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Lung Cancer Research Studies / Lung Cancer Treatments and Mutations · Journal article
Journal of Clinical Question · August 17, 2026
A consensus or society position rather than new primary data.
This narrative review synthesizes evidence on factors influencing PD-1/PD-L1 inhibitor efficacy in NSCLC, including PD-L1 expression, tumor mutational burden, oncogenic drivers, and combination strategies. It concludes that PD-L1 is the most widely implemented biomarker but no single factor adequately predicts response, and integrated, dynamic, context-specific biomarker models supported by prospective validation are needed to improve precision immuno-oncology.
Narrative review. Non-small cell lung cancer patients treated with PD-1/PD-L1 inhibitors across multiple treatment lines and settings.. Intervention: PD-1/PD-L1 inhibitors, with discussion of immune checkpoint combinations and biomarker-driven selection..
In historical studies of broadly selected patients receiving ICI monotherapy, objective responses occurred in approximately 20%. Response rates vary substantially according to treatment line, PD-L1 expression, molecular subtype, patient selection, and combination regimens. Current evidence supports PD-L1 as the most widely implemented biomarker, but no single factor adequately captures biological and temporal heterogeneity of treatment response.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should recognize that PD-L1 expression alone is insufficient for predicting ICI response in NSCLC. Integrated biomarker models incorporating tumor microenvironment, TMB, oncogenic drivers, and patient factors are needed to better select candidates and optimize precision immuno-oncology.
A narrative review synthesizing evidence on factors influencing PD-1/PD-L1 inhibitor efficacy in NSCLC, prioritizing pivotal trials and consensus statements, to inform clinical decision-making and biomarker selection.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians should recognize that PD-L1 expression alone is insufficient for predicting ICI response in NSCLC. Integrated biomarker models incorporating tumor microenvironment, TMB, oncogenic drivers, and patient factors are needed to better select candidates and optimize precision immuno-oncology.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Immune checkpoint inhibitors (ICIs), particularly programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors, are central to the treatment of non-small cell lung cancer (NSCLC), and a subset of patients achieves durable benefit. In historical studies of broadly selected patients receiving ICI monotherapy, objective responses occurred in approximately 20%, although response rates vary substantially according to treatment line, PD-L1 expression, molecular subtype, patient selection, and the use of combination regimens. This narrative review used targeted searches of PubMed/MEDLINE, ClinicalTrials.gov, and reference lists of key publications to identify English-language evidence available through May 2026, prioritizing pivotal randomized trials, prospective translational studies, consensus statements, guidelines, and recent high-quality reviews. We critically examine tumor-microenvironmental features, PD-L1 expression, tumor mutational burden (TMB), oncogenic driver alterations, combination strategies, and patient-related factors that may influence PD-(L)1 inhibitor efficacy. Although other immune checkpoints, including cytotoxic T-lymphocyte-associated protein 4 and lymphocyte-activation gene 3, are discussed when directly relevant to combination therapy, the principal focus is PD-1/PD-L1-directed treatment. Current evidence supports PD-L1 as the most widely implemented biomarker, but no single factor adequately captures the biological and temporal heterogeneity of treatment response. Integrated, dynamic, and context-specific biomarker models, supported by prospective validation, are therefore required to improve precision immuno-oncology.
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