Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/725
Title: A urinary microRNA signature can predict the presence of bladder urothelial carcinoma in patients undergoing surveillance.
Epworth Authors: Costello, Anthony
Corcoran, Niall
Clarkson, Michael
Hovens, Christopher
Other Authors: Sapre, Nikhil
Macintyre, Geoff
Naeem, Haroon
Cmero, Marek
Kowalczyk, Adam
Anderson, Paul
Keywords: Bladder Urothelial Carcinoma
Urothelial Carcinoma of the Bladder
UCB
MicroRNA
MiRNA
MicroRNA Profiling
MiRNA Signature
Cystoscopy
Tumour Recurrence
Support Vector Machine Classifier
Cancer Screening
Australian Prostate Cancer Research Centre Epworth HealthCare, Victoria, Australia
Issue Date: Feb-2016
Publisher: Springer Nature
Citation: Br J Cancer. 2016 Feb 16;114(4)
Abstract: Background: The objective of this study was to determine whether microRNA (miRNA) profiling of urine could identify the presence of urothelial carcinoma of the bladder (UCB) and to compare its performance characteristics to that of cystoscopy. Methods: In the discovery cohort we screened 81 patients, which included 21 benign controls, 30 non-recurrers and 30 patients with active cancer (recurrers), using a panel of 12 miRNAs. Data analysis was performed using a machine learning approach of a Support Vector Machine classifier with a Student’s t-test feature selection procedure. This was trained using a three-fold cross validation approach and performance was measured using the area under the receiver operator characteristic curve (AUC). The miRNA signature was validated in an independent cohort of a further 50 patients. Results: The best predictor to distinguish patients with UCB from non-recurrers was achieved using a combination of six miRNAs (AUC=0.85). This validated in an independent cohort (AUC=0.74) and detected UCB with a high sensitivity (88%) and sufficient specificity (48%) with all significant cancers identified. The performance of the classifier was best in detecting clinically significant disease such as presence of T1 Stage disease (AUC=0.92) and high-volume disease (AUC=0.81). Cystoscopy rates in the validation cohort would have been reduced by 30%. Conclusions: Urinary profiling using this panel of miRNAs shows promise for detection of tumour recurrence in the surveillance of UCB. Such a panel may be useful in reducing the morbidity and costs associated with cystoscopic surveillance, and now merits prospective evaluation.
URI: http://hdl.handle.net/11434/725
DOI: doi:10.1038/bjc.2015.472
PubMed URL: http://www.ncbi.nlm.nih.gov/pubmed/26812572
ISSN: 0007-0920
1532-1827
Journal Title: British Journal of Cancer
Type: Journal Article
Affiliated Organisations: Department of Surgery, Division of Urology, Royal Melbourne Hospital
The University of Melbourne, Parkville, Melbourne, Victoria, Australia
NICTA Victoria Research Laboratory, Department of Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia
Department of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
Centre for Neural Engineering, University of Melbourne, Melbourne, Victoria, Australia
Type of Clinical Study or Trial: Cohort Study
Appears in Collections:Cancer Services
UroRenal, Vascular

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