Integration of ERG gene mapping and gene expression profiling identifies distinct categories of human prostate cancer

OBJECTIVE: Previous expression microarray analyses have failed to take into consideration the genetic heterogeneity and complex patterns of ERG gene alteration frequently found in cancerous prostates. The objective of this study is for the first time, to integrate the mapping of ERG gene alterations with the collection of expression microarray data. PATIENTS AND METHODS: We have deterimined genome-wide expression levels with Affymetrix GeneChip Human Exon 1.0 ST arrays using RNA prepared from 35 specimens of prostate cancer from 28 prostates. RESULTS: The expression profiles exhibit clustering, in unsupervised hierarchical analyses, into two distinct prostate cancer categories, with one group strongly associated with indicators of poor clinical outcome. The two categories are not tightly linked to ERG gene status. Through analysis of the data we identify a subgoup of cancers lacking ERG gene rearrangements that exhibit an outlier pattern of SPINK1 mRNA expression. We also show that a major distinction between ERG gene rearranged and non-rearranged cancers involves the levels of expression of genes linked to exposure to beta-estradiol, and to retinoic acid. CONCLUSIONS: Our studies show that expression profiling of prostate cancer samples containing single patterns of ERG alterations can provide novel insights into the mechanism of prostate cancer development, and support the view that factors other than ERG gene status are the major determinants of poor clinical outcome. Keywords: disease state analysis Genome-wide expression levels were obtained from Affymetrix GeneChip Human Exon 1.0 ST arrays using RNA prepared from 35 specimens of prostate cancer from 28 prostates. Additionally, experiments on three normal specimens were performed. 1 technical replicate was also run.