Cell ReportsGenomicsPeer-reviewed journal article
Principles governing A-to-I RNA editing in the breast cancer transcriptome
Debora Fumagalli, David Gacquer, Françoise Rothé, Anne Lefort, Frederick Libert, David Brown, Naima Kheddoumi, Adam Shlien, Tomasz Konopka, Roberto Salgado, Denis LarsimontiD, Kornelia Polyak, Karen Willard-Gallo, Christine Desmedt, Martine Piccart, Marc Abramowicz, Peter J CampbelliD, Christos Sotiriou, Vincent DetoursiD
Abstract
ABSTRACTA-to-I editing substitutes inosines for adenosines at specific positions in mRNAs and can substantially alter a cells transcriptome. Currently, little is known about how RNA editing operates in cancer. Transcriptome analysis of 68 normal and cancerous breast tissues revealed that the editing enzyme ADAR acts uniformly, on the same loci, across tissues. Controlled ADAR expression experiments demonstrated that the editing frequency at all loci is proportional to both ADAR expression levels and the individual locus editability--a propensity to be edited determined by the surrounding nucleotide sequence. Comparison of tumor transcriptomes to those of normal breast and breast organoids, i.e. pure normal breast epithelial cells, demonstrated that the editing frequency is increased in tumor cells. This was consistent with ADAR immunohistochemistry. We also demonstrated that type I interferon response and ADAR DNA copy number explain together 53% of ADAR expression in breast cancers, an observation also valid in nearly all of 20 other cancer types in The Cancer Genome Atlas. Interferon exposure increased ADAR mRNA, protein expression and editing in four breast cell lines. Finally, ADAR silencing using shRNA lentivirus transduction in breast cancer cell lines led to more cell proliferation and less apoptosis. Our results reveal that A-to-I editing is a pervasive, yet reproducible, source of variation that is controlled by two factors, 1q amplification and inflammation, both highly prevalent among human cancers. This suggests the potential for a new class of therapeutic targets and an unexpected role for inflammation in cancers.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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