Evidence map›Paper›PMID 41222746›Full record

ArticleCancer immunology, immunotherapy : CII2025

Combining molecular patterns and clinical data for better immune checkpoint inhibitor prediction in metastatic urothelial carcinoma.

M Váradi, O Horváth, E Soós, A Csizmarik, B Németh, B Győrffy, I Kenessey, H Reis, F Koll, C Oláh and 17 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

27 authors.

M VáradiDepartment of Urology, Semmelweis University, Budapest, Hungary.
O HorváthDepartment of Genitourinary Medical Oncology and Pharmacology, National Institute of Oncology, Budapest, Hungary.
E SoósDepartment of Urology, Semmelweis University, Budapest, Hungary.
A CsizmarikDepartment of Urology, Semmelweis University, Budapest, Hungary.
B NémethDepartment of Urology, Semmelweis University, Budapest, Hungary.
B GyőrffyDepartment of Bioinformatics, Semmelweis University, Budapest, Hungary.
I KenesseyDepartment of Pathology, Forensic and Insurance Medicine, Semmelweis University, Budapest, Hungary.
H ReisDr. Senckenberg Institute of Pathology, Goethe University Frankfurt, Frankfurt Am Main, Germany.
F KollDepartment of Urology, Goethe University, Frankfurt Am Main, Germany.
C OláhDepartment of Urology, University of Duisburg-Essen, Essen, Germany.
B HadaschikDepartment of Urology, University of Duisburg-Essen, Essen, Germany.
U KrafftDepartment of Urology, University of Duisburg-Essen, Essen, Germany.
V GrünwaldDepartment of Urology, University of Duisburg-Essen, Essen, Germany.
F MairingerInstitute of Pathology, University of Duisburg-Essen, Essen, Germany.
M WessollyInstitute of Pathology, University of Duisburg-Essen, Essen, Germany.
M J HoffmannDepartment of Urology, University Hospital and Medical Faculty of the Heinrich-Heine University, Düsseldorf, Germany.
C M GrunewaldDepartment of Urology, University Hospital and Medical Faculty of the Heinrich-Heine University, Düsseldorf, Germany.
G NiegischDepartment of Urology, University Hospital and Medical Faculty of the Heinrich-Heine University, Düsseldorf, Germany.
C L CotareloMedical Faculty Mannheim, Institute of Pathology, Heidelberg University, Mannheim, Germany.
A MarázDepartment of Oncotherapy, University of Szeged, Szeged, Hungary.
L KuthiTumor Pathology Center, Department of Surgical and Molecular Pathology, National Institute of Oncology, Budapest, Hungary.
A M SzászDepartment of Internal Medicine and Oncology, Division of Oncology, Semmelweis University, Budapest, Hungary.
Z HeroldDepartment of Internal Medicine and Oncology, Division of Oncology, Semmelweis University, Budapest, Hungary.
M PostaDepartment of Bioinformatics, Semmelweis University, Budapest, Hungary.
B BátaiDepartment of Pathology and Experimental Cancer Research, HCEMM-SU Molecular Oncohematology Research Group, Semmelweis University, Budapest, Hungary.
P NyirádyDepartment of Urology, Semmelweis University, Budapest, Hungary.
T SzarvasDepartment of Urology, Semmelweis University, Budapest, Hungary. sztibusz@gmail.com.

Funding

Hungarian Academy of Sciences BO/00451/20/5Ministry for Innovation and Technology ÚNKP-21-5-SE-3
6 · The paper itself

Abstract

backgroundThe therapeutic landscape of advanced urothelial carcinoma (UC) is evolving, making the prediction of immune checkpoint inhibitor (ICI) therapy efficacy crucial. Standalone biomarkers offer limited predictive value, necessitating integrative approaches combining clinicopathological, laboratory, and molecular factors to enhance accuracy. This study aimed to evaluate clinical and molecular factors, including the real-life performance of PD-L1 IHC, to improve treatment outcome prediction in ICI-treated UC patients, ultimately developing a more precise therapy selection model.

methodsWe conducted a retrospective, multicenter study on advanced or metastatic UC patients with available formalin-fixed, paraffin-embedded tumor samples who underwent ICI therapy (n = 100). NanoString technology was used to analyze 770 immune-related genes and a 60-gene panel for molecular subtype classification. Identified genes were validated in the IMvigor210 dataset. Whole tissue PD-L1 expression was assessed using the Dako 22C3 antibody.

resultsOur findings show that PD-L1 IHC has limited predictive value for ICI response. However, among multigene molecular factors, the neuronal signature, MDA p53-like, and TCGA-luminal-infiltrated subtypes were linked to improved OS. Additionally, we identified and validated five novel ICI-predictive genes (PSMB10, HLA-E, IRF7, CXCL12, and C3), and by combining molecular and clinicopathological parameters, we developed a model with enhanced predictive value.

conclusionsOur real-life cohort analysis confirms the limitations of standalone biomarkers like PD-L1. We identified gene expression-based markers with strong prognostic and predictive value for ICI treatment outcomes.

Indexed as

Biomarkers, TumorCarcinoma, Transitional CellImmune Checkpoint InhibitorsUrinary Bladder NeoplasmsAgedAged, 80 and overB7-H1 AntigenFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesB7-H1 AntigenBiomarkers, TumorCD274 protein, humanImmune Checkpoint InhibitorsGene expression based markersImmune checkpoint inhibitorsMolecular subtypesPD-L1Urothelial carcinoma

Identifiers

PMID41222746
PMCPMC12612395

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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.