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.

Tretinoin Enhances the Effects of Chemotherapy in Juvenile Myelomonocytic Leukemia Using an Ex Vivo Drug Sensitivity Assay.

PURPOSE: Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric malignancy with myelodysplastic and myeloproliferative features. Curative treatment is restricted to hematopoietic stem-cell transplantation. Fludarabine combined with cytarabine (FLA) and 5-azacitidine (AZA) monotherapy are commonly used pre-transplant therapies. Here, we present a drug screening strategy using a flow cytometry-based precision medicine platform to identify potential additional therapeutic vulnerabilities. METHODS: We screened 120 dual- and 10 triple-drug combinations (DCs) on peripheral blood (n = 21) or bone marrow (n = 6) samples from 27 children with JMML to identify DCs more effectively reducing leukemic cells than the DCs' components on their own. If fewer leukemic cells survived a DC ex vivo treatment compared with that DC's most effective component alone, the drug effect was referred to as cooperative. The difference between the two resistant fractions is the effect size. RESULTS: We identified 26 dual- and one triple-DC more effective than their components. The differentiation agent tretinoin (TRET; all-trans retinoic acid) reduced the resistant fraction of FLA in 19/21 (90%) samples (decrease from 15% [2%-61%] to 11% [2%-50%] with a mean effect size of 3.8% [0.5%-11%]), and of AZA in 19/25 (76%) samples (decrease from 69% [34%-100+%] to 47% [17%-83%] with a mean effect size of 16% [0.3%-40%]). Among the resistant fractions, the mean proportion of CD38+ cells increased from 7% (0.03%-25%; FLA) to 17% (0.3%-38%; FLA + TRET) or from 10% (0.2%-31%; AZA) to 51% (0.8%-88%; AZA + TRET). CONCLUSION: TRET enhanced the effects of FLA and AZA in ex vivo assays with primary JMML samples.