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December 8, 2025BloodOpen Access

Machine 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 inhibitor

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Authors

JSJames T. SorrentinoAKAntonius KollerAKAlexis Khalil

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Overview

Predictive model shows protein signatures influence AML treatment response, indicating personalized therapy potential.

Key Points

  • Model predicts treatment success based on a 5-protein signature in AML patients, highlighting predictive capabilities.
  • The predictive model uses deep learning to derive insights from proteomics and dose-response data across 200 AML cases.
  • Application of transfer learning enhances the model's performance, integrating diverse proteomics data to improve predictions.
  • Clinical trial planned for 2025 aims to validate the mass spectrometry-based assay for personalized AML therapy selection.

Cite This Study

Sorrentino et al. (2025) studied this question.

synapsesocial.com/papers/69362f574fa91c937236d9fchttps://doi.org/10.1182/blood-2025-3498
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