Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review

Leukemia is a rare but fatal cancer of the blood. This cancer arises from abnormal bone marrow cells and requires prompt diagnosis for effective treatment and positive patient prognosis. Traditional diagnostic methods (e.g., microscopy, flow cytometry, and biopsy) pose challenges in both accuracy and time, demanding an inquisition on the development and use of deep learning (DL) models, such as convolutional neural networks (CNN), which could allow for a faster and more exact diagnosis. Using specific, objective criteria, DL might hold promise as a tool for physicians to diagnose leukemia. The purpose of this review was to report the relevant available published literature on using DL to diagnose leukemia. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles published between 2010 and 2023 were searched using Embase, Ovid MEDLINE, and Web of Science, searching the terms "leukemia" AND "deep learning" or "artificial neural network" OR "neural network" AND "diagnosis" OR "detection." After screening retrieved articles using pre-determined eligibility criteria, 20 articles were included in the final review and reported chronologically due to the nascent nature of the phenomenon. The initial studies laid the groundwork for subsequent innovations, illustrating the transition from specialized methods to more generalized approaches capitalizing on DL technologies for leukemia detection. This summary of recent DL models revealed a paradigm shift toward integrated architectures, resulting in notable enhancements in accuracy and efficiency. The continuous refinement of models and techniques, coupled with an emphasis on simplicity and efficiency, positions DL as a promising tool for leukemia detection. With the help of these neural networks, leukemia detection could be hastened, allowing for an improved long-term outlook and prognosis. Further research is warranted using real-life scenarios to confirm the suggested transformative effects DL models could have on leukemia diagnosis.

Advances in Acute Myeloid Leukemia Classification, Prognostication and Monitoring by Flow Cytometry

Although final classification of acute myeloid leukemia (AML) integrates morphologic, cytogenetic, and molecular data, flow cytometry remains an essential component of modern AML diagnostics. Here, we review the current role of flow cytometry in the classification, prognostication, and monitoring of AML. We cover immunophenotypic features of key genetically defined AML subtypes and their effects on biological and clinical behaviors, review clinically tractable strategies to differentiate leukemias with ambiguous immunophenotypes more accurately and discuss key principles of standardization for measurable residual disease monitoring. These advances underscore flow cytometry's continued growth as a powerful diagnostic, management, and discovery tool.

Novel Tools for Diagnosis and Monitoring of AML

In recent years, major advances in the understanding of acute myeloid leukemia (AML) pathogenesis, together with technological progress, have led us into a new era in the diagnosis and follow-up of patients with AML. A combination of immunophenotyping, cytogenetic and molecular studies are required for AML diagnosis, including the use of next-generation sequencing (NGS) gene panels to screen all genetic alterations with diagnostic, prognostic and/or therapeutic value. Regarding AML monitoring, multiparametric flow cytometry and quantitative PCR/RT-PCR are currently the most implemented methodologies for measurable residual disease (MRD) evaluation. Given the limitations of these techniques, there is an urgent need to incorporate new tools for MRD monitoring, such as NGS and digital PCR. This review aims to provide an overview of the different technologies used for AML diagnosis and MRD monitoring and to highlight the limitations and challenges of current versus emerging tools.

Clinical application of flow cytometry in patients with unexplained cytopenia and suspected myelodysplastic syndrome: A report of the European LeukemiaNet International MDS-Flow Cytometry Working Group

This article discusses the rationale for inclusion of flow cytometry (FCM) in the diagnostic investigation and evaluation of cytopenias of uncertain origin and suspected myelodysplastic syndromes (MDS) by the European LeukemiaNet international MDS Flow Working Group (ELN iMDS Flow WG). The WHO 2016 classification recognizes that FCM contributes to the diagnosis of MDS and may be useful for prognostication, prediction, and evaluation of response to therapy and follow-up of MDS patients.

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.

Quality Assessment of a Large Multi-Center Flow Cytometric Dataset of Acute Myeloid Leukemia Patients-A EuroFlow Study

Flowcytometric analysis allows for detailed identification and characterization of large numbers of cells in blood, bone marrow, and other body fluids and tissue samples and therefore contributes to the diagnostics of hematological malignancies. Novel data analysis tools allow for multidimensional analysis and comparison of patient samples with reference databases of normal, reactive, and/or leukemia/lymphoma patient samples. Building such reference databases requires strict quality assessment (QA) procedures. Here, we compiled a dataset and developed a QA methodology of the EuroFlow Acute Myeloid Leukemia (AML) database, based on the eight-color EuroFlow AML panel consisting of six different antibody combinations, including four backbone markers. In total, 1142 AML cases and 42 normal bone marrow samples were included in this analysis. QA was performed on 803 AML cases using multidimensional analysis of backbone markers, as well as tube-specific markers, and data were compared using classical analysis employing median and peak expression values. Validation of the QA procedure was performed by re-analysis of >300 cases and by running an independent cohort of 339 AML cases. Initial evaluation of the final cohort confirmed specific immunophenotypic patterns in AML subgroups; the dataset therefore can reliably be used for more detailed exploration of the immunophenotypic variability of AML. Our data show the potential pitfalls and provide possible solutions for constructing large flowcytometric databases. In addition, the provided approach may facilitate the building of other databases and thereby support the development of novel tools for (semi)automated QA and subsequent data analysis.

