A Consensus Molecular Classification of Muscle-invasive Bladder Cancer

Research output: Contribution to journalArticle


Background: Muscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application. Objective: To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. Design, setting, and participants: We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. Outcome measurements and statistical analysis: We performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models. Results and limitations: We report the results of an international effort to reach a consensus on MIBC molecular subtypes. We identified a consensus set of six molecular classes: luminal papillary (24%), luminal nonspecified (8%), luminal unstable (15%), stroma-rich (15%), basal/squamous (35%), and neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics, including outcomes. We provide a single-sample classifier that assigns a consensus class label to a tumor sample's transcriptome. Limitations of the work are retrospective clinical data collection and a lack of complete information regarding patient treatment. Conclusions: This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials. Patient summary: Bladder cancers are heterogeneous at the molecular level, and scientists have proposed several classifications into sets of molecular classes. While these classifications may be useful to stratify patients for prognosis or response to treatment, a consensus classification would facilitate the clinical use of molecular classes. Conducted by multidisciplinary expert teams in the field, this study proposes such a consensus and provides a tool for applying the consensus classification in the clinical setting.


  • Bladder Cancer Molecular Taxonomy Group
  • Aurélie Kamoun
  • Aurélien de Reyniès
  • Yves Allory
  • Gottfrid Sjödahl
  • A. Gordon Robertson
  • Roland Seiler
  • Katherine A. Hoadley
  • Clarice S. Groeneveld
  • Hikmat Al-Ahmadie
  • Woonyoung Choi
  • Mauro A.A. Castro
  • Jacqueline Fontugne
  • Pontus Eriksson
  • Qianxing Mo
  • Jordan Kardos
  • Alexandre Zlotta
  • Arndt Hartmann
  • Colin P. Dinney
  • Joaquim Bellmunt
  • Thomas Powles
  • Núria Malats
  • Keith S. Chan
  • William Y. Kim
  • David J. McConkey
  • Peter C. Black
  • Lars Dyrskjøt
  • Mattias Höglund
  • Seth P. Lerner
  • Francisco X. Real
  • François Radvanyi
External organisations
  • Ligue Nationale Contre le Cancer
  • Curie Institute, Paris
  • Skåne University Hospital
  • British Columbia Cancer Agency
  • Bern University Hospital
  • University of North Carolina
  • Federal University of Paraná
  • Memorial Sloan-Kettering Cancer Center
  • Johns Hopkins University
  • H. Lee Moffitt Cancer Center & Research Institute
  • University of Toronto
  • Friedrich-Alexander University Erlangen-Nürnberg
  • University of Texas
  • Queen Mary University
  • Spanish National Cancer Research Center (CNIO)
  • Cedars-Sinai Medical Center
  • University of British Columbia
  • Aarhus University Hospital
  • Baylor College of Medicine
  • Mount Sinai Hospital of University of Toronto
  • Harvard University
  • Barts Health NHS Trust
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Genetics
  • Cancer and Oncology


  • Consensus, Molecular taxonomy, Muscle-invasive bladder cancer, Transcriptomic classifier
Original languageEnglish
JournalEuropean Urology
Publication statusE-pub ahead of print - 2019 Sep 26
Publication categoryResearch