Identifying regulatory mechanisms underlying tumorigenesis using locus expression signature analysis

Abstract

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. genome-wide expression profiling | transcription factor activity | gene regulatory network | computational cancer biology | data integration M ouse retroviral insertional mutagenesis has been used as an efficient tool for identification of causal mutations in cancer (1). Such oncogenic mutations may either cause alteration of a gene product or influence the expression levels of one or more genes surrounding the insertion. More than 10,000 common insertion sites (CISs) at which retroviral insertions are found in multiple tumors have been identified (2). Methods exist that predict which genes are affected by insertions based on information such as orientation and position (3). However, it is not always straightforward to determine which genetic lesions near a CIS are playing a causal role. Moreover, these approaches are based on gene annotation and do not use or provide functional evidence. Analyzing the mRNA expression level of the genes near the insertion site is often not sufficient, as the lesion may exert its effect only at the posttranslational level. Furthermore, retroviral insertional mutagenesis screens alone rarely elucidate the regulatory mechanisms that drive tumorigenesis. These limitations highlight the need for new approaches that can integrate the genetic data with functional genomics data and other information to identify causal genes and regulatory mechanisms underlying cancer.Here, we carried out genome-wide expression profiling in a set of tumors induced by retroviral insertional mutagenesis. We also present a computational approach, locus expression signature analysis (LESA), that first defines and then analyzes the genome-wide mRNA expression response induced by the putative causal gene near the insertion locus. We analyzed genome-wide expression profiles for a panel of splenic tumors induced by retroviral insertional mutagenesis (3). To identify regulatory mechanisms underlying tumorigenesis, we hypothesized that gene expression is affected by insertional mutations through the regulation by sequence specific transcription ...

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Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression

“…Locus Expression Signature Analysis (LESA; [27]) is a variant of the aQTL approach. In this study, genome-wide signatures that quantify the gene expression response to genetic perturbation at a particular locus were constructed for a panel of mouse tumors created through viral insertional mutagenesis, as well as from TCGA data.…”

“…In this study, genome-wide signatures that quantify the gene expression response to genetic perturbation at a particular locus were constructed for a panel of mouse tumors created through viral insertional mutagenesis, as well as from TCGA data. These signatures were then further characterized in order to link genetic loci to the TFs whose activity they perturb, or to identify therapeutic locus-drug combinations [27]. Empirically defined regulons have also been used to infer TF activity across TCGA samples and analyze the regulatory connectivity between TFs and their upstream signaling pathways [28].…”