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Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

View ORCID ProfileSushant Kumar, View ORCID ProfileJonathan Warrell, View ORCID ProfileShantao Li, View ORCID ProfilePatrick D. McGillivray, View ORCID ProfileWilliam Meyerson, View ORCID ProfileLeonidas Salichos, View ORCID ProfileArif Harmanci, View ORCID ProfileAlexander Martinez-Fundichely, View ORCID ProfileCalvin W.Y. Chan, View ORCID ProfileMorten Muhlig Nielsen, View ORCID ProfileLucas Lochovsky, View ORCID ProfileYan Zhang, View ORCID ProfileXiaotong Li, View ORCID ProfileJakob Skou Pedersen, View ORCID ProfileCarl Herrmann, View ORCID ProfileGad Getz, View ORCID ProfileEkta Khurana, View ORCID ProfileMark B. Gerstein
doi: https://doi.org/10.1101/280446
Sushant Kumar
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
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  • ORCID record for Sushant Kumar
Jonathan Warrell
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
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Shantao Li
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
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Patrick D. McGillivray
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
4Yale School of Medicine, Yale University, New Haven, Connecticut, USA
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William Meyerson
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
4Yale School of Medicine, Yale University, New Haven, Connecticut, USA
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Leonidas Salichos
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
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  • ORCID record for Leonidas Salichos
Arif Harmanci
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
5Center for Precision Health, School of Biomedical Informatics, University of Texas Health Sciences Center, Houston, Texas, 77030, USA
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Alexander Martinez-Fundichely
6Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York 10021 USA
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Calvin W.Y. Chan
7Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
8Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
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Morten Muhlig Nielsen
10Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
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  • ORCID record for Morten Muhlig Nielsen
Lucas Lochovsky
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
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Yan Zhang
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
11Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio 43210, USA
12The Ohio State University Comprehensive Cancer Center (OSUCCC – James), Columbus, Ohio 43210, USA
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Xiaotong Li
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
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Jakob Skou Pedersen
10Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
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Carl Herrmann
7Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
9Institute of Pharmacy and Molecular Biotechnology, and Bioquant Center, University of Heidelberg, 69120 Heidelberg, Germany
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Gad Getz
13The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02124, USA
14Massachusetts General Hospital Center for Cancer Research, Charlestown, Massachusetts 02129, USA
15Harvard Medical School, 250 Longwood Avenue, Boston, 02115, MA, USA
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Ekta Khurana
6Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York 10021 USA
16Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY, 10065, USA
17Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
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Mark B. Gerstein
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
3Department of Computer Science, Yale University, New Haven, Connecticut, USA
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  • For correspondence: pi@gersteinlab.org
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Abstract

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

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Posted March 12, 2018.
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Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences
Sushant Kumar, Jonathan Warrell, Shantao Li, Patrick D. McGillivray, William Meyerson, Leonidas Salichos, Arif Harmanci, Alexander Martinez-Fundichely, Calvin W.Y. Chan, Morten Muhlig Nielsen, Lucas Lochovsky, Yan Zhang, Xiaotong Li, Jakob Skou Pedersen, Carl Herrmann, Gad Getz, Ekta Khurana, Mark B. Gerstein
bioRxiv 280446; doi: https://doi.org/10.1101/280446
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Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences
Sushant Kumar, Jonathan Warrell, Shantao Li, Patrick D. McGillivray, William Meyerson, Leonidas Salichos, Arif Harmanci, Alexander Martinez-Fundichely, Calvin W.Y. Chan, Morten Muhlig Nielsen, Lucas Lochovsky, Yan Zhang, Xiaotong Li, Jakob Skou Pedersen, Carl Herrmann, Gad Getz, Ekta Khurana, Mark B. Gerstein
bioRxiv 280446; doi: https://doi.org/10.1101/280446

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