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Updated: May 7, 2026

Modeling Chemotherapy Resistant Leukemia In Vitro
Published on: February 9, 2016
Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid
Tea Pemovska1, Mika Kontro, Bhagwan Yadav
11Institute for Molecular Medicine Finland, FIMM; 2Hematology Research Unit Helsinki, Helsinki University Central Hospital, University of Helsinki, Helsinki; 3Department of Clinical Chemistry and TYKSLAB, Turku University Central Hospital, University of Turku, Turku; 4Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; 5Department of Clinical Science, Hematology Section, University of Bergen; and 6Department of Internal Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway.
Unlabelled:
We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.
Significance:
Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.
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