Pd35-06 Association of a Polygenetic Risk Score and Prostate Cancer Detection

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology III (PD35)1 May 2024PD35-06 ASSOCIATION OF A POLYGENETIC RISK SCORE AND PROSTATE CANCER DETECTION Masatomo Kaneko, David Bogumil, Lorenzo Storino Ramacciotti, Peggy Wan, Manju Aron, David Conti, Michelle Hopstone, Inderbir S. Gill, Christopher Haiman, and Andre Luis Abreu Masatomo KanekoMasatomo Kaneko , David BogumilDavid Bogumil , Lorenzo Storino RamacciottiLorenzo Storino Ramacciotti , Peggy WanPeggy Wan , Manju AronManju Aron , David ContiDavid Conti , Michelle HopstoneMichelle Hopstone , Inderbir S. GillInderbir S. Gill , Christopher HaimanChristopher Haiman , and Andre Luis AbreuAndre Luis Abreu View All Author Informationhttps://doi.org/10.1097/01.JU.0001009396.21455.74.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To establish whether a non-invasive polygenetic risk score can be used to differentiate a patients' risk of prostate cancer versus benign. METHODS: Consecutive men undergoing 3T multiparametric MRI followed by MRI/transrectal ultrasound (TRUS) fusion prostate biopsy (PBx) as per standard of care were prospectively enrolled (IRB# HS-13-00663). MRI was acquired according to PIRADS v2 or v2.1. All participants underwent 12-14 core systematic biopsy with a minimum of additional two targeted biopsy cores per PIRADS≥3 lesion. All patients provided saliva samples immediately before the PBx. Saliva samples were used for genotyping. A polygenic risk score (PRS) of prostate cancer was then estimated in the sample using 451 variants identified via a multi-ancestry-genome-wide association study meta-analysis with 156,319 prostate cancer (PCa) cases and 788,443 controls. PRS was prospectively measured in association with PCa detection. The PCa detection performance of the PRS and other non-invasive clinical variables were assessed by logistic regression and the receiver operating characteristic (ROC) analysis. The optimal cutoff was determined by the Youden index. RESULTS: A total of 390 participants were enrolled. The baseline characteristics were: median age 66yr; PSA 6.2ng/mL; PSA density (PSAD) 0.12ng/mL2; Race/ethnicity, non-Hispanic white 62%, Hispanic 11%, Asian 16%, Black 8.5%; PIRADS 1-2 23%, PIRADS 3 19%, PIRADS 4 34%, and PIRADS 5 23%. The distribution of the highest GG on PBx was, benign 36%, GG1 19%, GG2 20%, GG3 9.5%, GG4 7.6%, and GG5 6.5%. On multivariable logistic regression, PRS (Odds ratio [OR] 1.70, 95%CI 1.30-2.23), age (OR 1.06, 95%CI 1.02-1.10), PSAD per 0.01 (OR 1.07, 95%CI 1.03-1.10), and PIRADS 3-5 (OR 3.12, 95%CI 1.73-5.61) were independent predictors for PCa detection. The addition of PRS in the non-invasive and pre-PBx predictors improved the area under the ROC curve (0.74 to 0.78), sensitivity (69% to 74%), and specificity (67% to 69%). CONCLUSIONS: A novel and non-invasive polygenetic risk score successfully predicted the presence of prostate cancer from saliva samples in the prospective study. Combined with non-invasive pre-biopsy patient characteristics, prostate cancer can be predicted. Download PPT Source of Funding: This study was supported by NCI P30 Cancer Center Support Grant (P30CA0140890) © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e726 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Masatomo Kaneko More articles by this author David Bogumil More articles by this author Lorenzo Storino Ramacciotti More articles by this author Peggy Wan More articles by this author Manju Aron More articles by this author David Conti More articles by this author Michelle Hopstone More articles by this author Inderbir S. Gill More articles by this author Christopher Haiman More articles by this author Andre Luis Abreu More articles by this author Expand All Advertisement PDF downloadLoading ...