Multiple myeloma gammopathies

Transcriptional profiling of circulating tumor cells in multiple myeloma: a new model to understand disease dissemination


The reason why a few myeloma cells egress from the bone marrow (BM) into peripheral blood (PB) remains unknown. Here, we investigated molecular hallmarks of circulating tumor cells (CTCs) to identify the events leading to myeloma trafficking into the bloodstream. After using next-generation flow to isolate matched CTCs and BM tumor cells from 32 patients, we found high correlation in gene expression at single-cell and bulk levels (r ≥ 0.94, P = 10−16), with only 55 genes differentially expressed between CTCs and BM tumor cells. CTCs overexpressed genes involved in inflammation, hypoxia, or epithelial–mesenchymal transition, whereas genes related with proliferation were downregulated in CTCs. The cancer stem cell marker CD44 was overexpressed in CTCs, and its knockdown significantly reduced migration of MM cells towards SDF1-α and their adhesion to fibronectin. Approximately half (29/55) of genes differentially expressed in CTCs were prognostic in patients with newly-diagnosed myeloma (n = 553; CoMMpass). In a multivariate analysis including the R-ISS, overexpression of CENPF and LGALS1 was significantly associated with inferior survival. Altogether, these results help understanding the presence of CTCs in PB and suggest that hypoxic BM niches together with a pro-inflammatory microenvironment induce an arrest in proliferation, forcing tumor cells to circulate in PB and seek other BM niches to continue growing.

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  1. 1.

    Palumbo A, Anderson K. Multiple myeloma. N Engl J Med. 2011;364:1046–60.

  2. 2.

    Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2015;15:e538–48.

  3. 3.

    Paiva B, Pérez-Andrés M, Vídriales MB, Almeida J, De Las Heras N, Mateos MV, et al. Competition between clonal plasma cells and normal cells for potentially overlapping bone marrow niches is associated with a progressively altered cellular distribution in MGUS vs myeloma. Leukemia. 2011;25:697–706.

  4. 4.

    Sanoja-Flores L, Flores-Montero J, Garcés JJ, Paiva B, Puig N, García-Mateo A, et al. Next generation flow for minimally-invasive blood characterization of MGUS and multiple myeloma at diagnosis based on circulating tumor plasma cells (CTPC). Blood Cancer J. 2018;8:117.

  5. 5.

    Kumar S, Rajkumar SV, Kyle RA, Lacy MQ, Dispenzieri A, Fonseca R, et al. Prognostic value of circulating plasma cells in monoclonal gammopathy of undetermined significance. J Clin Oncol. 2005;23:5668–74.

  6. 6.

    Bianchi G, Kyle RA, Larson DR, Witzig TE, Kumar S, Dispenzieri A, et al. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia. 2013;27:680–5.

  7. 7.

    Gonsalves WI, Rajkumar SV, Dispenzieri A, Dingli D, Timm MM, Morice WG, et al. Quantification of circulating clonal plasma cells via multiparametric flow cytometry identifies patients with smoldering multiple myeloma at high risk of progression. Leukemia. 2017;31:130–5.

  8. 8.

    Gonsalves WI, Rajkumar SV, Gupta V, Morice WG, Timm MM, Singh PP, et al. Quantification of clonal circulating plasma cells in newly diagnosed multiple myeloma: implications for redefining high-risk myeloma. Leukemia. 2014;28:2060–5.

  9. 9.

    Gonsalves WI, Morice WG, Rajkumar V, Gupta V, Timm MM, Dispenzieri A, et al. Quantification of clonal circulating plasma cells in relapsed multiple myeloma. Br J Haematol. 2014;167:500–5.

  10. 10.

    Chakraborty R, Muchtar E, Kumar SK, Jevremovic D, Buadi FK, Dingli D, et al. Serial measurements of circulating plasma cells before and after induction therapy have an independent prognostic impact in patients with multiple myeloma undergoing upfront autologous transplantation. Haematologica. 2017;102:1439–45.

