Innovation in Flow Cytometry Analysis: A New Paradigm Delineating Normal or Diseased Bone Marrow Subsets Through Machine Learning

Minimal Residual Disease in Acute Myeloid Leukemia: Old and New Concepts

Minimal residual disease (MRD) is of major importance in onco-hematology, particularly in acute myeloid leukemia (AML). MRD measures the amount of leukemia cells remaining in a patient after treatment, and is an essential tool for disease monitoring, relapse prognosis, and guiding treatment decisions. Patients with a negative MRD tend to have superior disease-free and overall survival rates. Considerable effort has been made to standardize MRD practices. A variety of techniques, including flow cytometry and molecular methods, are used to assess MRD, each with distinct strengths and weaknesses. MRD is recognized not only as a predictive biomarker, but also as a prognostic tool and marker of treatment efficacy. Expected advances in MRD assessment encompass molecular techniques such as NGS and digital PCR, as well as optimization strategies such as unsupervised flow cytometry analysis and leukemic stem cell monitoring. At present, there is no perfect method for measuring MRD, and significant advances are expected in the future to fully integrate MRD assessment into the management of AML patients.

Prognostic value of postinduction medullary myeloid recovery by flow cytometry in acute myeloid leukemia

Risk stratification and treatment response evaluation are key features in acute myeloid leukemia (AML) management. Immunophenotypic and molecular approaches all rely on the detection of persisting leukemic cells by measurable residual disease techniques. A new approach is proposed here by assessing medullary myeloid maturation by flow cytometry through a myeloid progenitor ratio (MPR). The normal MPR range was defined using reference normal bone marrows (n = 48). MPR was considered balanced if between 1 and 4 and unbalanced if < 1 or > 4. MPR was retrospectively assessed at baseline and post-induction for 206 newly diagnosed AML patients eligible for intensive treatment from two different French centers. All AML baseline MPR were unbalanced and thus significantly different from normal MPR (p < 0.0001). Patients with an unbalanced MPR after induction had worse 3-year overall survival (OS) (44.4% vs. 80.2%, HR, 2.96; 95% CI, 1.81-4.84, p < 0.0001) and 3-year relapse free survival (RFS) (38.7% vs. 64.4%, HR, 2.11; 95% CI, 1.39-3.18, p < 0.001). In multivariate analysis, postinduction unbalanced MPR was significantly associated with shorter OS and RFS regardless of the European LeukemiaNet 2010 risk stratification or NPM1/FLT3-ITD status. A balanced postinduction MPR conversely conferred favorable outcomes and reflects medullary myeloid recovery.

Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia

Undetectable measurable residual disease (MRD) is associated with favourable clinical outcomes in chronic lymphocytic leukaemia (CLL). While assessment is commonly performed using multiparameter flow cytometry (MFC), this approach is associated with limitations including user bias and expertise that may not be widely available. Implementation of unsupervised clustering algorithms in the laboratory can address these limitations and have not been previously reported in a systematic quantitative manner. We developed a computational pipeline to assess CLL MRD using FlowSOM. In the training step, a self-organising map was generated with nodes representing the full breadth of normal immature and mature B cells along with disease immunophenotypes. This map was used to detect MRD in multiple validation cohorts containing a total of 456 samples. This included an evaluation of atypical CLL cases and samples collected from two different laboratories. Computational MRD showed high correlation with expert analysis (Pearson's r > 0.99 for typical CLL). Binary classification of typical CLL samples as either MRD positive or negative demonstrated high concordance (>98%). Interestingly, computational MRD detected disease in a small number of atypical CLL cases in which MRD was not detected by expert analysis. These results demonstrate the feasibility and value of automated MFC analysis in a diagnostic laboratory.

Artificial intelligence in hematological diagnostics: Game changer or gadget?

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.

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.

Mixed Phenotype/Lineage Leukemia: Has Anything Changed for 2021 on Diagnosis, Classification, and Treatment?

Purpose of Review

Recent advances in the small field of the rare mixed phenotype acute leukemias (MPAL) are presented focusing on a better understanding of their pathophysiology and search for better therapeutic approaches.

Recent Findings

Three aspects of respective classification, therapy, and immunophenotype of MPAL are reviewed. New proposals have been made to segregate MPAL subtypes based on their genomic landscape. In parallel, it was found that a large array of therapeutic approaches has been tested in the past few years with increasingly good results. Finally, we explored the use of unsupervised flow cytometry analysis to dissect subtle variations in markers expression to better characterize the variegating aspect of MPALs.

Summary

Genomic and immunophenotypic aspects more clearly link MPAL subtypes with bona fide acute myeloblastic of lymphoblastic leukemias. This is likely to impact therapeutic strategies, towards a better management and outcome.

