Dyrskjøt Lars, Zieger Karsten, Real Francisco X, Malats Núria, Carrato Alfredo, Hurst Carolyn, Kotwal Sanjeev, Knowles Margaret, Malmström Per-Uno, de la Torre Manuel, Wester Kenneth, Allory Yves, Vordos Dimitri, Caillault Aurélie, Radvanyi François, Hein Anne-Mette K, Jensen Jens L, Jensen Klaus M E, Marcussen Niels, Orntoft Torben F
Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, Denmark.
Clin Cancer Res. 2007 Jun 15;13(12):3545-51. doi: 10.1158/1078-0432.CCR-06-2940.
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.
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.
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.
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.
为改善治疗效果，需要临床上有用的分子标志物来预测非肌层浸润性膀胱癌患者的临床病程。在此，我们验证了四个先前报道的基因表达特征，用于疾病分期和原位癌（CIS）的分子诊断以及预测疾病复发和进展。
我们使用定制微阵列分析了来自丹麦、瑞典、英国、西班牙和法国医院的404例膀胱癌患者的肿瘤。将分子分类与病理诊断和临床结果进行比较。
发现使用52基因分类器对疾病分期进行分类与病理分期高度显著相关（P < 0.001）。此外，该分类器增加了有关T(a)或T(1)肿瘤疾病进展的信息（P < 0.001）。分子88基因进展分类器与无进展生存期（P < 0.001）和癌症特异性生存期（P = 0.001）高度显著相关。多变量Cox回归分析显示，在调整年龄、性别、分期、分级和治疗后，进展分类器是与疾病进展相关的独立显著变量（风险比，2.3；P = 0.007）。在多变量逻辑回归分析中，使用68基因分类器诊断CIS与组织病理学CIS诊断高度显著相关（优势比，5.8；P < 0.001）。
这项多中心验证研究在一个独立队列中证实了分子分类器在预测初诊为非肌层浸润性膀胱癌患者预后方面的临床实用性。该信息可能有助于更好地指导患者治疗。
