Curated microRNAs in urine and blood fail to validate as predictive biomarkers for high-risk prostate cancer.
OBJECTIVE
The purpose of this study was to determine if microRNA profiling of urine and plasma at radical prostatectomy can distinguish potentially lethal from indolent prostate cancer.


METHODS
A panel of microRNAs was profiled in the plasma of 70 patients and the urine of 33 patients collected prior to radical prostatectomy. Expression of microRNAs was correlated to the clinical endpoints at a follow-up time of 3.9 years to identify microRNAs that may predict clinical response after radical prostatectomy. A machine learning approach was applied to test the predictive ability of all microRNAs profiled in urine, plasma, and a combination of both, and global performance assessed using the area under the receiver operator characteristic curve (AUC). Validation of urinary expression of miRNAs was performed on a further independent cohort of 36 patients.


RESULTS
The best predictor in plasma using eight miRs yielded only moderate predictive performance (AUC = 0.62). The best predictor of high-risk disease was achieved using miR-16, miR-21 and miR-222 measured in urine (AUC = 0.75). This combination of three microRNAs in urine was a better predictor of high-risk disease than any individual microRNA. Using a different methodology we found that this set of miRNAs was unable to predict high-volume, high-grade disease.


CONCLUSIONS
Our initial findings suggested that plasma and urinary profiling of microRNAs at radical prostatectomy may allow prognostication of prostate cancer behaviour. However we found that the microRNA expression signature failed to validate in an independent cohort of patients using a different platform for PCR. This highlights the need for independent validation patient cohorts and suggests that urinary microRNA signatures at radical prostatectomy may not be a robust way to predict the course of clinical disease after definitive treatment for prostate cancer.
Division of Urology, Department of Surgery, Royal Melbourne Hospital and the University of Melbourne, Parkville, Victoria, Australia; Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Nikhil
N
Sapre
Division of Urology, Department of Surgery, Royal Melbourne Hospital and the University of Melbourne, Parkville, Victoria, Australia; Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Matthew K H
MK
Hong
NICTA Victoria Research Laboratory, Department of Electronic and Electrical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.
Geoff
G
Macintyre
Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Heather
H
Lewis
NICTA Victoria Research Laboratory, Department of Electronic and Electrical Engineering, The University of Melbourne, Parkville, Victoria, Australia.
Adam
A
Kowalczyk
Division of Urology, Department of Surgery, Royal Melbourne Hospital and the University of Melbourne, Parkville, Victoria, Australia; Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Anthony J
AJ
Costello
Division of Urology, Department of Surgery, Royal Melbourne Hospital and the University of Melbourne, Parkville, Victoria, Australia; Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Niall M
NM
Corcoran
Division of Urology, Department of Surgery, Royal Melbourne Hospital and the University of Melbourne, Parkville, Victoria, Australia; Australian Prostate Cancer Research Epworth, Richmond, Victoria, Australia.
Christopher M
CM
Hovens
D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood* / Q000235:genetics / Q000652:urine*; D018450:Disease Progression; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000097:blood* / Q000652:urine*; D008875:Middle Aged; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000401:mortality / Q000652:urine; D015203:Reproducibility of Results; D012306:Risk; D059467:Transcriptome
D014408:Biomarkers, Tumor; D035683:MicroRNAs