Analysis of erythroid maturation in the nonlysed bone marrow with help of radar plots facilitates detection of flow cytometric aberrations in myelodysplastic syndromes

Analytical Appraisal of Hematogones in B-ALL MRD Assessment Using Multidimensional Dot-Plots by Multiparametric Flow Cytometry: A Critical Review and Update

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.

Flow Cytometric Assessment of Myelodysplastic Syndromes/Neoplasms

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.

Diagnosis of erythroid dysplasia by flow cytometry: a review

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.

CD36 relative mean fluorescence intensity of CD105+ nucleated erythroid cells can be used to differentiate myelodysplastic syndrome from megaloblastic anemia

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.

Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: Diagnostic power and prognostic impact

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.

Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group

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.

A series of case studies illustrating the role of flow cytometry in the diagnostic work-up of myelodysplastic syndromes

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.

Flow cytometric analysis of myelodysplasia: Pre-analytical and technical issues-Recommendations from the European LeukemiaNet

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.

Machine learning optimized multiparameter radar plots for B-cell acute lymphoblastic leukemia minimal residual disease analysis

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.

Proliferative and anti‐apoptotic fractions in maturing hematopoietic cell lineages and their role in homeostasis of normal bone marrow

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.

Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm

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.

Combined utility of CD177, P53, CD105 and c-kit immunohistochemical stains improves the detection of myelodysplastic syndrome

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.

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

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.

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes

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).

Monitoring treatment with 5-Azacitidine by flow cytometry predicts duration of hematological response in patients with myelodysplastic syndrome

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.

Radar plots facilitate differential diagnosis of acute promyelocytic leukemia and NPM1+ acute myeloid leukemia by flow cytometry

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.

Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine-learning assisted morphometrics

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.

Diagnostic significance of flow cytometry scales in diagnostics of myelodysplastic syndromes

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.