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Das N, Gajendra S, Gupta R. Analytical Appraisal of Hematogones in B-ALL MRD Assessment Using Multidimensional Dot-Plots by Multiparametric Flow Cytometry: A Critical Review and Update. Indian J Hematol Blood Transfus 2024; 40:12-24. [PMID: 38312180 PMCID: PMC10830989 DOI: 10.1007/s12288-023-01696-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/25/2023] [Indexed: 02/06/2024] Open
Abstract
The spectrum of benign B-cell precursors, known as hematogones (HGs), shows a significant morphological and immunophenotypic overlap with their malignant counterpart i.e. B-lymphoid blasts (BLBs). This results in a diagnostic dilemma in assessment of cases wherein there is a physiological preponderance of HGs and also poses a significant challenge in measurable residual disease assessment in B-cell acute lymphoblastic leukaemia. Consequently, expression patterns of various immunophenotypic markers are considered the most important tool in identification and delineation of HGs from BLBs. However, certain aspects of B-cell compartment evaluation by flow cytometric immunophenotyping and its relevance in clinical scenarios is yet to be defined precisely. This review summarizes current flowcytometric data on HGs and its discrimination from BLBs based on thorough review of literature and evaluation of in-house data. Furthermore, it focuses on the utility of an additional analytical tool i.e., radar plot for a comprehensive representation of various subsets of the B-cell compartment and their differentiation from BLBs. Supplementary Information The online version contains supplementary material available at 10.1007/s12288-023-01696-5.
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Affiliation(s)
- Nupur Das
- Laboratory Oncology, Dr. BRAIRCH, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029 India
| | - Smeeta Gajendra
- Laboratory Oncology, Dr. BRAIRCH, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029 India
| | - Ritu Gupta
- Laboratory Oncology, Dr. BRAIRCH, All India Institute of Medical Sciences (AIIMS), New Delhi, 110029 India
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Chen X, Johansson U, Cherian S. Flow Cytometric Assessment of Myelodysplastic Syndromes/Neoplasms. Clin Lab Med 2023; 43:521-547. [PMID: 37865501 DOI: 10.1016/j.cll.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2023]
Abstract
Myelodysplastic syndromes/neoplasms (MDS) are a heterogeneous class of hematopoietic stem cell neoplasms characterized by ineffective hematopoiesis leading to peripheral cytopenias. This group of diseases is typically diagnosed using a combination of clinical, morphologic, and genetic criteria. Many studies have described the value of multiparametric flow cytometry (MFC) in the diagnosis, classification, and prognostication of MDS. This review summarizes the approach to MDS diagnosis and immunophenotypic characterization using MFC and describes the current state while highlighting future opportunities and potential pitfalls.
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Affiliation(s)
- Xueyan Chen
- Translational Science and Therapeutics Division, Fred Hutch Cancer Center, Seattle, WA, USA; Department of Laboratory Medicine and Pathology, University of Washington, 825 Eastlake Avenue East, Seattle, WA 98109, USA
| | - Ulrika Johansson
- SI-HMDS, Haematology, UHBW NHS Foundation Trust, Bristol Royal Infirmary, Upper Maudlin Street, Bristol, BS2 8HW, UK
| | - Sindhu Cherian
- Department of Laboratory Medicine and Pathology, University of Washington, 825 Eastlake Avenue East, Seattle, WA 98109, USA.
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3
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Axler O, Bild F, Johansson ÅCM. An ultra-rapid screening method for acute leukemias. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2023; 104:474-477. [PMID: 37555457 DOI: 10.1002/cyto.b.22137] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/14/2023] [Accepted: 07/11/2023] [Indexed: 08/10/2023]
Affiliation(s)
- Olof Axler
- Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Filippa Bild
- Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Åsa C M Johansson
- Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
- Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund, Sweden
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Sorigue M. Diagnosis of erythroid dysplasia by flow cytometry: a review. Expert Rev Hematol 2023; 16:1049-1062. [PMID: 38018383 DOI: 10.1080/17474086.2023.2289534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/27/2023] [Indexed: 11/30/2023]
Abstract
INTRODUCTION The diagnosis of myelodysplastic syndrome (MDS) is complex. Flow cytometric analysis of the myelomonocytic compartment can be helpful, but it is highly subjective and reproducibility by non-specialized groups is unclear. Analysis of the erythroid lineage by flow cytometry is emerging as potentially more reproducible and easier to conduct, while keeping a high diagnostic performance. AREAS COVERED We review the evidence in this area, including 1) the use of well-established markers - CD71 and CD36 - and other less well-established markers and parameters; 2) the use of flow cytometric scores for the erythroid lineage; and 3) additional aspects, including the emergence of computational tools and the roles of flow cytometry beyond diagnosis. Finally, we discuss the limitations with the current evidence, including 1) the impact of the sample processing protocol and reagents on the results, 2) the lack of a standard gating strategy, and 3) conceptualization and design issues in the available publications. EXPERT OPINION We end by offering our recommendations for the current use - and our personal take on the value - of the analysis of erythroid lineage by flow cytometry.
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Affiliation(s)
- Marc Sorigue
- Medical Department, Trialing Health, Barcelona, Spain
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5
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Lu Y, Chen X, Zhang L. CD36 relative mean fluorescence intensity of CD105 + nucleated erythroid cells can be used to differentiate myelodysplastic syndrome from megaloblastic anemia. Sci Rep 2023; 13:8930. [PMID: 37264109 DOI: 10.1038/s41598-023-35994-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/27/2023] [Indexed: 06/03/2023] Open
Abstract
This study aims to evaluate the differences in CD105+ nucleated erythroid cell (NEC) immunophenotypes between myelodysplastic syndrome (MDS) and megaloblastic anemia (MA) using multiparameter flow cytometry and to screen potential markers. We analyzed bone marrow sample data from 37 patients with MDS, 35 with MA, 53 with iron-deficiency anemia (anemic controls), and 35 without anemia (normal controls). Compared with normal controls, the MDS and MA groups showed a decrease in the proportion of CD117+CD105+NEC and the relative mean fluorescence intensity (RMFI) of CD71 in CD105+NEC, accompanied by an increase in the coefficient of variation (CV) of CD71 and CD36. Additionally, CD36 RMFI of CD105+NEC increased in the MA group. Compared with anemia controls, the MDS and MA groups showed a significant increase in CD36 CV of CD105+NEC, and the CD36 RMFI in the MA group increased while that in the MDS group decreased. The proportions of CD117+CD105+NEC, CD36 CV, and CD36 RMFI in CD105+NEC differed significantly between MDS and MA groups. Among them, CD36 RMFI had good diagnostic performance (area under the curve: 0.844, 95% confidence interval: 0.753-0.935). CD36 RMFI of CD105+NEC may be a helpful marker in differentiating MDS and MA using multiparameter flow cytometry.
