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Gene Expression Signatures Predict Outcome in Non–Muscle-Invasive Bladder Carcinoma: A Multicenter Validation Study

Authors' Affiliations: ¹ Molecular Diagnostic Laboratory, Department of Clinical Biochemistry; Departments of ² Urology and ³ Theoretical Statistics, Aarhus University Hospital, Skejby, Denmark; ⁴ Institut Municipal d'Investigació Mèdica and ⁵ Center for Research in Environmental Epidemiology, Universitat Pompeu Fabra, Barcelona, Spain; ⁶ Hospital Universitario de Elche, Elche, Spain; ⁷ Cancer Research UK Clinical Centre and ⁸ Pyrah Department of Urology, St James's University Hospital, Leeds, England; Departments of ⁹ Urology and ¹⁰ Genetics and Pathology, University Hospital, Uppsala, Sweden; ¹¹ Hôpital Henri Mondor, Créteil, France; ¹² Oncologie Moléculaire, UMR144, Centre National de la Recherche Scientifique, Institut Curie, Paris, France; and ¹³ Department of Clinical Pathology, Odense University Hospital, Denmark

Requests for reprints: Torben F. Ørntoft, Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, Denmark. Phone: 45-89495100; Fax: 45-89496018; E-mail: orntoft{at}ki.au.dk.

Purpose: Clinically useful molecular markers predicting the clinical course of patients diagnosed with non–muscle-invasive bladder cancer are needed to improve treatment outcome. Here, we validated four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ (CIS) and for predicting disease recurrence and progression.

Experimental Design: We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain, and France using custom microarrays. Molecular classifications were compared with pathologic diagnosis and clinical outcome.

Results: Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathologic stage (P < 0.001). Furthermore, the classifier added information regarding disease progression of T_a or T₁ tumors (P < 0.001). The molecular 88-gene progression classifier was highly significantly correlated with progression-free survival (P < 0.001) and cancer-specific survival (P = 0.001). Multivariate Cox regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade, and treatment (hazard ratio, 2.3; P = 0.007). The diagnosis of CIS using a 68-gene classifier showed a highly significant correlation with histopathologic CIS diagnosis (odds ratio, 5.8; P < 0.001) in multivariate logistic regression analysis.

Conclusion: This multicenter validation study confirms in an independent series the clinical utility of molecular classifiers to predict the outcome of patients initially diagnosed with non–muscle-invasive bladder cancer. This information may be useful to better guide patient treatment.

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