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Research Collection
Article . 2020
License: CC BY
ETH Zürich Research Collection
Article . 2020
License: CC BY
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Analyses of non-coding somatic drivers in 2,658cancer whole genomes

Authors: Group, PCAWG Drivers; Functional Interpretation Working; Group, PCAWG Structural Variation Working; Consortium, PCAWG; PCAWG Consortium; Rheinbay, Esther; Nielsen, Morten M.; +92 Authors

Analyses of non-coding somatic drivers in 2,658cancer whole genomes

Abstract

The discovery of drivers of cancer has traditionally focused on protein-coding genes14. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5 region of TP53, in the 3 untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that althoughpoint mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Nature, 578 (7793)

ISSN:0028-0836

ISSN:1476-4687

Keywords

Cancer genomics; Computational biology and bioinformatics, Cancer genomics, Computational biology and bioinformatics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green