CombiFlow: combinatorial AML-specific plasma membrane expression profiles allow longitudinal tracking of clones

Acute myeloid leukemia (AML) often presents as an oligoclonal disease whereby multiple genetically distinct subclones can coexist within patients. Differences in signaling and drug sensitivity of such subclones complicate treatment and warrant tools to identify them and track disease progression. We previously identified >50 AML-specific plasma membrane (PM) proteins, and 7 of these (CD82, CD97, FLT3, IL1RAP, TIM3, CD25, and CD123) were implemented in routine diagnostics in patients with AML (n = 256) and myelodysplastic syndrome (n = 33). We developed a pipeline termed CombiFlow in which expression data of multiple PM markers is merged, allowing a principal component-based analysis to identify distinctive marker expression profiles and to generate single-cell t-distributed stochastic neighbor embedding landscapes to longitudinally track clonal evolution. Positivity for one or more of the markers after 2 courses of intensive chemotherapy predicted a shorter relapse-free survival, supporting a role for these markers in measurable residual disease (MRD) detection. CombiFlow also allowed the tracking of clonal evolution in paired diagnosis and relapse samples. Extending the panel to 36 AML-specific markers further refined the CombiFlow pipeline. In conclusion, CombiFlow provides a valuable tool in the diagnosis, MRD detection, clonal tracking, and understanding of clonal heterogeneity in AML.

A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data

OBJECTIVES

Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance.

METHODS

Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification.

RESULTS

High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties.

CONCLUSIONS

Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary

Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment.

Abstract

Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.

Ferritin-based targeted delivery of arsenic to diverse leukaemia types confers strong anti-leukaemia therapeutic effects

Trivalent arsenic (AsIII) is an effective agent for treating patients with acute promyelocytic leukaemia, but its ionic nature leads to several major limitations like low effective concentrations in leukaemia cells and substantial off-target cytotoxicity, which limits its general application to other types of leukaemia. Here, building from our clinical discovery that cancerous cells from patients with different leukaemia forms featured stable and strong expression of CD71, we designed a ferritin-based As nanomedicine, As@Fn, that bound to leukaemia cells with very high affinity, and efficiently delivered cytotoxic AsIII into a large diversity of leukaemia cell lines and patient cells. Moreover, As@Fn exerted strong anti-leukaemia effects in diverse cell-line-derived xenograft models, as well as in a patient-derived xenograft model, in which it consistently outperformed the gold standard, showing its potential as a precision treatment for a variety of leukaemias.

Database-Guided Analysis for Immunophenotypic Diagnosis and Follow-Up of Acute Myeloid Leukemia With Recurrent Genetic Abnormalities

Acute myeloid leukemias (AMLs) are hematologic malignancies with varied molecular and immunophenotypic profiles, making them difficult to diagnose and classify. High-dimensional analysis algorithms might increase the utility of multicolor flow cytometry for AML diagnosis and follow-up. The objective of the present study was to assess whether a Compass database-guided analysis can be used to achieve rapid and accurate diagnoses. We conducted this study to determine whether this method could be employed to pilote the genetic and molecular tests and to objectively identify different-from-normal (DfN) patterns to improve measurable residual disease follow-up in AML. Three Compass databases were built using Infinicyt 2.0 software, including normal myeloid-committed hematopoietic precursors (n = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities (n = 50). The diagnostic accuracy of the Compass database-guided analysis was evaluated in a prospective validation study (125 suspected AML patients). This method excluded AML associated with the following genetic abnormalities: t(8;21), t(15;17), inv(16), and KMT2A translocation, with 92% sensitivity [95% confidence interval (CI): 78.6%–98.3%] and a 98.5% negative predictive value (95% CI: 90.6%–99.8%). Our data showed that the Compass database-guided analysis could identify phenotypic differences between AML groups, representing a useful tool for the identification of DfN patterns.

A Novel Method for the Evaluation of Bone Marrow Samples from Patients with Pediatric B-Cell Acute Lymphoblastic Leukemia-Multidimensional Flow Cytometry

Simple Summary

By supporting the selection of the most suitable treatment protocol, the advancement of diagnostic methods contributes to achieving the best possible outcome for pediatric cases of acute lymphoblastic leukemia (ALL). In this study, we focused on a novel possibility in the flow cytometric (FC) analysis, as this method is the initial, crucial step in the diagnostic algorithm of ALL and can determine further diagnostic and therapeutic strategies. After the retrospective, multidimensional dot-plot-based FC analysis of 72 bone marrow samples of children with ALL, we found that the integrated appearance of immunophenotype resulted in a simple, quick, and accurate method. Furthermore, associations between immunophenotype and cytogenetic alterations were detected, which enabled the identification of cases with potential adverse outcome by completing the conventional FC analysis with multidimensional dot-plots. Standardized multi-center studies would be required to validate our results.

