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  • Review Article
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Single-cell profiling of tumour evolution in multiple myeloma — opportunities for precision medicine

Nature Reviews Clinical Oncology volume 19, pages 223–236 (2022)Cite this article

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Abstract

Multiple myeloma (MM) is a haematological malignancy of plasma cells characterized by substantial intraclonal genetic heterogeneity. Although therapeutic advances made in the past few years have led to improved outcomes and longer survival, MM remains largely incurable. Over the past decade, genomic analyses of patient samples have demonstrated that MM is not a single disease but rather a spectrum of haematological entities that all share similar clinical symptoms. Moreover, analyses of samples from monoclonal gammopathy of undetermined significance and smouldering MM have also shown the existence of genetic heterogeneity in precursor stages, in some cases remarkably similar to that of MM. This heterogeneity highlights the need for a greater dissection of underlying disease biology, especially the clonal diversity and molecular events underpinning MM at each stage to enable the stratification of individuals with a high risk of progression. Emerging single-cell sequencing technologies present a superlative solution to delineate the complexity of monoclonal gammopathy of undetermined significance, smouldering MM and MM. In this Review, we discuss how genomics has revealed novel insights into clonal evolution patterns of MM and provide examples from single-cell studies that are beginning to unravel the mutational and phenotypic characteristics of individual cells within the bone marrow tumour, immune microenvironment and peripheral blood. We also address future perspectives on clinical application, proposing that multi-omics single-cell profiling can guide early patient diagnosis, risk stratification and treatment strategies.

Key points

  • Multiple myeloma (MM) is a clinically and biologically heterogeneous haematological malignancy characterized by extensive tumour heterogeneity.

  • Bulk genomic studies have revealed the presence of clonal heterogeneity throughout all stages of disease from monoclonal gammopathy of undetermined significance and smouldering MM to MM, providing clonal evolution models defined by clonal stability or branching evolution.

  • Despite the genetic architecture of MM being well characterized, precision medicine is yet to be effective in patients with MM, among whom disease relapse is inevitable owing to the presence and selection of pre-existing resistant clones or their emergence in response to selective pressure during therapy.

  • Emerging single-cell technologies are poised to dissect the clonal complexity of tumour cells and concerted changes in the immune microenvironment to enable the high-resolution mapping of dysregulation occurring between disease stages.

  • Multi-omic single-cell characterization and integration of genomic (DNA), phenotype (RNA and protein) and epigenomic data can provide a holistic picture of ongoing tumour dynamics and improve the molecular stratification of patients with MM.

  • By targeting tumour and immune cells, single-cell technologies and mapping could be applied clinically to provide improved strategies for the diagnosis, prognostication and monitoring of response to treatment and residual disease.

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Fig. 1: Continuum of progression and somatic mutations involved in MM.
Fig. 2: Clonal evolution models in MM progression.
Fig. 3: Single-cell multi-omics approaches to molecular profiling of MM.

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References

  1. Chapman, M. A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5, 2997 (2014).

    Article  PubMed  Google Scholar 

  3. Bolli, N. et al. Genomic patterns of progression in smoldering multiple myeloma. Nat. Commun. 9, 3363 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dutta, A. K. et al. Subclonal evolution in disease progression from MGUS/SMM to multiple myeloma is characterised by clonal stability. Leukemia 33, 457–468 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Egan, J. B. et al. Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood 120, 1060–1066 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Keats, J. J. et al. Clonal competition with alternating dominance in multiple myeloma. Blood 120, 1067–1076 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rasche, L. et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat. Commun. 8, 268 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Walker, B. A. et al. Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J. Clin. Oncol. 33, 3911–3920 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Walker, B. A. et al. Intraclonal heterogeneity is a critical early event in the development of myeloma and precedes the development of clinical symptoms. Leukemia 28, 384–390 (2014).

    Article  PubMed  Google Scholar 

  11. Weston-Bell, N. et al. Exome sequencing in tracking clonal evolution in multiple myeloma following therapy. Leukemia 27, 1188–1191 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Zhao, S. et al. Serial exome analysis of disease progression in premalignant gammopathies. Leukemia 28, 1548–1552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Palumbo, A. & Anderson, K. Multiple myeloma. N. Engl. J. Med. 364, 1046–1060 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. National Cancer Institute. Cancer Stat Facts: Myeloma. https://seer.cancer.gov/statfacts/html/mulmy.html (2021).

