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.

Multiple myeloma gammopathies

The future of myeloma precision medicine: integrating the compendium of known drug resistance mechanisms with emerging tumor profiling technologies

Abstract

Multiple myeloma (MM) is a hematologic malignancy that is considered mostly incurable in large part due to the inability of standard of care therapies to overcome refractory disease and inevitable drug-resistant relapse. The post-genomic era has been a productive period of discovery where modern sequencing methods have been applied to large MM patient cohorts to modernize our current perception of myeloma pathobiology and establish an appreciation for the vast heterogeneity that exists between and within MM patients. Numerous pre-clinical studies conducted in the last two decades have unveiled a compendium of mechanisms by which malignant plasma cells can escape standard therapies, many of which have potentially quantifiable biomarkers. Exhaustive pre-clinical efforts have evaluated countless putative anti-MM therapeutic agents and many of these have begun to enter clinical trial evaluation. While the palette of available anti-MM therapies is continuing to expand it is also clear that malignant plasma cells still have mechanistic avenues by which they can evade even the most promising new therapies. It is therefore becoming increasingly clear that there is an outstanding need to develop and employ precision medicine strategies in MM management that harness emerging tumor profiling technologies to identify biomarkers that predict efficacy or resistance within an individual’s sub-clonally heterogeneous tumor. In this review we present an updated overview of broad classes of therapeutic resistance mechanisms and describe selected examples of putative biomarkers. We also outline several emerging tumor profiling technologies that have the potential to accurately quantify biomarkers for therapeutic sensitivity and resistance at genomic, transcriptomic and proteomic levels. Finally, we comment on the future of implementation for precision medicine strategies in MM and the clear need for a paradigm shift in clinical trial design and disease management.

This is a preview of subscription content

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
Fig. 2

References

  1. 1.

    Dimopoulos MA, Facon T, Terpos E. Multiple myeloma and other plasma cell neoplasms. New York: Springer; 2018.

  2. 2.

    Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. Cancer J Clin. 2017;67:7–30.

    Google Scholar 

  3. 3.

    Kuehl WM, Bergsagel PL. Multiple myeloma: evolving genetic events and host interactions. Nat Rev Cancer. 2002;2:175–87.

    CAS  PubMed  Google Scholar 

  4. 4.

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

    PubMed  Google Scholar 

  5. 5.

    Guang MHZ, McCann A, Bianchi G, Zhang L, Dowling P, Bazou D, et al. Overcoming multiple myeloma drug resistance in the era of cancer ‘omics’. Leuk Lymphoma. 2018;59:542–61.

    CAS  PubMed  Google Scholar 

  6. 6.

    Bolli N, Avet-Loiseau H, Wedge DC, Van Loo P, Alexandrov LB, Martincorena I, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun. 2014;5:2997.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Walker BA, Boyle EM, Wardell CP, Murison A, Begum DB, Dahir NM, et al. Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J Clin Oncol. 2015;33:3911–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Walker BA, Wardell CP, Melchor L, Brioli A, Johnson DC, Kaiser MF, et al. Intraclonal heterogeneity is a critical early event in the development of myeloma and precedes the development of clinical symptoms. Leukemia. 2014;28:384–90.

    PubMed  Google Scholar 

  9. 9.

    Bolli N, Li Y, Sathiaseelan V, Raine K, Jones D, Ganly P, et al. A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma. Blood Cancer J. 2016;6:e467.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471:467–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lohr JG, Stojanov P, Carter SL, Cruz-Gordillo P, Lawrence MS, Auclair D, et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell. 2014;25:91–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hoang PH, Dobbins SE, Cornish AJ, Chubb D, Law PJ, Kaiser M, et al. Whole-genome sequencing of multiple myeloma reveals oncogenic pathways are targeted somatically through multiple mechanisms. Leukemia 2018; https://doi.org/10.1038/s41375-018-0103-3.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple myeloma. Nat Rev Cancer. 2012;12:335–48.

    CAS  Google Scholar 

  14. 14.

    Manier S, Salem KZ, Park J, Landau DA, Getz G, Ghobrial IM. Genomic complexity of multiple myeloma and its clinical implications. Nat Rev Clin Oncol. 2017;14:100–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120:1067–76.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Weinhold N, Ashby C, Rasche L, Chavan SS, Stein C, Stephens OW, et al. Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma. Blood. 2016;128:1735–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Egan JB, Shi C-X, Tembe W, Christoforides A, Kurdoglu A, Sinari S, et al. Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood. 2012;120:1060–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Melchor L, Brioli A, Wardell CP, Murison A, Potter NE, Kaiser MF, et al. Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia. 2014;28:1705–15.

    CAS  Google Scholar 

  19. 19.

    Corre J, Cleynen A, Robiou du Pont S, Buisson L, Bolli N, Attal M, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia 2018; https://doi.org/10.1038/s41375-018-0153-6.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Gao M, Kong Y, Yang G, Gao L, Shi J. Multiple myeloma cancer stem cells. Oncotarget. 2016;7:35466–77.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Dimopoulos K, Gimsing P, Grønbæk K. The role of epigenetics in the biology of multiple myeloma. Blood Cancer J. 2014;4:e207–e207.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Alzrigat M, Párraga AA, Jernberg-Wiklund H. Epigenetics in multiple myeloma: From mechanisms to therapy. Semin Cancer Biol 2017; https://doi.org/10.1016/J.SEMCANCER.2017.09.007.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Issa ME, Takhsha FS, Chirumamilla CS, Perez-Novo C, Vanden Berghe W, Cuendet M. Epigenetic strategies to reverse drug resistance in heterogeneous multiple myeloma. Clin Epigenetics. 2017;9:17.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Dupéré-Richer D, Licht JD. Epigenetic regulatory mutations and epigenetic therapy for multiple myeloma. Curr Opin Hematol. 2017;24:336–44.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Alexanian R, Haut A, Khan AU, Lane M, McKelvey EM, Migliore PJ, et al. Treatment for multiple myeloma. Combination chemotherapy with different melphalan dose regimens. JAMA. 1969;208:1680–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Attal M, Harousseau J-L, Stoppa A-M, Sotto J-J, Fuzibet J-G, Rossi J-F, et al. A prospective, randomized trial of autologous bone marrow transplantation and chemotherapy in multiple myeloma. N Engl J Med. 1996;335:91–97.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Child JA, Morgan GJ, Davies FE, Owen RG, Bell SE, Hawkins K, et al. High-dose chemotherapy with hematopoietic stem-cell rescue for multiple myeloma. N Engl J Med. 2003;348:1875–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Kumar SK, Dispenzieri A, Lacy MQ, Gertz MA, Buadi FK, Pandey S, et al. Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients. Leukemia. 2014;28:1122–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    McKeage K. Daratumumab: first global approval. Drugs. 2016;76:275–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Magen H, Muchtar E. Elotuzumab: the first approved monoclonal antibody for multiple myeloma treatment. Ther Adv Hematol. 2016;7:187–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Nass J, Efferth T. Drug targets and resistance mechanisms in multiple myeloma. Cancer Drug Resist. 2018. https://doi.org/10.20517/cdr.2018.04.

