Diagnosis of prostate cancer using differentially expressed genes in stroma

Abstract

Over 1 million prostate biopsies are performed in the U.S. 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. More than a thousand significant expression changes were found and thereafter filtered using large numbers of other expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier was constructed based on 114 candidate genes and tested on 364 independent samples, including 243 tumor-bearing samples and 121 non-tumor 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 97.2% (sensitivity = 97.9% and specificity = 88.0%) 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.

Expression differences between African American and Caucasian prostate cancer tissue reveals that stroma is the site of aggressive changes

“…The NCI SPECS and EDRN consortium at UCI is an ongoing observational study that uses tissue and clinical data to generate gene signatures for the prognosis of prostate cancer. [16][17][18] As described previously, pathologic review was performed according to the TNM classification standard system. 19 Patient and clinical information was obtained through medical records and clinical pathologists verified the pathology of the tumor.…”

“…(1)] allowed us to associate gene expression with tissue type. [16][17][18] The model assumes that total observed gene expression is a sum of the contribution from the different tissue types,…”

“…It was previously reported that changes in RNA expression via microarray are statistically reliable to distinguish normal stroma from tumor-adjacent stroma. 16 Through the use of linear regression modeling, RNA expression can be associated with cell-type specific tissue from array data of prostate tumor samples. This work has led to classifiers that are useful for the diagnosis of prostate cancer of stroma-rich biopsies, even in the case where the percent tumor tissue is extremely low in the sample.…”

“…We have extended this modeling to understand the biological differences of prostate cancer by race using a subset of the U133A Affymetrix gene chip data (GSE08218) used in previous studies. 16 For our analysis, we developed a combination of pair-wise t-test between samples matched for tissue-type composition and multiple linear regression (MLR) models that identify differentially expressed genes between AA and CA patients and associate these expression changes with the percent tumor and stroma tissue found within the prostate cancer microarray samples. Our results show that specific processes related to the immune system and regulation of epithelial to mesenchymal transition (EMT) in the tumor-adjacent stroma are likely involved in the aggressive nature of prostate cancer in AA patients.…”

Overexpression of NCAPH is upregulated and predicts a poor prognosis in prostate cancer

“…Microarray data. Three public datasets GSE21034 (18), GSE17951 (19) and GSE55945 (20) were download from the Gene Expression Omnibus (GEO) database (www.ncbi.nlm. nih.gov/geo/).…”