Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

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.

ProGraME: A novel flow cytometry algorithm for the diagnosis of low-risk myelodysplastic syndromes in patients with cytopenia

OBJECTIVES

Flow cytometry (FC) is, together with morphology, genetics, and cytogenetics, used in the diagnostic assessment of cytopenia, as its value in evaluating bone marrow dysplasia been highlighted by several studies. However, despite the development of algorithms and guidelines, there is still a lack of standardization of the FC assessment of bone marrow dysplasia.

METHODS

By combining FC, together with morphological analysis and cytogenetic/molecular assessment in a training cohort of 209 patients, we created a novel score, ProGraME, which includes four parameters, each from a different cell lineage (Progenitor cells, Granulocytes, Monocytes, Erythroid precursors), solely based on relevant population gating. Points for ProGraME were attained for: lymphoid precursors ≤5% of all CD34+ cells (1.5 point); a granulocyte-to-lymphocyte side-scatter ratio ≤6 (1 point); a monocyte CD33-CV% ≥ 63 (2 points), and an erythroid precursor CD36-CV% ≥ 65 (2 points).

RESULTS

Using a cutoff of ≥2 as suggestive of dysplasia, ProGraME showed a sensitivity of 91% and a specificity of 81% in the training cohort and 95% and 75%, respectively, in an independent validation cohort of 159 patients. In addition, ProGraME had a very high negative predictive value of 97.1% and 97.8% in the training and validation cohorts, respectively, offering a useful tool for excluding bone marrow dysplasia. Finally, among the 23 CCUS patients that scored positive for dysplasia with ProGraME in the training cohort, 16 of them (69%) carried high-risk mutations, suggesting that FC might help identify early changes of dysplasia.

CONCLUSIONS

ProGraME can potentially optimize the FC diagnosis of low-risk myelodysplasia without minimal requirements of flow analysis other than accurate population gating.

CAR virus receptor mediates erythroid differentiation and migration and is downregulated in MDS

Myelodysplastic syndromes (MDS) are myeloid neoplasms presenting with dysplasia in the bone marrow (BM) and peripheral cytopenia. In most patients anemia develops. We screened for genes that are expressed abnormally in erythroid progenitor cells (EP) and contribute to the pathogenesis of MDS. We found that the Coxsackie-Adenovirus receptor (CAR = CXADR) is markedly downregulated in CD45low/CD105+ EP in MDS patients compared to control EP. Correspondingly, the erythroblast cell lines HEL, K562, and KU812 stained negative for CAR. Lentiviral transduction of the full-length CXADR gene into these cells resulted in an increased expression of early erythroid antigens, including CD36, CD71, and glycophorin A. In addition, CXADR-transduction resulted in an increased migration against a serum protein gradient, whereas truncated CXADR variants did not induce expression of erythroid antigens or migration. Furthermore, conditional knock-out of Cxadr in C57BL/6 mice resulted in anemia and erythroid dysplasia. Finally, decreased CAR expression on EP was found to correlate with high-risk MDS and decreased survival. Together, CAR is a functionally relevant marker that is down-regulated on EP in MDS and is of prognostic significance. Decreased CAR expression may contribute to the maturation defect and altered migration of EP and thus their pathologic accumulation in the BM in MDS.

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.

Multicenter prospective evaluation of diagnostic potential of flow cytometric aberrancies in myelodysplastic syndromes by the ELN iMDS flow working group

BACKGROUND

Myelodysplastic syndromes (MDS) represent a diagnostic challenge. This prospective multicenter study was conducted to evaluate pre-defined flow cytometric markers in the diagnostic work-up of MDS and chronic myelomonocytic leukemia (CMML).

METHODS

Thousand six hundred and eighty-two patients with suspected MDS/CMML were analyzed by both cytomorphology according to WHO 2016 criteria and flow cytometry according to ELN recommendations. Flow cytometric readout was categorized 'non-MDS' (i.e. no signs of MDS/CMML and limited signs of MDS/CMML) and 'in agreement with MDS' (i.e., in agreement with MDS/CMML).

RESULTS

Flow cytometric readout categorized 60% of patients in agreement with MDS, 28% showed limited signs of MDS and 12% had no signs of MDS. In 81% of cases flow cytometric readouts and cytomorphologic diagnosis correlated. For high-risk MDS, the level of concordance was 92%. A total of 17 immunophenotypic aberrancies were found independently related to MDS/CMML in ≥1 of the subgroups of low-risk MDS, high-risk MDS, CMML. A cut-off of ≥3 of these aberrancies resulted in 80% agreement with cytomorphology (20% cases concordantly negative, 60% positive). Moreover, >3% myeloid progenitor cells were significantly associated with MDS (286/293 such cases, 98%).

CONCLUSION

Data from this prospective multicenter study led to recognition of 17 immunophenotypic markers allowing to identify cases 'in agreement with MDS'. Moreover, data emphasizes the clinical utility of immunophenotyping in MDS diagnostics, given the high concordance between cytomorphology and the flow cytometric readout. Results from the current study challenge the application of the cytomorphologically defined cut-off of 5% blasts for flow cytometry and rather suggest a 3% cut-off for the latter.

