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

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

doi:10.1186/s12967-025-07408-z

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

J Transl Med. 2025 Dec 12;23(1):1391. doi: 10.1186/s12967-025-07408-z.

Authors

Jiacheng Dai^#^{1

2}, Huan Yan^#^{1

2

3}, Yangqian Chen², Yuda Zhang², Zhe Huang^{1

2}, Zhaohui Ruan^{1

2}, Fang Tian^{1

2

3}, Haoyue Qin², Qinqin Xu^{3

4}, Jin Wang⁵, Xing Li⁵, Peng Cheng⁵, Changbin Zhu⁵, Nong Yang^{2

6

7}, Liang Zeng^{1

2}, Yongchang Zhang^{8

9

10

11}

Affiliations

¹ Early Clinical Trial Center, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China.
² Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China.
³ School of Clinical Medicine, Qinghai University, Xining, Qinghai, 810000, China.
⁴ Department of Medical Oncology, Qinghai Provincial People's Hospital, Xining, Qinghai, 810000, China.
⁵ Department of Translational Medicine, Amoy Diagnostics, Xiamen, Fujian, 361000, China.
⁶ Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
⁷ Hunan Second People's Hospital, Changsha, Hunan, 410007, China.
⁸ Early Clinical Trial Center, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China. zhangyongchang@csu.edu.cn.
⁹ Department of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China. zhangyongchang@csu.edu.cn.
¹⁰ School of Clinical Medicine, Qinghai University, Xining, Qinghai, 810000, China. zhangyongchang@csu.edu.cn.
¹¹ Graduate Collaborative Training Base of Hunan Cancer Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China. zhangyongchang@csu.edu.cn.

^# Contributed equally.

Abstract

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.

Keywords: Immune checkpoint inhibitor; NSCLC; PFS; Prognostic prediction.

MeSH terms

Aged
Biomarkers, Tumor / metabolism
Carcinoma, Non-Small-Cell Lung* / drug therapy
Carcinoma, Non-Small-Cell Lung* / genetics
Carcinoma, Non-Small-Cell Lung* / immunology
Carcinoma, Non-Small-Cell Lung* / pathology
Female
Gene Expression Regulation, Neoplastic
Humans
Immune Checkpoint Inhibitors* / pharmacology
Immune Checkpoint Inhibitors* / therapeutic use
Lung Neoplasms* / drug therapy
Lung Neoplasms* / genetics
Lung Neoplasms* / immunology
Lung Neoplasms* / pathology
Male
Middle Aged
Proportional Hazards Models
Reproducibility of Results
Retrospective Studies
Treatment Outcome
Tumor Microenvironment* / drug effects
Tumor Microenvironment* / genetics
Tumor Microenvironment* / immunology

Substances

Immune Checkpoint Inhibitors
Biomarkers, Tumor