Pathway and network analysis of more than 2500 whole cancer genomes

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, Søren Brunak, Jose M. G. Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, Jüri Reimand (), Joshua M. Stuart () and Benjamin J. Raphael ()
Additional contact information
Matthew A. Reyna: Princeton University
David Haan: University of California, Santa Cruz
Marta Paczkowska: Computational Biology Program, Ontario Institute for Cancer Research, Toronto
Lieven P. C. Verbeke: Ghent University, IMEC
Miguel Vazquez: Barcelona Supercomputing Center (BSC)
Abdullah Kahraman: Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich
Sergio Pulido-Tamayo: Ghent University, IMEC
Jonathan Barenboim: Computational Biology Program, Ontario Institute for Cancer Research, Toronto
Lina Wadi: Computational Biology Program, Ontario Institute for Cancer Research, Toronto
Priyanka Dhingra: Weill Cornell Medicine
Raunak Shrestha: Vancouver Prostate Centre
Gad Getz: The Broad Institute of MIT and Harvard
Michael S. Lawrence: The Broad Institute of MIT and Harvard
Jakob Skou Pedersen: Aarhus University Hospital
Mark A. Rubin: Weill Cornell Medicine
David A. Wheeler: Human Genome Sequencing Center, Baylor College of Medicine
Søren Brunak: Technical University of Denmark, Kemitorvet
Jose M. G. Izarzugaza: Technical University of Denmark, Kemitorvet
Ekta Khurana: Weill Cornell Medicine
Kathleen Marchal: Ghent University, IMEC
Christian von Mering: Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich
S. Cenk Sahinalp: Vancouver Prostate Centre
Alfonso Valencia: Barcelona Supercomputing Center (BSC)
Jüri Reimand: Computational Biology Program, Ontario Institute for Cancer Research, Toronto
Joshua M. Stuart: University of California, Santa Cruz
Benjamin J. Raphael: Princeton University

Nature Communications, 2020, vol. 11, issue 1, 1-17

Abstract: Abstract 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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14367-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14367-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-14367-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().