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Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424–47.
Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A, et al. Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia. Cancer Discov. 2013;3:1416–29.
Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018; 562:526–31.
Snijder B, Vladimer GI, Krall N, Miura K, Schmolke A-S, Kornauth C, et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 2017;4:e595–e606.
Malani D, Murumagi A, Yadav B, Kontro M, Eldfors S, Kumar A, et al. Enhanced sensitivity to glucocorticoids in cytarabine-resistant AML. Leukemia. 2017;31:1187–95.
Wilding JL, Bodmer WF. Cancer cell lines for drug discovery and development. Cancer Res. 2014;74:2377–84.
Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–5.
Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.
Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569:503–8.
Lee S-I, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, et al. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9:42.
Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–54.
Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci Rep. 2014;4:5193.
Kumar A, Kankainen M, Parsons A, Kallioniemi O, Mattila P, Heckman CA. The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia. BMC Genomics. 2017;18:629.
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.
Heo S-K, Noh E-K, Kim JY, Jeong YK, Jo J-C, Choi Y, et al. Targeting c-KIT (CD117) by dasatinib and radotinib promotes acute myeloid leukemia cell death. Sci Rep. 2017;7:15278.
Acknowledgements
We are grateful to the patients who donated samples to the study and thank the FIMM High Throughput Biomedicine Unit and Breeze DSRT data analysis pipeline teams for their support. The research was funded by the Finnish Cultural Foundation (DM), the Blood Disease Foundation Finland (DM), Finnish Hematology Association SHY (DM and AK), Ida Montinin Foundation (DM), EMBO short-term fellowship (AK), the Academy of Finland (Center of Excellence for Translational Cancer Biology; Grants 310507, 313267, 326238 to TA; 277293 to KW; iCAN Digital Precision Cancer Medicine Flagship Grant 1320185 to TA and CAH), Cancer Society of Finland (DM, AK, OK, TA, KW, and CAH), Sigrid Jusélius Foundation (to OK, KP, TA, and KW), EU Systems Microscopy (FP7), TEKES/Business Finland (to OK and KP), and Novo Nordisk Foundation (to KW; NNF17CC0027852). OK supported by the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, VR environment grant. MK supported by the University of Helsinki and Finnish Medical Foundation.
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DM, AM, and OK designed the study. DM and AM performed drug testing experiments. DM, BY, and AK analyzed and visualized the data. DM generated hypotheses and interpreted results. DM and KKJ designed and performed flow cytometry experiments. MK performed cell line variant calling, and SP performed drug response data quality analysis. DM wrote the manuscript. MKo and KP obtained ethical permits, collected clinical samples and administered therapies. KP, MKo, TA, MW, KW, CAH, AM, and OK provided critical review. All authors contributed to and approved the final version of the manuscript.
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The senior authors have received collaborative research grants for other projects as listed: OK received research funding from Vinnova for collaboration between Astra-Zeneca, Takeda, Pelago, and Labcyte. OK is also a board member and a co-founder of Medisapiens and Sartar Therapeutics, and has received a royalty on patents licensed by Vysis-Abbot. KP received honoraria and research funding from Bristol-Myers Squibb, Celgene, Novartis, and Pfizer. CH received honoraria from Celgene, Novartis, and Roche, and research funding from Celgene, Novartis, Oncopeptides, Pfizer, and the IMI2 project HARMONY. KW received research funding from Novartis and Pfizer. MKo: research funding from AbbVie. The remaining authors declare that they have no conflict of interest.
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Malani, D., Yadav, B., Kumar, A. et al. KIT pathway upregulation predicts dasatinib efficacy in acute myeloid leukemia. Leukemia 34, 2780–2784 (2020). https://doi.org/10.1038/s41375-020-0978-7
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DOI: https://doi.org/10.1038/s41375-020-0978-7