Identification of therapeutic vulnerabilities in FLT3-mutated AML through customized CRISPR screens Free

Acute myeloid leukemia (AML) is a genetically diverse and aggressive blood cancer. FLT3 mutations occur in ~30% of AML cases and are targeted by tyrosine kinase inhibitors (TKIs) such as midostaurin, sorafenib, and gilteritinib across treatment stages. Gilteritinib, midostaurin, and sorafenib are FLT3-targeting tyrosine kinase inhibitors (TKIs); gilteritinib and midostaurin are type I TKIs active against both FLT3-ITD and FLT3-TKD, while sorafenib is a type II TKI with broader multi-kinase activity. Midostarurin is added to the standard care of chemotherapy and transplant, while gilteritinib is used in relapsed/refractory AML, and sorafenib in frontline or maintenance settings. Despite these advances, resistance remains a significant challenge due to secondary mutations and alternative pathway activation. In this study, we leveraged CRISPR/Cas9 screening of FLT3-ITD+ve AML cells to identify molecular pathways underlying resistance to FLT3-TKIs.

To identify FLT3 inhibitor response modulators, we performed a custom CRISPR/Cas9 screen in FLT3-ITD⁺ AML cell lines MOLM-13 and MV4-11 using a library targeting 2,440 AML-relevant and druggable genes. Following puromycin selection, cells were exposed to sublethal FLT3 inhibitors (sorafenib, midostaurin, or gilteritinib) for approximately seven cell doublings. Genomic DNA was PCR-amplified and sequenced at 500× coverage. MAGeCK-RRA was used to compute gene-level RRA scores using the ReadRRA() function, defined as –log₁₀ of the more significant p-value from either positive or negative selection. Genes were then classified as resistant or sensitizing based on sgRNA depletion or enrichment, respectively, using a ±1.5 SD cutoff from the mean RRA score distribution.

Our screen revealed a core set of modulators that reproducibly influence FLT3 inhibitor response regardless of drug or cellular context. Overall, 28 common resistance and 26 common sensitive genes were associated across the two cell lines and the three FLT3-TKIs tested. Of the 28 resistant genes, 6 are mapped to drugs approved or in trials, highlighting the potential therapeutic approaches to enhance the efficacy of TKIs. These include CDK6 (cyclin-dependent kinase 6), MDM2 (an oncogene), and TP53 with roles in multiple pathways, including oncogene-induced senescence, cell cycle, DNA methylation, and transcriptional repression, etc. Targeting these pathways and modulating p53 and microRNA regulation, which were also enriched in our pathway analysis, may help overcome resistance to FLT3 inhibitors in AML. FLT3 inhibitors induce resistance via p53 destabilization through the STAT5–MDM2 axis; consistent with current results, thus, the combination with MDM2 inhibitors has the potential to overcome resistance to FLT3-TKIs. DHODH, a dehydrogenase, and FASN, fatty acid synthase, play a role in metabolic adaptation to FLT3 inhibition, and drugs targeting these can potentially improve the therapeutic effects of FLT3 inhibitors. NPEPPS, an aminopeptidase, and TBCE emerged as druggable dependencies involved in chemoresistance and may serve as therapeutic targets to enhance treatment efficacy in AML. Conversely, MTIF2 and PEX16, involved in mitochondrial and peroxisomal metabolism, may function as biomarkers of FLT3 inhibitor resistance and merit further validation. In parallel, ASXL2, PTEN, and NF1 were identified as consistent sensitizer genes in our screen, consistent with their known tumor suppressor roles. These findings highlight key vulnerabilities and resistance mechanisms in FLT3-mutant AML and suggest actionable pathways and biomarkers for improving targeted therapy outcomes.

CRISPR/Cas9 screen identified effective genetic modulators of FLT3 inhibitor response in acute myeloid leukemia (AML). These findings reveal critical mechanisms underlying resistance, laying the groundwork for rational combination therapies to enhance treatment outcomes in FLT3-mutated AML. Future studies will validate these targets using gene knockdown in resistant and sensitive models to evaluate their impact on FLT3 inhibitor response.

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