  15. Cowan, A. J. et al. Global burden of multiple myeloma: a systematic analysis for the Global Burden of Disease Study 2016. JAMA Oncol. 4, 1221–1227 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dutta, A. K., Hewett, D. R., Fink, J. L., Grady, J. P. & Zannettino, A. C. W. Cutting edge genomics reveal new insights into tumour development, disease progression and therapeutic impacts in multiple myeloma. Br. J. Haematol. 178, 196–208 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Manier, S. et al. Genomic complexity of multiple myeloma and its clinical implications. Nat. Rev. Clin. Oncol. 14, 100–113 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Barwick, B. G., Gupta, V. A., Vertino, P. M. & Boise, L. H. Cell of origin and genetic alterations in the pathogenesis of multiple myeloma. Front. Immunol. 10, 1121 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kyle, R. A. et al. A long-term study of prognosis in monoclonal gammopathy of undetermined significance. N. Engl. J. Med. 346, 564–569 (2002).

    Article  PubMed  Google Scholar 

  20. Landgren, O. et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood 113, 5412–5417 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kyle, R. A. et al. Clinical course and prognosis of smoldering (asymptomatic) multiple myeloma. N. Engl. J. Med. 356, 2582–2590 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Weiss, B. M., Abadie, J., Verma, P., Howard, R. S. & Kuehl, W. M. A monoclonal gammopathy precedes multiple myeloma in most patients. Blood 113, 5418–5422 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fonseca, R. et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 23, 2210–2221 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sawyer, J. R., Waldron, J. A., Jagannath, S. & Barlogie, B. Cytogenetic findings in 200 patients with multiple myeloma. Cancer Genet. Cytogenet. 82, 41–49 (1995).

    Article  CAS  PubMed  Google Scholar 

  25. Kyle, R. A. et al. Prevalence of monoclonal gammopathy of undetermined significance. N. Engl. J. Med. 354, 1362–1369 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Murray, D. et al. Detection and prevalence of monoclonal gammopathy of undetermined significance: a study utilizing mass spectrometry-based monoclonal immunoglobulin rapid accurate mass measurement. Blood Cancer J. 9, 102 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Greenberg, A. J., Vachon, C. M. & Rajkumar, S. V. Disparities in the prevalence, pathogenesis and progression of monoclonal gammopathy of undetermined significance and multiple myeloma between blacks and whites. Leukemia 26, 609–614 (2012).

    Article  CAS  PubMed  Google Scholar 

  28. Marinac, C. R., Ghobrial, I. M., Birmann, B. M., Soiffer, J. & Rebbeck, T. R. Dissecting racial disparities in multiple myeloma. Blood Cancer J. 10, 19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ghobrial, I. M. & Landgren, O. How I treat smoldering multiple myeloma. Blood 124, 3380–3388 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Rajkumar, S. V. et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 15, e538–e548 (2014).

    Article  PubMed  Google Scholar 

  31. Kyle, R. A., Buadi, F. & Rajkumar, S. V. Management of monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Oncology 25, 578–586 (2011).

    PubMed  Google Scholar 

  32. Walker, B. A. et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood 132, 587–597 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Maura, F. et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat. Commun. 10, 3835 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Bustoros, M. et al. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J. Clin. Oncol. 38, 2380–2389 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ho, S. S., Urban, A. E. & Mills, R. E. Structural variation in the sequencing era. Nat. Rev. Genet. 21, 171–189 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Oben, B. et al. Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities. Nat. Commun. 12, 1861 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hoang, P. H. et al. Whole-genome sequencing of multiple myeloma reveals oncogenic pathways are targeted somatically through multiple mechanisms. Leukemia 32, 2459–2470 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Barwick, B. G. et al. Multiple myeloma immunoglobulin lambda translocations portend poor prognosis. Nat. Commun. 10, 1911 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Rajkumar, S. V. Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 91, 719–734 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mikulasova, A. et al. The spectrum of somatic mutations in monoclonal gammopathy of undetermined significance indicates a less complex genomic landscape than that in multiple myeloma. Haematologica 102, 1617–1625 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Fonseca, R. et al. Genomic abnormalities in monoclonal gammopathy of undetermined significance. Blood 100, 1417–1424 (2002).

