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 T(1) 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.
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 T(1) 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.
Authors: Jens-Peter Volkmer; Debashis Sahoo; Robert K Chin; Philip Levy Ho; Chad Tang; Antonina V Kurtova; Stephen B Willingham; Senthil K Pazhanisamy; Humberto Contreras-Trujillo; Theresa A Storm; Yair Lotan; Andrew H Beck; Benjamin I Chung; Ash A Alizadeh; Guilherme Godoy; Seth P Lerner; Matt van de Rijn; Linda D Shortliffe; Irving L Weissman; Keith S Chan Journal: Proc Natl Acad Sci U S A Date: 2012-01-19 Impact factor: 11.205
Authors: Pradeep Kumar Kopparapu; Stephen A Boorjian; Brian D Robinson; Martin Downes; Lorraine J Gudas; Nigel P Mongan; Jenny L Persson Journal: Anticancer Res Date: 2013-12 Impact factor: 2.480