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Pathway and network analysis of more than 2,500 whole cancer genomes

View ORCID ProfileMatthew A. Reyna, David Haan, Marta Paczkowska, View ORCID ProfileLieven P.C. Verbeke, View ORCID ProfileMiguel Vazquez, View ORCID ProfileAbdullah Kahraman, View ORCID ProfileSergio Pulido-Tamayo, Jonathan Barenboim, Lina Wadi, View ORCID ProfilePriyanka Dhingra, View ORCID ProfileRaunak Shrestha, View ORCID ProfileGad Getz, Michael S. Lawrence, View ORCID ProfileJakob Skou Pedersen, View ORCID ProfileMark A. Rubin, View ORCID ProfileDavid A. Wheeler, View ORCID ProfileSøren Brunak, View ORCID ProfileJose MG Izarzugaza, View ORCID ProfileEkta Khurana, View ORCID ProfileKathleen Marchal, View ORCID ProfileChristian von Mering, View ORCID ProfileS. Cenk Sahinalp, View ORCID ProfileAlfonso Valencia, Jüri Reimand, View ORCID ProfileJoshua M. Stuart, View ORCID ProfileBenjamin J. Raphael, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network
doi: https://doi.org/10.1101/385294
Matthew A. Reyna
1Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
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David Haan
2Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz CA 95060, USA
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Marta Paczkowska
3Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Lieven P.C. Verbeke
4Department of Information Technology, IDLab, Ghent University, IMEC
5Department of Plant Biotechnology and Bioinformatics, Ghent University
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Miguel Vazquez
6Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
7Norwegian University of Science and Technology, Trondheim, Norway
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Abdullah Kahraman
8Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zürich, Switzerland
9Department of Pathology and Molecular Pathology, University Hospital Zurich, CH-8091 Zürich, Switzerland
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Sergio Pulido-Tamayo
4Department of Information Technology, IDLab, Ghent University, IMEC
5Department of Plant Biotechnology and Bioinformatics, Ghent University
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Jonathan Barenboim
3Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Lina Wadi
3Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
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Priyanka Dhingra
10Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
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Raunak Shrestha
11Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
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Gad Getz
12The Broad Institute of MIT and Harvard, Cambridge, MA 02124, USA
13Massachusetts General Hospital, Center for Cancer Research, Charlestown, MA 02129, USA
14Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA
15Massachusetts General Hospital, Department of Pathology, Boston, MA 02114, USA
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Michael S. Lawrence
12The Broad Institute of MIT and Harvard, Cambridge, MA 02124, USA
13Massachusetts General Hospital, Center for Cancer Research, Charlestown, MA 02129, USA
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Jakob Skou Pedersen
16Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
17Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
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Mark A. Rubin
10Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
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David A. Wheeler
18Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
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Søren Brunak
19DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800 Kongens Lyngby, Denmark
20Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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Jose MG Izarzugaza
19DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800 Kongens Lyngby, Denmark
20Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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Ekta Khurana
10Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
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Kathleen Marchal
4Department of Information Technology, IDLab, Ghent University, IMEC
5Department of Plant Biotechnology and Bioinformatics, Ghent University
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Christian von Mering
8Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zürich, Switzerland
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S. Cenk Sahinalp
11Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC V6H 3Z6, Canada
21Department of Computer Science, Indiana University, Bloomington, IN 47405, USA
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Alfonso Valencia
6Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
22ICREA, Barcelona, 08010, Spain
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Jüri Reimand
3Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
23Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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  • For correspondence: braphael@princeton.edu jstuart@ucsc.edu juri.reimand@oicr.on.ca
Joshua M. Stuart
2Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz CA 95060, USA
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  • For correspondence: braphael@princeton.edu jstuart@ucsc.edu juri.reimand@oicr.on.ca
Benjamin J. Raphael
1Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
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  • ORCID record for Benjamin J. Raphael
  • For correspondence: braphael@princeton.edu jstuart@ucsc.edu juri.reimand@oicr.on.ca
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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. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 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 found 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 was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 07, 2018.
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Pathway and network analysis of more than 2,500 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 MG Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, Jüri Reimand, Joshua M. Stuart, Benjamin J. Raphael, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network
bioRxiv 385294; doi: https://doi.org/10.1101/385294
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Pathway and network analysis of more than 2,500 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 MG Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, Jüri Reimand, Joshua M. Stuart, Benjamin J. Raphael, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network
bioRxiv 385294; doi: https://doi.org/10.1101/385294

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