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November 1, 2025Lung CancerOpen Access

Plasma proteomics profiling identifies predictive biomarkers for immunotherapy response in small-cell lung cancer

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Authors

GJGuang‐Ling JieJZJiaxin ZhongJWJingze Wang

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Overview

Cohort study demonstrates that a plasma proteomic signature predicts chemo-immunotherapy response in small-cell lung cancer, indicating potential for personalized treatment.

Key Points

  • To identify and validate circulating plasma protein biomarkers that predict clinical response to combined anti-PD-L1 immunotherapy and chemotherapy in patients with small-cell lung cancer.
  • Enrolled 118 patients with small-cell lung cancer treated with anti-PD-L1 antibodies plus chemotherapy, collecting longitudinal plasma samples before and during therapy for mass spectrometry analysis.
  • Developed a three-protein predictive model (VASN, PARD3, and PTGES3) using LASSO machine learning in a training cohort (N=42) and an internal validation cohort (N=40).
  • Assessed model generalizability in an independent external validation cohort (N=36) using enzyme-linked immunosorbent assay (ELISA).
  • The three-protein (VPP) model achieved an AUC of 0.846 (95% CI: 0.723–0.968, P < 0.001) in the training cohort, 0.821 (95% CI: 0.686–0.955, P < 0.001) in the internal validation cohort, and 0.859 (95% CI: 0.730–0.981, P < 0.001) in the external cohort.
  • Patients classified into the low-risk group showed an 80% response rate compared to 20% in the high-risk group (χ² = 12.1, P < 0.001).
  • Low-risk patients demonstrated significantly longer median progression-free survival than high-risk patients (6.87 vs 3.97 months; HR = 0.39; 95% CI: 0.19–0.81; P = 0.004).

Cite This Study

Jie et al. (2025) studied this question.

synapsesocial.com/papers/6a830fce162ea9d73e9901fbhttps://doi.org/10.1016/j.lungcan.2025.108811
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