Browsing by Subject "DRUG-SENSITIVITY"

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  • Pietarinen, Paavo O.; Eide, Christopher A.; Ayuda-Duran, Pilar; Potdar, Swapnil; Kuusanmaki, Heikki; Andersson, Emma I.; Mpindi, John P.; Pemovska, Tea; Kontro, Mika; Heckman, Caroline A.; Kallioniemi, Olli; Wennerberg, Krister; Hjorth-Hansen, Henrik; Druker, Brian J.; Enserink, Jorrit M.; Tyner, Jeffrey W.; Mustjoki, Satu; Porkka, Kimmo (2017)
    Tyrosine kinase inhibitors (TKI) are the mainstay treatment of BCR-ABL1-positive leukemia and virtually all patients with chronic myeloid leukemia in chronic phase (CP CML) respond to TKI therapy. However, there is limited information on the cellular mechanisms of response and particularly on the effect of cell differentiation state to TKI sensitivity in vivo and ex vivo/in vitro. We used multiple, independent high-throughput drug sensitivity and resistance testing platforms that collectively evaluated 295 oncology compounds to characterize ex vivo drug response profiles of primary cells freshly collected from newly-diagnosed patients with BCR-ABL1positive leukemia (n = 40) and healthy controls (n = 12). In contrast to the highly TKI-sensitive cells from blast phase CML and Philadelphia chromosome-positive acute lymphoblastic leukemia, primary CP CML cells were insensitive to TKI therapy ex vivo. Despite maintaining potent BCR-ABL1 inhibitory activity, ex vivo viability of cells was unaffected by TKIs. These findings were validated in two independent patient cohorts and analysis platforms. All CP CML patients under study responded to TKI therapy in vivo. When CP CML cells were sorted based on CD34 expression, the CD34-positive progenitor cells showed good sensitivity to TKIs, whereas the more mature CD34-negative cells were markedly less sensitive. Thus in CP CML, TKIs predominantly target the progenitor cell population while the differentiated leukemic cells (mostly cells from granulocytic series) are insensitive to BCR-ABL1 inhibition. These findings have implications for drug discovery in CP CML and indicate a fundamental biological difference between CP CML and advanced forms of BCR-ABL1-positive leukemia.
  • Haapa-Paananen, Saija; Chen, Ping; Hellström, Kirsi; Kohonen, Pekka; Hautaniemi, Sampsa; Kallioniemi, Olli; Perala, Merja (2013)
  • Sundin, Iiris; Peltola, Tomi; Micallef, Luana; Afrabandpey, Homayun; Soare, Marta; Majumder, Muntasir Mamun; Daee, Pedram; He, Chen; Serim, Baris; Havulinna, Aki; Heckman, Caroline; Jacucci, Giulio; Marttinen, Pekka; Kaski, Samuel (2018)
    Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach.