Evidence map›Paper›PMID 40582068›Full record

ArticleTranslational oncology2025

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 and 1 more

Abstract read
In one paragraph

Article in Translational oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xianfei ZhangDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Zhengxin YinDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Xueyu ChenDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Nengchong ZhangDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Shengjia YuDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Congcong ZhuDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Lianggang ZhuDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Liulan ShaoDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine.
Bin LiCenter 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.
Runsen JinDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine. Electronic address: nkvincent@163.com.
Hecheng LiDepartment of Thoracic Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine. Electronic address: lihecheng2000@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

Growth differentiation factor 15ImmunotherapyLung cancerProgrammed cell death protein 1

Identifiers

PMID40582068
PMCPMC12266572

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.