  11. 11.

    Cowan AJ, Stevenson PA, Libby EN, Becker PS, Coffey DG, Green DJ, et al. Circulating plasma cells at the time of collection of autologous PBSC for transplant in multiple myeloma patients is a negative prognostic factor even in the age of post-transplant maintenance therapy. Biol Blood Marrow Transplant. 2018;24:1386–91.

  12. 12.

    Foulk B, Schaffer M, Gross S, Rao C, Smirnov D, Connelly MC, et al. Enumeration and characterization of circulating multiple myeloma cells in patients with plasma cell disorders. Br J Haematol. 2018;180:71–81.

  13. 13.

    Granell M, Calvo X, Garcia-Guiñón A, Escoda L, Abella E, Martínez CM, et al. Prognostic impact of circulating plasma cells in patients with multiple myeloma: implications for plasma cell leukemia definition. Haematologica. 2017;102:1099–104.

  14. 14.

    Huhn S, Weinhold N, Nickel J, Pritsch M, Hielscher T, Hummel M, et al. Circulating tumor cells as a biomarker for response to therapy in multiple myeloma patients treated within the GMMG-MM5 trial. Bone Marrow Transplant. 2017;52:1194–8.

  15. 15.

    Dingli D, Nowakowski GS, Dispenzieri A, Lacy MQ, Hayman SR, Rajkumar SV, et al. Flow cytometric detection of circulating myeloma cells before transplantation in patients with multiple myeloma: a simple risk stratification system. Blood. 2006;107:3384–8.

  16. 16.

    Paiva B, Paino T, Sayagues JM, Garayoa M, San-Segundo L, Martín M, et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood. 2013;122:3591–8.

  17. 17.

    Masri S, Sassone-Corsi P. The emerging link between cancer, metabolism, and circadian rhythms. Nat Med. 2018;24:1795–803.

  18. 18.

    Méndez-Ferrer S, Lucas D, Battista M, Frenette PS. Haematopoietic stem cell release is regulated by circadian oscillations. Nature. 2008;452:442–7.

  19. 19.

    Ghobrial IM. Myeloma as a model for the process of metastasis: implications for therapy. Blood. 2012;120:20–30.

  20. 20.

    Mishima Y, Paiva B, Shi J, Park J, Manier S, Takagi S, et al. The mutational landscape of circulating tumor cells in multiple myeloma. Cell Rep. 2017;19:218–24.

  21. 21.

    Lohr JG, Kim S, Gould J, Knoechel B, Drier Y, Cotton MJ, et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci Transl Med. 2016;8:1–10.

  22. 22.

    Ledergor G, Weiner A, Zada M, Wang S-Y, Cohen YC, Gatt ME, et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat Med. 2018;24:1867–76.

  23. 23.

    Flores-Montero J, Flores LS, Paiva B, Puig N, García-Sánchez O, Böttcher S, et al. Next generation flow (NGF) for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia. 2017;31:2094–103.

  24. 24.

    Puig N, Paiva B, Lasa M, Burgos L, Perez JJ, Merino J, et al. Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis. Leukemia. 2019;33:1256–67.

  25. 25.

    Fu GK, Wilhelmy J, Stern D, Fan HC, Fodor SPA. Digital encoding of cellular mRNAs enabling precise and absolute gene expression measurement by single-molecule counting. Anal Chem. 2014;86:2867–70.

  26. 26.

    Cole M, Risso D (2019). scone: Single Cell Overview of Normalized Expression data. R package version 1.8.0.

  27. 27.

    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.

  28. 28.

    Schlitzer A, Sivakamasundari V, Chen J, Sumatoh HR, Schreuder J, Lum J, et al. Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat Immunol. 2015;16:718–28.

  29. 29.

    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

  30. 30.

    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

  31. 31.

