Flow cytometry immunophenotyping in integrated diagnostics of patients with newly diagnosed cytopenia: one tube 10-color 14-antibody screening panel and 3-tube extensive panel for detection of MDS-related features

The role of flow cytometry in the classification of myeloid disorders

The World Health Organization classification (WHO-HAEM5) and the International Consensus Classification (ICC 2022) of myeloid neoplasms are based on the integration of clinical, morphologic, immunophenotypic, and genomic data. Flow cytometric immunophenotyping (FCIP) allows the identification, enumeration, and characterization of hematopoietic cells, and is therefore a powerful tool in the diagnosis, classification, and monitoring of hematological neoplasms. The vast majority of flow cytometry (FCM) studies in chronic myeloid neoplasms focus on its role in myelodysplastic neoplasms (MDS). FCM can also be helpful for the assessment of myeloproliferative neoplasms (MPN) and MDS/MPN, including the early detection of evolving myeloid or lymphoid blast crisis and the characterization of monocytic subsets. The classification of acute myeloid leukemia (AML) is primarily based on cytogenetic and molecular findings; however, FCIP is needed for subclassification of AML, not otherwise specified (NOS; ICC)/AML defined by differentiation (WHO-HAEM5). The main role of FCM in AML remains in making a rapid diagnosis and as a tool for measurable residual disease monitoring. Machine learning and artificial intelligence approaches can be used to analyze and classify FCM data. This article, based on an invited lecture at the 106th Annual Meeting of the German Society of Pathology in 2023, reviews the role of FCM in the classification of myeloid neoplasms, including recent publications on the application of artificial intelligence.

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.

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.

Advantages and Challenges of Differential Immune Cell Count Determination in Blood and Milk for Monitoring the Health and Well-Being of Dairy Cows

A key challenge of the 21st century will be to provide the growing world population with a sustainable and secure supply of food. Consequently, the dairy farming’s primary task is to lower milk losses and other inefficiencies associated with diseased cows. Moreover, a shift from curative to preventive health management would be desirable for mastitis and a wide variety of other infectious and non-infectious cattle diseases, some of which are known to have profound negative effects on the performance and well-being of cows. Differential cell counting (DCC), a procedure that aims to determine the proportions of different somatic cell types in raw milk samples, has not only the potential to optimize mastitis diagnostics, but it could furthermore serve as a diagnostic tool for monitoring the general and overall health status of dairy cows. Based on a broad search of the literature, the practical utility of various types of DCC is summarized and discussed in this review. Since it might be of advantage to interpret DCC with the aid of data from studies in humans, differences between the immune systems of humans and dairy cattle, with a special focus on surface marker expression profiles and γδ (gamma delta) T-cell characteristics, are also described.

A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification

Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.

The utility of a single tube 10-color flow cytometry for quantitative and qualitative analysis in myelodysplastic syndrome- a pilot study

INTRODUCTION

Assessment of myelodysplasia (MDS) by flow cytometry (FCM) includes elaborate panels, and interpretation is observer-dependent. This study evaluates single tube 10-color FCM in a test cohort of clinically suspected MDS patients.

METHODS

We analyzed fifty-six bone marrow (BM) samples from clinically suspected MDS patients in a morphology-blinded manner along with controls using a 10-color single tube flow cytometry. We analyzed the reproducibility of Ogata score and modified FCM scores, additionally incorporating the proportion of CD15, CD11b, CD56, and CD38MFI on CD34+CD19-cluster for each patient. Patients were grouped as proven-MDS, suspected-MDS, and non-MDS groups based on morphology and cytogenetics. Optimized multi-axial radar-plots were also used to analyze maturation patterns in the granulocytic, monocytic, and blast progenitor compartments of proven-MDS cases and controls.

RESULTS

Flow cytometric abnormalities ≥3 were present in proven-MDS (n = 23) with a sensitivity and specificity of 78 % and 94 %, respectively, as per Ogata score. The addition of CD38 MFI to the score yielded sensitivity and specificity of 82 % and 88 %, respectively. Additional analysis of aberrant expression of CD15, CD11b, and CD56 increased the diagnostic power of the FCM score. A qualitative analysis of data also showed differences in maturation patterns in proven-MDS compared to the control group.

CONCLUSION

Single tube 10-color FCM scoring, including Ogata score, modified-FCM scores, and radar plots pattern analysis, showed significant abnormalities in proven-MDS cases in this pilot study. Large databases, including FCM-scoring and pattern-based analysis for normal BM maturation, could be further validated and standardized for screening MDS.

A multicenter study evaluation of the ClearLLab 10C panels

Multiparameter flow cytometry plays an important role in the diagnosis, staging, and monitoring of patients with a suspected hematological malignancy. The ClearLLab 10C Panels consist of four reagent panels (B-Lineage Tube, T-Lineage Tube, and 2 Myeloid Lineage Tubes), each consisting of 10 color/10 antibody conjugates utilizing Beckman Coulters proprietary dry format optimized for investigating patients with suspected leukemia or lymphoma. A multicenter study was conducted to evaluate the performance of the ClearLLab 10C Panels for qualitative assessment of normal versus abnormal phenotype in peripheral blood, bone marrow, and lymph node samples with suspected hematological malignancies. ClearLLab 10C was compared to laboratory developed tests (LDTs) and final clinical diagnosis. Four clinical sites were used to enroll patient's spent specimens (n = 453); three laboratories in North America and one in Europe. Of the 453 specimens, 198 had no malignancy and 255 contained an abnormal population. The diagnostic accuracy of the ClearLLab 10C Panels was achieved with sensitivity of 96% and specificity of 95% with respect to patient final clinical diagnosis. The agreement of phenotyping between ClearLLab10C Panels and LDTs was 98%. Any differences noted between ClearLLab 10C and LDT were due to either the presence of populations below the level of detection, the lack of clinical information provided to the evaluators, or marker(s) not present in these panels. Overall, the ClearLLab 10C demonstrated excellent agreement to LDTs and diagnosis. These four reagent panels can be adopted by individual laboratories to assess the presence or absence of malignancy.

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.