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<p>Immune checkpoint inhibitors (ICIs) show limited efficacy in non-small cell lung cancer (NSCLC), highlighting the need for predictive biomarkers. Here we prospectively analysed serial plasma samples from 34 ICI-treated advanced NSCLC patients (plus 30 validation samples) using the Olink Immuno-Oncology panel. We assessed dynamic proteomic changes associated with ICI efficacy and immune-related adverse events (irAEs), and developed a prognostic model. Following ICIs, 42/92 proteins significantly elevated upon ICI treatment (<i>p</i> &lt; 0.05). Baseline levels of CD28, CXCL10, and TNFSF14, and increased CD40L post-treatment, correlated with inferior response. Baseline IL-4, IL-13 and increased GZMA post-treatment were associated with irAE occurrence. Using LASSO-Cox regression, we established an Immunosuppressive Signature of Combined Resistance Elements (I-SCORE) model based on eight plasma proteins (CCL23, ARG1, CD83, ADA, CXCL10, TNFSF14, CD28, GZMA). I-SCORE demonstrated strong predictive power for overall survival (12-month AUC = 0.94), progression-free survival (12-month AUC = 0.75), and treatment response (AUC = 0.62). Furthermore, a high I-SCORE was demonstrated to reflect an inflammatory and immunosuppressive phenotype, showing positive linear relationships with plasma IL-6, IL-8, IL-10, and monocyte count, and negative relationships with IL-33 and active T-cell proportion. Our study identifies I-SCORE, derived from plasma proteomics, as a promising integrated biomarker for predicting ICI outcomes in NSCLC. It suggests targeting specific proteins or the associated immunosuppressive microenvironment might enhance immunotherapy efficacy.</p>

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Liquid biopsy using plasma proteomics in predicting efficacy and tolerance of PD-1/PD-L1 blockades in NSCLC: a prospective exploratory study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月15日

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  • Yuan Gao,
  • Fei Qi,
  • Wenhao Zhou,
  • Peng Jiang,
  • Mingming Hu,
  • Ying Wang,
  • Congcong Song,
  • Yi Han,
  • Dongdong Li,
  • Na Qin,
  • Hongmei Zhang,
  • Haitao Luo,
  • Tongmei Zhang,
  • Hongxia Li

摘要

Immune checkpoint inhibitors (ICIs) show limited efficacy in non-small cell lung cancer (NSCLC), highlighting the need for predictive biomarkers. Here we prospectively analysed serial plasma samples from 34 ICI-treated advanced NSCLC patients (plus 30 validation samples) using the Olink Immuno-Oncology panel. We assessed dynamic proteomic changes associated with ICI efficacy and immune-related adverse events (irAEs), and developed a prognostic model. Following ICIs, 42/92 proteins significantly elevated upon ICI treatment (p < 0.05). Baseline levels of CD28, CXCL10, and TNFSF14, and increased CD40L post-treatment, correlated with inferior response. Baseline IL-4, IL-13 and increased GZMA post-treatment were associated with irAE occurrence. Using LASSO-Cox regression, we established an Immunosuppressive Signature of Combined Resistance Elements (I-SCORE) model based on eight plasma proteins (CCL23, ARG1, CD83, ADA, CXCL10, TNFSF14, CD28, GZMA). I-SCORE demonstrated strong predictive power for overall survival (12-month AUC = 0.94), progression-free survival (12-month AUC = 0.75), and treatment response (AUC = 0.62). Furthermore, a high I-SCORE was demonstrated to reflect an inflammatory and immunosuppressive phenotype, showing positive linear relationships with plasma IL-6, IL-8, IL-10, and monocyte count, and negative relationships with IL-33 and active T-cell proportion. Our study identifies I-SCORE, derived from plasma proteomics, as a promising integrated biomarker for predicting ICI outcomes in NSCLC. It suggests targeting specific proteins or the associated immunosuppressive microenvironment might enhance immunotherapy efficacy.