Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 18, 2024

Abstract

BACKGROUND AND OBJECTIVE: The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the BeatAML dataset employing Machine Learning algorithms. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the BeatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluated by the project.

Authors

  • Nestoras Karathanasis
    Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus. Electronic address: nestorask@cing.ac.cy.
  • Panayiota L Papasavva
    Molecular Genetics Thalassemia Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus.
  • Anastasis Oulas
    Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus.
  • George M Spyrou
    Department of Bioinformatics, Cyprus Institute of Neurology & Genetics, and professor at the Cyprus School of Molecular Medicine, Cyprus.

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