    Raimondi L, Amodio N, Teresa M, Martino D, Altomare E, Leotta M, et al. Targeting of multiple myeloma-related angiogenesis by miR- 199a-5p mimics: in vitro and in vivo anti-tumor activity. Oncotarget. 2014;5:3039–54.

  32. 32.

    Alboukadel Kassambara (2019). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.2.1.

  33. 33.

    Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9.

  34. 34.

    Lavin Y, Kobayashi S, Leader A, Amir ED, Elefant N, Bigenwald C, et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell. 2017;169:750–7. e17

  35. 35.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

  36. 36.

    Therneau T (2015). A Package for Survival Analysis in S. R package version 2.38.

  37. 37.

    Alboukadel Kassambara and Marcin Kosinski (2019). survminer: Drawing Survival Curves using ‘ggplot2’. R package version 0.4.4.

  38. 38.

    McKenzie CW, Craige B, Kroeger TV, Finn R, Wyatt TA, Sisson JH, et al. CFAP54 is required for proper ciliary motility and assembly of the central pair apparatus in mice. Mol Biol Cell. 2015;26:3140–9.

  39. 39.

    Martin TA, Harrison G, Mansel RE, Jiang WG. The role of the CD44/ezrin complex in cancer metastasis. Crit Rev Oncol Hematol. 2003;46:165–86.

  40. 40.

    Ivetic A, Ridley AJ. Ezrin/radixin/moesin proteins and Rho GTPase signalling in leucocytes. Immunology. 2004;112:165–76.

  41. 41.

    Zhu Y, Zhu MX, Zhang XD, Xu XE, Wu ZY, Liao LD, et al. SMYD3 stimulates EZR and LOXL2 transcription to enhance proliferation, migration, and invasion in esophageal squamous cell carcinoma. Hum Pathol. 2016;52:153–63.

  42. 42.

    Hsieh Y-H, Chou R-H, Hsieh S-C, Cheng C-W, Lin C-L, Yang S-F, et al. Targeting EMP3 suppresses proliferation and invasion of hepatocellular carcinoma cells through inactivation of PI3K/Akt pathway. Oncotarget. 2015;6:34859–74.

  43. 43.

    Dumitru CA, Bankfalvi A, Gu X, Zeidler R, Brandau S, Lang S. AHNAK and inflammatory markers predict poor survival in laryngeal carcinoma. PLoS ONE. 2013;8:e56420.

  44. 44.

    Davis TA, Loos B, Engelbrecht A-M. AHNAK: the giant jack of all trades. Cell Signal. 2014;26:2683–93.

  45. 45.

    Paiva B, Corchete LA, Vidriales M-B, Puig N, Maiso P, Rodriguez I, et al. Phenotypic and genomic analysis of multiple myeloma minimal residual disease tumor cells: a new model to understand chemoresistance. Blood. 2016;127:1896–906.

  46. 46.

    Waldschmidt JM, Simon A, Wider D, Müller SJ, Follo M, Ihorst G, et al. CXCL12 and CXCR7 are relevant targets to reverse cell adhesion-mediated drug resistance in multiple myeloma. Br J Haematol. 2017;179:36–49.

  47. 47.

    Anreddy N, Hazlehurst LA. Targeting intrinsic and extrinsic vulnerabilities for the treatment of multiple myeloma. J Cell Biochem. 2017;118:15–25.

  48. 48.

    Storti P, Marchica V, Giuliani N. Role of galectins in multiple myeloma. Int J Mol Sci. 2017;18:2740.

  49. 49.

    Glavey SV, Naba A, Manier S, Clauser K, Tahri S, Park J, et al. Proteomic characterization of human multiple myeloma bone marrow extracellular matrix. Leukemia. 2017;31:2426–34.

  50. 50.

    Ma Y, Jin Z, Huang J, Zhou S, Ye H, Jiang S, et al. IQGAP1 plays an important role in the cell proliferation of multiple myeloma via the MAP kinase (ERK) pathway. Oncol Rep. 2013;30:3032–8.

