Pathway and network analysis of more than 2500 whole cancer genomes

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Matthew A. Reyna, Princeton University, Emory University
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  • David Haan, University of California, Santa Cruz
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  • Marta Paczkowska, Ontario Institute for Cancer Research
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  • Lieven P.C. Verbeke, Universiteit Gent
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  • Miguel Vazquez, Polytechnic University of Catalonia, Norwegian University of Science and Technology
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  • Abdullah Kahraman, Physik-Institut, Universitat Zürich-Irchel, University Hospital Zurich
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  • Sergio Pulido-Tamayo, Universiteit Gent
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  • Jonathan Barenboim, Ontario Institute for Cancer Research
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  • Lina Wadi, Ontario Institute for Cancer Research
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  • Priyanka Dhingra, Weill Cornell Medicine
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  • Raunak Shrestha, Vancouver Prostate Centre
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  • Gad Getz, Broad Institute, Massachusetts General Hospital, Boston, Harvard Medical School
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  • Michael S. Lawrence, Broad Institute, Massachusetts General Hospital, Boston
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  • Jakob Skou Pedersen
  • Mark A. Rubin, Weill Cornell Medicine
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  • David A. Wheeler, Baylor College of Medicine
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  • Søren Brunak, Technical University of Denmark, University of Copenhagen
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  • Jose M.G. Izarzugaza, Technical University of Denmark, University of Copenhagen
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  • Ekta Khurana, Weill Cornell Medicine
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  • Kathleen Marchal, Universiteit Gent
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  • Christian von Mering, Physik-Institut, Universitat Zürich-Irchel
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  • S. Cenk Sahinalp, Vancouver Prostate Centre, Indiana University
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  • Alfonso Valencia, Polytechnic University of Catalonia, Theoretical and Computational Neuroscience Group, Center of Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.
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  • PCAWG Drivers and Functional Interpretation Working Group
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  • Jüri Reimand, University of Toronto
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  • Joshua Stewart, University of California
  • ,
  • Benjamin Raphael, Princeton University
  • ,
  • 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
Volume11
Issue1
ISSN2041-1723
DOIs
Publication statusPublished - 2020

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