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Affiliation(s)
- Yan Lu
- Clinical Laboratory, Dongyang People's Hospital, 60 West Wuning Road, Dongyang, 322100, Zhejiang, China
| | - Xuya Chen
- Clinical Laboratory, Dongyang People's Hospital, 60 West Wuning Road, Dongyang, 322100, Zhejiang, China
| | - Longyi Zhang
- Clinical Laboratory, Dongyang People's Hospital, 60 West Wuning Road, Dongyang, 322100, Zhejiang, China.
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Oelschlaegel U, Oelschlaeger L, von Bonin M, Kramer M, Sockel K, Mohr B, Wagenfuehr L, Kroschinsky F, Bornhaeuser M, Platzbecker U. Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: Diagnostic power and prognostic impact. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2023; 104:141-150. [PMID: 34390327 DOI: 10.1002/cyto.b.22030] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2021] [Accepted: 08/04/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Flow cytometry (FCM) is a co-criterion in myelodysplastic syndromes (MDS) diagnostics according to the WHO classification. The presented study compared diagnostic power and prognostic impact of different FCM-based scores. METHODS A total of 807 bone marrow (BM) samples of patients with cytopenia (543 MDS, 153 non-clonal cytopenias, 111 non-MDS myeloid malignancies) and 78 healthy controls have been investigated using a standardized 8-color-FCM procedure. FCSS, Ogata-score, iFS, RED-score, and ELN-NEC were analyzed for sensitivity and specificity in comparison to standard diagnostic tools. Median follow up for patients was 26 month (range: 0.2-89). RESULTS The iFS showed the highest accuracy (80%) with the best balance between sensitivity (79%) and specificity (86%). This was also valid in MDS with very low IPSS-R and even in MDS without ring sideroblasts, with normal blast count and karyotype, where iFS could confirm diagnosis in 62% and 65% of patients. Besides the high diagnostic power, the established iFS category "consistent with MDS" was associated with inferior overall survival (OS) independent from WHO classification (median: 51 month vs. not reached, p < 0.0001). Remarkably, this iFS category redefined a subgroup of patients with worse OS within IPSS-R low-risk category (73 month vs. not reached, p = 0.0433). Finally, multivariable analysis showed that iFS added independent prognostic information regarding OS besides IPSS-R. CONCLUSIONS The iFS separates non-clonal cytopenias and MDS with the highest accuracy, provided information in addition to standard diagnostic procedures, and refined established prognostic tools for outcome prediction.
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Affiliation(s)
- Uta Oelschlaegel
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Lorenz Oelschlaeger
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Malte von Bonin
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU Dresden, and German Cancer Consortium (DKTK), partner site Dresden, and German Cancer Research Center (DKFZ) Heidelberg, Dresden, Germany
| | - Michael Kramer
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Katja Sockel
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Brigitte Mohr
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Lisa Wagenfuehr
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Frank Kroschinsky
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU, Dresden, Germany
| | - Martin Bornhaeuser
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU Dresden, German Cancer Research Center (DKFZ), and National Center for Tumor Diseases (NCT) Heidelberg, Dresden, Germany
| | - Uwe Platzbecker
- Medical Clinic and Policlinic 1, Hematology and Cellular Therapy, University Hospital Leipzig, Leipzig, Germany
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Porwit A, Béné MC, Duetz C, Matarraz S, Oelschlaegel U, Westers TM, Wagner-Ballon O, Kordasti S, Valent P, Preijers F, Alhan C, Bellos F, Bettelheim P, Burbury K, Chapuis N, Cremers E, Della Porta MG, Dunlop A, Eidenschink-Brodersen L, Font P, Fontenay M, Hobo W, Ireland R, Johansson U, Loken MR, Ogata K, Orfao A, Psarra K, Saft L, Subira D, Te Marvelde J, Wells DA, van der Velden VHJ, Kern W, van de Loosdrecht AA. Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2023; 104:27-50. [PMID: 36537621 PMCID: PMC10107708 DOI: 10.1002/cyto.b.22108] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/20/2022] [Accepted: 11/29/2022] [Indexed: 01/18/2023]
Abstract
Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34+ CD19- ) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.
