UC Irvine

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.

In this work, 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. By integrating these three tools, we used ComboFlow to efficiently screen over 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. ComboFlow identified known synergies as well as a novel synergistic drug combination, dactinomycin and fludarabine, that specifically kills leukemic cells in a subset of AML samples. 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 of small molecule therapies for cancers amenable to ex vivo models.

Lastly, to move beyond the discovery of synergistic small molecule therapies, we then developed a “priming” therapy approach to identify more effective combinations for antigen directed therapies (like mABs, ADCs, and CAR-Ts). With this approach, “priming” agents are used to selectively enhance the expression of antigen targets on cancerous cells while sparing healthy tissue. By amplifying the difference in antigen expression level between cancerous and healthy cells, priming agents can potentially increase the sensitivity of cancerous cells to antigen directed therapy and improve their therapeutic window. We show that HTS flow cytometry assays can be used to screen for priming agents that modulate expression levels of antigen targets for hematologic malignancies. We further identify priming agents that restore expression of cancer antigens that are lost after treatment with antigen directed therapy and demonstrate that priming is a powerful combination approach for improving the efficacy of antigen directed therapies.