    Article  Google Scholar 

  32. 32.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Nijhof IS, van de Donk NWCJ, Zweegman S, Lokhorst HM. Current and new therapeutic strategies for relapsed and refractory multiple myeloma: an update. Drugs. 2018;78:19–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Rajkumar SV, Harousseau JL, Durie B, Anderson KC, Dimopoulos M, Kyle R. Consensus recommendations for the uniform reporting of clinical trials: report of the International Myeloma Workshop Consensus Panel 1. Blood. 2011;117:4691–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Alessandrini M, Chaudhry M, Dodgen TM, Pepper MS. Pharmacogenomics and global precision medicine in the context of adverse drug reactions: top 10 opportunities and challenges for the next decade. OMICS. 2016;20:593–603.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92:414–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Gupta N, Hanley MJ, Venkatakrishnan K, Bessudo A, Rasco DW, Sharma S, et al. Effects of strong CYP3A inhibition and induction on the pharmacokinetics of ixazomib, an oral proteasome inhibitor: results of drug-drug interaction studies in patients with advanced solid tumors or lymphoma and a physiologically based pharmacokinetic ana. J Clin Pharmacol. 2018;58:180–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Hassen W, Kassambara A, Reme T, Sahota S, Seckinger A, Vincent L, et al. Drug metabolism and clearance system in tumor cells of patients with multiple myeloma. Oncotarget. 2015;6:6431–47.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Argyriou AA, Bruna J, Genazzani AA, Cavaletti G. Chemotherapy-induced peripheral neurotoxicity: management informed by pharmacogenetics. Nat Rev Neurol. 2017;13:492–504.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Campo C, Da Silva Filho MI, Weinhold N, Goldschmidt H, Hemminki K, Merz M, et al. Genetic susceptibility to bortezomib-induced peripheral neuroropathy: replication of the reported candidate susceptibility loci. Neurochem Res. 2017;42:925–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Vangsted A, Klausen TW, Vogel U. Genetic variations in multiple myeloma II: association with effect of treatment. Eur J Haematol. 2012; https://doi.org/10.1111/j.1600-0609.2011.01696.x.

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Lendvai N, Tsakos I, Devlin SM, Schaffer WL, Hassoun H, Lesokhin AM, et al. Predictive biomarkers and practical considerations in the management of carfilzomib-associated cardiotoxicity. Leuk Lymphoma. 2018;59:1981–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Giglia JL, White MJ, Hart AJ, Toro JJ, Freytes CO, Holt CC, et al. A single nucleotide polymorphism in SLC7A5 is associated with gastrointestinal toxicity after high-dose melphalan and autologous stem cell transplantation for multiple myeloma. Biol Blood Marrow Transplant. 2014;20:1014–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Grogan T, Dalton W, Rybski J, Spier C, Meltzer P, Richter L, et al. Optimization of immunocytochemical P-glycoprotein assessment in multidrug-resistant plasma cell myeloma using three antibodies. Lab Invest. 1990;63:815–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Dalton WS, Grogan TM, Meltzer PS, Scheper RJ, Durie BG, Taylor CW, et al. Drug-resistance in multiple myeloma and non-Hodgkin’s lymphoma: detection of P-glycoprotein and potential circumvention by addition of verapamil to chemotherapy. J Clin Oncol. 1989;7:415–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Abraham J, Salama NN, Azab AK. The role of P-glycoprotein in drug resistance in multiple myeloma. Leuk Lymphoma. 2015;56:26–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    O’Connor R, Ooi MG, Meiller J, Jakubikova J, Klippel S, Delmore J, et al. The interaction of bortezomib with multidrug transporters: implications for therapeutic applications in advanced multiple myeloma and other neoplasias. Cancer Chemother Pharmacol. 2013;71:1357–68.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Hawley TS, Riz I, Yang W, Wakabayashi Y, DePalma L, Chang Y-T, et al. Identification of an ABCB1 (P-glycoprotein)-positive carfilzomib-resistant myeloma subpopulation by the pluripotent stem cell fluorescent dye CDy1. Am J Hematol. 2013;88:265–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Zhou W, Yang Y, Xia J, Wang H, Salama ME, Xiong W, et al. NEK2 induces drug resistance mainly through activation of efflux drug pumps and is associated with poor prognosis in myeloma and other cancers. Cancer Cell. 2013;23:48–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Besse A, Stolze SC, Rasche L, Weinhold N, Morgan GJ, Kraus M, et al. Carfilzomib resistance due to ABCB1/MDR1 overexpression is overcome by nelfinavir and lopinavir in multiple myeloma. Leukemia. 2018;32:391–401.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Mondello P, Cuzzocrea S, Navarra M, Mian M. Bone marrow micro-environment is a crucial player for myelomagenesis and disease progression. Oncotarget. 2017;8:20394–409.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Shay G, Hazlehurst L, Lynch CC. Dissecting the multiple myeloma-bone microenvironment reveals new therapeutic opportunities. J Mol Med. 2016;94:21–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Di Marzo L, Desantis V, Solimando AG, Ruggieri S, Annese T, Nico B, et al. Microenvironment drug resistance in multiple myeloma: emerging new players. Oncotarget. 2016;7:60698–711.