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Affiliation(s)
- Anna Porwit
- Division of Oncology and Pathology, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | - Marie C Béné
- Hematology Biology, Nantes University Hospital, CRCINA Inserm 1232, Nantes, France
| | - Carolien Duetz
- Department of Hematology, Amsterdam UMC, VU University Medical Center Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Sergio Matarraz
- Cancer Research Center (IBMCC-USAL/CSIC), Department of Medicine and Cytometry Service, Institute for Biomedical Research of Salamanca (IBSAL) and CIBERONC, University of Salamanca, Salamanca, Spain
| | - Uta Oelschlaegel
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, TU Dresden, Dresden, Germany
| | - Theresia M Westers
- Department of Hematology, Amsterdam UMC, VU University Medical Center Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Orianne Wagner-Ballon
- Department of Hematology and Immunology, Assistance Publique-Hôpitaux de Paris, University Hospital Henri Mondor, Créteil, France
- Inserm U955, Université Paris-Est Créteil, Créteil, France
| | | | - Peter Valent
- Department of Internal Medicine I, Division of Hematology & Hemostaseology and Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Vienna, Austria
| | - Frank Preijers
- Laboratory of Hematology, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Canan Alhan
- Department of Hematology, Amsterdam UMC, VU University Medical Center Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Peter Bettelheim
- Department of Hematology, Ordensklinikum Linz, Elisabethinen, Linz, Austria
| | - Kate Burbury
- Department of Haematology, Peter MacCallum Cancer Centre, & University of Melbourne, Melbourne, Australia
| | - Nicolas Chapuis
- Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Centre-Université de Paris, Cochin Hospital, Paris, France
- Institut Cochin, INSERM U1016, CNRS UMR, Université de Paris, Paris, France
| | - Eline Cremers
- Division of Hematology, Department of Internal Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Matteo G Della Porta
- IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Alan Dunlop
- Department of Haemato-Oncology, Royal Marsden Hospital, London, UK
| | | | - Patricia Font
- Department of Hematology, Hospital General Universitario Gregorio Marañon-IiSGM, Madrid, Spain
| | - Michaela Fontenay
- Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Centre-Université de Paris, Cochin Hospital, Paris, France
- Institut Cochin, INSERM U1016, CNRS UMR, Université de Paris, Paris, France
| | - Willemijn Hobo
- Department of Internal Medicine I, Division of Hematology & Hemostaseology and Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Vienna, Austria
| | - Robin Ireland
- Department of Haematology and SE-HMDS, King's College Hospital NHS Foundation Trust, London, UK
| | - Ulrika Johansson
- Laboratory Medicine, SI-HMDS, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | | | - Kiyoyuki Ogata
- Metropolitan Research and Treatment Centre for Blood Disorders (MRTC Japan), Tokyo, Japan
| | - Alberto Orfao
- Cancer Research Center (IBMCC-USAL/CSIC), Department of Medicine and Cytometry Service, Institute for Biomedical Research of Salamanca (IBSAL) and CIBERONC, University of Salamanca, Salamanca, Spain
| | - Katherina Psarra
- Department of Immunology - Histocompatibility, Evangelismos Hospital, Athens, Greece
| | - Leonie Saft
- Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital and Institute Solna, Stockholm, Sweden
| | - Dolores Subira
- Department of Hematology, Flow Cytometry Unit, Hospital Universitario de Guadalajara, Guadalajara, Spain
| | - Jeroen Te Marvelde
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Vincent H J van der Velden
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Arjan A van de Loosdrecht
- Department of Hematology, Amsterdam UMC, VU University Medical Center Cancer Center Amsterdam, Amsterdam, The Netherlands
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Westers TM, Saft L, van der Velden VHJ, Te Marvelde JG, Dunlop A, Ireland R, Valent P, Porwit A, Béné MC, van de Loosdrecht AA. A series of case studies illustrating the role of flow cytometry in the diagnostic work-up of myelodysplastic syndromes. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2023; 104:87-97. [PMID: 35179296 PMCID: PMC10078764 DOI: 10.1002/cyto.b.22061] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 01/24/2022] [Accepted: 02/10/2022] [Indexed: 01/19/2023]
Abstract
Current guidelines recommend flow cytometric analysis as part of the diagnostic assessment of patients with cytopenia suspected for myelodysplastic syndrome. Herein we describe the complete work-up of six cases using multimodal integrated diagnostics. Flow cytometry assessments are illustrated by plots from conventional and more recent analysis tools. The cases demonstrate the added value of flow cytometry in case of hypocellular, poor quality, or ambiguous bone marrow cytomorphology. Moreover, they demonstrate how immunophenotyping results support clinical decision-making in inconclusive and clinically 'difficult' cases.
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Affiliation(s)
- Theresia M Westers
- Department of Hematology, Amsterdam University Medical Centers, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Leonie Saft
- Department of Pathology, Division of Hematopathology, Karolinska University Hospital and Institute, Stockholm, Sweden
| | - Vincent H J van der Velden
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen G Te Marvelde
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alan Dunlop
- Department of Haemato-Oncology, Royal Marsden Hospital, London, UK
- Department of Haematology and SE-HMDS, King's College Hospital NHS Foundation Trust, London, UK
| | - Robin Ireland
- Department of Haematology and SE-HMDS, King's College Hospital NHS Foundation Trust, London, UK
| | - Peter Valent
- Department of Internal Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Vienna, Austria
| | - Anna Porwit
- Department of Clinical Sciences, Oncology and Pathology, Faculty of Medicine, Lund University, Lund, Sweden
| | - Marie C Béné
- Hematology Biology, Nantes University Hospital and CRCINA, Nantes, France
| | - Arjan A van de Loosdrecht
- Department of Hematology, Amsterdam University Medical Centers, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
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9
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van der Velden VHJ, Preijers F, Johansson U, Westers TM, Dunlop A, Porwit A, Béné MC, Valent P, Te Marvelde J, Wagner-Ballon O, Oelschlaegel U, Saft L, Kordasti S, Ireland R, Cremers E, Alhan C, Duetz C, Hobo W, Chapuis N, Fontenay M, Bettelheim P, Eidenshink-Brodersen L, Font P, Loken MR, Matarraz S, Ogata K, Orfao A, Psarra K, Subirá D, Wells DA, Della Porta MG, Burbury K, Bellos F, Weiß E, Kern W, van de Loosdrecht A. Flow cytometric analysis of myelodysplasia: Pre-analytical and technical issues-Recommendations from the European LeukemiaNet. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2023; 104:15-26. [PMID: 34894176 PMCID: PMC10078694 DOI: 10.1002/cyto.b.22046] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/18/2021] [Accepted: 11/29/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Flow cytometry (FCM) aids the diagnosis and prognostic stratification of patients with suspected or confirmed myelodysplastic syndrome (MDS). Over the past few years, significant progress has been made in the FCM field concerning technical issues (including software and hardware) and pre-analytical procedures. METHODS Recommendations are made based on the data and expert discussions generated from 13 yearly meetings of the European LeukemiaNet international MDS Flow working group. RESULTS We report here on the experiences and recommendations concerning (1) the optimal methods of sample processing and handling, (2) antibody panels and fluorochromes, and (3) current hardware technologies. CONCLUSIONS These recommendations will support and facilitate the appropriate application of FCM assays in the diagnostic workup of MDS patients. Further standardization and harmonization will be required to integrate FCM in MDS diagnostic evaluations in daily practice.
