SynthesisFrontiers in immunology2026
Soluble immune checkpoints in lung cancer: linking prognostic signatures to immunotherapy response - a meta-analysis.
Synthesis in Frontiers in immunology, 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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4 authors.
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Abstract
Introduction: Non-small cell lung cancer (NSCLC) remains one of the leading causes of cancer-related death, with most patients diagnosed at advanced, unresectable stages. Although immune-checkpoint inhibitors have transformed the therapeutic landscape, only a subset of patients achieves meaningful and sustained benefit. In this context, circulating soluble immune-checkpoints, particularly soluble PD-L1 (sPD-L1) have emerged as promising non-invasive biomarkers with potential prognostic and predictive value. Methods: To clarify their relevance, we conducted a systematic review and meta-analysis of studies published between 2015 and 2025, evaluating the association between serum levels of these biomarkers and clinical outcomes in NSCLC. A total of 20 studies including 1967 patients were analyzed, despite heterogeneity in study design and treatment regimens. Results: Lower baseline sPD-L1 levels consistently correlated with longer progression-free (PFS: random effect model with a pooled HR of 2.26 [95% CI: 1.77-2.87, p < 0.0001]) and overall survival (OS: random effect model with a pooled HR of 2.04 [95% CI 1.55-2.68, p < 0.0001]), whereas elevated concentrations were frequently observed in males, smokers, individuals with advanced disease, and those with liver metastases. Associations between soluble checkpoints and tumor histology, mutational status, or tissue PD-L1 expression varied widely across studies. Conclusions: Overall, our findings highlight baseline sPD-L1 with a median cut-off of 90 pg/mL as a promising prognostic biomarker for PFS in NSCLC, suggesting that dynamic monitoring combined with clinical and molecular parameters could enhance patient stratification. Prospective, standardized studies are needed to define optimal cut-offs and validate clinical utility across treatment settings.
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