Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-j5c6p Total loading time: 0 Render date: 2025-03-03T19:34:27.417Z Has data issue: false hasContentIssue false

Chapter 8 - Plasma Cell Myeloma and Related Disorders

Published online by Cambridge University Press:  30 January 2025

By
Ruth M. de Tute ,
Andrew C. Rawstron ,
Roger G. Owen ,
Anna Porwit  and
Marie-Christine Béné
Edited by
Anna Porwit  and
Marie Christine Béné
Anna Porwit
Affiliation:
Lunds Universitet, Sweden
Marie Christine Béné
Affiliation:
Université de Nantes, France
Get access

Summary

Malignant plasma cell proliferations are characterised by specific clinical, immunophenotypic and genetic features. Multiparameter flow cytometry (MFC) is an essential component of the diagnosis of these diseases. Clonal proliferations can be identified through their aberrant cell-surface immunophenotype or, more precisely, by demonstrating monotypy, i.e. selective expression of the same light chain in the cytoplasm of plasma-cells. This chapter reviews these immunophenotypic features, the technical points of caution to observe for proper use of MFC at diagnosis and during therapy to assess measurable residual disease.

Keywords

flow cytometryimmunophenotypingplasma cellmyelomapre-analyticspanelscytoplasmic stainingmonotypyminimal/measurable residual diseasetherapeutic targets
Type
Chapter
Information
Multiparameter Flow Cytometry in the Diagnosis of Hematologic Malignancies , pp. 127 - 140
Publisher: Cambridge University Press
Print publication year: 2025

