Figure 5

Workflow for the identification of robust gene signatures and gene sets for clustering prostate cancer cases. In step 1, we identified all statistically significant differentially expressed Affymetrix array probes in a small dataset consisting of 13 macrodissected clinical samples encompassing localised benign prostatic hyperplasia, localised prostate cancer and metastatic disease (GSE3325). We then generated gene signatures from these based on gene coexpression at varying stringency thresholds. These gene signatures were then applied to two additional datasets, a microdissected dataset (Tomlins et al.) and a multi-tissue site cancer and metastatic dataset (Ramaswamy et al.). A large number of the coexpression gene signatures clustered localised prostate cancers from metastatic disease and prostate metastases from metastases at other organ sites. The most compact gene signature able to do so consisted of 71 genes and we assessed its expression pattern in two additional datasets, an exon-array dataset (Taylor et al.) and in a RNA-sequenced dataset (TCGA-PRAD). Few of the genes in the significant coexpression gene signatures were overexpressed genes in localised prostate cancers. In the second phase of the study, we abstracted all of the overexpressed genes and refined this down to a set of 33 genes based on significant overexpression in additional publicly available prostate cancer microarray datasets housed within the Oncomine database. These genes also effectively clustered benign versus cancer cases in an exon-array dataset (Taylor et al.) an expression microarray dataset (Grasso et al.) and a RNA-sequenced dataset (TCGA-PRAD). In conclusion, it is possible to generate gene classifiers of clinical prostate cancer from a small dataset of macrodissected samples with the capacity to classify larger sequenced and microdissected datasets based on clinical characteristics.