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Acute myeloid leukemia

KIT pathway upregulation predicts dasatinib efficacy in acute myeloid leukemia

Leukemia volume 34, pages 2780–2784 (2020)Cite this article

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Acute myeloid leukemia (AML) is an aggressive malignant disease with a poor prognosis. Although the recent approval of several new targeted drugs provides new treatment options for subsets of patients, molecular heterogeneity in AML still poses a major challenge for the patient treatment [1]. Novel treatments are needed to cover the entire molecular spectrum of the disease. We and others have previously shown that functional ex vivo drug testing of patient-derived primary AML cells provides additional insights on the potential utility of, e.g., dasatinib, venetoclax, and dexamethasone for treatment of subsets of AML patients [2,3,4,5]. However, in most cases, the mechanism of action and biomarkers associated with the drug effects have remained unknown. Cell lines originating from different cancer types have provided valuable information about the complexity of cancer at the genomic, epigenomic, transcriptomic, and drug response levels [6,7,8,9], including observations in AML [10, 11]. However, the representability of the AML cell lines of patient AML specimens has remained unclear. Here, we aimed to (i) integrate and compare pharmacological profiles between AML cell lines and patient samples to identify differential drug sensitivities; and (ii) define molecular determinants and biomarkers of drug response by the integration of in vitro, ex vivo, and in vivo patient data, focusing on the KIT pathway and its inhibitors.

We compared drug response profiles between ex vivo AML patient samples (n = 45) (Supplementary Table S1), and established AML cell lines (n = 28) (Supplementary Table S2) using high-throughput drug sensitivity and resistance testing (DSRT) with 290 approved and investigational oncology compounds (Supplementary Table S3). Drug responses were quantified as drug sensitivity score (DSS) [12]. Briefly, DSS is a quantitative measurement of drug response to define drug efficacy using dose–response parameters. The differential drug sensitivity score (dDSS) for each sample was based on comparing the DSS for that sample with the mean over all patient samples (Supplementary Table S4) or mean over all cell lines (Supplementary Table S5). The mutation spectrum of the AML cell lines was obtained from cancer gene panel sequencing (Supplementary Table S6) while exome sequencing was applied to the patient samples and analyzed as described previously [2]. RNA-seq data for the AML patient specimens were generated and analyzed as described previously [13] and for the AML cell lines obtained from a published study [9].

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Fig. 1: Dasatinib has high sensitivity in AML patient samples compared to AML cell lines.
Fig. 2: KIT pathway enrichment is associated with dasatinib efficacy.

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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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Authors and Affiliations

  1. Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland

    Disha Malani, Bhagwan Yadav, Ashwini Kumar, Swapnil Potdar, Mika Kontro, Komal K. Javarappa, Maija Wolf, Tero Aittokallio, Krister Wennerberg, Caroline A. Heckman, Astrid Murumägi & Olli Kallioniemi

  2. Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland

    Mika Kontro, Matti Kankainen & Kimmo Porkka

  3. Department of Hematology, University Hospital Comprehensive Cancer Center, Helsinki, Finland

    Mika Kontro & Kimmo Porkka

  4. Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, and Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway

    Tero Aittokallio

  5. Biotech Research & Innovation Centre, BRIC and Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, University of Copenhagen, Copenhagen, Denmark

    Krister Wennerberg

  6. Science for Life Laboratory, Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden

    Olli Kallioniemi

Authors
  1. Disha Malani
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  2. Bhagwan Yadav
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  14. Olli Kallioniemi
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Contributions

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.

Corresponding author

Correspondence to Disha Malani.

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Conflict of interest

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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