed, the Cochrane Library, Web of Science, and Embase databases for randomized controlled trials reporting overall survival (OS) and progression-free survival (PFS) outcomes. The search spanned from database inception to November 3, 2023. Bayesian network meta-analysis was employed for a comprehen-sive analysis. To ensure scientific rigor and transparency, this study is registered in the Interna-tional Prospective Register of Systematic Reviews (PROSPERO) under the registration number CRD42022349712. The NMA encompassed 9 randomized controlled trials (RCTs), involving 2170 patients and investigating 9 distinct immunotherapy regimens. For OS, the combination of camrelizumab and chemotherapy demonstrated the highest probability (36.68%) of efficacy, fol-lowed by cemiplimab (33.86%) and atezolizumab plus chemotherapy (23.87%). Regarding PFS, the camrelizumab and chemotherapy combination had the highest probability (39.70%) of efficacy, followed by pembrolizumab (22.88%) and pembrolizumab plus chemotherapy (17.69%). Compared to chemotherapy, first-line treatment with immune checkpoint inhibitors (ICIs) in Sq-NSCLC pa-tients exhibited significant improvements in OS (HR 0.59, 95% CI 0.47-0.75) and PFS (HR 0.44, 95% CI 0.37-0.52). This study suggests that, for Sq-NSCLC patients with PD-L1 expression ≥50%, the first-line immunotherapy regimen of camrelizumab plus chemotherapy provides superior OS and PFS outcomes. Furthermore, ICIs demonstrate enhanced efficacy compared to chemotherapy in this patient population. https://www.crd.york.ac.uk/prospero/, identifier: CRD 42022349712.