Pd56-11 Analysis of Methylated Dna Markers for Prediction of Cancer Progression After Radical Prostatectomy
Year: 2018
Type: article
Study design: Observational Study
Abstract: You have accessJournal of UrologyProstate Cancer: Markers II1 Apr 2018PD56-11 ANALYSIS OF METHYLATED DNA MARKERS FOR PREDICTION OF CANCER PROGRESSION AFTER RADICAL PROSTATECTOMY Matthew Gettman, Ilya Sobol, Brian Dukek, William Taylor, Douglas Mahoney, Tracy Yab, Calise Berger, John Cheville, Jeffrey Karnes, and David Ahlquist Matthew GettmanMatthew Gettman More articles by this author , Ilya SobolIlya Sobol More articles by this author , Brian DukekBrian Dukek More articles by this author , William TaylorWilliam Taylor More articles by this author , Douglas MahoneyDouglas Mahoney More articles by this author , Tracy YabTracy Yab More articles by this author , Calise BergerCalise Berger More articles by this author , John ChevilleJohn Cheville More articles by this author , Jeffrey KarnesJeffrey Karnes More articles by this author , and David AhlquistDavid Ahlquist More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2643AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Therapeutic decisions for prostate cancer (CAP) are often guided by Gleason grade, which is subjective and lacks precision. In discovery and early validation, we identified methylated DNA markers (MDMs) with prognostic association (AUA 2017). We now assess value of novel MDMs in predicting recurrence using archival tissue from an independent group with > 12 years follow-up after radical prostatectomy (RP). METHODS From 737 men undergoing RP in 2004 at our institution, 446 were randomly selected and 155 met quality criteria. An expert pathologist re-reviewed all specimens in blinded fashion using updated Gleason criteria and marked tumors for macro-dissection prior to DNA extraction, bisulfite treatment and methylation-specific PCR. MDMs were standardized to beta-actin. Recurrence was defined as PSA > 0.4 ng/mL. Top MDMs were selected by regression partitioning tree models to assign recurrence risk and grouped by quartiles (M1 (lowest) to M4 (highest)). Prognostic values of MDMs and Gleason grade groups (GGG) were assessed and compared based on their concordance with postoperative RP outcomes. RESULTS Among 155 men studied, 43 recurrences occurred. MDMs selected by the model included WNT3A, AGPS7497, FNBP1, GSDMD, KCNK4, Chr1.61519554, GALR3, and RASSF2. Concordance with post-RP outcomes was 0.85 (95% CI 0.76-0.94) by MDM modeling and 0.63 (CI 0.53-0.71) by GGG, p , 0.0001. Concordance was 0.86 (CI 0.77 - 0.95) by combined approaches and did not significantly differ from MDM modeling alone. MDM-defined risk groups are compared to GGG for prediction of CAP recurrence (table). M1 & M2 quartiles showed no recurrences (0/72), yet 36 % (26/72) of these were histologically advanced (GGG 2-5). Conversely, the M4 quartile comprised 69% (34/49) recurrences, and 32% (11) of the 34 accurately predicted by M4 were GGG 1. CONCLUSIONS MDMs appear to enhance prediction of CAP recurrence. With optimization and further corroboration, MDMs testing has potential to contribute objectively to CAP management decisions before & after treatment. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1066 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Matthew Gettman More articles by this author Ilya Sobol More articles by this author Brian Dukek More articles by this author William Taylor More articles by this author Douglas Mahoney More articles by this author Tracy Yab More articles by this author Calise Berger More articles by this author John Cheville More articles by this author Jeffrey Karnes More articles by this author David Ahlquist More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Source: The Journal of Urology
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2796201153