Abstract

Multicolor flow cytometry (FC) evaluation has a key role in the diagnosis and prognostic stratification of ALL. Our aim was to create new analyzing protocols using multidimensional dot-plots. Seventy-two pediatric patients with ALL were included in this single-center study. Data of a normal BM sample and three BM samples of patients with BCP-ALL were merged, then all B cell populations of the four samples were presented in a single radar dot-plot, and those parameters and locations were selected in which the normal and pathological cell populations differed from each other the most. The integrated profile of immunophenotype resulted in a simple, rapid, and accurate method. There were no significant differences between the percentages of lymphoblasts in the detection of minimal residual disease (MRD) by multidimensional or conventional FC method (p = 0.903 at Day 15 and p = 0.155 at Day 33). Furthermore, we found associations between the position and the number of clusters of blast cells in the radar plots and cytogenetic properties (p = 0.002 and p < 0.0001 by the position and p = 0.02 by the number of subclones). FC analysis based on multidimensional dot-plots is not only a rapid, easy-to-use method, but can also provide additional information to screen cases which require detailed genetic examination.

Advanced Flow Cytometry Analysis Algorithms for Optimizing the Detection of "Different From Normal" Immunophenotypes in Acute Myeloid Blasts

Acute myeloid leukemias (AMLs) are a group of hematologic malignancies that are heterogeneous in their molecular and immunophenotypic profiles. Identification of the immunophenotypic differences between AML blasts and normal myeloid hematopoietic precursors (myHPCs) is a prerequisite to achieving better performance in AML measurable residual disease follow-ups. In the present study, we applied high-dimensional analysis algorithms provided by the Infinicyt 2.0 and Cytobank software to evaluate the efficacy of antibody combinations of the EuroFlow AML/myelodysplastic syndrome panel to distinguish AML blasts with recurrent genetic abnormalities (n = 39 AML samples) from normal CD45low CD117+ myHPCs (n = 23 normal bone marrow samples). Two types of scores were established to evaluate the abilities of the various methods to identify the most useful parameters/markers for distinguishing between AML blasts and normal myHPCs, as well as to distinguish between different AML groups. The Infinicyt Compass database-guided analysis was found to be a more user-friendly tool than other analysis methods implemented in the Cytobank software. According to the developed scoring systems, the principal component analysis based algorithms resulted in better discrimination between AML blasts and myHPCs, as well as between blasts from different AML groups. The most informative markers for the discrimination between myHPCs and AML blasts were CD34, CD36, human leukocyte antigen-DR (HLA-DR), CD13, CD105, CD71, and SSC, which were highly rated by all evaluated analysis algorithms. The HLA-DR, CD34, CD13, CD64, CD33, CD117, CD71, CD36, CD11b, SSC, and FSC were found to be useful for the distinction between blasts from different AML groups associated with recurrent genetic abnormalities. This study identified both benefits and the drawbacks of integrating multiple high-dimensional algorithms to gain complementary insights into the flow-cytometry data.

LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL

Background

Although some studies have demonstrated that lncRNAs are dysregulated in hematopoietic malignancies and may regulate the progression of leukemia, the detailed mechanism underlying tumorigenesis is still unclear. This study aimed to investigate lncRNAs that are differentially expressed in childhood B-cell acute lymphoblastic leukemia (B-ALL) and T-cell acute lymphoblastic leukemia (T-ALL) and their potential roles in the progression of childhood ALL.

Methods

Microarrays were used to detect differentially expressed lncRNAs and mRNAs. Several aberrantly expressed lncRNAs were validated by qRT-PCR. Leukemia-free survival was analyzed using the Kaplan–Meier method with a log-rank test. The co-expression correlations of lncRNAs and mRNAs were determined by Spearman’s correlation coefficient. CCK-8 assays and flow cytometry were performed to measure cell proliferation and apoptosis.

Results

We revealed that many lncRNAs were abnormally expressed in B-ALL and T-ALL. LncRNA/mRNA co-expression and the gene locus network showed that dysregulated lncRNAs are involved in diverse cellular processes. We also assessed the diagnostic value of the differentially expressed lncRNAs and confirmed the optimal combination of TCONS_00026679, uc002ubt.1, ENST00000411904, and ENST00000547644 with an area under the curve of 0.9686 [95 % CI: 0.9369–1.000, P < 0.001], with 90.7 % sensitivity and 92.19 % specificity, at a cut-off point of -0.5700 to distinguish childhood B-ALL patients from T-ALL patients, implying that these specific lncRNAs may have potential to detect subsets of childhood ALL. Notably, we found that the 8-year leukemia-free survival of patients with high TCONS_00026679 (p = 0.0081), ENST00000522339 (p = 0.0484), ENST00000499583 (p = 0.0381), ENST00000457217 (p = 0.0464), and ENST00000451368 (p = 0.0298) expression levels was significantly higher than that of patients with low expression levels of these lncRNAs, while patients with high uc002ubt.1 (p = 0.0499) and ENST00000547644 (p = 0.0451) expression levels exhibited markedly shorter 8-year leukemia-free survival. In addition, some lncRNAs were found to play different roles in cell proliferation and apoptosis in T-ALL and B-ALL.

