Noninvasive biomarkers to guide personalized treatment for castration-resistant prostate cancer (CRPC) are needed. In this study, we analyzed hypermethylation patterns of two genes (GSTP1 and APC) in plasma cell-free DNA (cfDNA) of CRPC patients. The aim of this study was to analyze the cfDNA concentrations and levels of the epigenetic markers and to assess the value of these biomarkers for prognosis.
In this prospective study, patients were included before starting new treatment after developing CRPC. The blood samples were collected prior to start of the treatment and at three time points thereafter. cfDNA was extracted from 1.5 mL of plasma and before performing a methylation-specific PCR, bisulfate modification was carried out.
The median levels of cfDNA, GSTP1, and APC copies in the baseline samples of CRPC patients (n = 47) were higher than in controls (n = 30). In the survival analysis, the group with baseline marker levels below median had significant less PCa-related deaths (P-values <0.02) and did not reach the median survival point. The survival distributions for the groups were statistically significant for the cfDNA concentration, GSTP1 and APC copies, as well as PSA combined with GSTP1 + APC (P-values <0.03). Furthermore, there were strong positive correlations between PSA and marker response after starting treatment (P-values <0.04).
In conclusion, this study showed the kinetics of methylated cfDNA (GSTP1 and APC) in plasma of CRPC patients after starting treatment. Furthermore, the value of the markers before treatment is prognostic for overall survival. These results are promising for developing a test to guide treatment-decision-making for CRPC patients.
The Prostate. 2018 Jan 12 [Epub ahead of print]
Rianne J Hendriks, Siebren Dijkstra, Frank P Smit, Johan Vandersmissen, Hendrik Van de Voorde, Peter F A Mulders, Inge M van Oort, Wim Van Criekinge, Jack A Schalken
Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands., MDxHealth, Inc., Irvine, California., Department of Mathematical Modeling, Statistics and Bio-Informatics, Ghent University, Ghent, Belgium.