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.

Ancient fish lineages illuminate toll-like receptor diversification in early vertebrate evolution.

Since its initial discovery over 50 years ago, understanding the evolution of the vertebrate RAG- mediated adaptive immune response has been a major area of research focus for comparative geneticists. However, how the evolutionary novelty of an adaptive immune response impacted the diversity of receptors associated with the innate immune response has received considerably less attention until recently. Here, we investigate the diversification of vertebrate toll-like receptors (TLRs), one of the most ancient and well conserved innate immune receptor families found across the Tree of Life, integrating genomic data that represent all major vertebrate lineages with new transcriptomic data from Polypteriformes, the earliest diverging ray-finned fish lineage. Our analyses reveal TLR sequences that reflect the 6 major TLR subfamilies, TLR1, TLR3, TLR4, TLR5, TLR7, and TLR11, and also currently unnamed, yet phylogenetically distinct TLR clades. We additionally recover evidence for a pulse of gene gain coincident with the rise of the RAG-mediated adaptive immune response in jawed vertebrates, followed by a period of rapid gene loss during the Cretaceous. These gene losses are primarily concentrated in marine teleost fish and synchronous with the mid Cretaceous anoxic event, a period of rapid extinction for marine species. Finally, we reveal a mismatch between phylogenetic placement and gene nomenclature for up to 50% of TLRs found in clades such as ray-finned fishes, cyclostomes, amphibians, and elasmobranchs. Collectively, these results provide an unparalleled perspective of TLR diversity and offer a ready framework for testing gene annotations in non-model species.