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Kinase Signaling Networks pp 485–506Cite as

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1636))

Abstract

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

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Acknowledgments

This work was supported by the European Research Council Starting Grant project DrugComb (grant number: 716063 to J.T.).

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Authors and Affiliations

  1. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, 00290, Helsinki, Finland

    Jing Tang

  2. Department of Mathematics and Statistics, University of Turku, Turku, Finland

    Jing Tang

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  1. Jing Tang
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Correspondence to Jing Tang .

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Editors and Affiliations

  1. Division of Medical Oncology, University of Colorado, Aurora, Colorado, USA

    Aik-Choon Tan

  2. Protein Networks Laboratory, Institute of Cancer Research, London, United Kingdom

    Paul H. Huang

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Tang, J. (2017). Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations. In: Tan, AC., Huang, P. (eds) Kinase Signaling Networks. Methods in Molecular Biology, vol 1636. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7154-1_30

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  • DOI: https://doi.org/10.1007/978-1-4939-7154-1_30

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