1274 NONINVASIVE TEST FOR DIAGNOSIS AND AGGRESSIVENESS ASSESSMENT OF BLADDER CANCER: VALIDATION STUDY

Abstract

You have accessJournal of UrologyBladder Cancer: Detection and Screening1 Apr 20121274 NONINVASIVE TEST FOR DIAGNOSIS AND AGGRESSIVENESS ASSESSMENT OF BLADDER CANCER: VALIDATION STUDY Lourdes Mengual, Maria J. Ribal, Juan J. Lozano, Mercedes Ingelmo-Torres, Moises Burset, Pedro L. Fernandez, and Antonio Alcaraz Lourdes MengualLourdes Mengual Barcelona, Spain More articles by this author , Maria J. RibalMaria J. Ribal Barcelona, Spain More articles by this author , Juan J. LozanoJuan J. Lozano Barcelona, Spain More articles by this author , Mercedes Ingelmo-TorresMercedes Ingelmo-Torres Barcelona, Spain More articles by this author , Moises BursetMoises Burset Barcelona, Spain More articles by this author , Pedro L. FernandezPedro L. Fernandez Barcelona, Spain More articles by this author , and Antonio AlcarazAntonio Alcaraz Barcelona, Spain More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1606AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Cystoscopy and cytology, the standard methods used to detect and monitor bladder urothelial cell carcinoma (UCC), are invasive or present low sensitivity. The objective of the present work was to validate the performance of our previously reported noninvasive test for bladder cancer diagnosis and prediction of disease aggressiveness based on the gene expression patterns of urine, in an independent set of samples. METHODS The previously reported 12+2 gene expression signature was validated using an independent set of 207 urines; 96 urines from patients subjected to transurethral resection of the bladder for a primary or recurrent bladder tumour and with histologically confirmed tumours ( 35 Ta, 35 T1, 21 T2-T4, 4 Tis and 1 Tx) and 111 control urine samples from patients with non neoplasic urological disease. For the prediction of UCC tumour aggressiveness, tumour samples were grouped as high grade (HG, n=63) and low grade (LG, n=32). RNA was extracted from urine samples and was reverse transcribed and pre-amplified using standard protocols. Subsequently, target cDNAs were amplified in singleplex reactions using TaqMan Arrays. Data normalization was carried out with the geometric average of the measured CT of the two reference genes (GUSB and PPIA). Logistic regression was used to assess the performance of the 12+2 gene signature in the independent validation set of samples. RESULTS The SN and SP of the signature in the validation set were 80% and 86%, respectively (AUC: 0.914). As in the training set of samples, SN increases through the UCC risk groups, being lower in low risk non muscle invasive bladder cancer and achieving 100% in muscle invasive bladder cancer. The overall SN and SP for discriminating urine from HG and LG UCC patients in the validation set of samples was 75% and 75%, respectively (AUC: 0.83). The overall accuracy for diagnosis and prognosis in the independent validation set was 84% and 75%, respectively. CONCLUSIONS This validation study strongly supports the feasibility of using a urine-based gene expression signature as an accurate tool in the assessment of bladder cancer. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e516 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lourdes Mengual Barcelona, Spain More articles by this author Maria J. Ribal Barcelona, Spain More articles by this author Juan J. Lozano Barcelona, Spain More articles by this author Mercedes Ingelmo-Torres Barcelona, Spain More articles by this author Moises Burset Barcelona, Spain More articles by this author Pedro L. Fernandez Barcelona, Spain More articles by this author Antonio Alcaraz Barcelona, Spain More articles by this author Expand All Advertisement Advertisement PDF DownloadLoading ...