Conclusions

Dysregulated lncRNAs involved in different regulatory mechanisms underlying the progression of childhood T-ALL and B-ALL might serve as novel biomarkers to distinguish ALL subsets and indicate poor outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-021-00303-x.

The potential of proliferative and apoptotic parameters in clinical flow cytometry of myeloid malignancies

Standardization of the detection and quantification of leukocyte differentiation markers by the EuroFlow Consortium has led to a major step forward in the integration of flow cytometry into classification of leukemia and lymphoma. In our opinion, this now enables introduction of markers for more dynamic parameters, such as proliferative and (anti)apoptotic markers, which have proven their value in the field of histopathology in the diagnostic process of solid tumors and lymphoma. Although use of proliferative and (anti)apoptotic markers as objective parameters in the diagnostic process of myeloid malignancies was studied in the past decades, this did not result in the incorporation of these biomarkers into clinical diagnosis. This review addresses the potential of these markers for implementation in the current, state-of-the-art multiparameter analysis of myeloid malignancies. The reviewed studies clearly recognize the importance of proliferation and apoptotic mechanisms in the pathogenesis of bone marrow (BM) malignancies. The literature is, however, contradictory on the role of these processes in myelodysplastic syndrome (MDS), MDS/myeloproliferative neoplasms, and acute myeloid leukemia. Furthermore, several studies underline the need for the analysis of the proliferative and apoptotic rates in subsets of hematopoietic BM cell lineages and argue that these results can have diagnostic and prognostic value in patients with myeloid malignancies. Recent developments in multiparameter flow cytometry now allow quantification of proliferative and (anti)apoptotic indicators in myeloid cells during their different maturation stages of separate hematopoietic cell lineages. This will lead to a better understanding of the biology and pathogenesis of these malignancies.

Immature acute leukaemias: lessons from the haematopoietic roadmap

It is essential to relate the biology of acute leukaemia to normal blood cell development. In this review, we discuss how modern models of haematopoiesis might inform approaches to diagnosis and management of immature leukaemias, with a specific focus on T-lymphoid and myeloid cases. In particular, we consider whether next-generation analytical tools could provide new perspectives that could improve our understanding of immature blood cancer biology.

CD2 and CD7 are sensitive flow cytometry screening markers for T-lineage acute leukemia(s): a study of 465 acute leukemia cases

T-lymphoblastic leukemia/lymphoma (T-ALL/LBL) is a rare acute leukemia that expresses cytoplasmic CD3 (cCD3) and frequently lacks surface CD3. Given that routine flow cytometric testing for cCD3 may not be feasible and cCD3 interpretation may be difficult, we investigate if surface CD2 and/or CD7 expression on blasts can be used by flow cytometry to screen for T-lineage acute leukemia. We retrospectively reviewed flow cytometric data from 233 acute leukemias (36 T-ALL/LBL, 8 mixed-phenotype acute leukemia T/myeloid, 80 acute myeloid leukemia, 97 B-ALL/LBL, 8 mixed-phenotype acute leukemia B/myeloid, and 4 acute undifferentiated leukemia cases). Uniform expression (≥75% of blasts) of CD2 and/or CD7 was seen in all 44 cCD3-positive cases but in only 11% (20/189) of cCD3-negative acute leukemias, thus demonstrating 100% sensitivity and 89% specificity in the identification of cCD3-positive (T-lineage) acute leukemia. To avoid selection bias, we prospectively studied 232 consecutive acute leukemias for which cCD3, CD2, and CD7 were automatically performed in all cases. Similar to the retrospective study, uniform expression of CD2 and/or CD7 on blasts showed 100% sensitivity and 88% specificity in the screening for cCD3-positive (T-lineage) acute leukemia. Therefore, acute leukemias with uniform expression of CD2 and/or CD7 warrant further testing for cCD3 to evaluate for T-lineage acute leukemia. Blasts that lack both uniform CD2 and CD7 expression do not require additional cCD3 testing. We propose that CD2 and CD7 could be utilized in a limited antibody flow cytometry panel as a sensitive, robust, and cost-effective way to screen for T-lineage acute leukemia.

Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia

Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

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).

