Direct Transcriptional Consequences of Somatic Mutation in Breast Cancer

Adam Shlien, Keiran Raine, Fabio Fuligni, Roland Arnold, Serena Nik-Zainal, Serge Dronov, Lira Mamanova, Andrej Rosic, Young Seok Ju, Susanna L Cooke, Manasa Ramakrishna, Elli Papaemmanuil, Helen R Davies, Patrick S Tarpey, Peter Van Loo, David C Wedge, David R Jones, Sancha Martin, John Marshall, Elizabeth AndersonClaire Hardy, ICGC Breast Cancer Working Group, Oslo Breast Cancer Research Consortium, Violetta Barbashina, Samuel A J R Aparicio, Torill Sauer, Øystein Garred, Anne Vincent-Salomon, Odette Mariani, Sandrine Boyault, Aquila Fatima, Anita Langerød, Åke Borg, Gilles Thomas, Andrea L Richardson, Anne-Lise Børresen-Dale, Kornelia Polyak, Michael R Stratton, Peter J Campbell

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
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Abstract

Disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription, coordinated secondary pathway alterations, and increased transcriptional noise. To catalog the rules governing how somatic mutation exerts direct transcriptional effects, we developed an exhaustive pipeline for analyzing RNA sequencing data, which we integrated with whole genomes from 23 breast cancers. Using X-inactivation analyses, we found that cancer cells are more transcriptionally active than intermixed stromal cells. This is especially true in estrogen receptor (ER)-negative tumors. Overall, 59% of substitutions were expressed. Nonsense mutations showed lower expression levels than expected, with patterns characteristic of nonsense-mediated decay. 14% of 4,234 rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusions, and premature polyadenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data reveals the rules by which transcriptional machinery interprets somatic mutation.

Original languageEnglish
Pages (from-to)2032-46
Number of pages15
JournalCell Reports
Volume16
Issue number7
Early online date4 Aug 2016
DOIs
Publication statusPublished - 16 Aug 2016

Keywords

  • Algorithms
  • Breast Neoplasms
  • Data Interpretation, Statistical
  • Exome
  • Female
  • Gene Expression Regulation, Neoplastic
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Mutation
  • Oncogene Proteins, Fusion
  • Polyadenylation
  • Receptors, Estrogen
  • Transcriptome
  • X Chromosome Inactivation
  • Journal Article

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