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CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures

  • Jang, Su-Kyeong (Ewha Research Center for Systems Biology, Department of Life Science, Division of Molecular & Life Sciences, Ewha Womans University) ;
  • Yoon, Byung-Ha (Gene Editing Research Center, KRIBB) ;
  • Kang, Seung Min (Ewha Research Center for Systems Biology, Department of Life Science, Division of Molecular & Life Sciences, Ewha Womans University) ;
  • Yoon, Yeo-Gha (Ewha Research Center for Systems Biology, Department of Life Science, Division of Molecular & Life Sciences, Ewha Womans University) ;
  • Kim, Seon-Young (Gene Editing Research Center, KRIBB) ;
  • Kim, Wankyu (Ewha Research Center for Systems Biology, Department of Life Science, Division of Molecular & Life Sciences, Ewha Womans University)
  • Received : 2018.10.21
  • Accepted : 2019.01.29
  • Published : 2019.03.31

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 resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale 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).

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Fig. 1. The process of extracting drug resistance signatures.

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Fig. 2. Web interface design of CDRgator.

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Fig. 3. Analysis of resistance-induced signatures for EGFR inhibitors Illustrative case results available in the ‘Analysis’ menu of CDRgator, generated using resistance-induced signatures for five EGFR inhibitors.

Acknowledgement

Supported by : National Research Foundation of Korea, Institution for Information & communications Technology Promotion (IITP)

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