Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance

Xianfei Zhang; Zhengxin Yin; Xueyu Chen; Nengchong Zhang; Shengjia Yu; Congcong Zhu; Lianggang Zhu; Liulan Shao; Bin Li; Runsen Jin; Hecheng Li

doi:10.1016/j.tranon.2025.102459

Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance

Transl Oncol. 2025 Jun 28:59:102459. doi: 10.1016/j.tranon.2025.102459. Online ahead of print.

Authors

Affiliations

¹ Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
² Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Respiratory and Critical Care Medicine of Ruijin Hospital, Department of Thoracic Surgery of Ruijin Hospital, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: binli@shsmu.edu.cn.
³ Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine. Electronic address: nkvincent@163.com.
⁴ Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine. Electronic address: lihecheng2000@hotmail.com.

PMID: 40582068
DOI: 10.1016/j.tranon.2025.102459

Abstract

Immune checkpoint blockade (ICB) has transformed non-small cell lung cancer (NSCLC) treatment, but durable clinical responses remain limited, underscoring the need for robust predictive biomarkers. We integrated multiomics profiling with machine learning to systematically identify determinants of ICB efficacy. Comparative evaluation of 22 survival algorithms across four NSCLC cohorts (n=156) led to the development of an Accelerated Oblique Random Survival Forest model, which outperformed conventional Cox regression and deep learning methods in predictive accuracy (training C-index=0.864; test C-index=0.748). Single-cell RNA sequencing of an immunotherapy-treated cohort revealed that high-risk tumors harbor malignant epithelial subclusters expressing growth differentiation factor 15 (GDF15), a transforming growth factor-β superfamily member implicated in immune evasion. Single-cell non-negative matrix factorization identified GDF15 as a network hub regulating proliferative dominance. External validation using melanoma cohorts (GSE91061) confirmed the pan-cancer predictive relevance of GDF15 and its associated tumor cluster. Functional studies utilizing GDF15-knockdown Lewis lung carcinoma cells showed no significant effect on intrinsic tumor proliferation or growth under immune stress (both p>0.05). GDF15 deletion significantly potentiated PD-1 inhibitor efficacy in vivo, reducing tumor mass by 94.41±6.53 % (SH1) and 94.54±5.21 % (SH2) compared with 3.39±54.90 % in empty vector controls (p<0.01 for all comparisons). CD8⁺ T cell infiltration was also substantially enhanced (81.62±4.79 % [SH1] and 123.50±10.02 % [SH2] vs. 29.63±22.17 % [EV], p<0.05). These findings implicate GDF15 as a regulator of the immunosuppressive tumor microenvironment. Our findings position GDF15 as a first-in-class biomarker for predicting ICB resistance; they establish a translational framework that bridges computational prediction with single-cell mechanistic insights to inform NSCLC immunotherapy.

Keywords: Growth differentiation factor 15; Immunotherapy; Lung cancer; Programmed cell death protein 1.