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Affiliation(s)
- Vincent H J van der Velden
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Frank Preijers
- Department of Laboratory Medicine - Laboratory for Hematology, Radboudumc, Nijmegen, The Netherlands
| | - Ulrika Johansson
- Laboratory Medicine, SI-HMDS, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Theresia M Westers
- Department of Hematology, Amsterdam UMC, location VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Alan Dunlop
- Department of Haemato-Oncology, Royal Marsden Hospital, Sutton, Surrey, UK
| | - Anna Porwit
- Department of Clinical Sciences, Division of Oncology And Pathology, Faculty of Medicine, Lund University, Lund, Sweden
| | - Marie C Béné
- Hematology Biology, Nantes University Hospital and CRCINA, Nantes, France
| | - Peter Valent
- Department of Internal Medicine I, Division of Hematology & Hemostaseology, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute for Hematology and Oncology, Medical University of Vienna, Vienna, Austria
| | - Jeroen Te Marvelde
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Orianne Wagner-Ballon
- Department of Hematology and Immunology; and Université Paris-Est Créteil, Assistance Publique-Hôpitaux de Paris, University Hospital Henri Mondor, Inserm U955, Créteil, France
| | - Uta Oelschlaegel
- Department of Internal Medicine, University Hospital Carl-Gustav-Carus, Dresden, TU, Germany
| | - Leonie Saft
- Department of Clinical Pathology and Oncology, Karolinska University Hospital and Institute, Solna, Stockholm, Sweden
| | - Sharham Kordasti
- Comprehensive Cancer Centre, King's College London and Hematology Department, Guy's Hospital, London, UK
| | - Robin Ireland
- Comprehensive Cancer Centre, King's College London and Hematology Department, Guy's Hospital, London, UK
| | - Eline Cremers
- Department of Internal Medicine, Division of Hematology, Maastricht University Medical Center, AZ, Maastricht, The Netherlands
| | - Canan Alhan
- Department of Hematology, Amsterdam UMC, location VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Carolien Duetz
- Department of Hematology, Amsterdam UMC, location VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Willemijn Hobo
- Department of Laboratory Medicine - Laboratory for Hematology, Radboudumc, Nijmegen, The Netherlands
| | - Nicolas Chapuis
- Assistance Publique-Hôpitaux de Paris. Centre-Université de Paris, Cochin Hospital, Laboratory of Hematology and Université de Paris, Institut Cochin, INSERM U1016, CNRS UMR8104, Paris, France
| | - Michaela Fontenay
- Assistance Publique-Hôpitaux de Paris. Centre-Université de Paris, Cochin Hospital, Laboratory of Hematology and Université de Paris, Institut Cochin, INSERM U1016, CNRS UMR8104, Paris, France
| | - Peter Bettelheim
- Department of Internal Medicine, Ordensklinikum Linz Barmherzige Schwestern - Elisabethinen, Linz, Austria
| | | | - Patricia Font
- Department of Hematology, Hospital General Universitario Gregorio Marañon-IiSGM, Madrid, Spain
| | | | - Sergio Matarraz
- Cancer Research Center (IBMCC, USAL-CSIC), Department of Medicine and Cytometry Service, University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
- Biomedical Research Networking Centre Consortium of Oncology (CIBERONC), Instituto Carlos III, Salamanca, Spain
| | - Kiyoyuki Ogata
- Metropolitan Research and Treatment Centre for Blood Disorders (MRTC Japan), Tokyo, Japan
| | - Alberto Orfao
- Cancer Research Center (IBMCC, USAL-CSIC), Department of Medicine and Cytometry Service, University of Salamanca, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
- Biomedical Research Networking Centre Consortium of Oncology (CIBERONC), Instituto Carlos III, Salamanca, Spain
| | - Katherina Psarra
- Immunology Histocompatibility Department, Evangelismos Hospital, Athens, Greece
| | - Dolores Subirá
- Flow Cytometry Unit. Department of Hematology, Hospital Universitario de Guadalajara, Guadalajara, Spain
| | | | - Matteo G Della Porta
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy & Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Kate Burbury
- Department of Haematology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Australia
| | | | | | | | - Arjan van de Loosdrecht
- Department of Hematology, Amsterdam UMC, location VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
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10
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Shopsowitz KE, Liu L, Setiadi A, Al-Bakri M, Vercauteren S. Machine learning optimized multiparameter radar plots for B-cell acute lymphoblastic leukemia minimal residual disease analysis. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2022; 102:342-352. [PMID: 35726954 DOI: 10.1002/cyto.b.22084] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/05/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Flow cytometry is widely used for B-ALL minimal residual disease (MRD) analysis given its speed, availability, and sensitivity; however, distinguishing B-lymphoblasts from regenerative B-cells is not always straightforward. Radar plots, which project multiple markers onto a single plot, have been applied to other MRD analyses. Here we aimed to develop optimized radar plots for B-ALL MRD analysis. METHODS We compiled Children's Oncology Group (COG) flow data from 20 MRD-positive and 9 MRD-negative B-ALL cases (enriched for hematogones) to create labeled training and test data sets with equal numbers of B-lymphoblasts, hematogones, and mature B-cells. We used an automated approach to create hundreds of radar plots and ranked them based on the ability of support vector machine (SVM) models to separate blasts from normal B-cells in the training data set. Top-performing radar plots were compared with PCA, t-SNE, and UMAP plots, evaluated with the test data set, and integrated into clinical workflows. RESULTS SVM area under the ROC curve (AUC) for COG tube 1/2 radar plots improved from 0.949/0.921 to 0.989/0.968 after optimization. Performance was superior to PCA plots and comparable to UMAP, but with better generalizability to new data. When integrated into an MRD workflow, optimized radar plots distinguished B-lymphoblasts from other CD19-positive populations. MRD quantified by radar plots and serial gating were strongly correlated. DISCUSSION Radar plots were successfully optimized to discriminate between diverse B-lymphoblast populations and non-malignant CD19-positive populations in B-ALL MRD analysis. Our novel radar plot optimization strategy could be adapted to other MRD panels and clinical scenarios.
