Aug 2026· Journal of Clinical Question· 0 citations· 113 references
TL;DR
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, so integrated, dynamic, and context-specific biomarker models are required to improve precision immuno-oncology.
Abstract
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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A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
