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December 8, 2025Blood

Determining sensitivity to FLT3 inhibitors prior to therapy in FLT3 mutant acute myelogenous leukemia

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Authors

ÉMÉlodie De MagalhaesMAMahan AbbasianSKSteven Kornblau

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Overview

Mass spectrometry and machine learning identify biomarkers of FLT3 inhibitor resistance in AML, indicating improved therapy selection.

Key Points

  • This research aims to validate biomarkers predicting FLT3 inhibitor sensitivity in FLT3 mutant acute myelogenous leukemia (AML).
  • Measured 429 proteins in 806 AML samples using RPPA;
  • Employ mass spectrometry for proteomic profiling of 57 AML samples;
  • Used machine learning and Consensus Clustering to identify FLT3i sensitivity subgroups;
  • Conducted ex-vivo drug assays to assess FLT3i sensitivity in AML cells.
  • Identified 7 clusters of distinct FLT3i sensitivity using mass spectrometry;
  • Validated high correlation of select proteins with FLT3i sensitivity;
  • Three proteins (SMARCA2, PXN, DPF2) serve as robust biomarkers for predicting FLT3i resistance;
  • Demonstrated that cell lines match primary AML samples in FLT3i sensitivity.

Cite This Study

Magalhaes et al. (2025) studied this question.

synapsesocial.com/papers/69362f574fa91c937236da44https://doi.org/10.1182/blood-2025-3507
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning-powered integration of global proteomics and ex vivo sensitivity unveils a protein signature predictive of treatment success to AML therapy: Validation in patients treated with FHD-286, a SMARCA2/4 dual inhibitor2025
  2. 2Abstract P09: AN EX-VIVO COMBINATION DRUG SENSITIVITY PLATFORM FOR PREDICTING SENSITIVITY TO FLT3 INHIBITOR-BASED COMBINATION IN ACUTE MYELOID LEUKEMIA2024
  3. 3Multi-drug algorithm to accurately predict best first-line treatments in newly-diagnosed acute myeloid leukemia (AML).2024
  4. 4Prognostic impact of co-occurring FLT3 mutations across molecular subgroups in intensively treated acute myeloid leukemia: Insights from real-world genomic data2025
  5. 5A blood-based protein signature adds to genomic prognostication of survival in TP53-mutated Acute Myeloid Leukemia2025