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Affiliation(s)
- Kevin E Shopsowitz
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Lorraine Liu
- Division of Hematopathology, British Columbia Children's Hospital, Vancouver, Canada
| | - Audi Setiadi
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
- Division of Hematopathology, British Columbia Children's Hospital, Vancouver, Canada
| | - Maryam Al-Bakri
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Suzanne Vercauteren
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
- Division of Hematopathology, British Columbia Children's Hospital, Vancouver, Canada
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11
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Wallace PK. Issue Highlights-September 2022. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2022; 102:337-341. [PMID: 36106576 DOI: 10.1002/cyto.b.22091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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12
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Mestrum SGC, Vanblarcum RBY, Drent RJM, Boonen BT, van Hemert WLW, Ramaekers FCS, Hopman AHN, Leers MPG. Proliferative and anti‐apoptotic fractions in maturing hematopoietic cell lineages and their role in homeostasis of normal bone marrow. Cytometry A 2022; 101:552-563. [PMID: 35429122 PMCID: PMC9540078 DOI: 10.1002/cyto.a.24558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/04/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022]
Abstract
Recent developments in clinical flow cytometry allow the simultaneous assessment of proliferative and anti‐apoptotic activity in the different hematopoietic cell lineages and during their maturation process. This can further advance the flow cytometric diagnosis of myeloid malignancies. In this study we established indicative reference values for the Ki‐67 proliferation index and Bcl‐2 anti‐apoptotic index in blast cells, as well as maturing erythroid, myeloid, and monocytic cells from normal bone marrow (BM). Furthermore, the cell fractions co‐expressing both proliferation and anti‐apoptotic markers were quantified. Fifty BM aspirates from femoral heads of patients undergoing hip replacement were included in this study. Ten‐color/twelve‐parameter flow cytometry in combination with a software‐based maturation tool was used for immunophenotypic analysis of Ki‐67 and Bcl‐2 positive fractions during the erythro‐, myelo‐, and monopoiesis. Indicative reference values for the Ki‐67 and Bcl‐2 positive fractions were established for different relevant hematopoietic cell populations in healthy BM. Ki‐67 and Bcl‐2 were equally expressed in the total CD34 positive blast cell compartment and 30% of Ki‐67 positive blast cells also showed Bcl‐2 positivity. The Ki‐67 and Bcl‐2 positive fractions were highest in the more immature erythroid, myeloid and monocytic cells. Both fractions then gradually declined during the subsequent maturation phases of these cell lineages. We present a novel application of an earlier developed assay that allows the simultaneous determination of the Ki‐67 proliferative and Bcl‐2 anti‐apoptotic indices in maturing hematopoietic cell populations of the BM. Their differential expression levels during the maturation process were in accordance with the demand and lifespan of these cell populations. The indicative reference values established in this study can act as a baseline for further cell biological and biomedical studies involving hematological malignancies.
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Affiliation(s)
- Stefan G. C. Mestrum
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Roanalis B. Y. Vanblarcum
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Roosmarie J. M. Drent
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
| | - Bert T. Boonen
- Department of Orthopedic Surgery Zuyderland Medical Center Heerlen The Netherlands
| | | | - Frans C. S. Ramaekers
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
- Nordic‐MUbio, Susteren The Netherlands
| | - Anton H. N. Hopman
- Department of Molecular Cell Biology, GROW‐School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht The Netherlands
| | - Math P. G. Leers
- Department of Clinical Chemistry & Hematology Zuyderland Medical Center Sittard‐Geleen The Netherlands
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13
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DiGiuseppe JA. Issue Highlights-March 2022. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2022; 102:85-87. [PMID: 35293132 DOI: 10.1002/cyto.b.22064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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14
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Porwit A, Violidaki D, Axler O, Lacombe F, Ehinger M, Béné MC. Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2022; 102:134-142. [PMID: 35150187 PMCID: PMC9306598 DOI: 10.1002/cyto.b.22059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/20/2021] [Accepted: 01/25/2022] [Indexed: 01/27/2023]
Abstract
Background The Flow‐Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open‐source R‐project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data. Methods Applied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list‐mode files of 16 patients (5 with non‐clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed. Results Unsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem‐cell transplantation. Conclusion Unsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.