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hope C, Ollar SJ, Heninger E, Hebron E, Jensen JL, Kim J, et al. TPL2 kinase regulates the inflammatory milieu of the myeloma niche. Blood. 2014;123:3305–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Wader KF, Fagerli UM, Holt RU, Stordal B, Børset M, Sundan A, et al. Elevated serum concentrations of activated hepatocyte growth factor activator in patients with multiple myeloma. Eur J Haematol. 2008;81:380–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Kuhn DJ, Berkova Z, Jones RJ, Woessner R, Bjorklund CC, Ma W, et al. Targeting the insulin-like growth factor-1 receptor to overcome bortezomib resistance in preclinical models of multiple myeloma. Blood. 2012;120:3260–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Spencer A, Yoon S-S, Harrison SJ, Morris SR, Smith DA, Brigandi RA, et al. The novel AKT inhibitor afuresertib shows favorable safety, pharmacokinetics, and clinical activity in multiple myeloma. Blood. 2014;124:2190–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Yang Y, Shi J, Gu Z, Salama ME, Das S, Wendlandt E, et al. Bruton tyrosine kinase is a therapeutic target in stem-like cells from multiple myeloma. Cancer Res. 2015;75:594–604.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Roccaro AM, Sacco A, Purschke WG, Moschetta M, Buchner K, Maasch C, et al. SDF-1 inhibition targets the bone marrow niche for cancer therapy. Cell Rep. 2014;9:118–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Moreaux J, Cremer FW, Reme T, Raab M, Mahtouk K, Kaukel P, et al. The level of TACI gene expression in myeloma cells is associated with a signature of microenvironment dependence versus a plasmablastic signature. Blood. 2005;106:1021–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Chen J, He D, Chen Q, Guo X, Yang L, Lin X, et al. BAFF is involved in macrophage-induced bortezomib resistance in myeloma. Cell Death Dis. 2017;8:e3161.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Tai Y-T, Acharya C, An G, Moschetta M, Zhong MY, Feng X, et al. APRIL and BCMA promote human multiple myeloma growth and immunosuppression in the bone marrow microenvironment. Blood. 2016;127:3225–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Wang J, Faict S, Maes K, De Bruyne E, Van Valckenborgh E, Schots R, et al. Extracellular vesicle cross-talk in the bone marrow microenvironment: implications in multiple myeloma. Oncotarget. 2016;7:38927–45.

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Wang J, De Veirman K, Faict S, Frassanito MA, Ribatti D, Vacca A, et al. Multiple myeloma exosomes establish a favourable bone marrow microenvironment with enhanced angiogenesis and immunosuppression. J Pathol. 2016;239:162–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Vallabhaneni KC, Penfornis P, Dhule S, Guillonneau F, Adams KV, Mo YY, et al. Extracellular vesicles from bone marrow mesenchymal stem/stromal cells transport tumor regulatory microRNA, proteins, and metabolites. Oncotarget. 2015;6:4953–67.

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Wang J, Hendrix A, Hernot S, Lemaire M, De Bruyne E, Van Valckenborgh E, et al. Bone marrow stromal cell-derived exosomes as communicators in drug resistance in multiple myeloma cells. Blood. 2014;124:555–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Yamamoto T, Kosaka N, Hattori Y, Ochiya T. A challenge to aging society by microRNA in extracellular vesicles: microRNA in extracellular vesicles as promising biomarkers and novel therapeutic targets in multiple myeloma. J Clin Med. 2018; https://doi.org/10.3390/jcm7030055.

    Google Scholar 

  70. 70.

    Nefedova Y, Cheng P, Alsina M, Dalton WS, Gabrilovich DI. Involvement of Notch-1 signaling in bone marrow stroma-mediated de novo drug resistance of myeloma and other malignant lymphoid cell lines. Blood. 2004;103:3503–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Gandolfi S, Laubach JP, Hideshima T, Chauhan D, Anderson KC, Richardson PG. The proteasome and proteasome inhibitors in multiple myeloma. Cancer Metastas. 2017;36:561–84.

    CAS  Google Scholar 

  72. 72.

    Grigoreva TA, Tribulovich VG, Garabadzhiu AV, Melino G, Barlev NA. The 26S proteasome is a multifaceted target for anti-cancer therapies. Oncotarget. 2015;6:24733–49.

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Shah C, Bishnoi R, Wang Y, Zou F, Bejjanki H, Master S, et al. Efficacy and safety of carfilzomib in relapsed and/or refractory multiple myeloma: systematic review and meta-analysis of 14 trials. Oncotarget. 2018;9:23704–17.

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Tsakiri EN, Trougakos IP. The amazing ubiquitin-proteasome system: structural components and implication in aging. Int Rev Cell Mol Biol. 2015;314:171–237.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Kisselev AF, Goldberg AL. Proteasome inhibitors: from research tools to drug candidates. Chem Biol. 2001;8:739–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Obeng EA, Carlson LM, Gutman DM, Harrington WJ, Lee KP, Boise LH, et al. Proteasome inhibitors induce a terminal unfolded protein response in multiple myeloma cells. Blood. 2006;107:4907–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Wallington-Beddoe CT, Sobieraj-Teague M, Kuss BJ, Pitson SM. Resistance to proteasome inhibitors and other targeted therapies in myeloma. Br J Haematol. 2018; https://doi.org/10.1111/bjh.15210.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Barrio S, Stühmer T, Da-Viá M, Barrio-Garcia C, Lehners N, Besse A, et al. Spectrum and functional validation of PSMB5 mutations in multiple myeloma. Leukemia 2018; https://doi.org/10.1038/s41375-018-0216-8.

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Oerlemans R, Franke NE, Assaraf YG, Cloos J, van Zantwijk I, Berkers CR, et al. Molecular basis of bortezomib resistance: proteasome subunit 5 (PSMB5) gene mutation and overexpression of PSMB5 protein. Blood. 2008;112:2489–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Li B, Fu J, Chen P, Ge X, Li Y, Kuiatse I, et al. The nuclear factor (Erythroid-derived 2)-like 2 and proteasome maturation protein axis mediate bortezomib resistance in multiple myeloma. J Biol Chem. 2015;290:29854–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Dytfeld D, Luczak M, Wrobel T, Usnarska-Zubkiewicz L, Brzezniakiewicz K, Jamroziak K, et al. Comparative proteomic profiling of refractory/relapsed multiple myeloma reveals biomarkers involved in resistance to bortezomib-based therapy. Oncotarget. 2016;7:56726–36.

    PubMed  PubMed Central  Google Scholar 

  82. 82.

    Rastgoo N, Abdi J, Hou J, Chang H. Role of epigenetics-microRNA axis in drug resistance of multiple myeloma. J Hematol Oncol. 2017;10:121.

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Zhang L, Fok JHL, Davies FE. Heat shock proteins in multiple myeloma. Oncotarget. 2014;5:1132–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Adomako A, Calvo V, Biran N, Osman K, Chari A, Paton JC, et al. Identification of markers that functionally define a quiescent multiple myeloma cell sub-population surviving bortezomib treatment. BMC Cancer. 2015;15:444.

