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Reverse phase protein arrays in signaling pathways: a data integration perspective

Authors Creighton C, Huang S

Received 24 February 2015

Accepted for publication 8 May 2015

Published 7 July 2015 Volume 2015:9 Pages 3519—3527

DOI https://doi.org/10.2147/DDDT.S38375

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 5

Editor who approved publication: Professor Shu-Feng Zhou


Chad J Creighton,1,3 Shixia Huang2,3

1Department of Medicine, 2Department of Molecular and Cellular Biology, 3Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA

Abstract: The reverse phase protein array (RPPA) data platform provides expression data for a prespecified set of proteins, across a set of tissue or cell line samples. Being able to measure either total proteins or posttranslationally modified proteins, even ones present at lower abundances, RPPA represents an excellent way to capture the state of key signaling transduction pathways in normal or diseased cells. RPPA data can be combined with those of other molecular profiling platforms, in order to obtain a more complete molecular picture of the cell. This review offers perspective on the use of RPPA as a component of integrative molecular analysis, using recent case examples from The Cancer Genome Altas consortium, showing how RPPA may provide additional insight into cancer besides what other data platforms may provide. There also exists a clear need for effective visualization approaches to RPPA-based proteomic results; this was highlighted by the recent challenge, put forth by the HPN-DREAM consortium, to develop visualization methods for a highly complex RPPA dataset involving many cancer cell lines, stimuli, and inhibitors applied over time course. In this review, we put forth a number of general guidelines for effective visualization of complex molecular datasets, namely, showing the data, ordering data elements deliberately, enabling generalization, focusing on relevant specifics, and putting things into context. We give examples of how these principles can be utilized in visualizing the intrinsic subtypes of breast cancer and in meaningfully displaying the entire HPN-DREAM RPPA dataset within a single page.

Keywords: RPPA, proteomics, molecular profiling, integrative analysis, breast cancer, TCGA
 

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