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Affiliation(s)
- Anna Porwit
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Faculty of Medicine, Lund, Sweden.,Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Despoina Violidaki
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Faculty of Medicine, Lund, Sweden.,Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Olof Axler
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Faculty of Medicine, Lund, Sweden.,Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Francis Lacombe
- Hematology Biology, Bordeaux University Hospital Haut Leveque, Bordeaux, France
| | - Mats Ehinger
- Department of Clinical Sciences, Oncology and Pathology, Lund University, Faculty of Medicine, Lund, Sweden.,Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden
| | - Marie C Béné
- Hematology Biology, Nantes University Hospital & CRCINA, Nantes, France
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15
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Béné MC. Issue Highlights-September 2021. CYTOMETRY PART B-CLINICAL CYTOMETRY 2021; 100:537-540. [PMID: 34536066 DOI: 10.1002/cyto.b.22031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Marie C Béné
- Hematology Biology, Nantes University Hospital, Inserm 1232, CRCINA, Nantes, France
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16
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Aladily TN, Obiedat S, Bustami N, Alhesa A, Altantawi AM, Khader M, Mansour AT. Combined utility of CD177, P53, CD105 and c-kit immunohistochemical stains improves the detection of myelodysplastic syndrome. Ann Diagn Pathol 2021; 55:151810. [PMID: 34482217 DOI: 10.1016/j.anndiagpath.2021.151810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/01/2021] [Accepted: 08/17/2021] [Indexed: 11/28/2022]
Abstract
The diagnosis of myelodysplastic syndrome (MDS) relies primarily on identifying peripheral blood cytopenia and morphologic dysplasia as well as detecting cytogenetic aberrations in a subset of patients. Accumulating data points to the importance of examining certain immunophenotypic changes characteristic of MDS, most of which are tested by flow cytometry. The role of immunohistochemistry in the diagnostic workup of MDS is less known. In this study, we used immunohistochemistry to survey the expression patterns of CD177, P53, CD105 and c- kit in a cohort of MDS bone marrow specimens (n = 57) and compared the results with a control group of patients who had cytopenia for other benign conditions (n = 49). MDS cases showed significant higher rates of: CD177-loss (13/57, 23% vs 1/49, 2%; P = .0016), P53 overexpression (8/57, 14% vs none; P = .005) and the presence of clusters of CD105-positive cells (6/57, 11% vs none; P = .021). Increased c-kit-positive cells was more common in MDS patients, but not statistically significant (17/57, 30% vs 8/49, 16%; P = .102). On multivariate analysis, only loss of CD177 expression was significantly higher in MDS group (P = .014). These findings suggest that a panel of immunohistochemical stains could serve as an adjunct tool in investigating unexplained cytopenias and warrant further comparative studies with flow cytometry.
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Affiliation(s)
- Tariq N Aladily
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan.
| | - Sara Obiedat
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan
| | - Nadwa Bustami
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan
| | - Ahmad Alhesa
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan
| | - Ahmad M Altantawi
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan
| | - Majd Khader
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan
| | - Ahmad T Mansour
- Department of Hematopathology, The University of Jordan, Amman 11910, Jordan; Department of Pathology and Laboratory Medicine, University of Cincinnati, OH 45220, USA.
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17
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The Plasmacytoid Dendritic Cell CD123+ Compartment in Acute Leukemia with or without RUNX1 Mutation: High Inter-Patient Variability Disclosed by Immunophenotypic Unsupervised Analysis and Clustering. HEMATO 2021. [DOI: 10.3390/hemato2030036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Plasmacytoid dendritic cells (PDC) constitute a small subset of normal bone marrow (BM) cells but have also been shown to be present, sometimes in large numbers, in several hematological malignancies such as acute myeloid leukemia with RUNX1 mutation, chronic myelomonocytic leukemia or, obviously, blastic plasmacytoid dendritic cell neoplasms. These cells have been reported to display somewhat variable immunophenotypic features in different conditions. However, little is known of their plasticity within individual patients. Using an unsupervised clustering tool (FlowSOM) to re-visit flow cytometry results of seven previously analyzed cases of hematological malignancies (6 acute myeloid leukemia and one chronic myelomonocytic leukemia) with a PDC contingent, we report here on the unexpectedly high variability of PDC subsets. Although five of the studied patients harbored a RUNX1 mutation, no consistent feature of PDCs could be disclosed as associated with this variant. Moreover, the one normal single-node small subset of PDC detected in the merged file of six normal BM could be retrieved in the remission BM samples of three successfully treated patients. This study highlights the capacity of unsupervised flow cytometry analysis to delineate cell subsets not detectable with classical supervised tools.
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18
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DiGiuseppe JA. Issue Highlights-July 2021. CYTOMETRY PART B-CLINICAL CYTOMETRY 2021; 100:393-396. [PMID: 34292659 DOI: 10.1002/cyto.b.22027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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19
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Duetz C, Van Gassen S, Westers TM, van Spronsen MF, Bachas C, Saeys Y, van de Loosdrecht AA. Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes. Cytometry A 2021; 99:814-824. [PMID: 33942494 PMCID: PMC8453916 DOI: 10.1002/cyto.a.24360] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/06/2021] [Accepted: 04/26/2021] [Indexed: 12/03/2022]
Abstract
The diagnostic work‐up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected‐MDS. The computational diagnostic workflow consists of methods for pre‐processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).
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Affiliation(s)
- Carolien Duetz
- Department of Hematology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Sofie Van Gassen
- VIB Inflammation Research Center, Ghent University, Ghent, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Theresia M Westers
- Department of Hematology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Margot F van Spronsen
- Department of Hematology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Costa Bachas
- Department of Hematology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Yvan Saeys
- VIB Inflammation Research Center, Ghent University, Ghent, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Arjan A van de Loosdrecht
- Department of Hematology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, Netherlands
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20
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Subirá D, Alhan C, Oelschlaegel U, Porwit A, Psarra K, Westers TM, Golbano N, Nilsson L, van de Loosdrecht AA, de Miguel D. Monitoring treatment with 5-Azacitidine by flow cytometry predicts duration of hematological response in patients with myelodysplastic syndrome. Ann Hematol 2021; 100:1711-1722. [PMID: 33423077 DOI: 10.1007/s00277-021-04411-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 01/05/2021] [Indexed: 11/28/2022]
Abstract
5-Azacitidine (AZA) therapy is used in high-risk myelodysplastic syndrome (MDS) patients who often show abnormalities in their immunophenotype. We explored the potential impact of AZA on these immunophenotypic abnormalities in serial bone marrow studies performed in 81 patients from five centers. We compared the immunophenotypic features before and after therapy with AZA, established definitions consistent with flow cytometry immunophenotyping (FCI) improvement, and explored its clinical significance. After a median of 6 cycles of AZA, 41% of patients showed a FCI improvement and this finding associated with best possible clinical response (P < 0.001). FCI improvement also correlated with hematological improvement (HI) (53/78 patients; 68%), independently of their eligibility for stem cell transplantation. Among patients who achieved a HI after 6 cycles of AZA, the probability of maintaining this response at 12 cycles of AZA was twice as large (67%) for those patients who also achieved a FCI improvement after 6 cycles of AZA as compared to patients who did not (33%, P < 0.01). These findings support that monitoring of the immunophenotypic abnormalities during therapy with AZA may assist in redefining the quality of response in patients with MDS.
