| Literature DB >> 27683457 |
Carsten Stephan1, Harry Rittenhouse2, Xinhai Hu3, Henning Cammann4, Klaus Jung1.
Abstract
PSA screening reduces PCa-mortality but the disadvantages overdiagnosis and overtreatment require multivariable risk-prediction tools to select appropriate treatment or active surveillance. This review explains the differences between the two largest screening trials and discusses the drawbacks of screening and its meta-analysisxs. The current American and European screening strategies are described. Nonetheless, PSA is one of the most widely used tumor markers and strongly correlates with the risk of harboring PCa. However, while PSA has limitations for PCa detection with its low specificity there are several potential biomarkers presented in this review with utility for PCa currently being studied. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved prostate health index (phi) shows improved specificity over percent free and total PSA. Another kallikrein panel, 4K, which includes KLK2 has recently shown promise in clinical research studies but has not yet undergone formal validation studies. In urine, prostate cancer gene 3 (PCA3) has also been validated and approved by the FDA for its utility to detect PCa. The potential correlation of PCA3 with cancer aggressiveness requires more clinical studies. The detection of the fusion of androgen-regulated genes with genes of the regulatory transcription factors in tissue of (~)50% of all PCa-patients is a milestone in PCa research. A combination of the urinary assays for TMPRSS2:ERG gene fusion and PCA3 shows an improved accuracy for PCa detection. Overall, the field of PCa biomarker discovery is very exciting and prospective.Entities:
Keywords: Biomarkers; MPRSS2; PCA3; PSA; Prostate cancer; Prostate health index; Screening
Year: 2014 PMID: 27683457 PMCID: PMC4975191
Source DB: PubMed Journal: EJIFCC ISSN: 1650-3414
Examples for multivariate models using %fPSA for diagnosis of PCa (1998-2004)
| First Author [Ref.] | Year | Screening | Model (ranking) | PSA assays (company) | tPSA range (ng/ml) | contributing factors (if numbered, by value) | AUC | Specificity at 95% sensitivity |
|---|---|---|---|---|---|---|---|---|
| 1998 1999 | no yes | LR | Tosoh (Dianon) | 4-20 | l.%fPSA, 2.age 3.tPSA | n.a. | 34 (LR) 23(%fPSA) | |
| 1999 | yes | l.LR | ProStatus (Wallac) | 3-10 | l.%fPSA | 0.81 (LR for tPSA3-45) %fPSAn.a. | n.a. | |
| 2000 | yes | 1.ANN | ProStatus (Wallac) | 4-10 | l.%fPSA | n.a. | 33 (ANN) | |
| 2000 | yes | ANN | Tandem R | 2.5-4 | %fPSA, tPSA, age, PAP, CK | 0.74 ANN | 51 (ANN) | |
| 2001 | yes | ANN | Abbot IMX | n.a. | age, tPSA, %f PSA, DRE, volume, PSAD, PSAD-TZ, TZ-volume | n.a. | ~27 (ANN) | |
| 2002 | no | ANN | IMMULITE | 2-20 | 1.DRE 2.%f PSA | 0.86 (ANN) | 43 (ANN) | |
| 2003 | no | ANN, LR | AxSYM | 4-10 | tPSA,%f PSA, volume, PSAD, PSAD-TZ, TZ-volume | 0.83 (ANN) | 68 (ANN) | |
| 2004 | yes | 1. LR | ProStatus (Wallac) | 4-10 | 1.DRE 2.%f PSA 3.volume 4.tPSA | 0.764 (LR) | 22 (LR) | |
| 2010 | not available | 0.79 (LR model) | 80 | 45 |
Abbreviations: AUC: area under the (ROC) curve; n.a.: not available; LR: logistic regression; ANN: artificial neural network, PAP: prostate alkaline phosphatase, CK: creatinkinase; PSAD: PSA density, PSAD-TZ: transition zone density; DRE: digital rectal examination
Fig. 1.1. The program at www.finne.info to estimate the risk of PCa based on ANN and LR at the 95% sensitivity level.
Fig. 2.Program “ProstataClass” version 2008 for 5 different PSA assays at http://urologie.charite.de and the link: “ProstataClass”. Provided example of the ANN output (only available in German) indicating “Risiko” (risk)” at the 95% sensitivity level.
Fig. 3.Molecular forms of PSA and the prostate health index phi including the respective times of detection.
Selected studies with more than 200 subjects on Phi (2010-2013)
| First author [Ref.] | Year | Phi cutoff | AUC | Sensitivity | Specificity |
|---|---|---|---|---|---|
| 2010 | not available | 0.79 (LR model) | 80 | 45 | |
| 2010 | not available | 0.75(0.71) | 90 | 31 | |
| 2011 | 36.45 (at 90% spec.) | 0.73 | 42 | 90 | |
| 2011 | 48.5 (at 90% spec.) Hybr. calibr. | 0.76 | 43 | 90 | |
| 2011 | 21.3(24.1) | 0.70 | 95( | 16( | |
| 2013 | 24.3(27.9) | 0.70 | 95( | 16( | |
| 2012 | 28.8 | 0.67 | 90 | 25 | |
| 2013 | 31( | 0.74 | 95 ( | 15( | |
| 2013 | 27.5 | 0.68 | 90 | 21 | |
| 2013 | 31.6 | 0.77 | 90 | 40 | |
| 2013 | 23.9(24.9) | 0.72 | 95( | 28( | |
| 2013 | 27.6 | 0.67 | 90 | 19 | |
| 2013 | 28.3(30.6) | 0.70 all | 90( | 16-34 | |
| 2013 | 26.5 | 0.78 | 90 | 50 |
*alsoPCA3 values available
Abbreviations: AUC: area under the (ROC) curve; bx: biopsy; Hybr. calibr.: Hybritech calibration (for PSA & fPSA); n.a.: not available; WHO calibr, calculated (not measured) as WHO calibrated
Selected studies with more than 200 subjects on PCA3 (2007-2013)
| First author [Ref.](n of pts.; % of PCa) | Year | PCA3 cutoff | AUC | Sensitivity | Specificity |
|---|---|---|---|---|---|
| 2007 | 35 | 0.68 | 58 | 72 | |
| 2008 | 35 | 0.66 | 47 | 72 | |
| 2008 | 35 | 0.69 | 54 | 74 | |
| 2008 | 25 | 0.665 | 63 | 60 | |
| 2009 | 17 | 0.68 | 81 | 45 | |
| 2010 | 35 | 0.72 | 61 | 74 | |
| 2010 | 17 ( | 0.73-0.75 | 88 | 45 | |
| 2010 | 35 | 0.635 | 68 | 56 | |
| 2010 | 35, ( | 0.69 | 44-59 | 67-79 | |
| 2010 | 35 | 0.69 | 48 | 79 | |
| 2011 | 35 | 0.76 | 64 | 76 | |
| 2011 | 51 | 0.83 | 70 | 81 | |
| 2012 | 35 ( | 0.68 | 73 | 49 | |
| 2012 | 10 ( | 0.71 | 86.5 | 37 | |
| 2013 | 28 | 0.74 | 73 | 64 | |
| 2013 | 21 | 0.74 | 79 | 59 | |
| 2013 | 16.5(13.5, 23.5) | 0.59 | 80 ( | 16-34 | |
| 2013 | 20 | n.a. | 87 | 55 | |
| 2013 | 25 | 0.71 | 77.5 | 57 | |
| 2013 | 22 | 0.73 | 90 | 40 | |
| 2013 | 35 | 0.73 | 62 | 75 | |
| 2013 | 35 | 0.74 | 63 | 72 |