SLAS Discovery (Jun 2023)

High-throughput approaches to uncover synergistic drug combinations in leukemia

  • Emma J. Chory,
  • Meng Wang,
  • Michele Ceribelli,
  • Aleksandra M Michalowska,
  • Stefan Golas,
  • Erin Beck,
  • Carleen Klumpp-Thomas,
  • Lu Chen,
  • Crystal McKnight,
  • Zina Itkin,
  • Kelli M. Wilson,
  • David Holland,
  • Sanjay Divakaran,
  • James Bradner,
  • Javed Khan,
  • Berkley E. Gryder,
  • Craig J. Thomas,
  • Benjamin Z. Stanton

Journal volume & issue
Vol. 28, no. 4
pp. 193 – 201

Abstract

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ABSTRACT: We report a comprehensive drug synergy study in acute myeloid leukemia (AML). In this work, we investigate a panel of cell lines spanning both MLL-rearranged and non-rearranged subtypes. The work comprises a resource for the community, with many synergistic drug combinations that could not have been predicted a priori, and open source code for automation and analyses. We base our definitions of drug synergy on the Chou-Talalay method, which is useful for visualizations of synergy experiments in isobolograms, and median-effects plots, among other representations. Our key findings include drug synergies affecting the chromatin state, specifically in the context of regulation of the modification state of histone H3 lysine-27. We report open source high throughput methodology such that multidimensional drug screening can be accomplished with equipment that is accessible to most laboratories. This study will enable preclinical investigation of new drug combinations in a lethal blood cancer, with data analysis and automation workflows freely available to the community.

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