Metastasis is the result of stochastic genomic and epigenetic events leading to gene expression profiles that drive
tumor dissemination. Here we exploit the principle that metastatic propensity is modified by the genetic background to generate prognostic gene expression signatures that illuminate regulators of
metastasis. We also identify multiple
microRNAs whose germline variation is causally linked to
tumor progression and
metastasis. We employ network analysis of global gene expression profiles in
tumors derived from a panel of recombinant inbred mice to identify a network of co-expressed genes centered on Cnot2 that predicts
metastasis-free survival. Modulating Cnot2 expression changes
tumor cell metastatic potential in vivo, supporting a functional role for Cnot2 in
metastasis. Small
RNA sequencing of the same
tumor set revealed a negative correlation between expression of the Mir216/217 cluster and
tumor progression. Expression quantitative trait locus analysis (eQTL) identified cis-eQTLs at the Mir216/217 locus, indicating that differences in expression may be inherited. Ectopic expression of Mir216/217 in
tumor cells suppressed
metastasis in vivo. Finally, small
RNA sequencing and
mRNA expression profiling data were integrated to reveal that miR-3470a/b target a high proportion of network transcripts. In vivo analysis of Mir3470a/b demonstrated that both promote
metastasis. Moreover, Mir3470b is a likely regulator of the Cnot2 network as its overexpression down-regulated expression of network hub genes and enhanced
metastasis in vivo, phenocopying Cnot2 knockdown. The resulting data from this strategy identify Cnot2 as a novel regulator of
metastasis and demonstrate the power of our systems-level approach in identifying modifiers of
metastasis.