  51. 51.

    Gocke CB, McMillan R, Wang Q, Begum A, Penchev VR, Ali SA, et al. IQGAP1 scaffold-MAP kinase interactions enhance multiple myeloma clonogenic growth and self-renewal. Mol Cancer Ther. 2016;15:2733–9.

  52. 52.

    Aust G, Zhu D, Van Meir EG, Xu L. Adhesion GPCRs in tumorigenesis. Handb Exp Pharmacol. 2016;234:369–96.

  53. 53.

    Demchenko YN, Glebov OK, Zingone A, Keats JJ, Bergsagel PL, Kuehl WMClassical. and/or alternative NF-kB pathway activation in multiple myeloma. Cancer. 2010;115:3541–52.

  54. 54.

    Troppan K, Hofer S, Wenzl K, Lassnig M, Pursche B, Steinbauer E, et al. Frequent down regulation of the tumor suppressor gene A20 in multiple myeloma. PLoS ONE. 2015;10:e0123922.

  55. 55.

    Broyl A, Hose D, Lokhorst H, De Knegt Y, Peeters J, Jauch A, et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Gene Expr. 2010;116:2543–53.

  56. 56.

    Li D, Wu C, Cai Y, Liu B. Association of NFKB1 and NFKBIA gene polymorphisms with susceptibility of gastric cancer. Tumor Biol. 2017;39:1–6.

  57. 57.

    Zhang M, Huang J, Tan X, Bai J, Wang H, Ge Y, et al. Common polymorphisms in the NFKBIA gene and cancer susceptibility: a meta-analysis. Med Sci Monit. 2015;21:3186–96.

  58. 58.

    Wang Q, Wang X, Liang Q, Wang S, Xiwen L, Pan F, et al. Distinct prognostic value of mRNA expression of guanylate-binding protein genes in skin cutaneous melanoma. Oncol Lett. 2018;15:7914–22.

  59. 59.

    Godoy P, Cadenas C, Hellwig B, Marchan R, Stewart J, Reif R, et al. Interferon-inducible guanylate binding protein (GBP2) is associated with better prognosis in breast cancer and indicates an efficient T cell response. Breast Cancer. 2014;21:491–9.

  60. 60.

    Thijssen VL, Heusschen R, Caers J, Griffioen AW. Galectin expression in cancer diagnosis and prognosis: a systematic review. Biochim Biophys Acta. 2015;1855:235–47.

  61. 61.

    Cheng CL, Hou HA, Lee MC, Liu CY, Jhuang JY, Lai YJ, et al. Higher bone marrow LGALS3 expression is an independent unfavorable prognostic factor for overall survival in patients with acute myeloid leukemia. Blood. 2013;121(Apr):3172–80.

  62. 62.

    Albright RA, Chang WC, Robert D, Ornstein DL, Cao W, Liu L, et al. NPP4 is a procoagulant enzyme on the surface of vascular endothelium. Blood. 2012;120:4432–40.

  63. 63.

    Xu X, Han K, Zhu J, Mao H, Lin X, Zhang Z, et al. An inhibitor of cholesterol absorption displays anti-myeloma activity by targeting the JAK2-STAT3 signaling pathway. Oncotarget. 2016;7:75539–50.

  64. 64.

    Murai T. Cholesterol lowering: role in cancer prevention and treatment. Biol Chem. 2015;396:1–11.

  65. 65.

    Silvente-Poirot S, Poirot M. Cancer. Cholesterol and cancer, in the balance. Science. 2014;343:1445–6.

  66. 66.

    Cohen Y, Gutwein O, Garach-Jehoshua O, Bar-Haim A, Kornberg A. The proliferation arrest of primary tumor cells out-of-niche is associated with widespread downregulation of mitotic and transcriptional genes. Hematology. 2014;19:286–92.

  67. 67.

