Patient-tailored design for selective co-inhibition of leukemic cell subpopulations
Open Access
- 19 February 2021
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 7 (8), eabe4038
- https://doi.org/10.1126/sciadv.abe4038
Abstract
The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.Keywords
Funding Information
- Helse Sør-Øst RHF (2020026)
- Novo Nordisk Foundation Center for Basic Metabolic Research (NNF17CC0027852)
- Business Finland
- Academy of Finland (310507, 313267, 326238, 344698)
- Academy of Finland (1320185)
- Finnish Medical Foundation
- Sigrid Juselius Foundation
- Cancer Society of Finland
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