Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

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DOI

  • Henrik Hornshøj
  • ,
  • Morten Muhlig Nielsen
  • Nicholas A Sinnott-Armstrong, 2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02140 USA.
  • ,
  • Michał P Świtnicki
  • ,
  • Malene Juul
  • ,
  • Tobias Madsen
  • ,
  • Richard Sallari, 2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02140 USA.
  • ,
  • Manolis Kellis, 2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02140 USA.
  • ,
  • Torben Ørntoft
  • Asger Hobolth
  • Jakob Skou Pedersen

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from 10 cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g., TERT and CDH10) and survival (e.g., CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217, and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. In conclusion, the screen for significant mutation patterns coupled with correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

OriginalsprogEngelsk
Tidsskriftnpj Genomic Medicine
Vol/bind3
Sider (fra-til)1
DOI
StatusUdgivet - 11 jan. 2018

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