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Mitsiades CS, Mitsiades NS, McMullan CJ, Poulaki V, Kung AL, Davies FE, et al. Antimyeloma activity of heat shock protein-90 inhibition. Blood. 2005;107:1092–1100.

    PubMed  Google Scholar 

  86. 86.

    Hamouda M-A, Belhacene N, Puissant A, Colosetti P, Robert G, Jacquel A, et al. The small heat shock protein B8 (HSPB8) confers resistance to bortezomib by promoting autophagic removal of misfolded proteins in multiple myeloma cells. Oncotarget. 2014;5:6252–66.

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark Even Warburg did not anticipate. Cancer Cell. 2012;21:297–308.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Maiso P, Huynh D, Moschetta M, Sacco A, Aljawai Y, Mishima Y, et al. Metabolic signature identifies novel targets for drug resistance in multiple myeloma. Cancer Res. 2015;75:2071–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Zaal EA, Wu W, Jansen G, Zweegman S, Cloos J, Berkers CR. Bortezomib resistance in multiple myeloma is associated with increased serine synthesis. Cancer Metab. 2017;5:7.

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Soriano GP, Besse L, Li N, Kraus M, Besse A, Meeuwenoord N, et al. Proteasome inhibitor-adapted myeloma cells are largely independent from proteasome activity and show complex proteomic changes, in particular in redox and energy metabolism. Leukemia. 2016;30:2198–207.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Raninga PV, Di Trapani G, Vuckovic S, Bhatia M, Tonissen KF. Inhibition of thioredoxin 1 leads to apoptosis in drug-resistant multiple myeloma. Oncotarget. 2015;6:15410–24.

    PubMed  PubMed Central  Google Scholar 

  92. 92.

    Dytfeld D, Rosebeck S, Kandarpa M, Mayampurath A, Mellacheruvu D, Alonge MM, et al. Proteomic profiling of naïve multiple myeloma patient plasma cells identifies pathways associated with favourable response to bortezomib-based treatment regimens. Br J Haematol. 2015;170:66–79.

    CAS  PubMed  Google Scholar 

  93. 93.

    Zheng Z, Fan S, Zheng J, Huang W, Gasparetto C, Chao NJ, et al. Inhibition of thioredoxin activates mitophagy and overcomes adaptive bortezomib resistance in multiple myeloma. J Hematol Oncol. 2018;11:29.

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Leung-Hagesteijn C, Erdmann N, Cheung G, Keats JJ, Stewart AK, Reece DE. Xbp1s-negative tumor B cells and pre-plasmablasts mediate therapeutic proteasome inhibitor resistance in multiple myeloma. Cancer Cell. 2013;24:289–304.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Paiva B, Puig N, Cedena MT, de Jong BG, Ruiz Y, Rapado I, et al. Differentiation stage of myeloma plasma cells: biological and clinical significance. Leukemia. 2017;31:382–92.

    CAS  PubMed  Google Scholar 

  96. 96.

    Mimura N, Fulciniti M, Gorgun G, Tai Y-T, Cirstea D, Santo L, et al. Blockade of XBP1 splicing by inhibition of IRE1 is a promising therapeutic option in multiple myeloma. Blood. 2012;119:5772–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Alonso S, Hernandez D, Chang Y-T, Gocke CB, McCray M, Varadhan R, et al. Hedgehog and retinoid signaling alters multiple myeloma microenvironment and generates bortezomib resistance. J Clin Invest. 2016;126:4460–8.

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Chen Q, Van der Sluis PC, Boulware D, Hazlehurst LA, Dalton WS. The FA/BRCA pathway is involved in melphalan-induced DNA interstrand cross-link repair and accounts for melphalan resistance in multiple myeloma cells. Blood. 2005;106:698–705.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Sousa MML, Zub KA, Aas PA, Hanssen-Bauer A, Demirovic A, Sarno A, et al. An inverse switch in DNA base excision and strand break repair contributes to melphalan resistance in multiple myeloma cells. PLoS One. 2013;8:e55493.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    XIONG T, WEI H, CHEN X, XIAO H. PJ34, a poly(ADP-ribose) polymerase (PARP) inhibitor, reverses melphalan-resistance and inhibits repair of DNA double-strand breaks by targeting the FA/BRCA pathway in multidrug resistant multiple myeloma cell line RPMI8226/R. Int J Oncol. 2015;46:223–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Marchesini M, Ogoti Y, Fiorini E, Aktas Samur A, Nezi L, D’Anca M, et al. ILF2 is a regulator of RNA splicing and DNA damage response in 1q21-amplified multiple myeloma. Cancer Cell. 2017;32:88–100.e6.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Sundahl N, Clarisse D, Bracke M, Offner F, B W Vanden, Beck IM. Selective glucocorticoid receptor-activating adjuvant therapy in cancer treatments. Oncoscience. 2016;3:188–202.

    PubMed  PubMed Central  Google Scholar 

  103. 103.

    Kfir-Erenfeld S, Yefenof E. Non-genomic events determining the sensitivity of hemopoietic malignancies to glucocorticoid-induced apoptosis. Cancer Immunol Immunother. 2014;63:37–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Ratman D, Vanden Berghe W, Dejager L, Libert C, Tavernier J, Beck IM, et al. How glucocorticoid receptors modulate the activity of other transcription factors: a scope beyond tethering. Mol Cell Endocrinol. 2013;380:41–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Moalli Pa, Pillay S, Weiner D, Leikin R, Rosen ST. A mechanism of resistance to glucocorticoids in multiple myeloma: transient expression of a truncated glucocorticoid receptor mRNA. Blood. 1992;79:213–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Sánchez-Vega B, Gandhi V. Glucocorticoid resistance in a multiple myeloma cell line is regulated by a transcription elongation block in the glucocorticoid receptor gene (NR3C1). Br J Haematol. 2009;144:856–64.

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Clarisse D, Thommis J, Van Wesemael K, Houtman R, Ratman D, Tavernier J, et al. Coregulator profiling of the glucocorticoid receptor in lymphoid malignancies. Oncotarget. 2017;8:109675–91.

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Kronke J, Udeshi ND, Narla A, Grauman P, Hurst SN, McConkey M, et al. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science. 2014;343:301–5.

