ArticleJournal of thoracic disease2026
Comprehensive characterization of non-small cell lung cancer of different PD-L1 expression classes: a study of 1,038 Chinese patients.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.
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7 authors.
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Abstract
Background: Programmed death ligand 1 (PD-L1) is one of the most well-established biomarker for predicting response to immunotherapy in non-small cell lung cancer (NSCLC). Multiple clinicohistologic and genetic features have been proposed as correlates of PD-L1 expression. However, few studies have investigated the relevance of immune cell infiltration or chromosomal arm-level alterations. This study aimed to comprehensively characterize the clinicopathologic, genomic, and immunologic landscape across different PD-L1 expression classes in 1,038 Chinese patients with NSCLC. Methods: We retrospectively included 1,038 NSCLC patients who had genomic profiles and PD-L1 Tumor Proportion Score (TPS) by the 22C3 assay. Multiplex immunohistochemistry (IHC) was used to assess immune cell filtration. Association analyses were performed to identify clinicopathologic and molecular features associated with PD-L1 expression. Results: The negative (TPS <1%), low (1-49%), and high (≥50%) PD-L1 expression groups accounted for 25.2%, 43.2%, and 31.5% of cases, respectively. PD-L1 expression was positively associated with puncture biopsy specimens, male sex, squamous histology, Conclusions: This study enriches the current volume of evidence on histopathologic, genetic, immunologic correlates of PD-L1 expression and, to our knowledge, is the first comprehensive analysis of arm-level alterations. Further studies are warranted to validate these finding and to determine their associations with clinical outcomes.
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