Evidence map›Paper›PMID 39848689›Full record

ArticleJournal for immunotherapy of cancer2025

Stratification system with dual human endogenous retroviruses for predicting immunotherapy efficacy in metastatic clear-cell renal cell carcinoma.

Xiaofan Lu, Yann-Alexandre Vano, Xiaoping Su, Virginie Verkarre, Cheng-Ming Sun, Wenxuan Cheng, Li Xu, Fangrong Yan, Salma Kotti, Wolf Hervé Fridman and 3 more

Abstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
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  5. 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

13 authors.

Xiaofan LuDepartment of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology (IGBMC), CNRS/INSERM/UNISTRA, Illkirch-Graffenstaden, France.ORCID http://orcid.org/0000-0003-2417-6548
Yann-Alexandre VanoDepartment of Medical Oncology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, APHP, Université Paris Cité, Paris, France.ORCID http://orcid.org/0000-0002-9410-1368
Xiaoping SuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Virginie VerkarreDepartment of Pathology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, APHP, Université Paris Cité, Paris, France.
Cheng-Ming SunCentre de Recherche Cordeliers, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, Paris, France.
Wenxuan ChengDepartment of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology (IGBMC), CNRS/INSERM/UNISTRA, Illkirch-Graffenstaden, France.
Li XuDepartment of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology (IGBMC), CNRS/INSERM/UNISTRA, Illkirch-Graffenstaden, France.
Fangrong YanResearch Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, China.
Salma KottiDepartment of Medical Oncology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, APHP, Université Paris Cité, Paris, France.
Wolf Hervé FridmanCentre de Recherche Cordeliers, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, Paris, France.ORCID http://orcid.org/0000-0002-1332-0973
Catherine Sautes-FridmanCentre de Recherche Cordeliers, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, Paris, France.ORCID http://orcid.org/0000-0003-1735-8722
Stéphane OudardDepartment of Medical Oncology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, APHP, Université Paris Cité, Paris, France.
Gabriel G MaloufDepartment of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology (IGBMC), CNRS/INSERM/UNISTRA, Illkirch-Graffenstaden, France maloufg@igbmc.fr.ORCID http://orcid.org/0000-0003-3527-0417

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndogenous retrovirus (ERV) elements are genomic footprints of ancestral retroviral infections within the human genome. While the dysregulation of ERV transcription has been linked to immune cell infiltration in various cancers, its relationship with immune checkpoint inhibitor (ICI) response in solid tumors, particularly metastatic clear-cell renal cell carcinoma (ccRCC), remains inadequately explored.

methodsThis study analyzed patients with metastatic ccRCC from two prospective clinical trials, encompassing 181 patients receiving nivolumab in the CheckMate trials (-009 to -010 and -025) and 48 patients treated with the ipilimumab-nivolumab combination in the BIONIKK trial. ERV expression was quantified using the ERVmap algorithm from RNA sequencing data. Our primary objective was to correlate ERV expression with progression-free survival, with overall survival and time-to-second-treatment survival as secondary endpoints. We used bootstrap methods with univariate Cox regression on 666 substantially expressed ERVs to evaluate their prognostic significance and stability.

resultsOur analysis centered on two ERVs, E4421_chr17 and E1659_chr4, which consistently exhibited opposing prognostic impacts across both cohorts. We developed a stratification system based on their median expression levels, categorizing patients into four ERV subgroups. These subgroups were further consolidated into a three-tier risk model that significantly correlated with ICI treatment outcomes. The most responsive ERV risk category showed enhanced endothelial cell infiltration, whereas the resistant category was characterized by higher levels of myeloid dendritic cells, regulatory T cells, myeloid-derived suppressor cells, and markers of T-cell exhaustion. Notably, this ERV-based classification outperformed traditional transcriptomic signatures in predicting ICI efficacy and showed further improvement when combined with epigenetic DNA methylation markers.

conclusionsOur findings introduce a dual ERV-based stratification system that effectively categorizes patient risk and predicts clinical outcomes for ccRCC patients undergoing ICI therapy. Beyond enhancing the predictive precision of existing transcriptomic models, this system paves the way for more targeted and individualized approaches in the realm of precision oncology.

Indexed as

Carcinoma, Renal CellEndogenous RetrovirusesImmune Checkpoint InhibitorsImmunotherapyKidney NeoplasmsAgedFemaleHumansMaleMiddle AgedPrognosisProspective StudiesImmune Checkpoint InhibitorsBiomarkerImmune Checkpoint InhibitorsImmunotherapyKidney CancerTumor microenvironment - TME

Identifiers

PMID39848689
PMCPMC11784120

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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.