A pan-cancer proteomic perspective on The Cancer Genome Atlas

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Abstract

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumors. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data is integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumor lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumor lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101528555

Journal ID (pubmed-jr-id): 37539

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature communications

ISSN (Electronic): 2041-1723

Publication date Nihms-submitted: 10 June 2014

Publication date (Electronic): 29 May 2014

Publication date Collection: 2014

Publication date PMC-release: 29 November 2014

Volume: 5

Page: 3887

Affiliations

[1 ]Department of Bioinformatics and Computational Biology, 1400 Pressler St., The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

[2 ]Department of Systems Biology, 1515 Holcombe Blvd, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

[3 ]Centre for Cancer Biomarkers, Department of Clinical Science, The University of Bergen, 5023 Bergen, Norway

[4 ]Department of Biochemistry, The Netherlands Cancer Institute, Postbox 90203 1006 BE Amsterdam, The Netherlands

[5 ]Medical Research Council Biostatistics Unit, Cambridge CB2 0SR, UK

[6 ]Cancer Research UK Cambridge Institute, School of Clinical Medicine, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK

[7 ]Department of Applied Mathematics, Kumoh National Institute of Technology, Gumi 730-701, South Korea

[8 ]Hamon Center for Therapeutic Oncology, Internal Medicine, Pharmacology, 1801 Inwood Rd, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA

[9 ]Department of Thoracic/ Head and Neck Medical Oncology, 1515 Holcombe Blvd, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

[10 ]Department of Surgical Oncology, 1515 Holcombe Blvd, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Author notes

Corresponding author, G.B. Mills gmills@ 123456mdanderson.org +1-713-563-4200, Y. Lu yilinglu@ 123456mdanderson.org +1-713- 563-4218, R. Akbani rakbani@ 123456mdanderson.org +1-713-794-5043

[*]

These authors contributed equally to this work

Article

Manuscript ID: NIHMS586714

DOI: 10.1038/ncomms4887

PMC ID: 4109726

PubMed ID: 24871328

SO-VID: 6f629217-46b2-4b1a-b55b-84600d9761a4

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