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Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells


Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.

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Fig. 1: CyTOF probes for detection of BCL-2 family members.
Fig. 2: Substantial changes in cell cycle and signaling states accompany apoptotic cell death following treatment of multiple myeloma cells with cytotoxic drugs.
Fig. 3: FLOW-MAP visualizations demonstrate the cell state dynamics of multiple myeloma cells following cytotoxic drug treatment.
Fig. 4: Random forest models trained on mass cytometry time course data identify the key features of cytotoxic drug-induced apoptosis.
Fig. 5: FLOW-MAP comparison of key model features following bortezomib or dexamethasone treatment.
Fig. 6: FLOW-MAP visualization of the key features in the apoptotic response to dexamethasone in multiple myeloma cells reveals a transitional population.
Fig. 7: Synergistic killing of multiple myeloma cells with MCL-1 inhibition combined with dexamethasone.
Fig. 8: Synergistic killing of myeloma cells from patients by combining MCL-1 inhibition with dexamethasone.


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CET is supported by an Australian NHMRC Early Career Fellowship (1089072) and a Fulbright Australia-America Postdoctoral Fellowship. PLF is supported by a Leukaemia Foundation of Australia Clinical PhD Scholarship. MSYL is supported by a NHMRC/RACP Gus Nossal PhD scholarship (1075151). GPN is supported by the Rachford and Carlotta A. Harris Endowed Chair. DHDG is supported by Australian NHMRC Fellowships (1090236 and 1158024). AS is supported by an Australian NHMRC Senior Principal Research Fellowship (1020363). MEK is supported by the National Cancer Institute of the National Institutes of Health under Award Number F99CA212231 and Stanford University’s Diversifying Academia, Recruiting Excellence Fellowship. This work was supported by grants to GPN: U19 AI057229, 1U19AI100627, Department of Defense (CDMRP), Northrop-Grumman Corporation, R01CA184968, 1R33CA183654–01, R33CA183692, R01CA184968, 1R21CA183660, 1R01NS08953304, OPP1113682, 5UH2AR067676, 1R01CA19665701, R01HL120724; grants to DHDG: Cancer Council of Victoria Grants-in-Aid (1146518 and 1102104); grants to AWR/DCSH: NHRMC (1113577, 1016701, 1079560); grants to AS: Leukemia and Lymphoma Society SCOR 7001–13, NHMRC program grant 1016701. This work was made possible through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS. The authors thank Prof. Silvia Plevritis (Stanford University) for advice and critical discussions, Dr Andrew Mitchell (University of Melbourne) for assistance with mass cytometry maintenance/operation, Chris Riffkin (WEHI) for assistance with cell culture and Assoc. Prof Grant Dewson for assistance with densitometric analysis of western blot data. This work was performed in part at the Materials Characterization and Fabrication Platform (MCFP) at the University of Melbourne and the Victorian Node of the Australian National Fabrication Facility (ANFF) with support from the Victorian Comprehensive Cancer Centre.

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Conception and design: CET, DCSH, GPN, DHDG, and MEK. Development of methodology: CET and MEK. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): CET, JG, DS, TT, CJV, PLF, MSYL, GG, SJH, AWR, DCSH, and MEK. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, and computational analysis): CET, JG, and MEK. Writing, review, and/or revision of the paper: CET, MEK, DHDG, CJV, PLF, MSYL, AS, AWR, DCSH, and GPN.

Corresponding authors

Correspondence to Garry P. Nolan or Daniel H. D. Gray.

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Conflict of interest

GPN is a paid consultant for Fluidigm, the manufacturer that produced some of the reagents and instrumentation used in this study. CET, JG, DS, TT, CJV, PLF, MSYL, AS, AWR, DCSH, and DHDG are employees of Walter and Eliza Hall Institute of Medical Research which receives milestone and royalty payments related to venetoclax (iBCL-2). SJH has received research funding and has participated in Advisory boards from Janssen Cilag (bortezomib), Abbvie (venetoclax), and Amgen (iMCL-1). Researchers at the Walter and Eliza Hall Institute of Medical Research in the Strasser, Roberts, Huang, and Gray laboratories collaborate with Servier on the development of MCL-1 inhibitors. All other authors declare no competing financial interests.

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Teh, C.E., Gong, JN., Segal, D. et al. Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells. Cell Death Differ 27, 2217–2233 (2020).

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