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Molecular & Cellular Proteomics

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Technological Innovation and Resources

Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis

Wenke Liu, View ORCID ProfileSamuel H. Payne, Sisi Ma  Correspondence email and View ORCID ProfileDavid Fenyö  Correspondence email
Molecular & Cellular Proteomics August 9, 2019, First published on June 18, 2019, 18 (8 suppl 1) S169-S182; https://doi.org/10.1074/mcp.TIR119.001442
Wenke Liu
‡Institute for System Genetics, NYU School of Medicine, New York, New York 10016§Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York 10016
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Samuel H. Payne
¶Biology Department, Brigham Young University, Provo, Utah 84602
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  • ORCID record for Samuel H. Payne
Sisi Ma
‖Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota 55455
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  • For correspondence: sisima@umn.edu
David Fenyö
‡Institute for System Genetics, NYU School of Medicine, New York, New York 10016§Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, New York 10016
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Graphical Abstract

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Highlights

  • Unsupervised feature extraction from proteogenomics data.

  • Pathway level integration of multi-omics data based on clinical features.

Abstract

Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.

  • Proteogenomics
  • Cancer biology
  • Computational biology
  • Breast cancer
  • Mass spectrometry

Footnotes

  • Author contributions: W.L., S.H.P., S.M., and D.F. designed research; W.L. performed research; W.L. analyzed data; W.L., S.M., and D.F. wrote the paper.

  • ↵* We would like to acknowledge funding by the National Cancer Institute (NCI) through CPTAC award U24 CA210972 and a contract 13XS068 from Leidos Biomedical Research, Inc., and by a grant from the Shifrin-Myers Breast Cancer Discovery Fund.

  • ↵Embedded Image This article contains supplemental Figures and Tables.

  • Received March 12, 2019.
  • Revision received June 1, 2019.
  • © 2019 Liu et al.

Published under exclusive license by The American Society for Biochemistry and Molecular Biology, Inc.

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Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis
Wenke Liu, Samuel H. Payne, Sisi Ma, David Fenyö
Molecular & Cellular Proteomics August 9, 2019, First published on June 18, 2019, 18 (8 suppl 1) S169-S182; DOI: 10.1074/mcp.TIR119.001442

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Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis
Wenke Liu, Samuel H. Payne, Sisi Ma, David Fenyö
Molecular & Cellular Proteomics August 9, 2019, First published on June 18, 2019, 18 (8 suppl 1) S169-S182; DOI: 10.1074/mcp.TIR119.001442
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Molecular & Cellular Proteomics: 18 (8 suppl 1)
Molecular & Cellular Proteomics
Vol. 18, Issue 8 suppl 1
9 Aug 2019
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