BMX kinase mediates gilteritinib resistance in FLT3-mutated AML through microenvironmental factors.

Source reference: Blood advances • 2022 • DOI: 10.1182/bloodadvances.2022007952 • PMID: 35797240 Despite the clinical benefit associated with gilteritinib in relapsed/refractory acute myeloid leukemia (AML), most patients eventually develop resistance through unknown mechanisms. To delineate the mechanistic basis of resistance to gilteritinib, we performed targeted sequencing and scRNASeq on primary FLT3-ITD-mutated AML samples. Co-occurring mutations in RAS pathway genes were the most common genetic abnormalities, and unresponsiveness to gilteritinib was associated with increased expression of bone marrow-derived hematopoietic cytokines and chemokines. In particular, we found elevated expression of the TEK-family kinase, BMX, in gilteritinib-unresponsive patients pre- and post-treatment. BMX contributed to gilteritinib resistance in FLT3-mutant cell lines in a hypoxia-dependent manner by promoting pSTAT5 signaling, and these phenotypes could be reversed with pharmacological inhibition and genetic knockout. We also observed that inhibition of BMX in primary FLT3-mutated AML samples decreased chemokine secretion and enhanced the activity of gilteritinib. Collectively, these findings indicate a crucial role for microenvironment-mediated factors modulated by BMX in the escape from targeted therapy and have implications for the development of novel therapeutic interventions to restore sensitivity to gilteritinib. Instructions for use: README BMX Kinase Mediates Gilteritinib Resistance in FLT3-mutated AML through Microenvironmental Factors This is a data package. It provides processed experimental data to be used with the analysis project at https://github.com/blaserlab/flt3_aml_bakerlab. Together, the analysis project and the data package will reproduce all R-generated figures from the manuscript. Individual figure panels can be recreated running the code in R/figs/fig_staging.R Full figures can be generated by running the code in R/figs/fig_*_composition.R Tables and summary stats can be generated by sourcing R/tables/supplemental_tables.R If you want to inspect the code used to generate the data package, it will be installed in your package library in the directory, flt3.aml.datapkg/data-raw. Each data object has an associated manual page which can be accessed by clicking on "flt3.aml.datapkg" in the packages panel of RStudio. System Requirements R v4.5.0 Loading the complete dataset occupies close to 8 GB memory. Installing this data package Using a standard workflow The data package can be installed from the .tar.gz file like any other R package. Because the single cell objects are stored with the matrix on disk, you will have to use monocle3 functions to load them from the package installation directory with something like load_monocle_objects("</path/to/package/directory/>flt3.aml.bakerlab.datapkg/extdata/cds_main") Other data objects can be loaded using data(<object name>) Using blaseRtools (recommended) Alternatively, you can load the data package using functions from the blaseRtools package. This will handle loading the on-disk single cell objects and will load other objects as promises which will come into memory when and if they are called. To install blaseRtools, run: install.packages('blaseRtools', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) Running blaseRtools::project_data(<path_to_datapkg_tarball>) will install the .tar.gz file and load it into your R session within a dedicated data environment. Additional documentation is available at https://blaserlab.github.io/blaseRtools/. Install dependencies The tsv file library_catalogs/blas02_flt3.aml.bakerlab.datapkg.tsv lists all R package dependencies for the analysis. Filtering the status column for status == "active" will list the direct dependencies. All others are available at the time of publication and include indirect dependencies and unrelated/unused packages. You should install the "active" packages and their dependencies using your method of choice. Instructions for use After installing the data package: edit system-specific details on R/dependencies.R and R/configs.R. At a minimum, point the blaseRtools::project_data() function to where you saved the data package. source R/dependencies.R source R/configs.R source R/figs/fig_staging.R. This will generate all computationally-derived figures in the manuscript. source R/supplemental_tables.R. This will generate all supplementary tables in the manuscript. If properly configured, these scripts should run to completion in 1-2 minutes.