OBJECTIVE: To investigate whether preoperative plasma levels of free DNA can discriminate between men with localized prostate cancer and benign prostatic hyperplasia (BPH). PATIENTS AND METHODS: In all, 161 referred patients suspicious for prostate cancer either by an elevated prostate-specific antigen (PSA) level and/or abnormal digital rectal examination (DRE) were included in this prospective study. Peripheral plasma was taken before prostate biopsy and genomic DNA was extracted from the plasma using the a commercial kit and a vacuum chamber. After controlling for age, PSA level, the percentage free/total (f/t) PSA and prostate volume, the median prostate cancer plasma DNA concentration served as diagnostic threshold in uni- and multivariate logistic regression models. Multivariate models were subjected to 200 bootstraps for internal validation and to reduce over-fit bias. RESULTS: Subgroups consisted of 142 men with clinically localized prostate cancer and 19 with BPH. The median plasma concentration of cell-free DNA was 267 ng/mL in men with BPH vs 709 ng/mL in men with prostate cancer. In univariate analyses, plasma DNA concentration was a statistically significant and informative predictor (P = 0.032 and predictive accuracy 0.643). In multivariate analyses, it remained statistically significant after controlling for age, tPSA, f/tPSA and prostate volume, increasing the predictive accuracy by 5.6%. CONCLUSIONS: Our data suggest that plasma DNA level is a highly accurate and informative predictor in uni- and multivariate models for the presence of prostate cancer on needle biopsy. The predictive accuracy was substantially increased by adding plasma DNA level. However, larger-scale studies are needed to further confirm its clinical impact on prostate cancer detection.
OBJECTIVE: To investigate whether preoperative plasma levels of free DNA can discriminate between men with localized prostate cancer and benign prostatic hyperplasia (BPH). PATIENTS AND METHODS: In all, 161 referred patients suspicious for prostate cancer either by an elevated prostate-specific antigen (PSA) level and/or abnormal digital rectal examination (DRE) were included in this prospective study. Peripheral plasma was taken before prostate biopsy and genomic DNA was extracted from the plasma using the a commercial kit and a vacuum chamber. After controlling for age, PSA level, the percentage free/total (f/t) PSA and prostate volume, the median prostate cancer plasma DNA concentration served as diagnostic threshold in uni- and multivariate logistic regression models. Multivariate models were subjected to 200 bootstraps for internal validation and to reduce over-fit bias. RESULTS: Subgroups consisted of 142 men with clinically localized prostate cancer and 19 with BPH. The median plasma concentration of cell-free DNA was 267 ng/mL in men with BPH vs 709 ng/mL in men with prostate cancer. In univariate analyses, plasma DNA concentration was a statistically significant and informative predictor (P = 0.032 and predictive accuracy 0.643). In multivariate analyses, it remained statistically significant after controlling for age, tPSA, f/tPSA and prostate volume, increasing the predictive accuracy by 5.6%. CONCLUSIONS: Our data suggest that plasma DNA level is a highly accurate and informative predictor in uni- and multivariate models for the presence of prostate cancer on needle biopsy. The predictive accuracy was substantially increased by adding plasma DNA level. However, larger-scale studies are needed to further confirm its clinical impact on prostate cancer detection.
Authors: Julia A Beaver; Danijela Jelovac; Sasidharan Balukrishna; Rory Cochran; Sarah Croessmann; Daniel J Zabransky; Hong Yuen Wong; Patricia Valda Toro; Justin Cidado; Brian G Blair; David Chu; Timothy Burns; Michaela J Higgins; Vered Stearns; Lisa Jacobs; Mehran Habibi; Julie Lange; Paula J Hurley; Josh Lauring; Dustin VanDenBerg; Jill Kessler; Stacie Jeter; Michael L Samuels; Dianna Maar; Leslie Cope; Ashley Cimino-Mathews; Pedram Argani; Antonio C Wolff; Ben H Park Journal: Clin Cancer Res Date: 2014-02-06 Impact factor: 12.531
Authors: Marcelo L Wroclawski; Ary Serpa-Neto; Fernando L A Fonseca; Oseas Castro-Neves-Neto; Alexandre S F L Pompeo; Marcos T Machado; Antonio C L Pompeo; Auro del Giglio Journal: Tumour Biol Date: 2013-05-29
Authors: Heidi Schwarzenbach; Klaus Pantel; Birthe Kemper; Cord Beeger; Friedrich Otterbach; Rainer Kimmig; Sabine Kasimir-Bauer Journal: Breast Cancer Res Date: 2009 Impact factor: 6.466