Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.
artificial intelligence
monoclonal gammopathy of undetermined significance
multiparametric flow cytometry
multiple myeloma
Journal
British journal of haematology
ISSN: 1365-2141
Titre abrégé: Br J Haematol
Pays: England
ID NLM: 0372544
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
revised:
13
10
2021
received:
10
08
2021
accepted:
18
10
2021
pubmed:
4
11
2021
medline:
11
3
2022
entrez:
3
11
2021
Statut:
ppublish
Résumé
Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1175-1183Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021 British Society for Haematology and John Wiley & Sons Ltd.
Références
Cowan AJ, Allen C, Barac A, Basaleem H, Bensenor I, Curado MP, et al. Global burden of multiple myeloma: a systematic analysis for the global burden of disease study 2016. JAMA Oncol. 2018;4:1221.
Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15:e538-48.
Kyle RA, Durie BG, Rajkumar SV, Landgren O, Blade J, Merlini G, et al. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. 2010;24:1121-7.
Landgren O, Kyle RA, Pfeiffer RM, Katzmann JA, Caporaso NE, Hayes RB, et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood. 2009;113:5412-7.
Gupta S, Karandikar NJ, Ginader T, Bellizzi AM, Holman CJ. Flow cytometric aberrancies in plasma cell myeloma and MGUS - correlation with laboratory parameters. Cytometry B Clin Cytom. 2018;94:500-8.
Alaterre E, Raimbault S, Goldschmidt H, Bouhya S, Requirand G, Robert N, et al. CD24, CD27, CD36 and CD302 gene expression for outcome prediction in patients with multiple myeloma. Oncotarget. 2017;8:98931-44.
Guikema JE, Hovenga S, Vellenga E, Conradie JJ, Abdulahad WH, Bekkema R, et al. CD27 is heterogeneously expressed in multiple myeloma: low CD27 expression in patients with high-risk disease. Br J Haematol. 2003;121:36-43.
Morgan TK, Zhao S, Chang KL, Haddix TL, Domanay E, Cornbleet PJ, et al. Low CD27 expression in plasma cell dyscrasias correlates with high-risk disease: an immunohistochemical analysis. Am J Clin Pathol. 2006;126:545-51.
Chen F, Hu Y, Wang X, Fu S, Liu Z, Zhang J. Expression of CD81 and CD117 in plasma cell myeloma and the relationship to prognosis. Cancer Med. 2018;7:5920-7.
Paiva B, Chen X, Vídriales MB, Montalbán MÁ, Rosiñol L, Oriol A, et al. Clinical significance of CD81 expression by clonal plasma cells in high-risk smoldering and symptomatic multiple myeloma patients. Leukemia. 2012;26:1862-9.
Kovarova L, Buresova I, Buchler T, Suska R, Pour L, Zahradova L, et al. Phenotype of plasma cells in multiple myeloma and monoclonal gammopathy of undetermined significance. Neoplasma. 2009;56:526-32.
Ocqueteau M, Orfao A, Almeida J, Bladé J, González M, García-Sanz R, et al. Immunophenotypic characterization of plasma cells from monoclonal gammopathy of undetermined significance patients. Implications for the differential diagnosis between MGUS and multiple myeloma. Am J Pathol. 1998;152:1655-65.
Paiva B, Vidriales MB, Mateo G, Pérez JJ, Montalbán MA, Sureda A, 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;114:4369-72.
Frébet E, Abraham J, Geneviève F, Lepelley P, Daliphard S, Bardet V, et al. A GEIL flow cytometry consensus proposal for quantification of plasma cells: Application to differential diagnosis between MGUS and myeloma. Cytometry B Clin Cytom. 2011;80:176-85.
Byrne E, Naresh KN, Giles C, Rahemtulla A. Excess bone marrow B-cells in patients with multiple myeloma achieving complete remission following autologous stem cell transplantation is a biomarker for improved survival: correspondence. Br. J. Haematol. 2011;155:509-11.
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.
Rajkumar SV, Kyle RA, Therneau TM, Melton LJ, Bradwell AR, Clark RJ, et al. Serum free light chain ratio is an independent risk factor for progression in monoclonal gammopathy of undetermined significance. Blood. 2005;106:812-7.
Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees [Internet]. 1st ed. Routledge; 2017 [cited 2021 Apr 20]. Available from: https://www.taylorfrancis.com/books/9781351460491
Le TT, Moore JH. treeheatr: an R package for interpretable decision tree visualizations. Bioinformatics. 2020;37(2):282-284.
Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38:367-78.
Madhira BR, Konala VM, Adapa S, Naramala S, Ravella PM, Parikh K, et al. Recent advances in the management of smoldering multiple myeloma. World J Oncol. 2020;11:45-54.
Guikema JE, Vellenga E, Abdulahad WH, Hovenga S, Bos NA. CD27-triggering on primary plasma cell leukaemia cells has anti-apoptotic effects involving mitogen activated protein kinases. Br J Haematol. 2004;124:299-308.
Chu B, Bao L, Wang Y, Lu M, Shi L, Gao S, et al. CD27 antigen negative expression indicates poor prognosis in newly diagnosed multiple myeloma. Clin Immunol. 2020;213:108363.
Witzig TE, Timm M, Larson D, Therneau T, Greipp PR. Measurement of apoptosis and proliferation of bone marrow plasma cells in patients with plasma cell proliferative disorders. Br J Haematol. 1999;104:131-7.
Lisenko K, Schönland S, Hegenbart U, Wallenwein K, Braun U, Mai EK, et al. Potential therapeutic targets in plasma cell disorders: a flow cytometry study. Cytometry B Clin Cytom. 2017;92:145-52.
van de Donk NW, Usmani SZ. CD38 Antibodies in multiple myeloma: mechanisms of action and modes of resistance. Front Immunol. 2018;9:2134.
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.
Arai Y, Kondo T, Fuse K, Shibasaki Y, Masuko M, Sugita J, et al. Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Blood Adv. 2019;3(22):3626-34.
Goswami C, Poonia S, Kumar L, Sengupta D. Staging system to predict the risk of relapse in multiple myeloma patients undergoing autologous stem cell transplantation. Front Oncol. 2019;9:633.
Ni W, Hu B, Zheng C, Tong Y, Wang L, Li Q, et al. Automated analysis of acute myeloid leukemia minimal residual disease using a support vector machine. Oncotarget. 2016;7:71915-21.
Deulofeu M, Kolářová L, Salvadó V, María Peña-Méndez E, Almáši M, Štork M, et al. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. Sci Rep. 2019;9:7975.
Soh KT, Wallace PK. Monitoring of measurable residual disease in multiple myeloma by multiparametric flow cytometry. Curr Protoc Cytom. 2019;90:e63.