Immunophenotypic changes of monocytes in myelodysplastic syndrome and clinical significance

Background

Myelodysplastic syndrome (MDS) is a type of heterogeneous myeloid clonal disorder usually diagnosed based on a combination of multiple laboratory examinations, including analysis of peripheral blood cells, bone marrow cell morphology and cytogenetics. However, there is a certain difficulty in cases with no distinct changes in hematology and marrow cell morphology.

Methods

We adopt flow cytometry to quantitatively analyze the immunophenotypic changes of marrow monocytes according to the surface antigens and their combinations at different differentiation stages, so as to study the changes of monocytes during differentiation in patients with bone marrow failure. In the meantime, the relationship between the immunophenotypic changes of marrow monocytes and IPSS-R score and prognosis of MDS patients was analyzed.

Results

Our results demonstrated disorders of maturation and differentiation of monocytes in patients with MDS and clonal cytopenias of undetermined significance as compared to those with aplastic anemia and healthy individuals. In addition, the differentiation abnormality gradually increased with the disease progression. Furthermore, CD300e expression was found to show significant associations with the clinical stage and disease progression of MDS, and the progression-free survival and AML-free survival were much longer in MDS patients highly expressing CD300e on monocytes.

Conclusions

CCUS and MDS patients have disorders of differentiation and maturation of monocytes, which tends to be more critical with MDS progression or transforms to AML. Moreover, high CD300e expression has the potential to be a favorable prognostic marker for MDS. This study provides important insights to the role of monocyte immunotyping in the diagnosis, differentiation and prognosis of MDS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10238-022-00856-7.

Flow Cytometric Findings in Clonal Cytopenia of Undetermined Significance

OBJECTIVES

To examine flow cytometric (FCM) findings in clonal cytopenia of undetermined significance (CCUS) in relation to variant allele fraction (VAF) and mutation risk.

METHODS

Nine FCM parameters, including 5 FCM metrics (Meyerson-Alayed scoring scheme [MASS] parameters) we previously used to identify myelodysplastic syndromes (MDS), were compared among 96 CCUS samples, 100 low-grade MDS samples and 100 samples from patients without somatic alterations (controls).

RESULTS

FCM findings did not differ between CCUS samples with less than 20% VAF and controls. CCUS samples with more than 20% VAF (CCUS >20% VAF) demonstrated more than 1 abnormal FCM parameter at a frequency between MDS and controls. Abnormalities in CCUS with high-risk alterations (CCUS(hi)) were similar to MDS, with no statistical difference in the percentage of cases with more than 1 FCM abnormality or a positive MASS score. The positive predictive value (PPV) for clinically significant myeloid processes; MDS, CCUS(hi), and CCUS >20% VAF compared with other CCUS samples and controls was 94.8%, with 96.5% specificity and 61% sensitivity using a modified MASS score. A subset of MDS (43%) was distinguished from CCUS(hi) and CCUS >20% VAF using 3 parameters, with a 93.5% PPV and 83.3% specificity.

CONCLUSIONS

FCM abnormalities can distinguish high-risk CCUS based on VAF or alteration type from low-risk CCUS and MDS in many cases. The findings are of potential utility in the evaluation of patients with cytopenias.

Prognostic impact of immunophenotypic aberrancies of blasts in lower risk myelodysplastic syndrome

Objective/background: Low risk myelodysplastic syndrome (MDS) is a marrow failure state eventually leading to transfusion dependence. Flow cytometry has previously been demonstrated as prognostic tool in MDS, however not thoroughly studied in lower risk MDS. In this study, we assessed whether assessment for immunophenotypic blast aberrancies by flow in low risk MDS patients has a prognostic role in these patients.

Methods: A total of 63 consecutive patients diagnosed with low/intermediate risk MDS were included. We recorded initial flow results, and collected time to transfusion dependence, and AML progression.

Results: On multivariate cox regression analysis, increasing IPSS-R score, an increase in the number of blast aberrancies on flow cytometry, and aberrant expression of CD7 on myeloid blasts increased likelihood of transfusion dependence.

Conclusion:Low risk MDS patients with increasingly aberrant blast phenotypes by flow may be at risk for earlier transfusion dependence.

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

How I investigate dysgranulopoiesis

Dysgranulopoiesis is a condition in which granulocytic production is defective and is most often described in neoplastic conditions. However, it can also be frequently seen in non-neoplastic conditions. Early suspicion and detection of these non-neoplastic causes may prevent further invasive and expensive interventions. In this review, we take a look at the various causes of dysgranulopoiesis with an emphasis on non-neoplastic etiologies, followed by a detailed outline of the laboratory approach for determining its many causes.

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).

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.

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

PURPOSE OF REVIEW

This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies.

RECENT FINDINGS

In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary.

SUMMARY

Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.