2. Academic Validation
  3. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  • Sci Adv. 2021 Feb 19;7(8):eabe4038. doi: 10.1126/sciadv.abe4038.
Aleksandr Ianevski 1 2,  Jenni Lahtela 1,  Komal K Javarappa 1,  Philipp Sergeev 1,  Bishwa R Ghimire 1,  Prson Gautam 1,  Markus Vähä-Koskela 1,  Laura Turunen 1,  Nora Linnavirta 1,  Heikki Kuusanmäki 1 3 4,  Mika Kontro 4,  Kimmo Porkka 5,  Caroline A Heckman 1,  Pirkko Mattila 1,  Krister Wennerberg 6 3,  Anil K Giri 6,  Tero Aittokallio 6 2 7 8
Affiliations

Affiliations

  • 1 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
  • 2 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland.
  • 3 Biotech Research and Innovation Centre (BRIC) and Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, Copenhagen, Denmark.
  • 4 Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.
  • 5 Helsinki University Hospital Comprehensive Cancer Center, Hematology Research Unit Helsinki, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland.
  • 6 Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland. krister.wennerberg@bric.ku.dk anil.kumar@helsinki.fi tero.aittokallio@helsinki.fi.
  • 7 Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.
  • 8 Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway.
PMID: 33608276 DOI: 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.

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