Literature DB >> 27569435

Molecular Analysis of Low Grade Prostate Cancer Using a Genomic Classifier of Metastatic Potential.

Eric A Klein1, María Santiago-Jiménez2, Kasra Yousefi2, Bruce A Robbins3, Edward M Schaeffer4, Bruce J Trock5, Jeffrey Tosoian5, Zaid Haddad2, Seong Ra3, R Jeffrey Karnes6, Robert B Jenkins7, John C Cheville7, Robert B Den8, Adam P Dicker8, Elai Davicioni2, Stephen J Freedland9, Ashley E Ross5.   

Abstract

PURPOSE: We determined how frequently histological Gleason 3 + 3 = 6 tumors have the molecular characteristics of disease with metastatic potential.
MATERIALS AND METHODS: We analyzed prostatectomy tissue from 337 patients with Gleason 3 + 3 disease. All tissue was re-reviewed in blinded fashion by genitourinary pathologists using 2005 ISUP (International Society of Urological Pathology) Gleason grading criteria. A previously validated Decipher® metastasis signature was calculated in each case based on a locked model. To compare patient characteristics across pathological Gleason score categories we used the Fisher exact test or the ANOVA F test. The distribution of Decipher scores among different clinicopathological groups was compared with the Wilcoxon rank sum test. The association of Decipher score with adverse pathology features was examined using logistic regression models. The significance level of all statistical tests was 0.05.
RESULTS: Of men with Gleason 3 + 3 = 6 disease only 269 (80%) had a low Decipher score with intermediate and high scores in 43 (13%) and 25 (7%), respectively. Decipher scores were significantly higher among pathological Gleason 3 + 3 = 6 specimens from cases with adverse pathological features such as extraprostatic extension, seminal vesicle involvement or positive margins (p <0.001). The median Decipher score in patients with margin negative pT2 disease was 0.23 (IQR 0.09-0.42) compared to 0.30 (IQR 0.17-0.42) in patients with pT3 disease or positive margins (p = 0.005).
CONCLUSIONS: Using a robust and validated prognostic signature we found that a small but not insignificant proportion of histological Gleason 6 tumors harbored molecular characteristics of aggressive cancer. Molecular profiling of such tumors at diagnosis may better select patients for active surveillance at diagnosis and trigger appropriate intervention during followup. Copyright Â
© 2017 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  genomics; neoplasm grading; neoplasm metastasis; prostatic neoplasms; watchful waiting

Mesh:

Year:  2016        PMID: 27569435     DOI: 10.1016/j.juro.2016.08.091

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  9 in total

1.  Management of prostate cancer: NYU Case of the Month, July 2017.

Authors:  Samir S Taneja
Journal:  Rev Urol       Date:  2017

2.  Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.

Authors:  Andrei S Purysko; Cristina Magi-Galluzzi; Omar Y Mian; Sarah Sittenfeld; Elai Davicioni; Marguerite du Plessis; Christine Buerki; Jennifer Bullen; Lin Li; Anant Madabhushi; Andrew Stephenson; Eric A Klein
Journal:  Eur Radiol       Date:  2019-03-07       Impact factor: 5.315

3.  Performance of a Prostate Cancer Genomic Classifier in Predicting Metastasis in Men with Prostate-specific Antigen Persistence Postprostatectomy.

Authors:  Daniel E Spratt; Darlene L Y Dai; Robert B Den; Patricia Troncoso; Kasra Yousefi; Ashley E Ross; Edward M Schaeffer; Zaid Haddad; Elai Davicioni; Rohit Mehra; Todd M Morgan; Walter Rayford; Firas Abdollah; Edouard Trabulsi; Mary Achim; Elsa Li Ning Tapia; Mireya Guerrero; Robert Jeffrey Karnes; Adam P Dicker; Mark A Hurwitz; Paul L Nguyen; Felix F Y Feng; Stephen J Freedland; John W Davis
Journal:  Eur Urol       Date:  2017-12-10       Impact factor: 20.096

4.  Clinical Utility of Gene Expression Classifiers in Men With Newly Diagnosed Prostate Cancer.

Authors:  Jonathan C Hu; Jeffrey J Tosoian; Ji Qi; Deborah Kaye; Anna Johnson; Susan Linsell; James E Montie; Khurshid R Ghani; David C Miller; Kirk Wojno; Frank N Burks; Daniel E Spratt; Todd M Morgan
Journal:  JCO Precis Oncol       Date:  2018-10-19

Review 5.  The Use of Biomarkers in Prostate Cancer Screening and Treatment.

Authors:  Ashley V Alford; Joseph M Brito; Kamlesh K Yadav; Shalini S Yadav; Ashutosh K Tewari; Joseph Renzulli
Journal:  Rev Urol       Date:  2017

Review 6.  Imaging for the selection and monitoring of men on active surveillance for prostate cancer.

Authors:  Maria C Velasquez; Nachiketh Soodana Prakash; Vivek Venkatramani; Bruno Nahar; Sanoj Punnen
Journal:  Transl Androl Urol       Date:  2018-04

7.  Identification of candidate miRNAs in early-onset and late-onset prostate cancer by network analysis.

Authors:  Rafael Parra-Medina; Liliana López-Kleine; Sandra Ramírez-Clavijo; César Payán-Gómez
Journal:  Sci Rep       Date:  2020-07-23       Impact factor: 4.379

8.  Molecular network-based identification of competing endogenous RNAs and mRNA signatures that predict survival in prostate cancer.

Authors:  Ning Xu; Yu-Peng Wu; Hu-Bin Yin; Xue-Yi Xue; Xin Gou
Journal:  J Transl Med       Date:  2018-10-04       Impact factor: 5.531

9.  PTEN status assessment in the Johns Hopkins active surveillance cohort.

Authors:  Jeffrey J Tosoian; Liana B Guedes; Carlos L Morais; Mufaddal Mamawala; Ashley E Ross; Angelo M De Marzo; Bruce J Trock; Misop Han; H Ballentine Carter; Tamara L Lotan
Journal:  Prostate Cancer Prostatic Dis       Date:  2018-10-02       Impact factor: 5.554

  9 in total

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