Protein network construction using reverse phase protein array data

• Published in: Methods (San Diego, Calif.) Calif.), 2017-07, Vol.124, p.89-99
• Main Authors: Varghese, Rency S., Zuo, Yiming, Zhao, Yi, Zhang, Yong-Wei, Jablonski, Sandra A., Pierobon, Mariaelena, Petricoin, Emanuel F., Ressom, Habtom W., Weiner, Louis M.
• Format: Article
• Language: English
• Subjects: ATPases Associated with Diverse Cellular Activities - antagonists & inhibitors; ATPases Associated with Diverse Cellular Activities - genetics; ATPases Associated with Diverse Cellular Activities - metabolism; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cell Line, Tumor; Cysteine-Rich Protein 61 - antagonists & inhibitors; Cysteine-Rich Protein 61 - genetics; Cysteine-Rich Protein 61 - metabolism; Female; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Humans; Intracellular Signaling Peptides and Proteins - antagonists & inhibitors; Intracellular Signaling Peptides and Proteins - genetics; Intracellular Signaling Peptides and Proteins - metabolism; MANOVA; MCF-7 Cells; Multivariate Analysis; Neoplasm Proteins - antagonists & inhibitors; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Network construction; Phosphorylation; Proteasome Endopeptidase Complex - genetics; Proteasome Endopeptidase Complex - metabolism; Protein Array Analysis - statistics & numerical data; Protein Processing, Post-Translational; RNA Polymerase II - antagonists & inhibitors; RNA Polymerase II - genetics; RNA Polymerase II - metabolism; RNA, Small Interfering - genetics; RNA, Small Interfering - metabolism; RPPA; Signal Transduction; Topology analysis; Tumor Suppressor Proteins - antagonists & inhibitors; Tumor Suppressor Proteins - genetics; Tumor Suppressor Proteins - metabolism
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2017.06.017

•Protein network construction using RPPA data to identify complex patterns in protein signaling.•Protein networks constructed by analyzing the expression levels of protein pairs using MANOVA.•A new scoring criterion introduced to select relevant top protein pairs.•Key proteins identified through network topology analysis of various experimental conditions. 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.