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Affiliation(s)
- Dolores Subirá
- Flow Cytometry Unit, Department of Hematology, Hospital Universitario de Guadalajara, c/Donante de Sangre s.n., 19002, Guadalajara, Spain.
| | - Canan Alhan
- Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, Netherlands
| | - Uta Oelschlaegel
- Medical Clinic and Policlinic I, University Hospital of TU Dresden, Dresden, Germany
| | - Anna Porwit
- Department of Clinical Sciences, Division Oncology and Pathology, Lund University, Lund, Sweden
| | - Katherina Psarra
- Department of Immunology and Histocompatibility, Evangelismos Hospital, Athens, Greece
| | - Theresia M Westers
- Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, Netherlands
| | - Nuria Golbano
- Department of Hematology, Hospital Universitario de Guadalajara, Guadalajara, Spain
| | - Lars Nilsson
- Department of Haematology and Coagulation Disorders, Skåne University Hospital, Lund, Sweden
| | | | - Dunia de Miguel
- Department of Hematology, Hospital Universitario de Guadalajara, Guadalajara, Spain
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21
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Definition of Erythroid Differentiation Subsets in Normal Human Bone Marrow Using FlowSOM Unsupervised Cluster Analysis of Flow Cytometry Data. Hemasphere 2020; 5:e512. [PMID: 33364551 PMCID: PMC7755522 DOI: 10.1097/hs9.0000000000000512] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 10/09/2020] [Indexed: 11/26/2022] Open
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22
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Gupta M, Jafari K, Rajab A, Wei C, Mazur J, Tierens A, Hyjek E, Musani R, Porwit A. Radar plots facilitate differential diagnosis of acute promyelocytic leukemia and NPM1+ acute myeloid leukemia by flow cytometry. CYTOMETRY PART B-CLINICAL CYTOMETRY 2020; 100:409-420. [PMID: 33301193 PMCID: PMC8359362 DOI: 10.1002/cyto.b.21979] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 11/09/2020] [Accepted: 11/24/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Acute promyelocytic leukemia (APL) is one of the most life-threatening hematological emergencies and requires a prompt correct diagnosis by cytomorphology and flow cytometry (FCM) with later confirmation by cytogenetics/molecular genetics. However, nucleophosmin 1 muted acute myeloid leukemia (NPM1+ AML) can mimic APL, especially the hypogranular variant of APL. Our study aimed to develop a novel, Radar plot-based FCM strategy to distinguish APLs and NPM1+ AMLs quickly and accurately. METHOD Diagnostic samples from 52 APL and 32 NPM1+ AMLs patients were analyzed by a 3-tube panel of 10-color FCM. Radar plots combining all markers were constructed for each tube. Percentages of positive leukemic cells and mean fluorescence intensity were calculated for all the markers. RESULTS APL showed significantly higher expression of CD64, CD2, and CD13, whereas more leukemic cells were positive for CD11b, CD11c, CD15, CD36, and HLA-DR in NPM1+ AMLs. Radar plots featured CD2 expression, a lack of a monocytic component, lack of expression of HLA-DR and CD15, and a lack of a prominent CD11c+ population as recurring characteristics of APL. The presence of blasts with low SSC, presence of at least some monocytes, some expression of HLA-DR and/or CD15, and a prominent CD11c population were recurrent characteristics of NPM1+ AMLs. Radar plot analysis could confidently separate all hypergranular APL cases from any NPM1+ AML and in 90% of cases between variant APL and blastic NPM1+ AML. CONCLUSION Radar plots can potentially add to differential diagnostics as they exhibit characteristic patterns distinguishing APL and different types of NPM1+ AMLs.
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Affiliation(s)
- Monali Gupta
- Immunophenotyping Laboratory, Viapath Analytics LLP, Department of Hematology, Kings College Hospital, London, UK.,Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada
| | - Katayoon Jafari
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada.,Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Amr Rajab
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada.,Medical-Scientific Department, Lifelabs Medical Laboratory Services, Toronto, Ontario, Canada
| | - Cuihong Wei
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada.,Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Joanna Mazur
- Department of Humanization of Medicine and Sexology, Collegium Medicum, University of Zielona Gora, Zielona Gora, Poland.,Department of Child and Adolescent Health, Institute of Mother and Child, Warsaw, Poland
| | - Anne Tierens
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada
| | - Elizabeth Hyjek
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada.,Department of Pathology, University of Chicago, Chicago, Illinois, USA
| | - Rumina Musani
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada
| | - Anna Porwit
- Department of Pathobiology and Laboratory Medicine, Division of Hematopathology, University Health Network, Toronto, Ontario, Canada.,Faculty of Medicine, Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
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23
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Rosenberg CA, Bill M, Rodrigues MA, Hauerslev M, Kerndrup GB, Hokland P, Ludvigsen M. Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine-learning assisted morphometrics. CYTOMETRY PART B-CLINICAL CYTOMETRY 2020; 100:554-567. [PMID: 33285035 DOI: 10.1002/cyto.b.21975] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/19/2020] [Accepted: 11/19/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND The hallmark of myelodysplastic syndrome (MDS) remains dysplasia in the bone marrow (BM). However, diagnosing MDS may be challenging and subject to inter-observer variability. Thus, there is an unmet need for novel objective, standardized and reproducible methods for evaluating dysplasia. Imaging flow cytometry (IFC) offers combined analyses of phenotypic and image-based morphometric parameters, for example, cell size and nuclearity. Hence, we hypothesized IFC to be a useful tool in MDS diagnostics. METHODS Using a different-from-normal approach, we investigated dyserythropoiesis by quantifying morphometric features in a median of 5953 erythroblasts (range: 489-68,503) from 14 MDS patients, 11 healthy donors, 6 non-MDS controls with increased erythropoiesis, and 6 patients with cytopenia. RESULTS First, we morphometrically confirmed normal erythroid maturation, as immunophenotypically defined erythroid precursors could be sequenced by significantly decreasing cell-, nuclear- and cytoplasm area. In MDS samples, we demonstrated cell size enlargement and increased fractions of macronormoblasts in late-stage erythroblasts (both p < .0001). Interestingly, cytopenic controls with high-risk mutational patterns displayed highly aberrant cell size morphometrics. Furthermore, assisted by machine learning algorithms, we reliably identified and enumerated true binucleated erythroblasts at a significantly higher frequency in two out of three erythroblast maturation stages in MDS patients compared to normal BM (both p = .0001). CONCLUSION We demonstrate proof-of-concept results of the applicability of automated IFC-based techniques to study and quantify morphometric changes in dyserythropoietic BM cells. We propose that IFC holds great promise as a powerful and objective tool in the complex setting of MDS diagnostics with the potential for minimizing inter-observer variability.
