Gamma-Retrovirus integration marks cell type-specific cancer genes
A novel profiling tool in cancer genomics
Kathryn L. Gilroy (University of Glasgow)
Anne Terry (University of Glasgow)
Asif Naseer (University of Glasgow, Khyber Medical University)
Jeroen de Ridder (TU Delft - Pattern Recognition and Bioinformatics)
Amin Allahyar (TU Delft - Pattern Recognition and Bioinformatics)
Weiwei Wang (University of Alberta)
Eric Carpenter (University of Alberta)
Andrew Mason (University of Alberta)
Gane K.S. Wong (University of Alberta)
Ewan R. Cameron (University of Glasgow)
Anna Kilbey (University of Glasgow)
James C. Neil (University of Glasgow)
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Abstract
Retroviruses have been foundational in cancer research since early studies identified protooncogenes as targets for insertional mutagenesis. Integration of murine gamma-retroviruses into the host genome favours promoters and enhancers and entails interaction of viral integrase with host BET/bromodomain factors. We report that this integration pattern is conserved in feline leukaemia virus (FeLV), a gamma-retrovirus that infects many human cell types. Analysis of FeLV insertion sites in the MCF-7 mammary carcinoma cell line revealed strong bias towards active chromatin marks with no evidence of significant post-integration growth selection. The most prominent FeLV integration targets had little overlap with the most abundantly expressed transcripts, but were strongly enriched for annotated cancer genes. A meta-analysis based on several gamma-retrovirus integration profiling (GRIP) studies in human cells (CD34+, K562, HepG2) revealed a similar cancer gene bias but also remarkable cell-type specificity, with prominent exceptions including a universal integration hotspot at the long non-coding RNA MALAT1. Comparison of GRIP targets with databases of super-enhancers from the same cell lines showed that these have only limited overlap and that GRIP provides unique insights into the upstream drivers of cell growth. These observations elucidate the oncogenic potency of the gamma-retroviruses and support the wider application of GRIP to identify the genes and growth regulatory circuits that drive distinct cancer types.
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