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Kortum KM, Mai EK, Hanafiah NH, Shi C-X, Zhu Y-X, Bruins L, et al. Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and Ras pathway genes. Blood. 2016;128:1226–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110.

    Barrio Garcia S, Da Via’ M, Garitano-Trojaola A, Ruiz-Heredia Y, Bittrich M, Shi C, et al. IKZF1/3 and CRL4CRBN E3 ubiquitin ligase mutations associate with IMiD resistance in relapsed multiple myeloma. Blood Suppl. 2017;130:270.

  111. 111.

    Dimopoulos K, Søgaard Helbo A, Fibiger Munch-Petersen H, Sjö L, Christensen J, Sommer Kristensen L, et al. Dual inhibition of DNMTs and EZH2 can overcome both intrinsic and acquired resistance of myeloma cells to IMiDs in a cereblon-independent manner. Mol Oncol. 2018;12:180–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Heintel D, Rocci A, Ludwig H, Bolomsky A, Caltagirone S, Schreder M, et al. High expression of cereblon (CRBN) is associated with improved clinical response in patients with multiple myeloma treated with lenalidomide and dexamethasone. Br J Haematol. 2013;161:695–700.

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Bedewy AML, EL-Maghraby SM. Do baseline cereblon gene expression and IL-6 receptor expression determine the response to thalidomide-dexamethasone treatment in multiple myeloma patients? Eur J Haematol. 2014;92:13–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Schuster SR, Kortuem KM, Zhu YX, Braggio E, Shi C-X, Bruins LA, et al. The clinical significance of cereblon expression in multiple myeloma. Leuk Res. 2014;38:23–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115.

    Liu J, Song T, Zhou W, Xing L, Wang S, Ho M, et al. A genome-scale CRISPR-Cas9 screening in myeloma cells identifies regulators of immunomodulatory drug sensitivity. Leukemia 2018; https://doi.org/10.1038/s41375-018-0205-y.

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Nijhof IS, Casneuf T, van Velzen J, van Kessel B, Axel AE, Syed K, et al. CD38 expression and complement inhibitors affect response and resistance to daratumumab therapy in myeloma. Blood. 2016;128:959–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117.

    Krejcik J, Frerichs KA, Nijhof IS, van Kessel B, van Velzen JF, Bloem AC, et al. Monocytes and granulocytes reduce CD38 expression levels on myeloma cells in patients treated with daratumumab. Clin Cancer Res. 2017;23:7498–511.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Nijhof IS, Groen RWJ, Lokhorst HM, van Kessel B, Bloem AC, van Velzen J, et al. Upregulation of CD38 expression on multiple myeloma cells by all-trans retinoic acid improves the efficacy of daratumumab. Leukemia. 2015;29:2039–49.

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119.

    Zonder JA, Mohrbacher AF, Singhal S, van Rhee F, Bensinger WI, Ding H, et al. A phase 1, multicenter, open-label, dose escalation study of elotuzumab in patients with advanced multiple myeloma. Blood. 2012;120:552–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Lonial S, Weiss BM, Usmani SZ, Singhal S, Chari A, Bahlis NJ, et al. Daratumumab monotherapy in patients with treatment-refractory multiple myeloma (SIRIUS): an open-label, randomised, phase 2 trial. Lancet. 2016;387:1551–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Funaro A, Horenstein AL, Calosso L, Morra M, Tarocco RP, Franco L, et al. Identification and characterization of an active soluble form of human CD38 in normal and pathological fluids. Int Immunol. 1996;8:1643–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Tai Y-T, Dillon M, Song W, Leiba M, Li X-F, Burger P, et al. Anti-CS1 humanized monoclonal antibody HuLuc63 inhibits myeloma cell adhesion and induces antibody-dependent cellular cytotoxicity in the bone marrow milieu. Blood. 2008;112:1329–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Harding T, Swanson J, Van Ness B, Harding T, Swanson J, Van Ness B, et al. EZH2 inhibitors sensitize myeloma cell lines to panobinostat resulting in unique combinatorial transcriptomic changes. Oncotarget. 2018;9:21930–42.

    PubMed  PubMed Central  Google Scholar 

  124. 124.

    Rizq O, Mimura N, Oshima M, Saraya A, Koide S, Kato Y, et al. Dual inhibition of EZH2 and EZH1 sensitizes PRC2-dependent tumors to proteasome inhibition. Clin Cancer Res. 2017;23:4817–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Zeng D, Liu M, Pan J. Blocking EZH2 methylation transferase activity by GSK126 decreases stem cell-like myeloma cells. Oncotarget. 2016;8:3396–411.

    Google Scholar 

  126. 126.

    Alzrigat M, Párraga AA, Agarwal P, Zureigat H, Österborg A, Nahi H, et al. EZH2 inhibition in multiple myeloma downregulates myeloma associated oncogenes and upregulates microRNAs with potential tumor suppressor functions. Oncotarget. 2016;8:10213–24.

    Google Scholar 

  127. 127.

    Zhu B, Ju S, Chu H, Shen X, Zhang Y, Luo X, et al. The potential function of microRNAs as biomarkers and therapeutic targets in multiple myeloma. Oncol Lett. 2018;15:6094–106.

    PubMed  PubMed Central  Google Scholar 

  128. 128.

    Rajan AM, Rajkumar SV. Interpretation of cytogenetic results in multiple myeloma for clinical practice. Blood Cancer J. 2015;5:e365.

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Vu T, Gonsalves W, Kumar S, Dispenzieri A, Lacy MQ, Buadi F, et al. Characteristics of exceptional responders to lenalidomide-based therapy in multiple myeloma. Blood Cancer J. 2015;5:e363.

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B, Shaughnessy J. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005;106:296–303.

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S, et al. The molecular classification of multiple myeloma. Blood. 2006;108:2020–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    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. Blood. 2010;116:2543–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  133. 133.

    Chng WJ, Dispenzieri A, Chim C-S, Fonseca R, Goldschmidt H, Lentzsch S, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28:269–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135.

    Dingli D, Ailawadhi S, Bergsagel PL, Buadi FK, Dispenzieri A, Fonseca R, et al. Therapy for relapsed multiple myeloma: guidelines from the mayo stratification for myeloma and risk-adapted therapy. Mayo Clin Proc. 2017;92:578–98.

    PubMed  PubMed Central  Google Scholar 

  136. 136.

    Shaughnessy JD, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I, et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109:2276–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  137. 137.

