Identifying distinct classes of bladder carcinoma using microarrays

• Published in: Nature genetics Vol. 33; no. 1; pp. 90 - 96
• Main Authors: Hamilton-Dutoit, Stephen, Kruhøffer, Mogens, Wolf, Hans, Jensen, Jens Ledet, Thykjaer, Thomas, Marcussen, Niels, Ørntoft, Torben F, Dyrskjøt, Lars
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 01.01.2003
• Subjects: Biological and medical sciences; Biological properties; Biopsy; Bladder cancer; Cluster analysis; Diagnosis; Disease Progression; Diverse techniques; DNA microarrays; Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genes, Neoplasm; Genetic aspects; Health aspects; Humans; Immunohistochemistry; Measurement; Methods; Molecular and cellular biology; Neoplasm Staging; Oligonucleotide Array Sequence Analysis - methods; Pathology; Prognosis; Reproducibility of Results; Risk factors; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA, Neoplasm - analysis; RNA, Neoplasm - genetics; Tumors; Urinary Bladder Neoplasms - classification; Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - genetics; Urinary Bladder Neoplasms - pathology; Denmark; Aarhus Denmark; Human; Urinary system disease; Carcinoma; DNA chip; Identification; Bladder disease; Urinary tract disease; Method; Malignant tumor; Gene expression
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng1061

Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.