Abstract
5006 Background: Integrative analysis that combines expression data, copy number variation, sequence, and epigenetic data with thousands of known biological interactions can help identify strong biological associations and novel “genotype to phenotype” associations. Methods: We performed multi-dataset differential expression analysis with Statistical Analysis of Microarrays (SAM) and gene set enrichment analysis (GSEA) across 10 publicly-available PCa microarray datasets to identify genes and pathways differentially expressed between local (n=471) and metastatic (n=220) PCa. We used PARADIGM, an integrated pathway analysis method, on PCa tumor samples with mRNA expression, copy number variation, and TMPRSS2:ERG fusion status data to infer differential fusion gene-related “activities” for pathway features (genes, protein complexes, etc) within a “Superimposed Pathway” representing a comprehensive collection of genetic interactions currently containing 20,314 known interactions among 16,362 concepts representing 6916 proteins, 7345 complexes, 1449 families, 55 RNAs, 15 miRNAs and 582 processes. Results: Out of the 7571 genes tested (those genes having data in two or more studies), the meta-analysis on gene expression revealed 210 (1.8% FDR) positively associated with PCa metastasis and 403 (0.94% FDR) negatively associated genes (threshold was p<0.001, 2-tailed Student’s t-test). GSEA highlighted cell proliferation, cell cycle control, and DNA damage repair pathways with metastatic tumors. The PARADIGM analysis identified a network containing 914 features connected by 1137 edges. PLK1 was identified as both highly expressed in metastatic PCa and as one of the fourteen hubs in the largest (596-feature) sub-network identified by PARADIGM. PLK1 was also associated with high Gleason Sum and recurrent disease in independent local PCa datasets. Conclusions: Using an approach pioneered by members of our SU2C/PCF supported PCa Dream Team, integrated analysis across multiple PCa datasets associates PLK1 activity with aggressive PCa and suggests it may provide a novel treatment target for at least a genetic sub-set of advanced PCa.
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