The efficacy of single-agent immune checkpoint inhibitors as a first-line treatment for advanced non-small cell lung cancer (aNSCLC) patients with PD-L1 Tumor Proportion Score (TPS) < 50% remains variable. Network analysis is promising in addressing tumor biology and behavior, potentially predicting therapeutic response.

This study, based on the PEOPLE trial (NCT03447678) data, explores network analysis for predictive biomarker discovery in immunotherapy response. Utilizing circulating immune profiling (CIP) and gene expression profiling (GEP), key immune cells and gene interactions were identified.

Our findings confirm the central role of natural killer (NK) cells, with elevated baseline levels associated with a favorable response. Differential co-expression network (DCN) analysis of GEP identified 23 hub genes, with enrichment analysis linking CD48 to immune-related processes. Patient similarity network (PSN) analysis identified two patient clusters with significantly different survival outcomes. The integrated model outperformed single-layer approaches, supporting the added value of combining GEP and CIP data.

Despite limitations such as a non-randomized design and small sample size, the study’s innovative network approach provides valuable insights. The results suggest that baseline NK cell subsets and specific gene evaluations could guide personalized treatment strategies, optimizing the use of pembrolizumab in aNSCLC patients with PD-L1 TPS < 50%.