Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Abstract

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1.

    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.

    PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    Delbridge AR, Strasser A. The BCL-2 protein family, BH3-mimetics and cancer therapy. Cell Death Differ. 2015;22:1071–80.

    PubMed  PubMed Central  CAS  Google Scholar 

  3. 3.

    Green DR, Reed JC. Mitochondria and apoptosis. Science. 1998;281:1309–12.

    PubMed  CAS  Google Scholar 

  4. 4.

    Tait SW, Green DR. Mitochondria and cell death: outer membrane permeabilization and beyond. Nat Rev Mol Cell Biol. 2010;11:621–32.

    PubMed  CAS  Google Scholar 

  5. 5.

    Korsmeyer SJ, Wei MC, Saito M, Weiler S, Oh KJ, Schlesinger PH. Pro-apoptotic cascade activates BID, which oligomerizes BAK or BAX into pores that result in the release of cytochrome c. Cell Death Differ. 2000;7:1166–73.

    PubMed  CAS  Google Scholar 

  6. 6.

    Luo X, Budihardjo I, Zou H, Slaughter C, Wang X. Bid, a Bcl2 interacting protein, mediates cytochrome c release from mitochondria in response to activation of cell surface death receptors. Cell. 1998;94:481–90.

    PubMed  CAS  Google Scholar 

  7. 7.

    Kotschy A, Szlavik Z, Murray J, Davidson J, Maragno AL, Le Toumelin-Braizat G, et al. The MCL1 inhibitor S63845 is tolerable and effective in diverse cancer models. Nature. 2016;538:477–82.

    Google Scholar 

  8. 8.

    Leverson JD, Phillips DC, Mitten MJ, Boghaert ER, Diaz D, Tahir SK, et al. Exploiting selective BCL-2 family inhibitors to dissect cell survival dependencies and define improved strategies for cancer therapy. Sci Transl Med. 2015;7:279ra240.

    Google Scholar 

  9. 9.

    Roberts AW, Huang D. Targeting BCL2 with BH3 mimetics: basic science and clinical application of venetoclax in chronic lymphocytic leukemia and related B cell malignancies. Clin Pharm Ther. 2017;101:89–98.

    CAS  Google Scholar 

  10. 10.

    Kumar SK, Rajkumar V, Kyle RA, van Duin M, Sonneveld P, Mateos MV, et al. Multiple myeloma. Nat Rev Dis Prim. 2017;3:17046.

    PubMed  Google Scholar 

  11. 11.

    Braggio E, Kortum KM, Stewart AK. SnapShot: multiple myeloma. Cancer Cell. 2015;28:678.e671.

    PubMed  PubMed Central  CAS  Google Scholar 

  12. 12.

    Abdi J, Chen G, Chang H. Drug resistance in multiple myeloma: latest findings and new concepts on molecular mechanisms. Oncotarget. 2013;4:2186–207.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gomez-Bougie P, Wuilleme-Toumi S, Menoret E, Trichet V, Robillard N, Philippe M, et al. Noxa up-regulation and Mcl-1 cleavage are associated to apoptosis induction by bortezomib in multiple myeloma. Cancer Res. 2007;67:5418–24.

    PubMed  CAS  Google Scholar 

  14. 14.

    Podar K, Gouill SL, Zhang J, Opferman JT, Zorn E, Tai YT, et al. A pivotal role for Mcl-1 in Bortezomib-induced apoptosis. Oncogene. 2008;27:721–31.

    PubMed  CAS  Google Scholar 

  15. 15.

    Gong JN, Khong T, Segal D, Yao Y, Riffkin CD, Garnier JM, et al. Hierarchy for targeting prosurvival BCL2 family proteins in multiple myeloma: pivotal role of MCL1. Blood. 2016;128:1834–44.

    PubMed  CAS  Google Scholar 

  16. 16.

    Bodet L, Gomez-Bougie P, Touzeau C, Dousset C, Descamps G, Maiga S, et al. ABT-737 is highly effective against molecular subgroups of multiple myeloma. Blood. 2011;118:3901–10.

    PubMed  CAS  Google Scholar 

  17. 17.

    Kline MP, Rajkumar SV, Timm MM, Kimlinger TK, Haug JL, Lust JA, et al. ABT-737, an inhibitor of Bcl-2 family proteins, is a potent inducer of apoptosis in multiple myeloma cells. Leukemia. 2007;21:1549–60.

    PubMed  CAS  Google Scholar 

  18. 18.

    Kumar S, Kaufman JL, Gasparetto C, Mikhael J, Vij R, Pegourie B, et al. Efficacy of venetoclax as targeted therapy for relapsed/refractory t(11;14) multiple myeloma. Blood. 2017;130:2401–9.

    PubMed  CAS  Google Scholar 

  19. 19.

    Behbehani GK, Bendall SC, Clutter MR, Fantl WJ, Nolan GP. Single-cell mass cytometry adapted to measurements of the cell cycle. Cytom A. 2012;81:552–66.

    Google Scholar 

  20. 20.

    Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO, Finck R, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332:687–96.

    PubMed  PubMed Central  CAS  Google Scholar 

  21. 21.

    Bodenmiller B, Zunder ER, Finck R, Chen TJ, Savig ES, Bruggner RV, et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat Biotechnol. 2012;30:858–67.

    PubMed  PubMed Central  CAS  Google Scholar 

  22. 22.

    Fienberg HG, Simonds EF, Fantl WJ, Nolan GP, Bodenmiller B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytom A. 2012;81:467–75.

    Google Scholar 

  23. 23.

    Zunder ER, Lujan E, Goltsev Y, Wernig M, Nolan GP. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell. 2015;16:323–37.

    PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Zunder ER, Finck R, Behbehani GK, Amir el AD, Krishnaswamy S, Gonzalez VD, et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat Protoc. 2015;10:316–33.

    PubMed  PubMed Central  CAS  Google Scholar 

  25. 25.

    Finck R, Simonds EF, Jager A, Krishnaswamy S, Sachs K, Fantl W, et al. Normalization of mass cytometry data with bead standards. Cytom A. 2013;83:483–94.

    Google Scholar 

  26. 26.

    Kotecha N, Krutzik PO, Irish JM. Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom. 2010;53:10.17.1–10.17.24.

  27. 27.

    Bliss CI. The toxicity of poisons applied jointly. Ann Appl Biol. 1939;26:585–615.

    CAS  Google Scholar 

  28. 28.

    Westphal D, Kluck RM, Dewson G. Building blocks of the apoptotic pore: how Bax and Bak are activated and oligomerize during apoptosis. Cell Death Differ. 2014;21:196–205.

    PubMed  CAS  Google Scholar 

  29. 29.

    Dewson G, Kratina T, Sim HW, Puthalakath H, Adams JM, Colman PM, et al. To trigger apoptosis, Bak exposes its BH3 domain and homodimerizes via BH3:groove interactions. Mol Cell. 2008;30:369–80.

    PubMed  CAS  Google Scholar 

  30. 30.

    Kruger FJ. Pilot study of the potential use of leucocyte adherence to Schistosoma haematobium eggs as an indication of immunity to reinfection. Trans R Soc Trop Med Hyg. 1991;85:83.

    PubMed  CAS  Google Scholar 

  31. 31.

    Alsop AE, Fennell SC, Bartolo RC, Tan IK, Dewson G, Kluck RM. Dissociation of Bak alpha1 helix from the core and latch domains is required for apoptosis. Nat Commun. 2015;6:6841.

    PubMed  CAS  Google Scholar 

  32. 32.

    Hideshima T, Chauhan D, Hayashi T, Akiyama M, Mitsiades N, Mitsiades C, et al. Proteasome inhibitor PS-341 abrogates IL-6 triggered signaling cascades via caspase-dependent downregulation of gp130 in multiple myeloma. Oncogene. 2003;22:8386–93.

    PubMed  CAS  Google Scholar 

  33. 33.

    Hideshima T, Nakamura N, Chauhan D, Anderson KC. Biologic sequelae of interleukin-6 induced PI3-K/Akt signaling in multiple myeloma. Oncogene. 2001;20:5991–6000.

    PubMed  CAS  Google Scholar 

  34. 34.

    Hansmann L, Blum L, Ju CH, Liedtke M, Robinson WH, Davis MM. Mass cytometry analysis shows that a novel memory phenotype B cell is expanded in multiple myeloma. Cancer Immunol Res. 2015;3:650–60.

    PubMed  PubMed Central  CAS  Google Scholar 

  35. 35.

    Souers AJ, Leverson JD, Boghaert ER, Ackler SL, Catron ND, Chen J, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med. 2013;19:202–8.

    PubMed  CAS  Google Scholar 

  36. 36.

    Yadav B, Wennerberg K, Aittokallio T, Tang J. Searching for drug synergy in complex dose-response landscapes using an interaction potency model. Comput Struct Biotechnol J. 2015;13:504–13.

    PubMed  PubMed Central  CAS  Google Scholar 

  37. 37.

    Chauhan D, Velankar M, Brahmandam M, Hideshima T, Podar K, Richardson P, et al. A novel Bcl-2/Bcl-X(L)/Bcl-w inhibitor ABT-737 as therapy in multiple myeloma. Oncogene. 2007;26:2374–80.

    PubMed  CAS  Google Scholar 

  38. 38.

    Touzeau C, Dousset C, Le Gouill S, Sampath D, Leverson JD, Souers AJ, et al. The Bcl-2 specific BH3 mimetic ABT-199: a promising targeted therapy for t(11;14) multiple myeloma. Leukemia. 2014;28:210–2.

    PubMed  CAS  Google Scholar 

  39. 39.

    Trudel S, Stewart AK, Li Z, Shu Y, Liang SB, Trieu Y, et al. The Bcl-2 family protein inhibitor, ABT-737, has substantial antimyeloma activity and shows synergistic effect with dexamethasone and melphalan. Clin Cancer Res. 2007;13:621–9.

