Efficacy of PD-1/PD-L1 plus CTLA-4 inhibitors in advanced/metastatic NSCLC: a meta-analysis based on RCTs

Tianfu Dai1, Yu Chen2, Xuelian Dai1

  • 1The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.

Abstract

Insights

Dual immune checkpoint inhibitor (ICI) therapy significantly improves survival outcomes for advanced non-small cell lung cancer (NSCLC). Efficacy varies by patient subgroup, suggesting TMB as a potential biomarker.

Area of Science:

  • Oncology
  • Immunotherapy
  • Clinical Trials

Background:

  • Dual immune checkpoint inhibitor (ICI) therapy, combining PD-1/PD-L1 and CTLA-4 inhibitors, shows promise for advanced/metastatic non-small cell lung cancer (NSCLC).
  • Optimal patient selection and definitive efficacy data for dual ICI therapy in NSCLC are still under investigation.

Purpose of the Study:

  • To evaluate the efficacy of dual ICI therapy compared to control treatments in patients with advanced/metastatic NSCLC.
  • To identify patient subgroups that benefit most from dual ICI therapy.

Main Methods:

  • Systematic literature search of randomized controlled trials (RCTs) in major databases (PubMed, Embase, etc.) up to August 2025.
  • Meta-analysis of 10 RCTs involving 6,369 patients, focusing on overall survival (OS) and progression-free survival (PFS).

Main Results:

  • Dual ICI therapy significantly improved both OS (HR=0.84) and PFS (HR=0.78) compared to control groups.
  • Significant benefits were observed across various subgroups, including different cancer types, PD-1/PD-L1 inhibitor use, presence of metastases, patient demographics, and PD-L1 expression levels.
  • Tumor mutational burden (TMB) emerged as a potential predictive biomarker, with high TMB associated with greater efficacy.

Conclusions:

  • Dual ICI therapy offers a significant survival advantage for patients with advanced/metastatic NSCLC.
  • Patient selection is crucial, as efficacy differs across subgroups, highlighting the role of TMB as a complementary predictive biomarker.