Figure 1

Gene signatures capable of discriminating between prostate cancer subgroups and classify metastatic disease. Gene signatures generated using the Varambally dataset and found to be significant discriminators of metastatic disease and primary/localised cancers (Additional file 10: Table S8) when applied to the Tomlins and Rawaswamy datasets were used to cluster samples in these datasets in a heatmap. The gene signatures represented are those capable of characterising samples from at least one progression stage (Fischer’s exact < = 0.05). Gene signatures are rows and samples are columns. The colour coded bar at the base of the heatmap indicates the clinical grouping for each sample as also defined in the key. Metastatic hormone refractory, metastatic hormone naïve and hormone refractory vs. naïve represent prostate cancer cases from the Tomlins dataset, as do PIN (prostatic intraepithelial neoplasia) and primary carcinoma. The other categories (metastatic and primary) are samples from the Rawaswamy dataset and are metastatic and primary cancers from multiple organ sites, not simply the prostate gland. The blue bar graph on the right-hand side of the heatmap depicts the number of genes in each signature which are differentially expressed and contribute to the sample clustering in this analysis. For signature 1 (dist 101.6.1 and Additional file 5: Table S3) this is 1748 genes in total as highlighted and other bars are numbers of genes relative to this. The colour scale represents the mean log2 fold change for differential gene signatures (> = abs log2(2)). Red indicates module induction, green repression. Gene signatures significant in both directions are indicated in yellow generated by the addition of the corresponding red and green shades. Using the mean module log2 fold change we clustered the samples and modules using hierarchical clustering with euclidean distance as a measure of dissimilarity. Data points that contained both induced and repressed values have been excluded from the clustering.