Relative expansion of CD19-negative very-early normal B-cell precursors in children with acute lymphoblastic leukaemia after CD19 targeting by blinatumomab and CAR-T cell therapy: implications for flow cytometric detection of minimal residual disease

CD19-directed treatment in B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) frequently leads to the downmodulation of targeted antigens. As multicolour flow cytometry (MFC) application for minimal/measurable residual disease (MRD) assessment in BCP-ALL is based on B-cell compartment study, CD19 loss could hamper MFC-MRD monitoring after blinatumomab or chimeric antigen receptor T-cell (CAR-T) therapy. The use of other antigens (CD22, CD10, CD79a, etc.) as B-lineage gating markers allows the identification of CD19-negative leukaemia, but it could also lead to misidentification of normal very-early CD19-negative BCPs as tumour blasts. In the current study, we summarized the results of the investigation of CD19-negative normal BCPs in 106 children with BCP-ALL who underwent CD19 targeting (blinatumomab, n = 64; CAR-T, n = 25; or both, n = 17). It was found that normal CD19-negative BCPs could be found in bone marrow after CD19-directed treatment more frequently than in healthy donors and children with BCP-ALL during chemotherapy or after stem cell transplantation. Analysis of the antigen expression profile revealed that normal CD19-negative BCPs could be mixed up with residual leukaemic blasts, even in bioinformatic analyses of MFC data. The results of our study should help to investigate MFC-MRD more accurately in patients who have undergone CD19-targeted therapy, even in cases with normal CD19-negative BCP expansion.

Current trends in flow cytometry automated data analysis software

Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.

Monitoring minimal/measurable residual disease in B-cell acute lymphoblastic leukemia by flow cytometry during targeted therapy

B-cell acute lymphoblastic leukemia (B-ALL) is a hematologic malignancy of B-type lymphoid precursor cells. Minimal/measurable residual disease (MRD) is an important prognostic factor for B-ALL relapse. Traditional flow cytometry detection mainly relies on CD19-based gating strategies. However, relapse of CD19-negative B-ALL frequently occurs in patients who receive cellular and targeted therapy. This review will summarize the technical aspects of standard MRD assessment in B-ALL by flow cytometry, and then discuss the challenges of MRD strategies to deal with the scenario of CD19 negative or dim B-ALL relapse.

Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide toward the relevant classification panel and final diagnosis. In this study, we designed and validated an algorithm for automated (database-supported) gating and identification (AGI tool) of cell subsets within samples stained with ALOT. A reference database of normal peripheral blood (PB, n = 41) and bone marrow (BM; n = 45) samples analyzed with the ALOT was constructed, and served as a reference for the AGI tool to automatically identify normal cells. Populations not unequivocally identified as normal cells were labeled as checks and were classified by an expert. Additional normal BM (n = 25) and PB (n = 43) and leukemic samples (n = 109), analyzed in parallel by experts and the AGI tool, were used to evaluate the AGI tool. Analysis of normal PB and BM samples showed low percentages of checks (<3% in PB, <10% in BM), with variations between different laboratories. Manual analysis and AGI analysis of normal and leukemic samples showed high levels of correlation between cell numbers (r² > 0.95 for all cell types in PB and r² > 0.75 in BM) and resulted in highly concordant classification of leukemic cells by our previously published automated database-guided expert-supervised orientation tool for immunophenotypic diagnosis and classification of acute leukemia (Compass tool). Similar data were obtained using alternative, commercially available tubes, confirming the robustness of the developed tools. The AGI tool represents an innovative step in minimizing human intervention and requirements in expertise, toward a "sample-in and result-out" approach which may result in more objective and reproducible data analysis and diagnostics. The AGI tool may improve quality of immunophenotyping in individual laboratories, since high percentages of checks in normal samples are an alert on the quality of the internal procedures.

Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification

The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.

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.

Comments on EuroFlow standard operating procedures for instrument setup and compensation for BD FACS Canto II, Navios and BD FACS Lyric instruments

This commentary discusses particularities of application of the EuroFlow standardization of flow cytometric analyses on three different flow cytometers. The EuroFlow consortium developed a fully standardized approach for flow cytometric immunophenotyping of hematological malignancies and primary immunodeficiencies. Standardized instrument setup is an essential part of EuroFlow standardization. Initially, the EuroFlow Consortium developed and optimized a step-by-step standard operating procedure (SOP) to setup 8-color BD FACSCanto II flow cytometer (Canto), with the later inclusion of Navios (Beckman Coulter) and BD FACSLyric (Lyric). Those SOPs were developed to enable standardized and fully comparable fluorescence measurements in the three flow cytometers. In Canto and Navios, mean fluorescence intensity (MFI) of a reference peak of Rainbow beads calibration particles is used to set up photomultiplier (PMT) voltages for each detector channel in individual instruments to reach the same MFI across distinct instruments. In turn, a new feature of Lyric instruments allows to share collection of attributes that are used to place the positive population at the same position among instruments in the form of assays, as one of its components integrated in the Cytometer Setup and Tracking (CS&T) module. The EuroFlow Lyric assays thus allow for standardized acquisition of 8-color EuroFlow panels on Lyric without the need to setup the PMT voltages on the individual instruments manually. In summary, the standardized instrument setup developed by EuroFlow enables cross-platform inter- and intra-laboratory standardization of flow cytometric measurements. This commentary provides a perspective on the modifications of the standardized EuroFlow instrument setup of Canto, Navios and Lyric instruments that are described in detail in individual instrument-specfic SOPs available at the EuroFlow website.

