The landscape of prognostic outlier genes in high-risk prostate cancer

Shuang G. Zhao, University of Michigan, Ann Arbor
Joseph R. Evans, University of Michigan, Ann Arbor
Vishal Kothari, University of Michigan, Ann Arbor
Grace Sun, University of Michigan, Ann Arbor
Ashley Larm, University of Michigan, Ann Arbor
Victor Mondine, University of Michigan, Ann Arbor
Edward M. Schaeffer, Johns Hopkins Medicine
Ashley E. Ross, Johns Hopkins Medicine
Eric A. Klein, Cleveland Clinic Foundation
Robert B. Den, Thomas Jefferson University
Adam P. Dicker, Thomas Jefferson University
R. Jeffrey Karnes, Mayo Clinic
Nicholas Erho, Genome DX
Paul L. Nguyen, Brigham and Women's Hospital
Elai Davicioni, Genome DX
Felix Y. Feng, University of Michigan, Ann Arbor


© 2015 American Association for Cancer Research. Purpose: There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets. Experimental Design:Weinterrogated microarray gene expression data from prostatectomy samples from 545 high-risk prostate cancer patients with long-term follow-up (mean 13.4 years). Three independent clinical datasets totaling an additional 545 patients were used for validation. Novel prognostic outlier genes were interrogated for impact on oncogenic phenotypes in vitro using siRNA-based knockdown. Association with clinical outcomes and comparison with existing prognostic instruments was assessed with multivariable models using a prognostic outlier score. Results: Analysis of the discovery cohort identified 20 prognostic outlier genes. Three top prognostic outlier genes were novel prostate cancer genes; NVL, SMC4, or SQLE knockdown reduced migration and/or invasion and outlier expression was significantly associated with poor prognosis. Increased prognostic outlier score was significantly associated with poor prognosis independent of standard clinicopathologic variables. Finally, the prognostic outlier score prognostic association is independent of, and adds to existing genomic and clinical tools for prognostication in prostate cancer (Decipher, the cell-cycle progression signature, and CAPRA-S). Conclusions: To our knowledge, this study represents the first unbiased high-throughput investigation of prognostic outlier genes in prostate cancer and demonstrates the potential biomarker and therapeutic importance of this previously unstudied class of cancer genes.