Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

Fan Mo, Dong Lin, Mandeep Takhar, Varune Rohan Ramnarine, Xin Dong, Robert H. Bell, Stanislav V. Volik, Kendric Wang, Hui Xue, Yuwei Wang, Anne Haegert, Shawn Anderson, Sonal Brahmbhatt, Nicholas Erho, Xinya Wang, Peter W. Gout, James Morris, R. Jeffrey Karnes, Robert B. Den, Eric A. KleinEdward M. Schaeffer, Ashley Ross, Shancheng Ren, S. Cenk Sahinalp, Yingrui Li, Xun Xu, Jun Wang, Jian Wang, Martin E. Gleave, Elai Davicioni, Yinghao Sun, Yuzhuo Wang*, Colin C. Collins

*Corresponding author for this work

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

Background: Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa. Objective: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa). Design, setting, and participants: To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers. Outcome measurements and statistical analysis: The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures. Results and limitations: A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures. Conclusions: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors. Patient summary: Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors. A novel strategy incorporating the use of prostate cancer patient-derived xenograft models and sequence data analysis has led to the discovery of a stromal gene signature that can serve as a new independent biomarker for risk stratification for patients with primary prostate cancer.

Original languageEnglish (US)
Pages (from-to)524-532
Number of pages9
JournalEuropean urology
Volume73
Issue number4
DOIs
StatePublished - Apr 2018

Keywords

  • Genomic profiling
  • Prognostic biomarkers
  • Prostate cancer metastasis
  • RNA sequencing
  • Stromal gene

ASJC Scopus subject areas

  • Urology

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