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

Litovkin, Kirill; Van Eynde, Aleyde; Joniau, Steven; Lerut, Evelyne; Laenen, Annouschka; Gevaert, Thomas; Gevaert, Olivier; Spahn, Martin; Kneitz, Burkhard; Gramme, Pierre; Helleputte, Thibault; Isebaert, Sofie; Haustermans, Karin; Bollen, Mathieu (2015). DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer. PLoS ONE, 10(6), e0130651. Public Library of Science 10.1371/journal.pone.0130651

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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.

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5 Citations in Web of Science ®
6 Citations in Scopus

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