PD52-11 UNIQUE GENOMIC SIGNATURE TO PREDICT BIOCHEMICAL RECURRENCE FOLLOWING RADICAL PROSTATECTOMY

Abstract

You have accessJournal of UrologyProstate Cancer: Markers II (PD52)1 Apr 2020PD52-11 UNIQUE GENOMIC SIGNATURE TO PREDICT BIOCHEMICAL RECURRENCE FOLLOWING RADICAL PROSTATECTOMY John Corradi, Christine Cumarasamy, Ilene Staff, Joseph Tortora, Andrew Salner, and Joseph Wagner* John CorradiJohn Corradi More articles by this author , Christine CumarasamyChristine Cumarasamy More articles by this author , Ilene StaffIlene Staff More articles by this author , Joseph TortoraJoseph Tortora More articles by this author , Andrew SalnerAndrew Salner More articles by this author , and Joseph Wagner*Joseph Wagner* More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000954.011AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Identification of novel biomarkers associated with high risk prostate cancer or biochemical recurrence can drive improvement in detection, prognosis, and treatment. Using a large sample of prostate specimens, the objective was to identify gene expression signatures that would predict biochemical recurrence following radical prostatectomy. METHODS: Between 2008 and 2011, patients undergoing radical prostatectomy at Hartford Hospital were consented to submit specimens for whole genome gene expression as part of the Total Cancer Care Consortium. RNA isolated from formalin-fixed paraffin-embedded prostates was hybridized to a custom Affymetrix microarray. Regularized Least Absolute Shrinkage and Selection Operator(LASSO) Cox regression was performed with cross-validation to identify a gene expression signature that improves risk prediction. Recurrence was defined as post-operative prostate-specific antigen (PSA) >0.2 ng/mL or triggered salvage treatment. Model performance was assessed using time-dependent receiver operating characteristic (ROC) curves (with area under the curve, AUC) and survival plots. RESULTS: Pre- and post-operative PSA data were available for 606 prostate specimens. Using the LASSO model, a 5 gene signature was identified that independently predicted biochemical recurrence above Gleason grade and tumor stage. The time-dependent ROC AUC for the 5 gene signature with Gleason grade and tumor stage was 0.868 compared to an AUC of 0.767 for Gleason grade and tumor stage alone. The predictive model was subsequently validated on two independent gene expression data sets with AUC’s of 0.837 and 0.839. Patients stratified into low and high risk groups based on the predictive model score displayed significant differences in their recurrence-free survival curves. The model included genes (RHOU, MTX2, and ERP44) that have previously been implicated in prostate cancer biology. CONCLUSIONS: Our unique 5 gene signature panel can improve prediction of biochemical recurrence over the use of classical pathological hallmarks alone. Further validation should involve more specific sub-populations of prostate cancer patients, including those with earlier biochemical recurrence. Source of Funding: Unfunded. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1094-e1094 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information John Corradi More articles by this author Christine Cumarasamy More articles by this author Ilene Staff More articles by this author Joseph Tortora More articles by this author Andrew Salner More articles by this author Joseph Wagner* More articles by this author Expand All Advertisement PDF downloadLoading ...