    Article  CAS  PubMed  Google Scholar 

  42. Leshchiner, I. et al. Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment. Preprint at https://doi.org/10.1101/508127 (2019).

  43. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Melchor, L. et al. Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia 28, 1705–1715 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Jang, J. S. et al. Molecular signatures of multiple myeloma progression through single cell RNA-seq. Blood Cancer J. 9, 2 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Liu, R. et al. Co-evolution of tumor and immune cells during progression of multiple myeloma. Nat. Commun. 12, 2559 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ghobrial, I. M. Myeloma as a model for the process of metastasis: implications for therapy. Blood 120, 20–30 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Shen, S. Y. et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kis, O. et al. Circulating tumour DNA sequence analysis as an alternative to multiple myeloma bone marrow aspirates. Nat. Commun. 8, 15086 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Lohr, J. G. et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci. Transl. Med. 8, 363ra147 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Manier, S. et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat. Commun. 9, 1691 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Guo, G. et al. Genomic discovery and clonal tracking in multiple myeloma by cell-free DNA sequencing. Leukemia 32, 1838–1841 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mishima, Y. et al. The mutational landscape of circulating tumor cells in multiple myeloma. Cell Rep. 19, 218–224 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Garces, J. J. et al. Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma. Leukemia 34, 3007–3018 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  59. Foulk, B. et al. Enumeration and characterization of circulating multiple myeloma cells in patients with plasma cell disorders. Br. J. Haematol. 180, 71–81 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Dimopoulos, M. A. et al. Macrofocal multiple myeloma in young patients: a distinct entity with favorable prognosis. Leuk. Lymphoma 47, 1553–1556 (2006).

    Article  PubMed  Google Scholar 

  61. Rasche, L., Kortum, K. M., Raab, M. S. & Weinhold, N. The impact of tumor heterogeneity on diagnostics and novel therapeutic strategies in multiple myeloma. Int. J. Mol. Sci. 20, 1248 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  62. Zamagni, E., Tacchetti, P. & Cavo, M. Imaging in multiple myeloma: how? When? Blood 133, 644–651 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Dhodapkar, M. V. MGUS to myeloma: a mysterious gammopathy of underexplored significance. Blood 128, 2599–2606 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ghobrial, I. M., Detappe, A., Anderson, K. C. & Steensma, D. P. The bone-marrow niche in MDS and MGUS: implications for AML and MM. Nat. Rev. Clin. Oncol. 15, 219–233 (2018).

    Article  PubMed  Google Scholar 

  65. Raaijmakers, M. H. et al. Bone progenitor dysfunction induces myelodysplasia and secondary leukaemia. Nature 464, 852–857 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lawson, M. A. et al. Osteoclasts control reactivation of dormant myeloma cells by remodelling the endosteal niche. Nat. Commun. 6, 8983 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Das, R. et al. Microenvironment-dependent growth of preneoplastic and malignant plasma cells in humanized mice. Nat. Med. 22, 1351–1357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hewett, D. R. et al. DNA barcoding reveals habitual clonal dominance of myeloma plasma cells in the bone marrow microenvironment. Neoplasia 19, 972–981 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zavidij, O. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat. Cancer 1, 493–506 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Khoo, W. H. et al. A niche-dependent myeloid transcriptome signature defines dormant myeloma cells. Blood 134, 30–43 (2019).

    Article  CAS  PubMed  Google Scholar 

  71. Wilcock, P. & Webster, R. The multiple myeloma drug market. Nat. Rev. Drug Discov. 18, 579–580 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Andrulis, M. et al. Targeting the BRAF V600E mutation in multiple myeloma. Cancer Discov. 3, 862–869 (2013).

    Article  CAS  PubMed  Google Scholar 

  73. Paiva, B., van Dongen, J. J. & Orfao, A. New criteria for response assessment: role of minimal residual disease in multiple myeloma. Blood 125, 3059–3068 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Perrot, A. et al. Minimal residual disease negativity using deep sequencing is a major prognostic factor in multiple myeloma. Blood 132, 2456–2464 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Martinez-Lopez, J. et al. Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma. Blood 123, 3073–3079 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kortum, K. M. et al. Longitudinal analysis of 25 sequential sample-pairs using a custom multiple myeloma mutation sequencing panel (M(3)P). Ann. Hematol. 94, 1205–1211 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Magrangeas, F. et al. Minor clone provides a reservoir for relapse in multiple myeloma. Leukemia 27, 473–481 (2013).

