Pathway and network analysis of more than 2500 whole cancer genomes

Research output: Contribution to journalJournal articleResearchpeer-review


  • Matthew A. Reyna
  • David Haan
  • Marta Paczkowska
  • Lieven P.C. Verbeke
  • Miguel Vazquez
  • Abdullah Kahraman
  • Sergio Pulido-Tamayo
  • Jonathan Barenboim
  • Lina Wadi
  • Priyanka Dhingra
  • Raunak Shrestha
  • Gad Getz
  • Michael S. Lawrence
  • Jakob Skou Pedersen
  • Mark A. Rubin
  • David A. Wheeler
  • Brunak, Søren
  • Jose M.G. Izarzugaza
  • Ekta Khurana
  • Kathleen Marchal
  • Christian von Mering
  • S. Cenk Sahinalp
  • Alfonso Valencia
  • Federico Abascal
  • Samirkumar B. Amin
  • Gary D. Bader
  • Pratiti Bandopadhayay
  • Rameen Beroukhim
  • Johanna Bertl
  • Keith A. Boroevich
  • John Busanovich
  • Peter J. Campbell
  • Joana Carlevaro-Fita
  • Dimple Chakravarty
  • Calvin Wing Yiu Chan
  • Ken Chen
  • Jung Kyoon Choi
  • Jordi Deu-Pons
  • Klev Diamanti
  • Lars Feuerbach
  • J. Lynn Fink
  • Nuno A. Fonseca
  • Joan Frigola
  • Carlo Gambacorti-Passerini
  • Dale W. Garsed
  • Mark Gerstein
  • Erik Larsson
  • Morten Muhlig Nielsen
  • Nikos Sidiropoulos
  • Weischenfeldt, Joachim Lütken
  • PCAWG Drivers and Functional Interpretation Working Group
  • PCAWG Consortium

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Original languageEnglish
Article number729
JournalNature Communications
Issue number1
Pages (from-to)1-17
Publication statusPublished - 2020

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