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Affiliation(s)
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Mathias Hauerslev
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | - Gitte B Kerndrup
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Peter Hokland
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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24
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Davydova YO, Parovichnikova EN, Galtseva IV, Kokhno AV, Dvirnyk VN, Kovrigina AM, Obukhova TN, Kapranov NM, Nikiforova KA, Glinkina SA, Troitskaya VV, Mikhailova EA, Fidarova ZT, Moiseeva TN, Lukina EA, Tsvetaeva NV, Nikulina OF, Kuzmina LA, Savchenko VG. Diagnostic significance of flow cytometry scales in diagnostics of myelodysplastic syndromes. CYTOMETRY PART B-CLINICAL CYTOMETRY 2020; 100:312-321. [PMID: 33052634 DOI: 10.1002/cyto.b.21965] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 09/07/2020] [Accepted: 10/02/2020] [Indexed: 01/16/2023]
Abstract
BACKGROUND Myelodysplastic syndromes (MDS) can present a challenge for clinicians. Multicolor flow cytometry (MFC) can aid in establishing a diagnosis. The aim of this study was to determine the optimal MFC approach for MDS. METHODS The study included 102 MDS (39 low-grade MDS), 83 cytopenic patients without myeloid neoplastic disorders (control group), and 35 healthy donors. Bone marrow was analyzed using a six-color MFC. Analysis was conducted according to the "Ogata score," "Wells score," and the integrated flow cytometry (iFC) score. RESULTS The respective sensitivity and specificity values were 77.5% and 90.4% for the Ogata score, 79.4% and 81.9% for the Wells score, and 87.3% and 87.6% for the iFC score. Specificity was not 100% due to deviations of MFC parameters in the control group. Patients with paroxysmal nocturnal hemoglobinuria (PNH) had higher levels of CD34+ CD7+ myeloid cells than donors. Aplastic anemia and PNH were characterized by a high proportion of CD56+ cells among CD34+ precursors and neutrophils. The proportion of MDS-related features increased with the progression of MDS. The highest number of CD34+ blasts was found in MDS with excess blasts. MDS with isolated del(5q) was characterized by a high proportion of CD34+ CD7+ cells and low granularity of neutrophils. In 39 low-grade MDS, the sensitivities were 53.8%, 61.5%, and 71.8% for Ogata score, Wells score, and iFC, respectively. CONCLUSION The results support iFC as a useful diagnostic tool in MDS.
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Affiliation(s)
- Yulia O Davydova
- Laboratory of Immunophenotyping, National Research Center for Hematology, Moscow, Russia
| | - Elena N Parovichnikova
- Chemotherapy Department for Hemoblastoses, Hemopoiesis Depression and BMT, National Research Center for Hematology, Moscow, Russia
| | - Irina V Galtseva
- Laboratory of Immunophenotyping, National Research Center for Hematology, Moscow, Russia
| | - Alina V Kokhno
- Intensive High-Dose Chemotherapy Department for Hemoblastoses and Hematopoiesis Depressions, National Research Center for Hematology, Moscow, Russia
| | - Valentina N Dvirnyk
- Centralized Diagnostic Laboratory, National Research Center for Hematology, Moscow, Russia
| | - Alla M Kovrigina
- Department of Pathology, National Research Center for Hematology, Moscow, Russia
| | - Tatyana N Obukhova
- Karyology Laboratory, National Research Center for Hematology, Moscow, Russia
| | - Nikolay M Kapranov
- Laboratory of Immunophenotyping, National Research Center for Hematology, Moscow, Russia
| | - Ksenia A Nikiforova
- Laboratory of Immunophenotyping, National Research Center for Hematology, Moscow, Russia
| | - Svetlana A Glinkina
- Department of Pathology, National Research Center for Hematology, Moscow, Russia
| | - Vera V Troitskaya
- Intensive High-Dose Chemotherapy Department for Hemoblastoses and Hematopoiesis Depressions, National Research Center for Hematology, Moscow, Russia
| | - Elena A Mikhailova
- Intensive High-Dose Chemotherapy Department for Hemoblastoses and Hematopoiesis Depressions, National Research Center for Hematology, Moscow, Russia
| | - Zalina T Fidarova
- Intensive High-Dose Chemotherapy Department for Hemoblastoses and Hematopoiesis Depressions, National Research Center for Hematology, Moscow, Russia
| | - Tatyana N Moiseeva
- Department of Hematology Advisory, National Research Center for Hematology, Moscow, Russia
| | - Elena A Lukina
- Department of Orphan Diseases, National Research Center for Hematology, Moscow, Russia
| | - Nina V Tsvetaeva
- Department of Orphan Diseases, National Research Center for Hematology, Moscow, Russia
| | - Olga F Nikulina
- Department of Orphan Diseases, National Research Center for Hematology, Moscow, Russia
| | - Larisa A Kuzmina
- Department of Intensive High-Dose Chemotherapy and BMT, National Research Center for Hematology, Moscow, Russia
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