    Decaux O, Lodé L, Magrangeas F, Charbonnel C, Gouraud W, Jézéquel P, et al. Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: A Study of the Intergroupe Francophone du Myélome. J Clin Oncol. 2008;26:4798–805.

    CAS  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Moreaux J, Klein B, Bataille R, Descamps G, Maiga S, Hose D. A high-risk signature for patients with multiple myeloma established from the molecular classification of human myeloma cell lines. Haematologica. 2011;96:574–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Kuiper R, Broyl A, de Knegt Y, van Vliet MH, van Beers EH, van der Holt B, et al. A gene expression signature for high-risk multiple myeloma. Leukemia. 2012;26:2406–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Chung T-H, Mulligan G, Fonseca R, Chng WJ. A novel measure of chromosome instability can account for prognostic difference in multiple myeloma. PLoS One. 2013;8:e66361.

    CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Chng WJ, Braggio E, Mulligan G, Bryant B, Remstein E, Valdez R, et al. The centrosome index is a powerful prognostic marker in myeloma and identifies a cohort of patients that might benefit from aurora kinase inhibition. Blood. 2007;111:1603–9.

    PubMed  PubMed Central  Google Scholar 

  142. 142.

    Dickens NJ, Walker BA, Leone PE, Johnson DC, Brito JL, Zeisig A, et al. Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome. Clin Cancer Res. 2010;16:1856–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143.

    Hose D, Reme T, Hielscher T, Moreaux J, Messner T, Seckinger A, et al. Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma. Haematologica. 2011;96:87–95.

    PubMed  PubMed Central  Google Scholar 

  144. 144.

    Corre J, Cleynen A, Robiou du Pont S, Buisson L, Bolli N, Attal M, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia. 2018;32:2635–47.

    PubMed  PubMed Central  Google Scholar 

  145. 145.

    Kortüm KM, Langer C, Monge J, Bruins L, Egan JB, Zhu YX, et al. Targeted sequencing using a 47 gene multiple myeloma mutation panel (M 3 P) in -17p high risk disease. Br J Haematol. 2015;168:507–10.

    PubMed  PubMed Central  Google Scholar 

  146. 146.

    Jiménez C, Jara-Acevedo M, Corchete LA, Castillo D, Ordóñez GR, Sarasquete ME, et al. A next-generation sequencing strategy for evaluating the most common genetic abnormalities in multiple myeloma. J Mol Diagn. 2017;19:99–106.

    PubMed  PubMed Central  Google Scholar 

  147. 147.

    Ryland GL, Jones K, Chin M, Markham J, Aydogan E, Kankanige Y, et al. Novel genomic findings in multiple myeloma identified through routine diagnostic sequencing. J Clin Pathol. 2018;71:895–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Barrio S, DáVia M, Bruins L, Stühmer T, Steinbrunn T, Bittrich M et al. Protocol for M3P: a comprehensive and clinical oriented targeted sequencing panel for routine molecular analysis in multiple myeloma. In: Methods in molecular biology. New York, NY: Humana Press, 2018; p. 117–28.

    Google Scholar 

  149. 149.

    Rasche L, Chavan SS, Stephens OW, Patel PH, Tytarenko R, Ashby C, et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun. 2017;8:268.

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Bai Y, Orfao A, Chim CS. Molecular detection of minimal residual disease in multiple myeloma. Br J Haematol. 2018;181:11–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151.

    Landgren O, Lu SX, Hultcrantz M. MRD testing in multiple myeloma: the main future driver for modern tailored treatment. Semin Hematol. 2018;55:44–50.

    PubMed  PubMed Central  Google Scholar 

  152. 152.

    Innao V, Allegra A, Russo S, Gerace D, Vaddinelli D, Alonci A, et al. Standardisation of minimal residual disease in multiple myeloma. Eur J Cancer Care. 2017;26:e12732.

    Google Scholar 

  153. 153.

    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;8:363ra147.

    PubMed  PubMed Central  Google Scholar 

  154. 154.

    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.

    CAS  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Manier S, Park J, Capelletti M, Bustoros M, Freeman SS, Ha G, et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat Commun. 2018;9:1691.

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156.

    Kis O, Kaedbey R, Chow S, Danesh A, Dowar M, Li T, et al. Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates. Nat Commun. 2017;8:15086.

    PubMed  PubMed Central  Google Scholar 

  157. 157.

    Mithraprabhu S, Khong T, Ramachandran M, Chow A, Klarica D, Mai L, et al. Circulating tumour DNA analysis demonstrates spatial mutational heterogeneity that coincides with disease relapse in myeloma. Leukemia. 2017;31:1695–705.

    CAS  PubMed  Google Scholar 

  158. 158.

    Rustad EH, Coward E, Skytøen ER, Misund K, Holien T, Standal T, et al. Monitoring multiple myeloma by quantification of recurrent mutations in serum. Haematologica. 2017;102:1266–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Jung S-H, Lee S-E, Lee M, Kim S-H, Yim S-H, Kim TW, et al. Circulating microRNA expressions can predict the outcome of lenalidomide plus low-dose dexamethasone treatment in patients with refractory/relapsed multiple myeloma. Haematologica. 2017;102:e456–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Zhang L, Pan L, Xiang B, Zhu H, Wu Y, Chen M, et al. Potential role of exosome-associated microRNA panels and in vivo environment to predict drug resistance for patients with multiple myeloma. Oncotarget. 2016;7:30876–91.

    PubMed  PubMed Central  Google Scholar 

  161. 161.

    Kassambara A, Jourdan M, Bruyer A, Robert N, Pantesco V, Elemento O, et al. Global miRNA expression analysis identifies novel key regulators of plasma cell differentiation and malignant plasma cell. Nucleic Acids Res. 2017;45:5639–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  162. 162.

    Shaughnessy JD, Qu P, Usmani S, Heuck CJ, Zhang Q, Zhou Y, et al. Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with total therapy 3. Blood. 2011;118:3512–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Stessman HAF, Baughn LB, Sarver A, Xia T, Deshpande R, Mansoor A, et al. Profiling bortezomib resistance identifies secondary therapies in a mouse myeloma model. Mol Cancer Ther. 2013;12:1140–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164.

    Moreaux J, Reme T, Leonard W, Veyrune J-L, Requirand G, Goldschmidt H, et al. Gene expression-based prediction of myeloma cell sensitivity to histone deacetylase inhibitors. Br J Cancer. 2013;109:676–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  165. 165.

