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December 8, 2025BloodOpen Access

Longitudinal single-cell spatial proteomics reveals predictive immune and stem cell signatures in Relapsed/Refractory AML

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Authors

SFSebastian FernandoDLDaniel J. Lee

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Overview

Longitudinal analysis reveals immune signatures using the proximity network assay, suggesting predictive biomarkers for treatment response in R/R AML patients.

Key Points

  • Higher proportions of monocytes observed in patients responding to decitabine treatment, suggesting immune activation.
  • Monocytes and immune recognition molecules, including HLA and CD43, differentiate responders from non-responders in AML.
  • Analysis utilizing a random forest algorithm trained on PNA features achieved high prediction accuracy (AUC 0.889) for treatment response.
  • Dynamic spatial proteomic profiling in R/R AML patients may enable tracking of therapeutic response and stem cell behavior.

Cite This Study

Fernando et al. (2025) studied this question.

synapsesocial.com/papers/69362f764fa91c937236e3d7https://doi.org/10.1182/blood-2025-1696
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  4. 4Machine 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
  5. 5Multiomics-guided ex vivo drug sensitivity testing in Relapsed/Refractory Acute Myeloid Leukemia2025