Evidence map›Paper›PMID 38729997›Full record

ArticleBritish journal of cancer2024

Epithelial-mesenchymal transition induced by tumor cell-intrinsic PD-L1 signaling predicts a poor response to immune checkpoint inhibitors in PD-L1-high lung cancer.

Hyein Jeong, Jaemoon Koh, Sehui Kim, Seung Geun Song, Soo Hyun Lee, Youngjoo Jeon, Chul-Hwan Lee, Bhumsuk Keam, Se-Hoon Lee, Doo Hyun Chung and 1 more

Erratum issuedAbstract read
In one paragraph

Article in British journal of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Towards liquid biopsy-based analysis of antitumour immunity.Nature reviews. Clinical oncology · 2026
    Review
  3. Article
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  16. Article
  17. Review
  18. Article
  19. Review
  20. EMT and cancer stem cells: Drivers of therapy resistance and promising therapeutic targets.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2025
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Hyein JeongCancer Research Institute, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0001-8416-7091
Jaemoon KohDepartment of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sehui KimDepartment of Pathology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
Seung Geun SongDepartment of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Soo Hyun LeeCancer Research Institute, Seoul National University, Seoul, Republic of Korea.
Youngjoo JeonInterdiscipilinary Program of Cancer Biology, Seoul National University Graduate School, Seoul, Republic of Korea.
Chul-Hwan LeeCancer Research Institute, Seoul National University, Seoul, Republic of Korea.
Bhumsuk KeamCancer Research Institute, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0001-8196-4247
Se-Hoon LeeDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-9219-3350
Doo Hyun ChungDepartment of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Yoon Kyung JeonCancer Research Institute, Seoul National University, Seoul, Republic of Korea. ykjeon@snu.ac.kr.ORCID 0000-0001-8466-9681

Funding

Ministry of Trade, Industry and Energy (Ministry of Trade, Industry and Energy, Korea) 10067758National Research Foundation of Korea (NRF) 2020R1A2C3006535National Research Foundation of Korea (NRF) 2020R1A4A1017515National Research Foundation of Korea (NRF) 2020R1F1A1070825National Research Foundation of Korea (NRF) RS-2023-00217571National Research Foundation of Korea (NRF) RS-2023-00252806Seoul National University 0431-20220017
6 · The paper itself

Abstract

backgroundWe investigated the role of tumor cell-intrinsic PD-L1 signaling in the epithelial-mesenchymal transition (EMT) in non-small-cell lung cancer (NSCLC) and the role of EMT as a predictive biomarker for immune checkpoint inhibitor (ICI) therapy.

methodsPD-L1-overexpressing or PD-L1-knockdown NSCLC cells underwent RNA-seq and EMT phenotype assessment. Mouse lung cancer LLC cells were injected into nude mice. Two cohorts of patients with NSCLC undergoing ICI therapy were analyzed.

resultsRNA-seq showed that EMT pathways were enriched in PD-L1-high NSCLC cells. EMT was enhanced by PD-L1 in NSCLC cells, which was mediated by transforming growth factor-β (TGFβ). PD-L1 promoted the activation of p38-MAPK by binding to and inhibiting the protein phosphatase PPM1B, thereby increasing the TGFβ production. Tumor growth and metastasis increased in nude mice injected with PD-L1-overexpressing LLC cells. In the ICI cohort, EMT signature was higher in patients with progressive disease than in those with responses, and EMT was significantly associated with poor survival in PD-L1-high NSCLC. In PD-L1-high NSCLC, EMT was associated with increased M2-macrophage and regulatory T-cell infiltrations and decreased cytotoxic T-cell infiltration.

conclusionsTumor cell-intrinsic PD-L1 function contributes to NSCLC progression by promoting EMT. EMT may predict an unfavorable outcome after ICI therapy in PD-L1-high NSCLC.

Indexed as

B7-H1 AntigenCarcinoma, Non-Small-Cell LungEpithelial-Mesenchymal TransitionImmune Checkpoint InhibitorsLung NeoplasmsMice, NudeSignal TransductionAnimalsCell Line, TumorFemaleHumansMiceTransforming Growth Factor betaB7-H1 AntigenCD274 protein, humanImmune Checkpoint InhibitorsTransforming Growth Factor beta

Identifiers

PMID38729997
PMCPMC11231337

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Registered trials

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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.