Blood-based and urinary prostate cancer biomarkers: a review and comparison of novel biomarkers for detection and treatment decisions.

backgroundThe diagnosis of prostate cancer (PCa) is currently based on serum PSA testing and/or abnormal digital rectal examination and histopathologic evaluation of prostate biopsies. The main drawback of PSA testing is the lack of specificity for PCa. To improve early detection of PCa more specific biomarkers are needed. In the past few years, many new promising biomarkers have been identified; however, to date, only a few have reached clinical practice.

methodsIn this review, we discuss new blood-based and urinary biomarker models that are promising for usage in PCa detection, follow-up and treatment decision-making. These include Prostate Health Index (PHI), prostate cancer antigen 3 (PCA3), four-kallikrein panel (4K), transmembrane protease serine 2-ERG (TMPRSS2-ERG), ExoDx Prostate Intelliscore, SelectMDx and the Mi-Prostate score. Only few head-to-head studies are available for these new fluid-based biomarkers and/or models. The blood-based PHI and urinary PCA3 are two US Food and Drug Administration-approved biomarkers for diagnosis of PCa. In the second part of this review, we give an overview of published studies comparing these two available biomarkers for prediction of (1) PCa upon prostate biopsy, (2) pathological features in radical prostatectomy specimen and (3) significant PCa in patients eligible for active surveillance.

resultsStudies show opposing results in comparison of PHI with PCA3 for prediction of PCa upon initial and repeat prostate biopsy. PHI and PCA3 are able to predict pathological findings on radical prostatectomy specimen, such as tumor volume and Gleason score. Only PHI could predict seminal vesicle invasion and only PCA3 could predict multifocality. There is some evidence that PHI outperforms PCA3 in predicting significant PCa in an active surveillance population.

conclusionsFuture research should focus on independent validation of promising fluid-based biomarkers/models, and prospective comparison of biomarkers with each other.