A Consensus Molecular Classification of Muscle-invasive Bladder Cancer.
Consensus
Molecular taxonomy
Muscle-invasive bladder cancer
Transcriptomic classifier
Journal
European urology
ISSN: 1873-7560
Titre abrégé: Eur Urol
Pays: Switzerland
ID NLM: 7512719
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
03
05
2019
accepted:
02
09
2019
pubmed:
30
9
2019
medline:
28
5
2021
entrez:
30
9
2019
Statut:
ppublish
Résumé
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. To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. 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. 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. This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials. 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.
Sections du résumé
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.
Identifiants
pubmed: 31563503
pii: S0302-2838(19)30695-5
doi: 10.1016/j.eururo.2019.09.006
pmc: PMC7690647
mid: NIHMS1647024
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
420-433Subventions
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA076292
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES010126
Pays : United States
Investigateurs
Mattias Aine
(M)
Hikmat Al-Ahmadie
(H)
Yves Allory
(Y)
Joaquim Bellmunt
(J)
Isabelle Bernard-Pierrot
(I)
Peter C Black
(PC)
Mauro A A Castro
(MAA)
Keith S Chan
(KS)
Woonyoung Choi
(W)
Bogdan Czerniak
(B)
Colin P Dinney
(CP)
Lars Dyrskjøt
(L)
Pontus Eriksson
(P)
Jacqueline Fontugne
(J)
Ewan A Gibb
(EA)
Clarice S Groeneveld
(CS)
Arndt Hartmann
(A)
Katherine A Hoadley
(KA)
Mattias Höglund
(M)
Aurélie Kamoun
(A)
Jordan Kardos
(J)
Jaegil Kim
(J)
William Y Kim
(WY)
David J Kwiatkowski
(DJ)
Thierry Lebret
(T)
Seth P Lerner
(SP)
Fredrik Liedberg
(F)
Núria Malats
(N)
David J McConkey
(DJ)
Qianxing Mo
(Q)
Thomas Powles
(T)
François Radvanyi
(F)
Francisco X Real
(FX)
Aurélien de Reyniès
(A)
A Gordon Robertson
(AG)
Arlene Siefker-Radtke
(A)
Nanor Sirab
(N)
Roland Seiler
(R)
Gottfrid Sjödahl
(G)
Ann Taber
(A)
John Weinstein
(J)
Alexandre Zlotta
(A)
Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.
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