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Swerdlow, SH, Campo, E, Harris, NL, et al., eds. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised 4th ed. IARC Press; Lyon, France: 2017.Google Scholar
Rajkumar, SV. Updated diagnostic criteria and staging system for multiple myeloma. Am Soc Clin Oncol Educ Book 2016; 35: e418–e423.CrossRefGoogle ScholarPubMed
Rajkumar, SV, Kumar, S, Lonial, S, Mateos, MV. Smoldering multiple myeloma current treatment algorithms. Blood Cancer J 2022; 12: 129.CrossRefGoogle ScholarPubMed
Mateos, MV, Kumar, S, Dimopoulos, MA, et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J 2020; 10: 102.CrossRefGoogle Scholar
Drayson, M, Tang, LX, Drew, R, Mead, GP, Carr-Smith, H, Bradwell, AR. Serum free light-chain measurements for identifying and monitoring patients with nonsecretory multiple myeloma. Blood 2001; 97: 2900–2902.CrossRefGoogle ScholarPubMed
Fernández de Larrea, C, Kyle, R, Rosiñol, L, et al. Primary plasma cell leukemia: consensus definition by the International Myeloma Working Group according to peripheral blood plasma cell percentage. Blood Cancer J 2021; 11: 192.CrossRefGoogle Scholar
Pham, A, Mahindra, A. Solitary plasmacytoma: a review of diagnosis and management. Curr Hematol Malig Rep 2019; 14: 63–69.CrossRefGoogle ScholarPubMed
Merlini, G, Sarosiek, S, Benevolo, G, et al. Report of Consensus Panel 6 from the 11 th International Workshop on Waldenström’s Macroglobulinemia on Management of Waldenström’s Macroglobulinemia Related Amyloidosis. Semin Hematol 2023; 60: 113–117.CrossRefGoogle ScholarPubMed
Bou Zerdan, M, Nasr, L, Khalid, F, et al. Systemic AL amyloidosis: current approach and future direction. Oncotarget 2023; 14: 384–394.CrossRefGoogle ScholarPubMed
D’Sa, S, Khwaja, J, Keddie, S, et al. Comprehensive diagnosis and management of POEMS syndrome. Hemasphere 2022; 6: e796.CrossRefGoogle ScholarPubMed
Rawstron, AC, de Tute, RM, Haughton, J, Owen, RG. Measuring disease levels in myeloma using flow cytometry in combination with other laboratory techniques: lessons from the past 20 years at the Leeds Haematological Malignancy Diagnostic Service. Cytometry B Clin Cytom 2016; 90: 54–60.CrossRefGoogle ScholarPubMed
Rawstron, AC, Orfao, A, Beksac, M, et al.; European Myeloma Network. Report of the European Myeloma Network on multiparametric flow cytometry in multiple myeloma and related disorders. Haematologica 2008; 93: 431–438.CrossRefGoogle ScholarPubMed
Stetler-Stevenson, M, Paiva, B, Stoolman, L, et al. Consensus guidelines for myeloma minimal residual disease sample staining and data acquisition. Cytometry B Clin Cytom 2016; 90: 26–30.CrossRefGoogle ScholarPubMed
Kumar, D, Kiran, FNU, Wheeler, A, Dellamano, R, Hammer, RD. An alternative processing approach to increase CD138 intensity in flow cytometric analysis of plasma cells. Cytometry B Clin Cytom 2023 Nov 27. E-pub ahead of print.CrossRefGoogle Scholar
van Dongen, JJ, Lhermitte, L, Böttcher, S, et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia 2012; 26: 1908–1975.CrossRefGoogle ScholarPubMed
Mathis, S, Chapuis, N, Borgeot, J, et al. Comparison of cross-platform flow cytometry minimal residual disease evaluation in multiple myeloma using a common antibody combination and analysis strategy. Cytometry B Clin Cytom 2015; 88: 101–109.CrossRefGoogle ScholarPubMed
Roshal, M. Minimal residual disease detection by flow cytometry in multiple myeloma: why and how? Semin Hematol 2018; 55: 4–12.CrossRefGoogle Scholar
Roshal, M, Gao, Q. Flow cytometry in diagnosis, prognostication, and monitoring of multiple myeloma and related disorders. Clin Lab Med 2023; 43: 363–375.CrossRefGoogle ScholarPubMed
Moreau, P, Hulin, C, Perrot, A, et al. Maintenance with daratumumab or observation following treatment with bortezomib, thalidomide, and dexamethasone with or without daratumumab and autologous stem-cell transplant in patients with newly diagnosed multiple myeloma (CASSIOPEIA): an open-label, randomised, phase 3 trial. Lancet Oncol 2021; 22: 1378–1390.CrossRefGoogle ScholarPubMed
