Abstract
High-throughput drug sensitivity testing provides a powerful phenotypic profiling approach to identify effective drug candidates for individual cell lines or patient-derived samples. Here, we describe an experimental-computational pipeline, named target addiction scoring (TAS), which mathematically transforms the drug response profiles into target addiction signatures, and thereby provides a ranking of potential therapeutic targets according to their functional importance in a particular cancer sample. The TAS pipeline makes use of drug polypharmacology to integrate the drug sensitivity and selectivity profiles through systems-wide interconnection networks between drugs and their targets, including both primary protein targets as well as secondary off-targets. We show how the TAS pipeline enables one to identify not only single-target addictions but also combinatorial coaddictions among targets that often underlie synergistic drug combinations.
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Acknowledgments
This work was supported by the Academy of Finland (grants 272437, 269862, 279163, 292611, 295504, 310507); the Cancer Society of Finland (TA, KW); the Integrative Life Science Doctoral Program at the University of Helsinki (AJ).
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Jaiswal, A., Yadav, B., Wennerberg, K., Aittokallio, T. (2019). Integrated Analysis of Drug Sensitivity and Selectivity to Predict Synergistic Drug Combinations and Target Coaddictions in Cancer. In: Ziegler, S., Waldmann, H. (eds) Systems Chemical Biology. Methods in Molecular Biology, vol 1888. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8891-4_12
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DOI: https://doi.org/10.1007/978-1-4939-8891-4_12
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