A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer

Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers.

Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset.

Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025).

The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

The Prostate. 2018 Jun 28 [Epub ahead of print]

Shanshan Zhao, Amy Leonardson, Milan S Geybels, Andrew S McDaniel, Ming Yu, Suzanne Kolb, Hong Zong, Kelly Carter, Javed Siddiqui, Anqi Cheng, Jonathan L Wright, Colin C Pritchard, Raymond Lance, Dean Troyer, Jian-Bing Fan, Elaine A Ostrander, James Y Dai, Scott A Tomlins, Ziding Feng, Janet L Stanford

National Institute of Environmental Health Sciences, Biostatistics and Computational Biology Branch, Research Triangle Park, Durham, North Carolina., Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, Washington., Departments of Pathology and Urology, University of Michigan, Ann Arbor, Michigan., Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, Washington., Department of Laboratory Medicine, University of Washington School of Medicine, Seattle, Washington., Department of Urology, Eastern Virginia Medical School, Norfolk, Virginia., Departments of Pathology, Microbiology, and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia., Department of Oncology, Illumina, Inc., San Diego, California., Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland.

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