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.

More than one million prostate biopsies are performed in the United States every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. About 3,800 significant expression changes were found and thereafter filtered using independent expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier for nearby tumor was constructed on the basis of 114 candidate genes and tested on 364 independent samples including 243 tumor-bearing samples and 121 nontumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97% (sensitivity = 98% and specificity = 88%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.