• Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA.
• Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20850, USA.
• Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA.
• Genetics and Molecular Biology Branch, National Human Genome Research Institute, NIH, Bethesda, MD, 20892, USA. crawforn@mail.nih.gov.

Prostate cancer (PC) is very common in developed countries. However, the molecular determinants of PC metastasis are unclear. Previously, we reported that germline variation influences metastasis in the C57BL/6-Tg(TRAMP)8247Ng/J (TRAMP) mouse model of PC. These mice develop prostate tumors similar to a subset of poor outcome, treatment-associated human PC tumors. Here, we used TRAMP mice to nominate candidate genes and validate their role in aggressive human PC in PC datasets and cell lines. Candidate metastasis susceptibility genes were identified through quantitative trait locus (QTL) mapping in 201 (TRAMP × PWK/PhJ) F2 males. Two metastasis-associated QTLs were identified; one on chromosome 12 (LOD = 5.86), and one on chromosome 14 (LOD = 4.41). Correlation analysis using microarray data from (TRAMP × PWK/PhJ) F2 prostate tumors identified 35 metastasis-associated transcripts within the two loci. The role of these genes in susceptibility to aggressive human PC was determined through in silico analysis using multiple datasets. First, analysis of candidate gene expression in two human PC datasets demonstrated that five candidate genes were associated with an increased risk of aggressive disease and lower disease-free survival. Second, four of these genes (GNL3, MAT1A, SKA3, and ZMYM5) harbored SNPs associated with aggressive tumorigenesis in the PLCO/CGEMS GWAS of 1172 PC patients. Finally, over-expression of GNL3 and SKA3 in the PC-3 human PC cell line decreased in vitro cell migration and invasion. This novel approach demonstrates how mouse models can be used to identify metastasis susceptibility genes, and gives new insight into the molecular mechanisms of fatal PC.