Curated microRNAs in urine and blood fail to validate as predictive biomarkers for high-risk prostate cancer

• Published in: PloS one Vol. 9; no. 4; p. e91729
• Main Authors: Sapre, Nikhil, Hong, Matthew K H, Macintyre, Geoff, Lewis, Heather, Kowalczyk, Adam, Costello, Anthony J, Corcoran, Niall M, Hovens, Christopher M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-04-2014; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Analysis; Antigens; Biology and Life Sciences; Biomarkers; Biomarkers, Tumor - blood; Biomarkers, Tumor - genetics; Biomarkers, Tumor - urine; Cancer; Cancer surgery; Cancer therapies; Care and treatment; Cell cycle; Diagnosis; Disease Progression; DNA methylation; Electrical engineering; Ethics; Gene Expression Profiling; Genotype & phenotype; Health aspects; Health risks; Humans; Laboratories; Learning algorithms; Machine learning; Male; Medical research; Medical treatment; Medicine and Health Sciences; Metastasis; MicroRNA; MicroRNAs; MicroRNAs - blood; MicroRNAs - urine; Middle Aged; miRNA; Patients; Performance prediction; Plasma; Prognosis; Prostate cancer; Prostatectomy; Prostatic Neoplasms - blood; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - mortality; Prostatic Neoplasms - urine; Quality; Reproducibility of Results; Ribonucleic acid; Risk; RNA; Studies; Surgery; Transcriptome; Urine; Urological surgery; Urology; Australia; Victoria Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0091729

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.
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.
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.
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.