Quality of Response in Acute Myeloid Leukemia: The Role of Minimal Residual Disease

In the acute myeloid leukemia (AML) setting, research has extensively investigated the existence and relevance of molecular biomarkers, in order to better tailor therapy with newly developed agents and hence improve outcomes and/or save the patient from poorly effective therapies. In particular, in patients with AML, residual disease after therapy does reflect the sum of the contributions of all factors associated with diagnosis and post-diagnosis resistance. The evaluation of minimal/measurable residual disease (MRD) can be considered as a key tool to guide patient’s management and a promising endpoint for clinical trials. In this narrative review, we discuss MRD evaluation as biomarker for tailored therapy in AML patients; we briefly report current evidence on the use of MRD in clinical practice, and comment on the potential ability of MRD in the assessment of the efficacy of new molecules.

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.

EuroFlow Lymphoid Screening Tube (LST) data base for automated identification of blood lymphocyte subsets

In recent years the volume and complexity of flow cytometry data has increased substantially. This has led to a greater number of identifiable cell populations in a single measurement. Consequently, new gating strategies and new approaches for cell population definition are required. Here we describe how the EuroFlow Lymphoid Screening Tube (LST) reference data base for peripheral blood (PB) samples was designed, constructed and validated for automated gating of the distinct lymphoid (and myeloid) subsets in PB of patients with chronic lymphoproliferative disorders (CLPD). A total of 46 healthy/reactive PB samples which fulfilled pre-defined technical requirements, were used to construct the LST-PB reference data base. In addition, another set of 92 PB samples (corresponding to 10 healthy subjects, 51 B-cell CLPD and 31 T/NK-cell CLPD patients), were used to validate the automated gating and cell-population labeling tools with the Infinicyt software. An overall high performance of the LST-PB data base was observed with a median percentage of alarmed cellular events of 0.8% in 10 healthy donor samples and of 44.4% in CLPD data files containing 49.8% (range: 1.3-96%) tumor cells. The higher percent of alarmed cellular events in every CLPD sample was due to aberrant phenotypes (75.6% cases) and/or to abnormally increased cell counts (86.6% samples). All 18 (22%) data files that only displayed numerical alterations, corresponded to T/NK-cell CLPD cases which showed a lower incidence of aberrant phenotypes (41%) vs B-cell CLPD cases (100%). Comparison between automated vs expert-bases manual classification of normal (r² = 0.96) and tumor cell populations (rho = 0.99) showed a high degree of correlation. In summary, our results show that automated gating of cell populations based on the EuroFlow LST-PB data base provides an innovative, reliable and reproducible tool for fast and simplified identification of normal vs pathological B and T/NK lymphocytes in PB of CLPD patients.

From big flow cytometry datasets to smart diagnostic strategies: The EuroFlow approach

The rise in the analytical speed of mutiparameter flow cytometers made possible by the introduction of digital instruments, has brought up the possibility to manage progressively higher number of parameters simultaneously on significantly greater numbers of individual cells. This has led to an exponential increase in the complexity and volume of flow cytometry data generated about cells present in individual samples evaluated in a single measurement. This increase demands for new developments in flow cytometry data analysis, graphical representation, and visualization and interpretation tools to address the new big data challenges, i.e. processing data files of ≥10-25 parameters per cell in samples with >5-10 million cells (= up to 250 million data points per cell sample) obtained in a few minutes. Here, we present a comprehensive review of some of the tools developed by the EuroFlow consortium for processing flow cytometric big data files in diagnostic laboratories, particularly focused on automated EuroFlow approaches for: i) identification of all cell populations coexisting in a sample (automated gating); ii) smart classification of aberrant cell populations in routine diagnostics; iii) automated reporting; together with iv) new tools developed to visualize n-dimensional data in 2-dimensional plots to support expert-guided automated data analysis. The concept of using reference data bases implemented into software programs, in combination with multivariate statistical analysis pioneered by EuroFlow, provides an innovative, highly efficient and fast approach for diagnostic screening, classification and monitoring of patients with distinct hematological and immune disorders, as well as other diseases.

