Ex vivo discovery of synergistic drug combinations for hematologic malignancies.

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

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Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kamran A. Ali reports financial support was provided by Notable Labs. Kamran A. Ali reports a relationship with Notable Labs that includes: employment. Reecha D. Shah reports a relationship with Notable Labs that includes: employment. Anukriti Dhar reports a relationship with Notable Labs that includes: employment. Nina M. Myers reports a relationship with Notable Labs that includes: employment. Cameron Nguyen reports a relationship with Notable Labs that includes: employment. Arisa Paul reports a relationship with Notable Labs that includes: employment. Jordan E. Mancuso reports a relationship with Notable Labs that includes: employment. A. Scott Patterson reports a relationship with Notable Labs that includes: employment. Diane Heiser reports a relationship with Notable Labs that includes: employment.