BioSpace Collaborative

Academic/Biomedical Research
News & Jobs
Biotechnology and Pharmaceutical Channel Medical Device and Diagnostics Channel Clinical Research Channel BioSpace Collaborative    Job Seekers:  Register | Login          Employers:  Register | Login  

Free Newsletters
My Subscriptions

News by Subject
News by Disease
News by Date
Search News
Post Your News

Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

Regional News
US & Canada
  Biotech Bay
  Biotech Beach
  Pharm Country
  Bio NC
  Southern Pharm
  BioCanada East
  C2C Services & Suppliers™


Company Profiles

Research Store

Research Events
Post an Event
Real Estate
Business Opportunities

PLoS By Category | Recent PLoS Articles
Pathology - Urology

Expression Changes in the Stroma of Prostate Cancer Predict Subsequent Relapse
Published: Wednesday, August 01, 2012
Author: Zhenyu Jia et al.

by Zhenyu Jia, Farah B. Rahmatpanah, Xin Chen, Waldemar Lernhardt, Yipeng Wang, Xiao-Qin Xia, Anne Sawyers, Manuel Sutton, Michael McClelland, Dan Mercola

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.