    Article  CAS  PubMed  Google Scholar 

  78. Weinhold, N. et al. Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma. Blood 128, 1735–1744 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Corre, J. et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia 32, 2636–2647 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Jones, J. R. et al. Clonal evolution in myeloma: the impact of maintenance lenalidomide and depth of response on the genetics and sub-clonal structure of relapsed disease in uniformly treated newly diagnosed patients. Haematologica 104, 1440–1450 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lonial, S. et al. Randomized trial of lenalidomide versus observation in smoldering multiple myeloma. J. Clin. Oncol. 38, 1126–1137 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Mateos, M. V. et al. Lenalidomide plus dexamethasone versus observation in patients with high-risk smouldering multiple myeloma (QuiRedex): long-term follow-up of a randomised, controlled, phase 3 trial. Lancet Oncol. 17, 1127–1136 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Mateos, M. V. et al. Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma. N. Engl. J. Med. 369, 438–447 (2013).

    Article  CAS  PubMed  Google Scholar 

  84. Cohen, Y. C. et al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing. Nat. Med. 27, 491–503 (2021).

    Article  CAS  PubMed  Google Scholar 

  85. Acar, A. et al. Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. Nat. Commun. 11, 1923 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Gomez-Bougie, P. et al. BH3-mimetic toolkit guides the respective use of BCL2 and MCL1 BH3-mimetics in myeloma treatment. Blood 132, 2656–2669 (2018).

    Article  CAS  PubMed  Google Scholar 

  87. Miles, L. A. et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature 587, 477–482 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Shahi, P., Kim, S. C., Haliburton, J. R., Gartner, Z. J. & Abate, A. R. Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci. Rep. 7, 44447 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Demaree, B. et al. Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia. Nat Commun. 12, 1583 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Mateos, M. V. et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J. 10, 102 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Perez-Persona, E. et al. New criteria to identify risk of progression in monoclonal gammopathy of uncertain significance and smoldering multiple myeloma based on multiparameter flow cytometry analysis of bone marrow plasma cells. Blood 110, 2586–2592 (2007).

    Article  CAS  PubMed  Google Scholar 

  93. Feng, X. et al. Targeting CD38 suppresses induction and function of T regulatory cells to mitigate immunosuppression in multiple myeloma. Clin. Cancer Res. 23, 4290–4300 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Gorgun, G. et al. Immunomodulatory effects of lenalidomide and pomalidomide on interaction of tumor and bone marrow accessory cells in multiple myeloma. Blood 116, 3227–3237 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kohlhapp, F. J. et al. Venetoclax increases intratumoral effector T cells and antitumor efficacy in combination with immune checkpoint blockade. Cancer Discov. 11, 68–79 (2021).

    Article  CAS  PubMed  Google Scholar 

  96. Paiva, B. et al. Immune status of high-risk smoldering multiple myeloma patients and its therapeutic modulation under LenDex: a longitudinal analysis. Blood 127, 1151–1162 (2016).

    Article  CAS  PubMed  Google Scholar 

  97. Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550, 451–453 (2017).

    Article  CAS  PubMed  Google Scholar 

  99. Bagaev, A. et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell 39, 845–865.e7 (2021).

    Article  CAS  PubMed  Google Scholar 

  100. de Jong, M. M. E. et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat. Immunol. 22, 769–780 (2021).

    Article  PubMed  Google Scholar 

  101. Bianchi, G. et al. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia 27, 680–685 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. Kumar, S. et al. Prognostic value of circulating plasma cells in monoclonal gammopathy of undetermined significance. J. Clin. Oncol. 23, 5668–5674 (2005).

    Article  PubMed  Google Scholar 

  103. Paiva, B. et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood 122, 3591–3598 (2013).

    Article  CAS  PubMed  Google Scholar 

  104. Gonsalves, W. I. et al. Quantification of clonal circulating plasma cells in newly diagnosed multiple myeloma: implications for redefining high-risk myeloma. Leukemia 28, 2060–2065 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Granell, M. et al. Prognostic impact of circulating plasma cells in patients with multiple myeloma: implications for plasma cell leukemia definition. Haematologica 102, 1099–1104 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Sanoja-Flores, L. et al. Blood monitoring of circulating tumor plasma cells by next generation flow in multiple myeloma after therapy. Blood 134, 2218–2222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Nowakowski, G. S. et al. Circulating plasma cells detected by flow cytometry as a predictor of survival in 302 patients with newly diagnosed multiple myeloma. Blood 106, 2276–2279 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Zeune, L. L. et al. Deep learning of circulating tumour cells. Nat. Mach. Intell. 2, 124–133 (2020).

