PURPOSE: Early detection of prostate cancer (PC) using prostate-specific antigen (PSA) in blood reduces PC-death among unscreened men. However, due to modest specificity of PSA at commonly used cut-offs, there are urgent needs for additional biomarkers contributing enhanced risk classification among men with modestly elevated PSA. EXPERIMENTAL DESIGN: Recombinant antibody microarrays were applied for protein expression profiling of 80 plasma samples from routine PSA-measurements, a priori divided into four risk groups, based on levels of total and %free PSA. RESULTS: The results demonstrated that plasma protein profiles could be identified that pin-pointed PC (a malignant biomarker signature) and most importantly that showed moderate to high correlation with biochemically defined PC risk groups. Notably, the data also implied that the risk group with midrange PSA and low %free PSA, a priori known to be heterogeneous, could be further stratified into two subgroups, more resembling the lowest and highest risk groups, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: In this pilot study, we have shown that plasma protein biomarker signatures, associated with risk groups of PC, could be identified from crude plasma samples using affinity proteomics. This approach could in the longer perspective provide novel opportunities for improved risk classification of PC patients.
PURPOSE: Early detection of prostate cancer (PC) using prostate-specific antigen (PSA) in blood reduces PC-death among unscreened men. However, due to modest specificity of PSA at commonly used cut-offs, there are urgent needs for additional biomarkers contributing enhanced risk classification among men with modestly elevated PSA. EXPERIMENTAL DESIGN: Recombinant antibody microarrays were applied for protein expression profiling of 80 plasma samples from routine PSA-measurements, a priori divided into four risk groups, based on levels of total and %free PSA. RESULTS: The results demonstrated that plasma protein profiles could be identified that pin-pointed PC (a malignant biomarker signature) and most importantly that showed moderate to high correlation with biochemically defined PC risk groups. Notably, the data also implied that the risk group with midrange PSA and low %free PSA, a priori known to be heterogeneous, could be further stratified into two subgroups, more resembling the lowest and highest risk groups, respectively. CONCLUSIONS AND CLINICAL RELEVANCE: In this pilot study, we have shown that plasma protein biomarker signatures, associated with risk groups of PC, could be identified from crude plasma samples using affinity proteomics. This approach could in the longer perspective provide novel opportunities for improved risk classification of PC patients.
Authors: Frida Pauly; Linda Dexlin-Mellby; Sara Ek; Mats Ohlin; Niclas Olsson; Karin Jirström; Michael Dictor; Saskia Schoenmakers; Carl A K Borrebaeck; Christer Wingren Journal: J Proteome Res Date: 2013-10-09 Impact factor: 4.466
Authors: W J Catalona; A W Partin; K M Slawin; M K Brawer; R C Flanigan; A Patel; J P Richie; J B deKernion; P C Walsh; P T Scardino; P H Lange; E N Subong; R E Parson; G H Gasior; K G Loveland; P C Southwick Journal: JAMA Date: 1998-05-20 Impact factor: 56.272
Authors: A V D'Amico; R Whittington; S B Malkowicz; D Schultz; K Blank; G A Broderick; J E Tomaszewski; A A Renshaw; I Kaplan; C J Beard; A Wein Journal: JAMA Date: 1998-09-16 Impact factor: 56.272
Authors: Shahrokh F Shariat; Michael W Kattan; Erica Traxel; Ben Andrews; Kuichun Zhu; Thomas M Wheeler; Kevin M Slawin Journal: Clin Cancer Res Date: 2004-03-15 Impact factor: 12.531