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SWAN Identification of Common Aneuploidy-Based Oncogenic Drivers

Robert R. Bowers, Christian M. Jones, Edwin A. Paz, John K. Barrows, Kent E. Armeson, David T. Long, View ORCID ProfileJoe R. Delaney
doi: https://doi.org/10.1101/2021.05.05.442642
Robert R. Bowers
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States of America
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Christian M. Jones
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States of America
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Edwin A. Paz
2Departments of Neurology, Neurobiology, and Cell Biology, and the Duke Center for Neurodegeneration & Neurotherapeutics, Duke University School of Medicine, Durham, North Carolina, United States of America
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John K. Barrows
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States of America
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Kent E. Armeson
3Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States of America
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David T. Long
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States of America
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Joe R. Delaney
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, United States of America
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  • ORCID record for Joe R. Delaney
  • For correspondence: delaneyj@musc.edu
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Summary

Haploinsufficiency drives Darwinian evolution. Siblings, while alike in many aspects, differ due to monoallelic differences inherited from each parent. In cancer, solid tumors exhibit aneuploid genetics resulting in hundreds to thousands of monoallelic gene-level copy-number alterations (CNAs) in each tumor. Aneuploidy patterns are heterogeneous, posing a challenge to identify drivers in this high-noise genetic environment. Here, we developed Shifted Weighted Annotation Network (SWAN) analysis to assess biology impacted by cumulative monoallelic changes. SWAN enables an integrated pathway-network analysis of CNAs, RNA expression, and mutations via a simple web platform. SWAN is optimized to best prioritize known and novel tumor suppressors and oncogenes, thereby identifying drivers and potential druggable vulnerabilities within cancer CNAs. Protein homeostasis, phospholipid dephosphorylation, and ion transport pathways are commonly suppressed. An atlas of CNA pathways altered in each cancer type is released. These CNA network shifts highlight new, attractive targets to exploit in solid tumors.

Highlights

  • Copy-number alteration pathways define solid tumor biology

  • SWAN is released as an integrative point-and-click pathway analysis tool

  • Moderate impact drivers highlighted by SWAN validated in vitro

  • Copy-number altered pathways associate with mutations and survival

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.delaneyapps.com/#SWAN

Copyright 
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 4.0 International license.
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Posted May 06, 2021.
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SWAN Identification of Common Aneuploidy-Based Oncogenic Drivers
Robert R. Bowers, Christian M. Jones, Edwin A. Paz, John K. Barrows, Kent E. Armeson, David T. Long, Joe R. Delaney
bioRxiv 2021.05.05.442642; doi: https://doi.org/10.1101/2021.05.05.442642
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SWAN Identification of Common Aneuploidy-Based Oncogenic Drivers
Robert R. Bowers, Christian M. Jones, Edwin A. Paz, John K. Barrows, Kent E. Armeson, David T. Long, Joe R. Delaney
bioRxiv 2021.05.05.442642; doi: https://doi.org/10.1101/2021.05.05.442642

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