EuroFlow-Based Flowcytometric Diagnostic Screening and Classification of Primary Immunodeficiencies of the Lymphoid System

Guidelines for screening for primary immunodeficiencies (PID) are well-defined and several consensus diagnostic strategies have been proposed. These consensus proposals have only partially been implemented due to lack of standardization in laboratory procedures, particularly in flow cytometry. The main objectives of the EuroFlow Consortium were to innovate and thoroughly standardize the flowcytometric techniques and strategies for reliable and reproducible diagnosis and classification of PID of the lymphoid system. The proposed EuroFlow antibody panels comprise one orientation tube and seven classification tubes and corresponding databases of normal and PID samples. The 8-color 12-antibody PID Orientation tube (PIDOT) aims at identification and enumeration of the main lymphocyte and leukocyte subsets; this includes naïve pre-germinal center (GC) and antigen-experienced post-GC memory B-cells and plasmablasts. The seven additional 8(-12)-color tubes can be used according to the EuroFlow PID algorithm in parallel or subsequently to the PIDOT for more detailed analysis of B-cell and T-cell subsets to further classify PID of the lymphoid system. The Pre-GC, Post-GC, and immunoglobulin heavy chain (IgH)-isotype B-cell tubes aim at identification and enumeration of B-cell subsets for evaluation of B-cell maturation blocks and specific defects in IgH-subclass production. The severe combined immunodeficiency (SCID) tube and T-cell memory/effector subset tube aim at identification and enumeration of T-cell subsets for assessment of T-cell defects, such as SCID. In case of suspicion of antibody deficiency, PIDOT is preferably directly combined with the IgH isotype tube(s) and in case of SCID suspicion (e.g., in newborn screening programs) the PIDOT is preferably directly combined with the SCID T-cell tube. The proposed ≥8-color antibody panels and corresponding reference databases combined with the EuroFlow PID algorithm are designed to provide fast, sensitive and cost-effective flowcytometric diagnosis of PID of the lymphoid system, easily applicable in multicenter diagnostic settings world-wide.

Impact of blood storage and sample handling on quality of high dimensional flow cytometric data in multicenter clinical research

Obtaining reliable and reproducible high quality data in multicenter clinical research settings requires design of optimal standard operating procedures. While the need for standardization in sample processing and data analysis is well-recognized, the impact of sample handling in the pre-analytical phase remains underestimated. We evaluated the impact of sample storage time (≈transport time) and temperature, type of anticoagulant, and limited blood volume on reproducibility of flow cytometric studies. EDTA and Na-Heparin samples processed with the EuroFlow bulk lysis protocol, stained and stored at 4 °C showed fairly stable expression of cell surface markers and distribution of the major leukocyte populations for up to 72 h. Additional sample fixation (1% PFA, Fix & Perm) did not have any beneficial effects. Blood samples stored for <24 h at room temperature before processing and staining seemed suitable for reliable immunophenotyping, although losses in absolute cell numbers were observed. The major losses were observed in myeloid cells and monocytes, while lymphocytes seemed less affected. Expression of cell surface markers and population distribution were more stable in Na-Heparin blood than in EDTA blood. However, storage of Na-Heparin samples was associated with faster decrease in leukocyte counts over time. Whole blood fixation strategies (Cyto-Chex, TransFix) improved long-term population distribution, but were detrimental for expression of cellular markers. The main conclusions from this study on healthy donor blood samples were successfully confirmed in EDTA clinical (patient) blood samples with different time delays until processing. Finally, we recognized the need for adjustments in bulk lysis in case of insufficient blood volumes. Despite clear overall conclusions, individual markers and cell populations had different preferred conditions. Therefore, specific guidelines for sample handling should always be adjusted to the clinical application and the main target leukocyte population.

A novel flow cytometric method for enhancing acute promyelocytic leukemia screening by multidimensional dot-plots

Acute promyelocytic leukemia (APL) is generally characterized by t(15;17)(q24;q21). In some cases, the classic translocation cannot be identified by conventional methods, since the PML-RARA fusion protein results from complex, variant, or cryptic translocation. The diagnostic algorithm of APL starts with screening methods, such as flow cytometry (FC), followed by fluorescence in situ hybridization or polymerase chain reaction to confirm the diagnosis. Our aim was to develop a novel protocol for analyzing APL samples based on multidimensional dot-plots that can provide comprehensive information about several markers at the same time. The protocol included four optimized multidimensional dot-plots, which were tested by retrospective reanalysis of FC results in APL (n = 8) and non-APL (n = 12) acute myeloid leukemia (AML) cases. After predicting the potential position of hypergranular- and microgranular-type aberrant promyelocytes, the percentages of blast populations were examined within the gates in all AML cases. The percentage of blasts in each predefined gate was well above the cut-off value (95%) in APL cases in all tubes. In non-APL AML cases, the percentage of blasts in the same gates never reached the cut-off value in all investigated tubes, and even when it did in a single tube, the pattern was markedly different from that observed in APL cases. In conclusion, multidimensional dot-plots can be used for screening APL even in cryptic APL cases, although reproducibility across several laboratories would require standardization of antibodies and fluorochromes. This easy-to-use and quick method can support the diagnosis of APL and the prompt initiation of the appropriate treatment.

Defects in memory B-cell and plasma cell subsets expressing different immunoglobulin-subclasses in patients with CVID and immunoglobulin subclass deficiencies

BACKGROUND

Predominantly antibody deficiencies (PADs) are the most prevalent primary immunodeficiencies, but their B-cell defects and underlying genetic alterations remain largely unknown.

