To reduce overdiagnosis and overtreatment, a test is urgently needed to detect clinically significant prostate cancer (PCa).
To develop a multimodal model, incorporating previously identified messenger RNA (mRNA) biomarkers and traditional risk factors that could be used to identify patients with high-grade PCa (Gleason score ≥7) on prostate biopsy.
In two prospective multicenter studies, urine was collected for mRNA profiling after digital rectal examination (DRE) and prior to prostate biopsy. The multimodal risk score was developed on a first cohort (n=519) and subsequently validated clinically in an independent cohort (n=386).
The mRNA levels were measured using reverse transcription quantitative polymerase chain reaction. Logistic regression was used to model patient risk and combine risk factors. Models were compared using the area under the curve (AUC) of the receiver operating characteristic, and clinical utility was evaluated with a decision curve analysis (DCA).
HOXC6 and DLX1 mRNA levels were shown to be good predictors for the detection of high-grade PCa. The multimodal approach reached an overall AUC of 0.90 (95% confidence interval [CI], 0.85-0.95) in the validation cohort (AUC 0.86 in the training cohort), with the mRNA signature, prostate-specific antigen (PSA) density, and previous cancer-negative prostate biopsies as the strongest, most significant components, in addition to nonsignificant model contributions of PSA, age, and family history. For another model, which included DRE as an additional risk factor, an AUC of 0.86 (95% CI, 0.80-0.92) was obtained (AUC 0.90 in the training cohort). Both models were successfully validated, with no significant change in AUC in the validation cohort, and DCA indicated a strong net benefit and the best reduction in unnecessary biopsies compared with other clinical decision-making tools, such as the Prostate Cancer Prevention Trial risk calculator and the PCA3 assay.
The risk score based on the mRNA liquid biopsy assay combined with traditional clinical risk factors identified men at risk of harboring high-grade PCa and resulted in a better patient risk stratification compared with current methods in clinical practice. Therefore, the risk score could reduce the number of unnecessary prostate biopsies.
This study evaluated a novel urine-based assay that could be used as a noninvasive diagnostic aid for high-grade prostate cancer (PCa). When results of this assay are combined with traditional clinical risk factors, risk stratification for high-grade PCa and biopsy decision making are improved.
European urology. 2016 Apr 20 [Epub]
Leander Van Neste, Rianne J Hendriks, Siebren Dijkstra, Geert Trooskens, Erik B Cornel, Sander A Jannink, Hans de Jong, Daphne Hessels, Frank P Smit, Willem J G Melchers, Gisèle H J M Leyten, Theo M de Reijke, Henk Vergunst, Paul Kil, Ben C Knipscheer, Christina A Hulsbergen-van de Kaa, Peter F A Mulders, Inge M van Oort, Wim Van Criekinge, Jack A Schalken
Department of Pathology, Maastricht University Medical Center, Maastricht, The Netherlands., Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands., MDxHealth BV, Nijmegen, The Netherlands., Department of Urology, ZGT Hospital, Hengelo, The Netherlands., Department of Medical Microbiology, Radboud University Medical Center, Nijmegen, The Netherlands., Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Urology, AMC University Medical Centre, Amsterdam, The Netherlands., Department of Urology, AMC University Medical Centre, Amsterdam, The Netherlands., Department of Urology, CWZ Hospital, Nijmegen, The Netherlands., Department of Urology, St. Elisabeth Hospital, Tilburg, The Netherlands., Department of Urology, Scheper Hospital, Emmen, The Netherlands., Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands., Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium., Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: .