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.

Candidate driver genes involved in genome maintenance and DNA repair in Sézary syndrome.

Sézary syndrome (SS) is a leukemic variant of cutaneous T-cell lymphoma (CTCL) and represents an ideal model for study of T-cell transformation. We describe whole-exome and single-nucleotide polymorphism array-based copy number analyses of CD4(+) tumor cells from untreated patients at diagnosis and targeted resequencing of 101 SS cases. A total of 824 somatic nonsynonymous gene variants were identified including indels, stop-gain/loss, splice variants, and recurrent gene variants indicative of considerable molecular heterogeneity. Driver genes identified using MutSigCV include POT1, which has not been previously reported in CTCL; and TP53 and DNMT3A, which were also identified consistent with previous reports. Mutations in PLCG1 were detected in 11% of tumors including novel variants not previously described in SS. This study is also the first to show BRCA2 defects in a significant proportion (14%) of SS tumors. Aberrations in PRKCQ were found to occur in 20% of tumors highlighting selection for activation of T-cell receptor/NF-κB signaling. A complex but consistent pattern of copy number variants (CNVs) was detected and many CNVs involved genes identified as putative drivers. Frequent defects involving the POT1 and ATM genes responsible for telomere maintenance were detected and may contribute to genomic instability in SS. Genomic aberrations identified were enriched for genes implicated in cell survival and fate, specifically PDGFR, ERK, JAK STAT, MAPK, and TCR/NF-κB signaling; epigenetic regulation (DNMT3A, ASLX3, TET1-3); and homologous recombination (RAD51C, BRCA2, POLD1). This study now provides the basis for a detailed functional analysis of malignant transformation of mature T cells and improved patient stratification and treatment.