DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients.

METHODS: A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation.

RESULTS: Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis.

CONCLUSIONS: Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

PLoS One. 2015 Jun 18;10(6):e0130651. doi: 10.1371/journal.pone.0130651. eCollection 2015.

Litovkin K1, Van Eynde A1, Joniau S2, Lerut E3, Laenen A4, Gevaert T2, Gevaert O5, Spahn M6, Kneitz B7, Gramme P8, Helleputte T8, Isebaert S9, Haustermans K9, Bollen M1.

1Laboratory of Biosignaling & Therapeutics, KU Leuven Department of Cellular and Molecular Medicine, University of Leuven, Leuven, Belgium.

2Urology, University Hospitals Leuven & KU Leuven Department of Development and Regeneration, University of Leuven, Leuven, Belgium.

3Pathology, University Hospitals Leuven & KU Leuven Department of Imaging and Pathology, University of Leuven, Leuven, Belgium.

4KU Leuven Biostatistics and Statistical Bioinformatics Centre, University of Leuven, Leuven, Belgium.

5Stanford Center for Cancer Systems Biology, Stanford University School of Medicine, Stanford, California, United States of America; Laboratory of Cancer Data Fusion, KU Leuven Department of Oncology, University of Leuven, Leuven, Belgium.

6Department of Urology, University Hospital Bern, Inselspital, Bern, Switzerland; Department of Urology and Paediatric Urology, University Hospital Würzburg, Würzburg, Germany.

7Department of Urology and Paediatric Urology, University Hospital Würzburg, Würzburg, Germany.

8DNAlytics SA, Chemin du Cyclotron 6, 1348 Louvain-la-Neuve, Belgium.

9Radiation Oncology, University Hospitals Leuven & KU Leuven Department of Oncology, University of Leuven, Leuven, Belgium.

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