Gao, Q, Chen, X, Cherian, S, Roshal, M. Mature B- and plasma-cell flow cytometric analysis: a review of the impact of targeted therapy. Cytometry B Clin Cytom 2023; 104: 224–242.CrossRefGoogle ScholarPubMed
Del Giudice, ML, Galimberti, S, Buda, G. Beyond BCMA, why GPRC5D could be the right way: treatment strategies with immunotherapy at relapse after anti-BCMA agents. Cancer Immunol Immunother 2023; 72: 3931–3937.CrossRefGoogle ScholarPubMed
Zhou, X, Xiao, X, Kortuem, KM, Einsele, H. Bispecific antibodies in the treatment of multiple myeloma. Hematol Oncol Clin North Am 2024: S0889 –8588: 00170–00173.Google Scholar
Liu, D, Lin, P, Hu, Y, et al. Immunophenotypic heterogeneity of normal plasma cells: comparison with minimal residual plasma cell myeloma. J Clin Pathol 2012; 65: 823–829.CrossRefGoogle ScholarPubMed
Paiva, B, Gutiérrez, NC, Chen, X, et al. Clinical significance of CD81 expression by clonal plasma cells in high-risk smoldering and symptomatic multiple myeloma patients. Leukemia 2012; 26: 1862–1869.CrossRefGoogle ScholarPubMed
Tembhare, PR, Yuan, CM, Venzon, D, et al. Flow cytometric differentiation of abnormal and normal plasma cells in the bone marrow in patients with multiple myeloma and its precursor diseases. Leuk Res. 2014; 38: 371–376.CrossRefGoogle ScholarPubMed
Raja, KR, Kovarova, L, Hajek, R. Review of phenotypic markers used in flow cytometric analysis of MGUS and MM, and applicability of flow cytometry in other plasma cell disorders. Br J Haematol 2010; 149: 334–351.CrossRefGoogle ScholarPubMed
Robillard, N, Jego, G, Pellat-Deceunynck, C, et al. CD28, a marker associated with tumoral expansion in multiple myeloma. Clin Cancer Res 1998; 4: 1521–1526.Google ScholarPubMed
Paiva, B, Vidriales, MB, Mateo, G, et al. The persistence of immunophenotypically normal residual bone marrow plasma cells at diagnosis identifies a good prognostic subgroup of symptomatic multiple myeloma patients. Blood 2009; 114: 4369–4372.CrossRefGoogle ScholarPubMed
Paiva, B, Pérez-Andrés, M, Vídriales, MB, 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.CrossRefGoogle ScholarPubMed
Ghosh, T, Gonsalves, WI, Jevremovic, D, et al. The prognostic significance of polyclonal bone marrow plasma cells in patients with relapsing multiple myeloma. Am J Hematol 2017; 92: E507–E512.CrossRefGoogle ScholarPubMed
Paiva, B, Paino, T, Sayagues, JM, et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood 2013; 122: 3591–3598.CrossRefGoogle ScholarPubMed
Kumar, S, Rajkumar, SV, Kyle, RA, et al. Prognostic value of circulating plasma cells in monoclonal gammopathy of undetermined significance. J Clin Oncol 2005; 23: 5668–5674.CrossRefGoogle ScholarPubMed
Gonsalves, WI, Rajkumar, SV, Dispenzieri, A, 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: 130–135.CrossRefGoogle ScholarPubMed
Bianchi, G, Kyle, RA, Larson, DR, et al. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia 2013; 27: 680–685.CrossRefGoogle ScholarPubMed
Pérez-Escurza, O, Flores-Montero, J, Óskarsson, J, et al. Immunophenotypic assessment of clonal plasma cells and B-cells in bone marrow and blood in the diagnostic classification of early stage monoclonal gammopathies: an iSTOPMM study. Blood Cancer J 2023; 13: 182.CrossRefGoogle ScholarPubMed
Bataille, R, Pellat-Deceunynck, C, Robillard, N, Avet-Loiseau, H, Harousseau, JL, Moreau, P. CD117 (c-kit) is aberrantly expressed in a subset of MGUS and multiple myeloma with unexpectedly good prognosis. Leuk Res 2008; 32: 379–382.CrossRefGoogle Scholar
Schmidt-Hieber, M, Pérez-Andrés, M, Paiva, B, et al. CD117 expression in gammopathies is associated with an altered maturation of the myeloid and lymphoid hematopoietic cell compartments and favorable disease features. Haematologica 2011; 96: 328–332.CrossRefGoogle ScholarPubMed