    Deng Y, Jiang L, Wang Y, Xi Q, Zhong J, Liu J, et al. High expression of CDC6 is associated with accelerated cell proliferation and poor prognosis of epithelial ovarian cancer. Pathol Res Pract. 2016;212:239–46.

  68. 68.

    Avigdor A, Goichberg P, Shivtiel S, Dar A, Peled A, Samira S, et al. CD44 and hyaluronic acid cooperate with SDF-1 in the trafficking of human CD34+ stem/progenitor cells to bone marrow. Blood. 2004;103:2981–9.

  69. 69.

    Dimitroff CJ, Lee JY, Rafii S, Fuhlbrigge RC, Sackstein R. CD44 is a major E-selectin ligand on human hematopoietic progenitor cells. J Cell Biol. 2001;153:1277–86.

  70. 70.

    Kodama H, Murata S, Ishida M, Yamamoto H, Yamaguchi T, Kaida S, et al. Prognostic impact of CD44-positive cancer stem-like cells at the invasive front of gastric cancer. Br J Cancer. 2017;116:186–94.

  71. 71.

    Chanmee T, Ontong P, Kimata K, Itano N. Key roles of hyaluronan and its CD44 receptor in the stemness and survival of cancer stem cells. Front Oncol. 2015;5:180.

  72. 72.

    Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. J Clin Oncol. 2015;33:2863–9.

  73. 73.

    Storti P, Marchica V, Airoldi I, Donofrio G, Fiorini E, Ferri V, et al. Galectin-1 suppression delineates a new strategy to inhibit myeloma-induced angiogenesis and tumoral growth in vivo. Leukemia. 2016;30:2351–63.

  74. 74.

    Takagi S, Tsukamoto S, Park J, Johnson KE, Kawano Y, Moschetta M, et al. Platelets enhance multiple myeloma progression via il-1b upregulation. Clin Cancer Res. 2018;24:2430–9.

  75. 75.

    Leblanc R, Peyruchaud O. Metastasis: new functional implications of platelets and megakaryocytes. Blood. 2016;128:24–31.

  76. 76.

    Dongre A, Weinberg RA. New insights into the mechanisms of epithelial–mesenchymal transition and implications for cancer. Nat Rev Mol Cell Biol. 2019;20:69–84.

  77. 77.

    Batlle E, Clevers H. Cancer stem cells revisited. Nat Med. 2017;23:1124–34.

  78. 78.

    Zhang H, Brown RL, Wei Y, Zhao P, Liu S, Liu X, et al. CD44 splice isoform switching determines breast cancer stem cell state. Genes Dev. 2019;33:1–14.

  79. 79.

    Misra S, Heldin P, Hascall VC, Karamanos NK, Skandalis SS, Markwald RR, et al. Hyaluronan-CD44 interactions as potential targets for cancer therapy. FEBS J. 2011;278:1429–43.

  80. 80.

    Mueller N, Wicklein D, Eisenwort G, Jawhar M, Berger D, Stefanzl G, et al. CD44 is a RAS/STAT5-regulated invasion receptor that triggers disease expansion in advanced mastocytosis. Blood. 2018;132:1936–50.

  81. 81.

    Bjorklund CC, Baladandayuthapani V, Lin HY, Jones RJ, Kuiatse I, Wang H, et al. Evidence of a role for CD44 and cell adhesion in mediating resistance to lenalidomide in multiple myeloma: therapeutic implications. Leukemia. 2014;28:373–83.

  82. 82.

    Yan Y, Zuo X, Wei D. Concise review: emerging role of CD44 in cancer stem cells: a promising biomarker and therapeutic target. Stem Cells Transl Med. 2015;4:1033–43.

  83. 83.

    Natoni A, Smith TAG, Keane N, McEllistrim C, Connolly C, Jha A, et al. E-selectin ligands recognised by HECA452 induce drug resistance in myeloma, which is overcome by the E-selectin antagonist, GMI-1271. Leukemia. 2017;31:2642–51.

