Epigenetic risk score improves prostate cancer risk assessment

Leander Van Neste, Jack Groskopf, William E. Grizzle, George W. Adams, Mark S. DeGuenther, Peter N. Kolettis, James E. Bryant, Gary P. Kearney, Michael C. Kearney, Wim Van Criekinge*, Sandra M. Gaston

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


BackgroundEarly detection of aggressive prostate cancer (PCa) remains crucial for effective treatment of patients. However, PCa screening remains controversial due to a high rate of overdiagnosis and overtreatment. To better reconcile both objectives, more effective methods for assessing disease severity at the time of diagnosis are needed.

MethodsThe relationship between DNA-methylation and high-grade PCa was examined in a cohort of 102 prospectively enrolled men who received standard 12-core prostate biopsies. EpiScore, an algorithm that quantifies the relative DNA methylation intensities of GSTP1, RASSF1, and APC in prostate biopsy tissue, was evaluated as a method to compensate for biopsy under-sampling and improve risk stratification at the time of diagnosis.

ResultsDNA-methylation intensities of GSTP1, RASSF1, and APC were higher in biopsy cores from men diagnosed with GS7 cancer compared to men with diagnosed GS 6 disease. This was confirmed by EpiScore, which was significantly higher for subjects with high-grade biopsies and higher NCCN risk categories (both P

ConclusionsIn men diagnosed with PCa, DNA-methylation profiling can detect under-sampled high-risk PCa in prostate biopsy specimens through a field effect. Predictive accuracy increased when EpiScore was combined with other clinical risk factors. These results suggest that EpiScore could aid in the detection of occult high-grade disease at the time of diagnosis, thereby improving the selection of candidates for Active Surveillance.

Original languageEnglish
Pages (from-to)1259-1264
Number of pages6
Issue number12
Publication statusPublished - 1 Sept 2017


  • epigenetic
  • Gleason grade
  • logistic regression model
  • prognosis
  • prostate cancer
  • risk score
  • GSTP1
  • APC

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