    Article  Google Scholar 

  109. Yu, M. et al. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science 345, 216–220 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Touzeau, C. et al. BH3 profiling identifies heterogeneous dependency on Bcl-2 family members in multiple myeloma and predicts sensitivity to BH3 mimetics. Leukemia 30, 761–764 (2016).

    Article  CAS  PubMed  Google Scholar 

  111. Travaglini, K. J. et al. A molecular cell atlas of the human lung from single-cell RNA sequencing. Nature 587, 619–625 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Rebbeck, T. R. et al. Precision prevention and early detection of cancer: fundamental principles. Cancer Discov. 8, 803–811 (2018).

    Article  PubMed  Google Scholar 

  113. Turnbull, C., Sud, A. & Houlston, R. S. Cancer genetics, precision prevention and a call to action. Nat. Genet. 50, 1212–1218 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Auclair, D., Lonial, S., Anderson, K. C. & Kumar, S. K. Precision medicine in multiple myeloma: are we there yet? Expert Rev. Precis. Med. Drug Dev. 4, 51–53 (2019).

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge funding support from the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (AMRF), Multiple Myeloma Research Foundation (MMRF) and National Institutes of Health (NIH). The work of these authors is also supported by a Stand Up To Cancer Dream Team Research Grant (grant number: SU2C-AACR-DT-28-18). Stand Up To Cancer is a programme of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research (ACS), the scientific partner of Stand Up To Cancer. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by Stand Up To Cancer, the Entertainment Industry Foundation or the ACS. The authors thank A. V. Justis and O. C. Lomas (both at Dana-Farber Cancer Institute) for scientific writing support and review of the manuscript, respectively.

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Authors and Affiliations

  1. Center for Prevention of Progression of Blood Cancers, Dana-Farber Cancer Institute, Boston, MA, USA

    Ankit K. Dutta, Jean-Baptiste Alberge, Romanos Sklavenitis-Pistofidis, Elizabeth D. Lightbody & Irene M. Ghobrial

  2. Department of Medical Oncology, Harvard Medical School, Boston, MA, USA

    Ankit K. Dutta, Jean-Baptiste Alberge, Romanos Sklavenitis-Pistofidis, Elizabeth D. Lightbody & Irene M. Ghobrial

  3. Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ankit K. Dutta, Jean-Baptiste Alberge, Romanos Sklavenitis-Pistofidis, Elizabeth D. Lightbody, Gad Getz & Irene M. Ghobrial

  4. Cancer Center and Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Gad Getz

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Contributions

A.K.D. researched data for this article and wrote the first draft. All the authors contributed substantially to discussions of content and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Irene M. Ghobrial.

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Competing interests

A.K.D. and E.D.L. have acted as consultants for and received honoraria from Menarini Silicon Biosystems. G.G. receives research funding from IBM and Pharmacyclics; is an inventor on patent applications related to ABSOLUTE, MSIDetect, MSMutSig, MSMuTect, MutSig, MuTect, POLYSOLVER and TensorQTL; and is a founder, consultant and holds privately held equity in Scorpion Therapeutics. I.M.G. is a consultant for AbbVie, Adaptive, Aptitude, Bristol Myers Squibb, Cellectar, Curio Science, Genentech, GNS, GSK, Janssen, Karyopharm, Medscape, Oncopeptides, Sanofi, and Takeda and has received honoraria from Aptitude. J.-B.A. and R.S.-P. declare no competing interests.

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Nature Reviews Clinical Oncology thanks O. Landgren, G. Morgan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Dutta, A.K., Alberge, JB., Sklavenitis-Pistofidis, R. et al. Single-cell profiling of tumour evolution in multiple myeloma — opportunities for precision medicine. Nat Rev Clin Oncol 19, 223–236 (2022). https://doi.org/10.1038/s41571-021-00593-y

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