MP11-17 P-SCORE BASED ON THREE EMBRYONIC STEM CELL GENES IMPROVES PROGNOSTIC EVALUATION FOR METASTASIS AND DEATH IN PROSTATE CANCER - A RETROSPECTIVE VALIDATION STUDY

Abstract

You have accessJournal of UrologyProstate Cancer: Staging (MP11)1 Sep 2021MP11-17 P-SCORE BASED ON THREE EMBRYONIC STEM CELL GENES IMPROVES PROGNOSTIC EVALUATION FOR METASTASIS AND DEATH IN PROSTATE CANCER - A RETROSPECTIVE VALIDATION STUDY Li-Di Xu, Arni Saemundsson, Florian Meisgen, Rong Cao, and Göran Ahlgren Li-Di XuLi-Di Xu More articles by this author , Arni SaemundssonArni Saemundsson More articles by this author , Florian MeisgenFlorian Meisgen More articles by this author , Rong CaoRong Cao More articles by this author , and Göran AhlgrenGöran Ahlgren More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001984.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: A previously reported Prostatype algorithm (P-score) incorporating a three-gene signature (IGFBP3, F3, VGLL3) with clinical parameters (Gleason score, PSA and T-stage) was developed and validated in 596 historical patients selected from a population-based cohort. This study aims to retrospectively validate Prostatype algorithms´ capability to predict the risk of metastasis and prostate cancer (PCa) specific mortality in new diagnosed PCa patients. METHODS: All 716 patients included were diagnosed with PCa using core needle biopsy from January 2008 to December 2010 at Skåne University hospital Malmö and Lund Sweden with a follow-up time 8-10 years after diagnosis. Gene expression was assessed by RT-qPCR on all core needle biopsies from diagnosis. A predefined genes score was computed from the expression of the three-gene signature and then combined with clinical parameters (Gleason score, PSA and T-stage) to calculate the P-score (units: 0-15). Cox proportional hazard regression models were used to evaluate the association of gene signature with clinical outcomes. The risk stratification of the score system was evaluated by Kaplan-Meier curve. The receiver operating characteristic analysis and decision-curve analysis were used to assess the prediction accuracy and the net-benefit, respectively. Analysis results were compared to known risk scores such as D´Amico. RESULTS: Total 365 had valid data, 316 patients were without metastasis at diagnosis, 47 had secondary metastasis during follow up and 33 died due to PCa. All patients that died had a high P-score. The gene signature added significant prognostic information for metastasis (p<0.01) and PCa-specific death (p<0.01). One unit change in P-score has a hazard ratio (HR) of 1.6(95% CI: 1.41-1.82, p<0.0001) for predicting PCa death and HR of 1.48 (95% CI: 1.35-1.63, p<0.0001) for predicting PCa-specific death. P-score was significantly better than D´Amico for predicting end-point PCa death (0.89 v.s 0.77, p<0,0001) and metastasis (0.86 v.s 0.77, p<0,0001), unrelated to which treatment was given. Gene signature alone showed a similar prediction power as D’Amico for both end-points. P-score has a much higher net-benefit than D’Amico as revealed by decision curve analysis. CONCLUSIONS: P-score gave improved prognostic evaluation for metastasis and death in PCa compared to other known risk indicators. P-score can be a beneficial tool when treatment decision is made for patients with localised PCa. Source of Funding: Prostatype Genomics sponsored for the data collection © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e186-e186 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Li-Di Xu More articles by this author Arni Saemundsson More articles by this author Florian Meisgen More articles by this author Rong Cao More articles by this author Göran Ahlgren More articles by this author Expand All Advertisement Loading ...