    Moreaux J, Reme T, Leonard W, Veyrune J-L, Requirand G, Goldschmidt H, et al. Development of gene expression-based score to predict sensitivity of multiple myeloma cells to DNA methylation inhibitors. Mol Cancer Ther. 2012;11:2685–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166.

    Mitra AK, Harding T, Mukherjee UK, Jang JS, Li Y, HongZheng R, et al. A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma. Blood Cancer J. 2017;7:e581.

    CAS  PubMed  PubMed Central  Google Scholar 

  167. 167.

    Bhutani M, Zhang Q, Friend R, Voorhees PM, Druhan LJ, Barlogie B, et al. Investigation of a gene signature to predict response to immunomodulatory derivatives for patients with multiple myeloma: an exploratory, retrospective study using microarray datasets from prospective clinical trials. Lancet Haematol. 2017;4:e443–e451.

    PubMed  PubMed Central  Google Scholar 

  168. 168.

    Kim JK, Kolodziejczyk AA, Ilicic T, Teichmann SA, Marioni JC. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. 2015;6:8687.

    CAS  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65:631.

    CAS  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Mitra AK, Mukherjee UK, Harding T, Jang JS, Stessman H, Li Y. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia. 2016;30:1094–102.

    CAS  PubMed  PubMed Central  Google Scholar 

  171. 171.

    Thoren KL. Mass spectrometry methods for detecting monoclonal immunoglobulins in multiple myeloma minimal residual disease. Semin Hematol. 2018;55:41–43.

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Łuczak M, Kubicki T, Rzetelska Z, Szczepaniak T, Przybyłowicz-Chalecka A, Ratajczak B, et al. Comparative proteomic profiling of sera from patients with refractory multiple myeloma reveals potential biomarkers predicting response to bortezomib-based therapy. Pol Arch Intern Med. 2017;127:392–400.

    PubMed  PubMed Central  Google Scholar 

  173. 173.

    Dom M, Offner F, Vanden Berghe W, Van Ostade X. Proteomic characterization of Withaferin A-targeted protein networks for the treatment of monoclonal myeloma gammopathies. J Proteom. 2018;179:17–29.

    CAS  Google Scholar 

  174. 174.

    Walz S, Stickel JS, Kowalewski DJ, Schuster H, Weisel K, Backert L, et al. The antigenic landscape of multiple myeloma: mass spectrometry (re)defines targets for T-cell-based immunotherapy. Blood. 2015;126:1203–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  175. 175.

    St-Germain JR, Taylor P, Tong J, Jin LL, Nikolic A, Stewart II, et al. Multiple myeloma phosphotyrosine proteomic profile associated with FGFR3 expression, ligand activation, and drug inhibition. Proc Natl Acad Sci USA. 2009;106:20127–32.

    CAS  PubMed  Google Scholar 

  176. 176.

    Misiewicz-Krzeminska I, Corchete LA, Rojas EA, Martínez-López J, García-Sanz R, Oriol A, et al. A novel nano-immunoassay method for quantification of proteins from CD138-purified myeloma cells: biological and clinical utility. Haematologica. 2018;103:880–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  177. 177.

    Ramakrishnan V, Mager DE. Network-based analysis of bortezomib pharmacodynamic heterogeneity in multiple myeloma cells. J Pharmacol Exp Ther. 2018;365:734–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  178. 178.

    Harshman SW, Canella A, Ciarlariello PD, Agarwal K, Branson OE, Rocci A, et al. Proteomic characterization of circulating extracellular vesicles identifies novel serum myeloma associated markers. J Proteom. 2016;136:89–98.

    CAS  Google Scholar 

  179. 179.

    Kuhlmann L, Cummins E, Samudio I, Kislinger T. Cell-surface proteomics for the identification of novel therapeutic targets in cancer. Expert Rev Proteom. 2018;15:259–75.

    CAS  Google Scholar 

  180. 180.

    Hansmann L, Blum L, Ju C-H, 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  181. 181.

    Smets T, Stevenaert F, Adams H, Vanhoof G. Deep profiling of the immune system of multiple myeloma patients using cytometry by time-of-flight (CyTOF). New York, NY: Humana Press; 2018. p. 47–54.

    Google Scholar 

  182. 182.

    Baughn LB, Sachs Z, Noble-Orcutt KE, Mitra A, Van Ness BG, Linden MA. Phenotypic and functional characterization of a bortezomib-resistant multiple myeloma cell line by flow and mass cytometry. Leuk Lymphoma. 2017;58:1931–40.

    CAS  PubMed  Google Scholar 

  183. 183.

    Stessman HA, Lulla A, Xia T, Mitra A, Harding T, Mansoor A. High-throughput drug screening identifies compounds and molecular strategies for targeting proteasome inhibitor-resistant multiple myeloma. Leukemia. 2014;28:2263–7.

    CAS  Google Scholar 

  184. 184.

    Meurice N, Petit JL, DeCampos CB, Polito AN, Lopez Armenta ID, Ahman GJ, et al. ‘Direct to Drug’ screening as a route to individualized therapy in multiple myeloma. Blood. 2017;130:3080.

    Google Scholar 

  185. 185.

    Majumder MM, Silvennoinen R, Anttila P, Tamborero D, Eldfors S, Yadav B, et al. Identification of precision treatment strategies for relapsed/refractory multiple myeloma by functional drug sensitivity testing. Oncotarget. 2017;8:56338–50.

    PubMed  PubMed Central  Google Scholar 

  186. 186.

    Snijder B, Vladimer GI, Krall N, Miura K, Schmolke A-S, Kornauth C, et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 2017;4:e595–e606.

    PubMed  PubMed Central  Google Scholar 

  187. 187.

    Pak C, Callander NS, Young EWK, Titz B, Kim K, Saha S, et al. MicroC(3): an ex vivo microfluidic cis-coculture assay to test chemosensitivity and resistance of patient multiple myeloma cells. Integr Biol. 2015;7:643–54.

    CAS  Google Scholar 

  188. 188.

    Pauli C, Hopkins BD, Prandi D, Shaw R, Fedrizzi T, Sboner A, et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 2017;7:462–77.

    PubMed  PubMed Central  Google Scholar 

  189. 189.

    Schueler JB, Wider D, Klingner K, Siegers GM, May AM, Waldschmidt JM, et al. Novel patient derived multiple myeloma model reflects sensitivity towards anticancer treatment in multiple myeloma patients. Blood. 2015;126:3004.

