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Methods. 2017 Jul 15;124:89-99. doi: 10.1016/j.ymeth.2017.06.017. Epub 2017 Jun 24.

Protein network construction using reverse phase protein array data.

Author information

1
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA.
2
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA; Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.
3
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA; Department of Biostatistics, School of Public Health, Brown University, Rhode Island, Providence, USA.
4
Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA.
5
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. Electronic address: hwr@georgetown.edu.
6
Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. Electronic address: weinerl@georgetown.edu.

Abstract

In this paper, we introduce a novel computational method for constructing protein networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine relevant protein pairs. Through a network topology based analysis, we search for wiring patterns to identify key proteins that are associated with significant changes in expression levels across various experimental conditions.

KEYWORDS:

Breast cancer; MANOVA; Network construction; RPPA; Topology analysis

PMID:
28651964
PMCID:
PMC5603262
DOI:
10.1016/j.ymeth.2017.06.017
[Indexed for MEDLINE]
Free PMC Article

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