    PubMed  CAS  Google Scholar 

  40. 40.

    Tse C, Shoemaker AR, Adickes J, Anderson MG, Chen J, Jin S, et al. ABT-263: a potent and orally bioavailable Bcl-2 family inhibitor. Cancer Res. 2008;68:3421–8.

    CAS  Google Scholar 

  41. 41.

    Kaufman JL, Gasparetto CJ, Mikhael J, Moreau P, Touzeau C, Vij R, et al. Phase 1 study of venetoclax in combination with dexamethasone as targeted therapy for t(11;14) relapsed/refractory multiple myeloma. Blood. 2017;130:3131.

  42. 42.

    Kervoelen C, Menoret E, Gomez-Bougie P, Bataille R, Godon C, Marionneau-Lambot S, et al. Dexamethasone-induced cell death is restricted to specific molecular subgroups of multiple myeloma. Oncotarget. 2015;6:26922–34.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Matulis SM, Gupta VA, Nooka AK, Hollen HV, Kaufman JL, Lonial S, et al. Dexamethasone treatment promotes Bcl-2 dependence in multiple myeloma resulting in sensitivity to venetoclax. Leukemia. 2016;30:1086–93.

    PubMed  CAS  Google Scholar 

  44. 44.

    Punnoose EA, Leverson JD, Peale F, Boghaert ER, Belmont LD, Tan N, et al. Expression profile of BCL-2, BCL-XL, and MCL-1 predicts pharmacological response to the BCL-2 selective antagonist venetoclax in multiple myeloma models. Mol Cancer Ther. 2016;15:1132–44.

    PubMed  CAS  Google Scholar 

  45. 45.

    Agarwal R, Chan YC, Tam CS, Hunter T, Vassiliadis D, Teh CE, et al. Dynamic molecular monitoring reveals that SWI-SNF mutations mediate resistance to ibrutinib plus venetoclax in mantle cell lymphoma. Nat Med. 2019;25:119–29.

    PubMed  PubMed Central  CAS  Google Scholar 

  46. 46.

    Blombery P, Anderson MA, Gong JN, Thijssen R, Birkinshaw RW, Thompson ER, et al. Acquisition of the recurrent Gly101Val mutation in BCL2 confers resistance to venetoclax in patients with progressive chronic lymphocytic leukemia. Cancer Discov. 2019;9:342–53.

    PubMed  Google Scholar 

  47. 47.

    van Delft MF, Wei AH, Mason KD, Vandenberg CJ, Chen L, Czabotar PE, et al. The BH3 mimetic ABT-737 targets selective Bcl-2 proteins and efficiently induces apoptosis via Bak/Bax if Mcl-1 is neutralized. Cancer Cell. 2006;10:389–99.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Wuilleme-Toumi S, Robillard N, Gomez P, Moreau P, Le Gouill S, Avet-Loiseau H, et al. Mcl-1 is overexpressed in multiple myeloma and associated with relapse and shorter survival. Leukemia. 2005;19:1248–52.

    PubMed  CAS  Google Scholar 

  49. 49.

    Beck IM, Vanden Berghe W, Vermeulen L, Yamamoto KR, Haegeman G, De Bosscher K. Crosstalk in inflammation: the interplay of glucocorticoid receptor-based mechanisms and kinases and phosphatases. Endocr Rev. 2009;30:830–82.

    PubMed  PubMed Central  CAS  Google Scholar 

  50. 50.

    Shabestari RM, Safa M, Alikarami F, Banan M, Kazemi A. CREB knockdown inhibits growth and induces apoptosis in human pre-B acute lymphoblastic leukemia cells through inhibition of prosurvival signals. Biomed Pharmacother. 2017;87:274–9.

    PubMed  CAS  Google Scholar 

  51. 51.

    Carter BZ, Mak PY, Mu H, Zhou H, Mak DH, Schober W, et al. Combined targeting of BCL-2 and BCR-ABL tyrosine kinase eradicates chronic myeloid leukemia stem cells. Sci Transl Med. 2016;8:355ra117.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Brioli A, Melchor L, Cavo M, Morgan GJ. The impact of intra-clonal heterogeneity on the treatment of multiple myeloma. Br J Haematol. 2014;165:441–54.

    PubMed  Google Scholar 

  53. 53.

    Caenepeel S, Brown SP, Belmontes B, Moody G, Keegan KS, Chui D, et al. AMG 176, a selective MCL1 inhibitor, is effective in hematologic cancer models alone and in combination with established therapies. Cancer Discov. 2018;8:1582–97.

  54. 54.

    Ramsey HE, Fischer MA, Lee T, Gorska AE, Arrate MP, Fuller L, et al. A novel MCL1 inhibitor combined with venetoclax rescues venetoclax-resistant acute myelogenous leukemia. Cancer Discov. 2018;8:1566–81.

    PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

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.

Author information

Affiliations

Authors

Contributions

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.

Ethics declarations

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by G. Melino

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1038/s41418-020-0498-z

Download citation

Further reading

Search

Quick links