    Google Scholar 

  190. 190.

    Laganà A, Beno I, Melnekoff D, Leshchenko V, Madduri D, Ramdas D, et al. Precision medicine for relapsed multiple myeloma on the basis of an integrative multiomics approach. JCO Precis Oncol. 2018;2:1–17.

    Google Scholar 

  191. 191.

    Saad ED, Paoletti X, Burzykowski T, Buyse M. Precision medicine needs randomized clinical trials. Nat Rev Clin Oncol. 2017;14:317–23.

    Google Scholar 

  192. 192.

    Xu GW, Ali M, Wood TE, Wong D, Maclean N, Wang X, et al. The ubiquitin-activating enzyme E1 as a therapeutic target for the treatment of leukemia and multiple myeloma. Blood. 2010;115:2251–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  193. 193.

    Teoh PJ, Chng WJ. p53 Abnormalities and potential therapeutic targeting in multiple myeloma. Biomed Res Int. 2014;2014:1–9.

    Google Scholar 

  194. 194.

    Kapuria V, Peterson LF, Fang D, Bornmann WG, Talpaz M, Donato NJ. Deubiquitinase inhibition by small-molecule WP1130 triggers aggresome formation and tumor cell apoptosis. Cancer Res. 2010;70:9265–76.

    CAS  PubMed  Google Scholar 

  195. 195.

    Peterson LF, Sun H, Liu Y, Potu H, Kandarpa M, Ermann M, et al. Targeting deubiquitinase activity with a novel small-molecule inhibitor as therapy for B-cell malignancies. Blood. 2015;125:3588–97.

    CAS  PubMed  Google Scholar 

  196. 196.

    Chauhan D, Tian Z, Nicholson B, Kumar KGS, Zhou B, Carrasco R, et al. A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance. Cancer Cell. 2012;22:345–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  197. 197.

    Li J, Favata M, Kelley JA, Caulder E, Thomas B, Wen X, et al. INCB16562, a JAK1/2 selective inhibitor, is efficacious against multiple myeloma cells and reverses the protective effects of cytokine and stromal cell support. Neoplasia. 2010;12:28–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  198. 198.

    Monaghan KA, Khong T, Burns CJ, Spencer A. The novel JAK inhibitor CYT387 suppresses multiple signalling pathways, prevents proliferation and induces apoptosis in phenotypically diverse myeloma cells. Leukemia. 2011;25:1891–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  199. 199.

    Casimiro MC, Velasco-Velázquez M, Aguirre-Alvarado C, Pestell RG. Overview of cyclins D1 function in cancer and the CDK inhibitor landscape: past and present. Expert Opin Investig Drugs. 2014;23:295–304.

    CAS  PubMed  PubMed Central  Google Scholar 

  200. 200.

    Kawano T, Agata N, Kharbanda S, Avigan D, Kufe D. A novel isocoumarin derivative induces mitotic phase arrest and apoptosis of human multiple myeloma cells. Cancer Chemother Pharmacol. 2007;59:329–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  201. 201.

    Maginn EN, Browne PV, Hayden P, Vandenberghe E, MacDonagh B, Evans P, et al. PBOX-15, a novel microtubule targeting agent, induces apoptosis, upregulates death receptors, and potentiates TRAIL-mediated apoptosis in multiple myeloma cells. Br J Cancer. 2011;104:281–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  202. 202.

    Rozic G, Paukov L, Jakubikova J, Ben-Shushan D, Duek A, Leiba A, et al. The novel compound STK405759 is a microtubule-targeting agent with potent and selective cytotoxicity against multiple myeloma in vitro and in vivo. Oncotarget. 2016;7:62572–84.

    PubMed  PubMed Central  Google Scholar 

  203. 203.

    Drew AE, Moradei O, Jacques SL, Rioux N, Boriack-Sjodin AP, Allain C, et al. Identification of a CARM1 inhibitor with potent in vitro and in vivo activity in preclinical models of multiple myeloma. Sci Rep. 2017;7:17993.

    PubMed  PubMed Central  Google Scholar 

  204. 204.

    Alzrigat M, Jernberg-Wiklund H, Licht JD. Targeting EZH2 in multiple myeloma—multifaceted anti-tumor activity. Epigenomes. 2018;2:16.

    PubMed  PubMed Central  Google Scholar 

  205. 205.

    Alzrigat M, Párraga AA, Majumder MM, Ma A, Jin J, Österborg A, et al. The polycomb group protein BMI-1 inhibitor PTC-209 is a potent anti-myeloma agent alone or in combination with epigenetic inhibitors targeting EZH2 and the BET bromodomains. Oncotarget 2017; 5.

  206. 206.

    Jones RJ, Gu D, Bjorklund CC, Kuiatse I, Remaley AT, Bashir T, et al. The novel anticancer agent JNJ-26854165 induces cell death through inhibition of cholesterol transport and degradation of ABCA1. J Pharmacol Exp Ther. 2013;346:381–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  207. 207.

    Jiang H, Zhang W, Shang P, Zhang H, Fu W, Ye F, et al. Transfection of chimeric anti-CD138 gene enhances natural killer cell activation and killing of multiple myeloma cells. Mol Oncol. 2014;8:297–310.

    CAS  PubMed  PubMed Central  Google Scholar 

  208. 208.

    Chu J, Deng Y, Benson DM, He S, Hughes T, Zhang J, et al. CS1-specific chimeric antigen receptor (CAR)-engineered natural killer cells enhance in vitro and in vivo antitumor activity against human multiple myeloma. Leukemia. 2014;28:917–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  209. 209.

    Gebhard AW, Jain P, Nair RR, Emmons MF, Argilagos RF, Koomen JM, et al. MTI-101 (cyclized HYD1) binds a CD44 containing complex and induces necrotic cell death in multiple myeloma. Mol Cancer Ther. 2013;12:2446–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  210. 210.

    Meads MB, Fang B, Mathews L, Gemmer J, Nong L, Rosado-Lopez I, et al. Targeting PYK2 mediates microenvironment-specific cell death in multiple myeloma. Oncogene. 2016;35:2723–34.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Brian Van Ness.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harding, T., Baughn, L., Kumar, S. et al. The future of myeloma precision medicine: integrating the compendium of known drug resistance mechanisms with emerging tumor profiling technologies. Leukemia 33, 863–883 (2019). https://doi.org/10.1038/s41375-018-0362-z

Download citation

Further reading

Search

Quick links