Retroviral insertional mutagenesis is a powerful tool for identifying putative cancer genes in mice. To uncover the regulatory mechanisms by which common insertion loci affect downstream processes, we supplemented genotyping data with genome-wide mRNA expression profiling data for 97 tumors induced by retroviral insertional mutagenesis. We developed locus expression signature analysis, an algorithm to construct and interpret the differential gene expression signature associated with each common insertion locus. Comparing locus expression signatures to promoter affinity profiles allowed us to build a detailed map of transcription factors whose protein-level regulatory activity is modulated by a particular locus. We also predicted a large set of drugs that might mitigate the effect of the insertion on tumorigenesis. Taken together, our results demonstrate the potential of a locus-specific signature approach for identifying mammalian regulatory mechanisms in a cancer context.
Keywords: computational cancer biology; data integration; gene regulatory network; genome-wide expression profiling; transcription factor activity.