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  • Northcott, Paul A, et al. (author)
  • Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma.
  • 2014
  • In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 511:7510, s. 428-428
  • Journal article (peer-reviewed)abstract
    • Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate 'enhancer hijacking' as an efficient mechanism driving oncogene activation in a childhood cancer.
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2.
  • Zichner, Thomas, et al. (author)
  • Temporal analysis of oncogenesis using microRNA expression data
  • 2008
  • In: German Conference on Bioinformatics, GCB 2008. - Bonn : Gesellschaft für Informatik. - 9783885792260 ; , s. 128-137
  • Conference paper (peer-reviewed)abstract
    • MicroRNAs (miRNAs) have rapidly become the focus of many cancer research studies. These small non-coding RNAs have been shown to play important roles in the regulation of oncogenes and tumor suppressors. It has also been demonstrated that miRNA expression profiles differ significantly between normal and cancerous cells, which indicates the possibility of using miRNAs as markers for cancer diagnosis and prognosis. However, not much is known about the regulation of miRNA expression. One of the issues worth investigating is whether deregulations of miRNA expression in cancer cells occur according to some pattern or in a random order. We therefore selected two approaches, previously used to derive graph models of oncogenesis using chromosomal imbalance data, and adapted them to miRNA expression data. Applying the adapted algorithms to a breast cancer data set, we obtained results indicating the temporal order of miRNA deregulations during tumor development. When analyzing the specific deregulations appearing at different time points in the derived model, we found that several of the deregulations identified as early events could be supported through literature studies.
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