Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence

Wusheng Yan, Muhammad Jamal, Shyh Han Tan, Yingjie Song, Denise Young, Yongmei Chen, Shilpa Katta, Kai Ying, Lakshmi Ravindranath, Tarah Woodle, Indu Kohaar, Jennifer Cullen, Jacob Kagan, Sudhir Srivastava, Albert Dobi, David G. McLeod, Inger L. Rosner, Isabell A. Sesterhenn, Alagarsamy Srinivasan, Shiv Srivastava*Gyorgy Petrovics

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Background: As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered “low risk” at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression. Methods: We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes. Results: After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays. Conclusions: Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

Original languageEnglish
Pages (from-to)6466-6483
Number of pages18
JournalOncotarget
Volume10
Issue number60
DOIs
StatePublished - 2019
Externally publishedYes

Keywords

  • Biochemical recurrence
  • ERG
  • NanoString
  • Prognostic biomarker
  • Prostate cancer

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