Mateo, G, Montalbán, MA, Vidriales, MB, et al. Prognostic value of immunophenotyping in multiple myeloma: a study by the PETHEMA/GEM cooperative study groups on patients uniformly treated with high-dose therapy. J Clin Oncol 2008; 26: 2737–2744.CrossRefGoogle ScholarPubMed
An, G, Xu, Y, Shi, L, et al. t(11;14) multiple myeloma: a subtype associated with distinct immunological features, immunophenotypic characteristics but divergent outcome. Leuk Res 2013; 37: 1251–1257.CrossRefGoogle ScholarPubMed
Banerjee, R, Cicero, KI, Lee, SS, Cowan, AJ. Definers and drivers of functional high-risk multiple myeloma: insights from genomic, transcriptomic, and immune profiling. Front Oncol 2023; 13: 1240966.CrossRefGoogle ScholarPubMed
Zheng, D, Zhu, M, Li, Q, Wan, W, Chen, Y, Jing, H. Dual negativity of CD56 and CD117 links to unfavorable cytogenetic abnormalities and predicts poor prognosis in multiple myeloma. J Clin Med 2022; 11: 6524.CrossRefGoogle ScholarPubMed
Arana, P, Paiva, B, Cedena, MT, et al. Prognostic value of antigen expression in multiple myeloma: a PETHEMA/GEM study on 1265 patients enrolled in four consecutive clinical trials. Leukemia 2018; 32: 971–978.CrossRefGoogle Scholar
Paiva, B, Chandia, M, Vidriales, MB, et al. Multiparameter flow cytometry for staging of solitary bone plasmacytoma: new criteria for risk of progression to myeloma. Blood 2014;124: 1300–1303.CrossRefGoogle ScholarPubMed
Hill, QA, Rawstron, AC, de Tute, RM, Owen, RG. Outcome prediction in plasmacytoma of bone: a risk model utilizing bone marrow flow cytometry and light-chain analysis. Blood 2014; 124: 1296–1299.CrossRefGoogle Scholar
Sammartano, V, Franceschini, M, Fredducci, S, et al. Anti-BCMA novel therapies for multiple myeloma. Cancer Drug Resist 2023; 6: 169–181.Google ScholarPubMed
Rodriguez-Otero, P, van de Donk, NWCJ, Pillarisetti, K, et al. GPRC5D as a novel target for the treatment of multiple myeloma: a narrative review. Blood Cancer J 2024; 14: 24.CrossRefGoogle ScholarPubMed
Flores-Montero, J, de Tute, R, Paiva, B, et al. Immunophenotype of normal vs. myeloma plasma cells: toward antibody panel specifications for MRD detection in multiple myeloma. Cytometry B Clin Cytom 2016; 90: 61–72.CrossRefGoogle ScholarPubMed
Paiva, B, Cedena, MT, Puig, N, et al. Minimal residual disease monitoring and immune profiling in multiple myeloma in elderly patients. Blood 2016; 127: 3165–3174.CrossRefGoogle ScholarPubMed
Roshal, M, Gao, Q. Flow cytometry in diagnosis, prognostication, and monitoring of multiple myeloma and related disorders. Clin Lab Med 2023; 43: 363–375.CrossRefGoogle ScholarPubMed
Paiva, B, Martinez-Lopez, J, Vidriales, MB, et al. Comparison of immunofixation, serum free light chain, and immunophenotyping for response evaluation and prognostication in multiple myeloma. J Clin Oncol 2011; 29: 1627–1633.CrossRefGoogle ScholarPubMed
Rawstron, AC, Child, JA, de Tute, RM, et al. Minimal residual disease assessed by multiparameter flow cytometry in multiple myeloma: impact on outcome in the Medical Research Council Myeloma IX Study. J Clin Oncol 2013; 31: 2540–2547.CrossRefGoogle ScholarPubMed
Rawstron, AC, Gregory, WM, de Tute, RM, et al. Minimal residual disease in myeloma by flow cytometry: independent prediction of survival benefit per log reduction. Blood 2015; 125: 1932–1935.CrossRefGoogle ScholarPubMed
Soh, KT, Came, N, Otteson, GE, et al. Evaluation of multiple myeloma measurable residual disease by high sensitivity flow cytometry: an international harmonized approach for data analysis. Cytometry B Clin Cytom 2022; 102: 88–106.CrossRefGoogle ScholarPubMed
Halliley, JL, Tipton, CM, Liesveld, J, et al. Long-lived plasma cells are contained within the CD19(-)CD38(hi)CD138(+) subset in human bone marrow. Immunity 2015; 43: 132–145.CrossRefGoogle ScholarPubMed
Bhoj, VG, Arhontoulis, D, Wertheim, G, et al. Persistence of long-lived plasma cells and humoral immunity in individuals responding to CD19-directed CAR T-cell therapy. Blood 2016; 128: 360–370.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×