OBJECTIVE

We investigated patients with PADs for the distribution of 41 blood B-cell and plasma cell (PC) subsets, including subsets defined by expression of distinct immunoglobulin heavy chain subclasses.

METHODS

Blood samples from 139 patients with PADs, 61 patients with common variable immunodeficiency (CVID), 68 patients with selective IgA deficiency (IgAdef), 10 patients with IgG subclass deficiency with IgA deficiency, and 223 age-matched control subjects were studied by using flow cytometry with EuroFlow immunoglobulin isotype staining. Patients were classified according to their B-cell and PC immune profile, and the obtained patient clusters were correlated with clinical manifestations of PADs.

RESULTS

Decreased counts of blood PCs, memory B cells (MBCs), or both expressing distinct IgA and IgG subclasses were identified in all patients with PADs. In patients with IgAdef, B-cell defects were mainly restricted to surface membrane (sm)IgA⁺ PCs and MBCs, with 2 clear subgroups showing strongly decreased numbers of smIgA⁺ PCs with mild versus severe smIgA⁺ MBC defects and higher frequencies of nonrespiratory tract infections, autoimmunity, and affected family members. Patients with IgG subclass deficiency with IgA deficiency and those with CVID showed defects in both smIgA⁺ and smIgG⁺ MBCs and PCs. Reduced numbers of switched PCs were systematically found in patients with CVID (absent in 98%), with 6 different defective MBC (and clinical) profiles: (1) profound decrease in MBC numbers; (2) defective CD27⁺ MBCs with almost normal IgG₃⁺ MBCs; (3) absence of switched MBCs; and (4) presence of both unswitched and switched MBCs without and; (5) with IgG₂⁺ MBCs; and (6) with IgA₁⁺ MBCs.

CONCLUSION

Distinct PAD defective B-cell patterns were identified that are associated with unique clinical profiles.

Categorizing and Establishing CD4 Service Equivalency: Testing of Residual, Archived External Quality Assessment Scheme Sample Panels Enables Accelerated Virtual Peer Laboratory Review

BACKGROUND

Testing of collated, curated residual archived external quality assessment (EQA) trial material, with pre-established true (consensus) values collated into 25-sample panels enables virtual peer review of a laboratory's proficiency. In this study, we introduce how archived EQAS samples/panels can establish equivalency of CD4 reporting across multiple laboratories in a national program.

METHODS

Curated unused trial material from archived EQAS CD4 trials were collated into 25-sample panels comprising three sets of five-sample replicates and at least three duplicate samples. Panel-samples were tested using predicate methods of participating laboratories and proficiency determined by calculating a Standard Deviation Index (SDI) for each panel-sample reported according to retrospective consensus pooled trial outcomes. All data were plotted using MS Excel and Graphpad-Prism with SDI limits between -2 and +2 SDI to define acceptable performance. Percentage similarity analysis determined agreement. Bead-count-rate data was used to determine pipetting error.

RESULTS

Tight clustering of SDI outcomes predicted acceptable laboratory proficiency with acceptable accuracy and precision as evidenced by mean SDI < 0.5 and SD of SDI < 0.5. Random pipetting error was identified with aberrant BCR. Systematic under-reading of results was noted in one lab with excellent precision but mean SDI > 1.6. Additional training requirements were evident where a respective laboratory's SD of SDI exceeded 0.7.

CONCLUSIONS

Archival panel testing undertaken across a network of CD4 laboratories using the same CD4 method to simultaneously test the same panel prior to national implementation highlighted proficient laboratories and was useful for identifying sites with service deficiencies and immediate additional training needs. © 2019 International Clinical Cytometry Society.

Frequent issues and lessons learned from EuroFlow QA

EuroFlow Quality Assessment was designed to provide a feedback on the quality of the standardization effort in executing the EuroFlow protocols for sample preparation and instrument setup. It was first beta-tested by the members of the EuroFlow consortium internally (2010-2013) and opened to the external participants from 2015 onwards. The goal of participation in the EuroFlow QA is to evaluate whether the technical quality of the data generated by the laboratory is comparable to the data of the EuroFlow members and thus if a non-EuroFlow member participant can use the EuroFlow reference sample database for his own patient evaluation. Also it assesses whether data are sufficiently standardized for automated population gating and alarm notification. By spring 2018, a total 87 laboratories from 32 countries on five continents have registered for the EuroFlow QA program. We evaluated 163 results of 2015-2016 QA rounds, where we noted clear improvement in the score of first-time participants (median score of 91% correct) when they participated second time or later (median score of 94% correct, p = 0,017), which was comparable to EuroFlow member scores (median score of 97% correct). Among frequent mistakes, we found non-adherence to the EuroFlow protocols (improper reagent used), improper gating and some compensation issues. In summary, we show that EuroFlow QA has a positive impact on improvement of standardized data quality of non-member laboratories adhering to the EuroFlow standard operating procedures and reagent panels.