  84. 84.

    Lytle NK, Barber AG, Reya T. Stem cell fate in cancer growth, progression and therapy resistance. Nat Rev Cancer. 2018;18:669–80.

  85. 85.

    Roccaro AM, Mishima Y, Sacco A, Moschetta M, Tai Y-T, Shi J, et al. CXCR4 regulates extra-medullary myeloma through epithelial-mesenchymal-transition-like transcriptional activation. Cell Rep. 2015;12:622–35.

  86. 86.

    Peacock CD, Wang Q, Gesell GS, Corcoran-Schwartz IM, Jones E, Kim J, et al. Hedgehog signaling maintains a tumor stem cell compartment in multiple myeloma. Proc Natl Acad Sci USA 2007;104:4048–53.

  87. 87.

    Matsui W, Huff CA, Wang Q, Malehorn MT, Barber J, Tanhehco Y, et al. Characterization of clonogenic multiple myeloma cells. Blood. 2004;103:2332–6.

  88. 88.

    Thiago LS, Perez-Andres M, Balanzategui A, Sarasquete ME, Paiva B, Jara-Acevedo M, et al. Circulating clonotypic B cells in multiple myeloma and monoclonal gammopathy of undetermined significance. Haematologica. 2014;99:155–62.

  89. 89.

    Li H, Zhong A, Li S, Meng X, Wang X, Xu F, et al. The integrated pathway of TGFβ/Snail with TNFα/NFκB may facilitate the tumor-stroma interaction in the EMT process and colorectal cancer prognosis. Sci Rep. 2017;7:4915.

  90. 90.

    Manzi M, Bacigalupo ML, Carabias P, Elola MT, Wolfenstein-Todel C, Rabinovich GA, et al. Galectin-1 controls the proliferation and migration of liver sinusoidal endothelial cells and their interaction with hepatocarcinoma cells. J Cell Physiol. 2016;231:1522–33.

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We would like to thank to the patients and their families who participated in this study.


This study was supported by the Centro de Investigación Biomédica en Red —Área de Oncología— del Instituto de Salud Carlos III (CIBERONC; CB16/12/00369, CB16/12/00489, and CB16/12/00400), Cancer Research UK, FCAECC and AIRC under the Accelerator Award Programme, Instituto de Salud Carlos III and Asociación Española Contra el Cáncer by ERA-NET TRANSCAN-2 Programme (AC17/00101), the Black Swan Research Initiative of the International Myeloma Foundation, the European Research Council (ERC) 2015 Starting Grant (MYELOMANEXT, 680200), the Czech Science Foundation through Project No. 19-25354Y, the European Regional Development Fund—Project ENOCH (No. CZ.02.1.01/0.0/0.0/16_019/0000868), and the Ministry of Health of the Czech Republic (15-29667A).

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RH, JFSM, and BP conceived the idea and designed the study protocol; XA, LP, FP, RR, JML, PM, LP, RdO, APM, JFM, LSF, TJ, HEO, JK, AO, RH, and JFSM provided study material and patients; LB and BP analyzed flow cytometry data; DA, SG, and RB performed cell sorting; JJG, LB, KG, and ZC extracted the samples and processed the arrays; MV, DA, and SG executed the single-cell RNA-seq experiment; JJG, MV, LB, IG, TS, and CB did/supervised the bioinformatics processing and JJG, MS, MV, KG, and BP analyzed and interpreted data; in vitro experiments were performed by KG, AV, LB, ML, and PM. JJG, MS, MV, and BP wrote the paper and all authors reviewed and approved the paper.

Correspondence to Bruno Paiva.

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Garcés, J., Simicek, M., Vicari, M. et al. Transcriptional profiling of circulating tumor cells in multiple myeloma: a new model to understand disease dissemination. Leukemia 34, 589–603 (2020).

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