High-throughput approaches to uncover synergistic drug combinations in leukemia

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.

Synergy and antagonism between azacitidine and FLT3 inhibitors

Synergetic interactions between drugs can make a drug combination more effective. Alternatively, they may allow to use lower concentrations and thus avoid toxicities or side effects that not only cause discomfort but might also reduce the overall survival. Here, we studied whether synergy exists between agents that are used for treatment of acute myeloid leukaemia (AML). Azacitidine is a demethylation agent that is used in the treatment of AML patients that are unfit for aggressive chemotherapy. An activating mutation in the FLT3 gene is common in AML patients and in the absence of specific treatment makes prognosis worse. FLT3 inhibitors may be used in such cases. We sought to determine whether combination of azacitidine with a FLT3 inhibitor (gilteritinib, quizartinib, LT-850-166, FN-1501 or FF-10101) displayed synergy or antagonism. To this end, we calculated dose-response matrices of these drug combinations from experiments in human AML cells and subsequently analysed the data using a novel consensus scoring algorithm. The results show that combinations that involved non-covalent FLT3 inhibitors, including the two clinically approved drugs gilteritinib and quizartinib were antagonistic. On the other hand combinations with the covalent inhibitor FF-10101 had some range of concentrations where synergy was observed.

MSC.sensor: Capturing cancer cell interactions with stroma for functional profiling

Mesenchymal stromal cells (MSCs) contribute to the microenvironment regulating normal and malignant hematopoiesis, and thus may support subpopulations of cancer cells to escape therapeutic pressure. Here, we engineered bone marrow MSCs to express a synthetic CD19-sensor receptor to detect and display interacting primary CD19+ leukemia cells in coculture. This implementation provides a versatile platform facilitating ex vivo drug response profiling of primary CD19+ leukemia cells in coculture with high-sensitivity and scalability.

PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories

Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.