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Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

In many complex diseases, such as cancers, resistance to monotherapies easily occurs, and longer-term treatment responses often require combinatorial therapies as next-line regimens. However, due to a massive number of possible drug combinations to test, there is a need for systematic and rational approaches to finding safe and effective drug combinations for each individual patient. This protocol describes an ecosystem of computational methods to guide high-throughput combinatorial screening that help experimental researchers to identify optimal drug combinations in terms of synergy, efficacy, and/or selectivity for further preclinical and clinical investigation. The methods are demonstrated in the context of combinatorial screening in primary cells of leukemia patients, where the translational aim is to identify drug combinations that show not only high synergy but also maximal cancer-selectivity. The mechanism-agnostic and cost-effective computational methods are widely applicable to various cancer types, which are amenable to drug testing, as the computational methods take as input only the phenotypic measurements of a subset of drug combinations, without requiring target information or genomic profiles of the patient samples.

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Acknowledgments

The authors thank the High-Throughput Chemical Biology Screening Platform at the Centre for Molecular Medicine Norway (NCMM) for assistance with the combination screening experiments. The work was supported by the grants from Helse Sør-Øst (2020026 to TA), the Norwegian Cancer Society (216104 to TA), the Radium Hospital Foundation (TA), the Academy of Finland (310507, 313267, 326238, and 344698 to TA), the Sigrid Jusélius Foundation (TA), the Finnish Cancer Foundation (TA), and the ERANET PerMed Co-Fund (projects JAKSTAT-TARGET to TA and CLL-CLUE to TA and SS).

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Authors and Affiliations

  1. Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

    Paschalis Athanasiadis, Sigrid S. Skånland & Tero Aittokallio

  2. Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway

    Paschalis Athanasiadis & Tero Aittokallio

  3. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    Aleksandr Ianevski & Tero Aittokallio

  4. K. G. Jebsen Centre for B Cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Sigrid S. Skånland

Authors
  1. Paschalis Athanasiadis
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  2. Aleksandr Ianevski
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  3. Sigrid S. Skånland
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  4. Tero Aittokallio
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Corresponding author

Correspondence to Tero Aittokallio .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Athanasiadis, P., Ianevski, A., Skånland, S.S., Aittokallio, T. (2022). Computational Pipeline for Rational Drug Combination Screening in Patient-Derived Cells. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_14

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_14

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

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