CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures
Su-Kyeong Jang1,4, Byung-Ha Yoon2,3,4, Seung Min Kang1,4, Yeo-Gha Yoon1, Seon-Young Kim2,3,*, and Wankyu Kim1,*
1Ewha Research Center for Systems Biology, Department of Life Science, Division of Molecular & Life Sciences, Ewha Womans University, Seoul 03760, Korea, 2Gene Editing Research Center, KRIBB, Daejeon 34141, Korea, 3Department of Functional Genomics, University of Science and Technology (UST), Daejeon 34113, Korea, 4These authors contributed equally to this work.
*Correspondence: kimsy@kribb.re.kr (SYK); wkim@ewha.ac.kr (WK)
Received October 21, 2018; Revised December 26, 2018; Accepted January 29, 2019.; Published online February 12, 2019.
© Korean Society for Molecular and Cellular Biology. All rights reserved.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit (http://creativecommons.org/licenses/by-nc-sa/3.0/).
ABSTRACT
Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial?mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistanceinduced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two largescale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).
Keywords: cancer drug resistance, gene expression signatures, meta-analysis, microarray, RNA-seq analysis, transcriptome


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28 February 2019 Volume 42,
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