ArticleBlood science (Baltimore, Md.)2025
Multi-omics integration reveals immune hallmarks and biomarkers associated with FLT3 inhibitor sensitivity in FLT3-mutated AML.
Article in Blood science (Baltimore, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Transcriptomics-based multi-omics approach for optimizing risk stratification in acute myeloid leukemia.Blood science (Baltimore, Md.) · 2026Article
- Precision prediction of venetoclax-azacitidine treatment efficacy in acute myeloid leukemia via integrative drug screening and machine learning.Cell reports. Medicine · 2025Article
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Authors and funding
8 authors.
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Abstract
Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by poor clinical outcomes, frequently exacerbated by mutations in the FMS-like tyrosine kinase 3 (FLT3) gene. Although FLT3 inhibitors (FLT3i) have emerged as promising therapeutic agents, the absence of molecular biomarkers to predict FLT3i response remains a critical limitation in clinical practice. In this study, we performed a comprehensive multi-omics analysis integrating transcriptomic, proteomic, and pharmacogenomic data from the Beat AML cohort, the Cancer Cell Line Encyclopedia (CCLE), and the PXD023201 repository to elucidate the molecular consequences of FLT3 mutations in AML. Our analysis revealed significant differences in RNA and protein expression profiles between FLT3-mutant and wild-type AML cases, with a particularly striking association between FLT3 mutations and immune suppression. We further evaluated the drug sensitivity of FLT3-mutant patients to 3 FDA-approved FLT3i, gilteritinib, midostaurin, and quizartinib, and observed heightened sensitivity in FLT3-mutant cohorts, accompanied by the activation of immune-related pathways in treatment-responsive groups. These findings suggest a potential synergy between FLT3i efficacy and immune activation. Through rigorous bioinformatic analysis, we identified 3 candidate biomarkers: CD36, SASH1, and NIBAN2, associated with FLT3i sensitivity. These biomarkers were consistently upregulated in favorable prognostic subgroups and demonstrated strong correlations with immune activation pathways. The identification of CD36, SASH1, and NIBAN2 as predictive biomarkers offers a novel toolset for stratifying FLT3i response and prognosis.
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