Scholarlink

Background <p>Immune checkpoint inhibitor (ICI)-based regimens have become the standard first-line treatment for advanced non-small cell lung cancer (NSCLC). However, response rates vary widely, emphasizing the need for a predictive model to optimize patient selection and improve treatment outcomes.</p> Methods <p>We retrospectively analyzed 96 treatment-naïve patients with advanced NSCLC who received first-line ICI-based regimens (anti–PD-1 ± platinum chemotherapy) at Hunan Cancer Hospital. Pre-treatment tumor biopsies underwent bulk RNA sequencing to identify differentially expressed genes associated with progression-free survival (PFS). A five-gene Cox proportional hazards model, the <b>P</b>rediction model of <b>I</b>mmunotherapy <b>E</b>fficacy (PIE), was developed and validated in two independent cohorts: ORIENT-11 (<i>n</i> = 113; chemo-ICI) and OAK (<i>n</i> = 344; ICI monotherapy). Multiplex immunofluorescence staining (CD8, CD56, CD16, Pan-CK, PD-L1, MAGEA2) was performed to validate immune-cell infiltration patterns and candidate biomarkers across PIE-defined risk groups.</p> Results <p>Patients in the long-survival group exhibited enrichment of IL7R<sup>+</sup> NK cells, SLC4A10<sup>+</sup> CD8<sup>+</sup> T cells, and dendritic cells, which were significantly associated with longer PFS. PIE integrated transcriptomic signatures of five biomarkers (<i>CD274</i>, <i>KRT14</i>, <i>FOLR2</i>, <i>SLC31A2</i>, and <i>EFCAB14</i>) to predicting PFS. Compared with patients having low-risk PIE scores, those classified as high-risk demonstrated significantly worse PFS (<i>HR</i> = 2.37, <i>p</i> &lt; 0.001). PIE demonstrated predictive accuracy for 24-month PFS (<i>AUC</i> = 0.768). Additionally, <i>MAGEA2</i> and <i>MAGEA12</i> were identified as potential therapeutic targets.</p> Conclusion <p>PIE represents a clinically relevant, transcriptome-based predictive model for evaluating the benefit of ICI treatment in advanced NSCLC. With further prospective validation, PIE may facilitate personalized patient selection and guide the development of novel strategies to overcome primary resistance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Tumor immune microenvironment score predicts efficacy of immune checkpoint inhibitors-based regimens in advanced non-small cell lung cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

查看全文
  • Jiacheng Dai,
  • Huan Yan,
  • Yangqian Chen,
  • Yuda Zhang,
  • Zhe Huang,
  • Zhaohui Ruan,
  • Fang Tian,
  • Haoyue Qin,
  • Qinqin Xu,
  • Jin Wang,
  • Xing Li,
  • Peng Cheng,
  • Changbin Zhu,
  • Nong Yang,
  • Liang Zeng,
  • Yongchang Zhang

摘要

Background

Immune checkpoint inhibitor (ICI)-based regimens have become the standard first-line treatment for advanced non-small cell lung cancer (NSCLC). However, response rates vary widely, emphasizing the need for a predictive model to optimize patient selection and improve treatment outcomes.

Methods

We retrospectively analyzed 96 treatment-naïve patients with advanced NSCLC who received first-line ICI-based regimens (anti–PD-1 ± platinum chemotherapy) at Hunan Cancer Hospital. Pre-treatment tumor biopsies underwent bulk RNA sequencing to identify differentially expressed genes associated with progression-free survival (PFS). A five-gene Cox proportional hazards model, the Prediction model of Immunotherapy Efficacy (PIE), was developed and validated in two independent cohorts: ORIENT-11 (n = 113; chemo-ICI) and OAK (n = 344; ICI monotherapy). Multiplex immunofluorescence staining (CD8, CD56, CD16, Pan-CK, PD-L1, MAGEA2) was performed to validate immune-cell infiltration patterns and candidate biomarkers across PIE-defined risk groups.

Results

Patients in the long-survival group exhibited enrichment of IL7R+ NK cells, SLC4A10+ CD8+ T cells, and dendritic cells, which were significantly associated with longer PFS. PIE integrated transcriptomic signatures of five biomarkers (CD274, KRT14, FOLR2, SLC31A2, and EFCAB14) to predicting PFS. Compared with patients having low-risk PIE scores, those classified as high-risk demonstrated significantly worse PFS (HR = 2.37, p < 0.001). PIE demonstrated predictive accuracy for 24-month PFS (AUC = 0.768). Additionally, MAGEA2 and MAGEA12 were identified as potential therapeutic targets.

Conclusion

PIE represents a clinically relevant, transcriptome-based predictive model for evaluating the benefit of ICI treatment in advanced NSCLC. With further prospective validation, PIE may facilitate personalized patient selection and guide the development of novel strategies to overcome primary resistance.