Page 70 - Haematologica - Vol. 105 n. 6 - June 2020

M.M. Majumder et al.
healthy cells may give rise to untoward effects related to these entities. Although seminal studies have contributed to the understanding of signaling diversities across blood cells,5-8 a detailed characterization of cell-type specific vul- nerabilities within the hematopoietic hierarchy is still lacking.
Cell-based phenotypic screens of primary cells have shown tremendous potential to identify novel therapeu- tics in leukemia and to explore novel indications for approved drugs.9,10 However, classical drug screening methods that assess the sum of all cellular effects in the bone marrow (BM) or blood restrict the ability to evaluate drug responses in populations affected by rare diseases and is influenced by the more abundant cell types in the sample. Flow cytometry presents a functional platform for dissecting the complexity of hematopoiesis, allowing characterization of the different cell populations. Applying flow cytometry in functional screens allows for a higher throughput (HTS) assessment of vulnerabilities to a large set of oncology drugs in leukemic cells with improved precision, and to compartmentalize drug responses between malignant and healthy cell subsets. However, preclinical flow cytometric-based high through- put functional screens are still limited by numerous wash- ing steps and small cell population numbers, which can compromise the robustness of the assay.
In this study, we developed a high throughput no-wash flow cytometry assay that enabled us to monitor dose responses of 71 oncology compounds simultaneously on multiple hematopoietic cell populations defined by their surface antigen expression. To map the drug responses to the proteome and basal signaling profiles of the different cell types, we utilized mass spectrometry (MS) and mass cytometry (CyTOF) in both healthy and malignant hema- tologic samples. Finally, we compared inhibition profiles for those small molecules in a cohort of 281 primary sam- ples representing a diverse set of hematologic malignancies to assess whether healthy cell-specific responses can be exploited in a leukemic context. A graphical overview of the study and cohorts is provided in Figure 1. Our results strongly suggest that drug responses are highly specific to cell lineages and often linked to intrinsic cell signaling pres- ent in those cell types. We provide evidence that cell-spe- cific responses could potentially be applied to identify new clinical applications of therapies and discover relevant non- oncogenic-dependent activities of small molecules.
Methods
Patient specimens and cohorts
Bone marrow and peripheral blood (PB) samples from 332 donors were collected after written informed consent (Studies: 239/13/03/00/2010, 303/13/03/01/2011, REK2016/253 and REK2012/2247) following protocols approved by local institution- al review boards (Helsinki University Hospital Comprehensive Cancer Center and Haukeland University Hospital) in compliance with the Declaration of Helsinki. Samples were allocated to four patient cohorts (I-IV). Cohort I included three healthy PB samples used for flow cytometry screening with 71 drugs, plus three acute myeloid leukemia (AML) and ten multiple myeloma (MM) sam- ples which were tested with bortezomib, clofarabine, dexametha- sone, omipalisib, venetoclax and navitoclax. Cohort II included 17 samples from two healthy, eight AML with (n=5) or without FLT3-ITD mutations (n=3), and seven CLL patients tested against
midostaurin, trametinib and dasatinib. Cohort III (n=281) included 231 BM aspirates from a diverse collection of leukemia and 50 MM patients (CD138+ enriched). Four healthy BM aspirates sub- jected to magnetic bead-based enrichment using EasySepTM human CD138, CD3, CD19, CD14 and CD34 positive selection kits (StemCell Technologies), served as healthy cell-of-origin samples for comparison against the malignant cell counterparts. CyTOF was performed on 14 samples in Cohort IV. PB from healthy donors (n=3), AML (n=6), B-cell acute lymphoblastic leukemia (B- ALL) (n=2), and matched BM samples from the same healthy donors were included. An overview of the cohorts and experimen- tal design is provided in Figure 1.
Proteome analysis
10 μg of whole cell protein lysates, prepared from purified CD3, CD19 and CD14 fractions from healthy (n=2) and MM (n=4) sam- ples, were digested and loaded (500 ng) on to a Q-Exactive mass spectrometer connected to a Dionex Ultimate 3000 (RSLCnano) chromatography system (Thermo Scientific). Protein identifica- tion and label-free quantification (LFQ) normalization of tandem mass spectrometry (MS/MS) data were performed using MaxQuant v1.5.2.8.
Mass cytometry
For mass cytometry (CyTOF), the 14 samples described in cohort IV were fixed, barcoded (Fluidigm), pooled into a single sample and stained with the antibody panels (Online Supplementary Table S1). Acquisition of samples was performed using a Helios mass cytometer (Fluidigm). Data were analyzed using FlowJo v.10.2 and Cytobank (Cytobank Inc.).
High throughput flow cytometry and cell viability assay
High throughput flow cytometry (HTFC) assays were per- formed in both 384-well (n=3, 71 drugs, 5 concentrations) and 96- well plate formats (n=33) using IntelliCyt iQue Screener PLUS. A detailed optimization protocol is provided in the Online Supplementary Methods. A list of the antibodies is provided in Online Supplementary Table S1. Data were analyzed using ForeCyt software (Intellicyt). The gating strategy, cell composition and list of compounds are provided in Online Supplementary Figures S1-S3. CellTiter-Glo® luminescent viability assay was used based on a previously described method.9,16
Statistical analysis of drug sensitivity data
Cell counts (HTFC) or luminescence intensity were used as input for Dotmatics (Dotmatics Ltd.) or Graphpad Prism 8.0 to generate dose response graphs, which were subsequently applied to calculate drug sensitivity score (DSS) as described by Yadav et al.16 Comparisons between groups were tested with ANOVA and with Tukey’s multiple comparison test to derive significance. A two-tailed P<0.05 was considered significant.
Results
Distinct drug response profiles in hematologic cell subsets are tied to cell lineages
To simultaneously monitor drug effects on a large col- lection (n=71) of samples in multiple cell types, we applied a multiplexed, no-wash flow cytometry-based assay (detailed in the Online Supplementary Methods). We first tested ex vivo response to the 71 compounds (Online Supplementary Table S2 and Online Supplementary Figure S3) in B (CD19+), natural killer (NK, CD56+), T-helper cells (THC, CD3+CD4+), cytotoxic T lymphocytes (CTL,
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