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.
Original language | English |
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Title of host publication | Data Mining Techniques for the Life Sciences |
Editors | Oliviero Carugo, Frank Eisenhaber |
Number of pages | 22 |
Place of Publication | New York |
Publisher | Humana press |
Publication date | 2022 |
Pages | 327-348 |
ISBN (Print) | 978-1-0716-2094-6, 978-1-0716-2097-7 |
ISBN (Electronic) | 978-1-0716-2095-3 |
DOIs | |
Publication status | Published - 2022 |
MoE publication type | A3 Book chapter |
Publication series
Name | Methods in Molecular Biology |
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Volume | 2449 |
ISSN (Print) | 1064-3745 |
ISSN (Electronic) | 1940-6029 |
Bibliographical note
Funding Information: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).
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Fields of Science
- Drug combinations
- High-throughput screening
- Precision oncology
- Predictive modeling
- Synergy scoring
- Toxic effects
- 3111 Biomedicine