Dataset Information

A Consensus Molecular Classification of Muscle-invasive Bladder Cancer.

ABSTRACT: 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.

SUBMITTER: Kamoun A

PROVIDER: S-EPMC7690647 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A Consensus Molecular Classification of Muscle-invasive Bladder Cancer.

Kamoun Aurélie A de Reyniès Aurélien A Allory Yves Y Sjödahl Gottfrid G Robertson A Gordon AG Seiler Roland R Hoadley Katherine A KA Groeneveld Clarice S CS Al-Ahmadie Hikmat H Choi Woonyoung W Castro Mauro A A MAA Fontugne Jacqueline J Eriksson Pontus P Mo Qianxing Q Kardos Jordan J Zlotta Alexandre A Hartmann Arndt A Dinney Colin P CP Bellmunt Joaquim J Powles Thomas T Malats Núria N Chan Keith S KS Kim William Y WY McConkey David J DJ Black Peter C PC Dyrskjøt Lars L Höglund Mattias M Lerner Seth P SP Real Francisco X FX Radvanyi François F

European urology 20190926 4

<h4>Background</h4>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.<h4>Objective</h4>To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes.<h4>Design, setting, and participants</h4>We used 1750 MIBC transcriptomic profiles from 16 published datas ...[more]

PMID: 31563503

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Project description:BackgroundRecently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.MethodsThe mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.ResultsTwo TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.ConclusionThe present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

Project description:IntroductionIdentifying neoadjuvant chemotherapy (NAC) response in patients with muscle invasive bladder cancer (MIBC) has had limited success based on clinicopathological features and molecular subtyping. Identification of chemotherapy responsive cohorts would facilitate delivery to those most likely to benefit.ObjectiveDevelop a molecular signature that can identify MIBC NAC responders (R) and non-responders (NR) using a cohort of known NAC response phenotypes, and better understand differences in molecular pathways and subtype classifications between NAC R and NR.Materials and methodsPresented are the messenger RNA (mRNA) and microRNA (miRNA) differential expression profiles from initial transurethral resection of bladder tumor (TURBT) specimens of a discovery cohort of MIBC patients consisting of 7 known NAC R and 11 NR, and a validation cohort consisting of 3 R and 5 NR. Pathological response at time of cystectomy after NAC was used to classify initial TURBT specimens as R (pT0) versus NR (≥pT2). RNA and miRNA from FFPE blocks were sequenced using RNAseq and qPCR, respectively.ResultsThe discovery cohort had 2309 genes, while the validation cohort had 602 genes and 13 miRNA differentially expressed between R and NR. Gene set enrichment analysis identified mitochondrial gene expression, DNA replication initiation, DNA unwinding in the R discovery cohort and positive regulation of vascular associated smooth muscle cell proliferation in the NR discovery cohort. Canonical correlation (CC) analysis was applied to differentiate R versus NR. 3 CCs (CC13, CC16, and CC17) had an AUC >0.65 in the discovery and validation dataset. Gene ontology enrichment showed CC13 as nucleoside triphosphate metabolic process, CC16 as cell cycle and cellular response to DNA damage, CC17 as DNA packaging complex. All patients were classified using established molecular subtypes: Baylor, UNC, CIT, Lund, MD Anderson, TCGA, and Consensus Class. The MD Anderson p53-like subtype, CIT MC4 subtype and Consensus Class stroma rich subtype had the strongest correlation with a NR phenotype, while no subtype had a strong correlation with the R phenotype.ConclusionsOur results identify molecular signatures that can be used to differentiate MIBC NAC R versus NR, salient molecular pathway differences, and highlight the utility of molecular subtyping in relation to NAC response.

Dataset Information

A Consensus Molecular Classification of Muscle-invasive Bladder Cancer.

Publications

A Consensus Molecular Classification of Muscle-invasive Bladder Cancer.

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