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Lookup NU author(s): Dan Coleman, Dr Helen Blair, Sandeep Potluri, Professor Olaf Heidenreich
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). AML is characterized by mutations in genes associated with growth regulation such as internal tandem duplications (ITD) in the receptor kinase FLT3. Inhibitors targeting FLT3 (FLT3i) are being used to treat patients with FLT3-ITD+ but most relapse and become resistant. To elucidate the resistance mechanism, we compared the gene regulatory networks (GRNs) of leukemic cells from patients before and after relapse, which revealed that the GRNs of drug-responsive patients were altered by rewiring their AP-1-RUNX1 axis. Moreover, FLT3i induces the upregulation of signaling genes, and we show that multiple cytokines, including interleukin-3 (IL-3), can overcome FLT3 inhibition and send cells back into cycle. FLT3i leads to loss of AP-1 and RUNX1 chromatin binding, which is counteracted by IL-3. However, cytokine-mediated drug resistance can be overcome by a pan-RAS inhibitor. We show that cytokines instruct AML growth via the transcriptional regulators AP-1 and RUNX1 and that pan-RAS drugs bypass this barrier.
Author(s): Coleman DJL, Keane P, Chin PS, Ames L, Kellaway S, Blair H, Khan N, Griffin J, Holmes E, Maytum A, Potluri S, Strate L, Koscielniak K, Raghavan M, Bushweller J, Heidenreich O, Rabbitts T, Cockerill PN, Bonifer C
Publication type: Article
Publication status: Published
Journal: iScience
Year: 2024
Volume: 27
Issue: 4
Print publication date: 19/04/2024
Online publication date: 26/03/2024
Acceptance date: 25/03/2024
Date deposited: 18/04/2024
ISSN (electronic): 2589-0042
Publisher: Elsevier Inc.
URL: https://doi.org/10.1016/j.isci.2024.109576
DOI: 10.1016/j.isci.2024.109576
Data Access Statement: All sequencing data produced as part of this study are available on GEO and are publicly available as of the date of publication under the super series GEO: GSE241650. Python scripts used to construct the gene regulatory networks presented in this study, as well as the probability weight matrices for the transcription factor binding motifs and promoter-capture HiC data have been made available on GitHub at https://github.com/ petebio/Gene_regulatory_network_analysis and are free to use under an MIT license, https://doi.org/10.5072/zenodo.268, these scripts have been published previously. Any additional information required to reanalyse the data reported in this paper is available from the lead contact (Constanze Bonifer, c.bonifer@bham.ac.uk) upon request.
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