ArticleCancer cell international2026
Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 < 50.
Article in Cancer cell international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03447678 (Phase II Study to Test Pembrolizumab), which is not on this map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Phase II Study to Test Pembrolizumab (MK-3475) in First Line Treatment of Advanced NSCLC Patients With PD-L1 Low Tumors (<50%)_ PEOPLE TRIAL (Pembrolizumab in Pd-L1 Low Expressors).
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Authors and funding
18 authors.
Funding
Abstract
backgroundThe 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.
methodsThis 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.
resultsOur 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.
conclusionsDespite 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%.
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