2021 Update on MRD in acute myeloid leukemia: a consensus document from the European LeukemiaNet MRD Working Party

Measurable residual disease (MRD) is an important biomarker in acute myeloid leukemia (AML) that is used for prognostic, predictive, monitoring, and efficacy-response assessments. The European LeukemiaNet (ELN) MRD Working Party evaluated standardization and harmonization of MRD in an ongoing manner and has updated the 2018 ELN MRD recommendations based on significant developments in the field. New and revised recommendations were established during in-person and online meetings, and a 2-stage Delphi poll was conducted to optimize consensus. All recommendations are graded by levels of evidence and agreement. Major changes include technical specifications for next-generation sequencing-based MRD testing and integrative assessments of MRD irrespective of technology. Other topics include use of MRD as a prognostic and surrogate end point for drug testing; selection of the technique, material, and appropriate time points for MRD assessment; and clinical implications of MRD assessment. In addition to technical recommendations for flow- and molecular-MRD analysis, we provide MRD thresholds and define MRD response, and detail how MRD results should be reported and combined if several techniques are used. MRD assessment in AML is complex and clinically relevant, and standardized approaches to application, interpretation, technical conduct, and reporting are of critical importance.

CD158k and PD-1 expressions define heterogeneous subtypes of Sezary syndrome

SS can be divided into 3 subtypes, each with a different immune environment and response to treatment.

Sezary syndrome (SS) is a rare leukemic form of cutaneous T-cell lymphoma. Diagnosis mainly depends on flow cytometry, but results are not specific enough to be unequivocal. The difficulty in defining a single marker that could characterize Sezary cells may be the consequence of different pathological subtypes. In this study, we used multivariate flow cytometry analyses. We chose to investigate the expression of classical CD3, CD4, CD7, and CD26 and the new association of 2 markers CD158k and PD-1. We performed lymphocyte computational phenotypic analyses during diagnosis and follow-up of patients with SS to define new SS classes and improve the sensitivity of the diagnosis and the follow-up flow cytometry method. Three classes of SS, defined by different immunophenotypic profiles, CD158k⁺ SS, CD158k⁻PD-1⁺ SS, CD158k and PD-1 double-negative SS, showed different CD8⁺ and B-cell environments. Such a study could help to diagnose and define biological markers of susceptibility/resistance to treatment, including immunotherapy.

The Plasmacytoid Dendritic Cell CD123+ Compartment in Acute Leukemia with or without RUNX1 Mutation: High Inter-Patient Variability Disclosed by Immunophenotypic Unsupervised Analysis and Clustering

Plasmacytoid dendritic cells (PDC) constitute a small subset of normal bone marrow (BM) cells but have also been shown to be present, sometimes in large numbers, in several hematological malignancies such as acute myeloid leukemia with RUNX1 mutation, chronic myelomonocytic leukemia or, obviously, blastic plasmacytoid dendritic cell neoplasms. These cells have been reported to display somewhat variable immunophenotypic features in different conditions. However, little is known of their plasticity within individual patients. Using an unsupervised clustering tool (FlowSOM) to re-visit flow cytometry results of seven previously analyzed cases of hematological malignancies (6 acute myeloid leukemia and one chronic myelomonocytic leukemia) with a PDC contingent, we report here on the unexpectedly high variability of PDC subsets. Although five of the studied patients harbored a RUNX1 mutation, no consistent feature of PDCs could be disclosed as associated with this variant. Moreover, the one normal single-node small subset of PDC detected in the merged file of six normal BM could be retrieved in the remission BM samples of three successfully treated patients. This study highlights the capacity of unsupervised flow cytometry analysis to delineate cell subsets not detectable with classical supervised tools.

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.

Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia

The assessment of minimal residual disease (MRD) is increasingly considered to monitor response to therapy in hematological malignancies. In acute myeloblastic leukemia (AML), molecular MRD (mMRD) is possible for about half the patients while multiparameter flow cytometry (MFC) is more broadly available. However, MFC analysis strategies are highly operator-dependent. Recently, new tools have been designed for unsupervised MFC analysis, segregating cell-clusters with the same immunophenotypic characteristics. Here, the Flow-Self-Organizing-Maps (FlowSOM) tool was applied to assess MFC-MRD in 96 bone marrow (BM) follow-up (FU) time-points from 40 AML patients with available mMRD. A reference FlowSOM display was built from 19 healthy/normal BM samples (NBM), then simultaneously compared to the patient's diagnosis and FU samples at each time-point. MRD clusters were characterized individually in terms of cell numbers and immunophenotype. This strategy disclosed subclones with varying immunophenotype within single diagnosis and FU samples including populations absent from NBM. Detectable MRD was as low as 0.09% in MFC and 0.051% for mMRD. The concordance between mMRD and MFC-MRD was 80.2%. MFC yielded 85% specificity and 69% sensitivity compared to mMRD. Unsupervised MFC is shown here to allow for an easy and robust assessment of MRD, applicable also to AML patients without molecular markers.

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.

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.

Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry

Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named "Clouds", are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the "Abnormality Ratio" (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as "Leukemic Clouds" (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient's L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (R2 = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice.

Minimal residual disease and stem cell transplantation outcomes

Risk classification and tailoring of treatment are essential for improving outcome for patients with acute myeloid leukemia or high-risk myelodysplastic syndrome. Both patient and leukemia-specific characteristics assessed using morphology, cytogenetics, molecular biology, and multicolor flow cytometry are relevant at diagnosis and during induction, consolidation, and maintenance phases of the treatment. In particular, minimal residual disease (MRD) during therapy has potential as a prognostic factor of outcome, determination of response to therapy, and direction of targeted therapy. MRD can be determined by cell surface markers using multicolor flow cytometry, whereas leukemia-specific translocations and mutations are measured using polymerase chain reaction-based techniques and recently using next-generation sequencing. All these methods of MRD detection have their (dis)advantages, and all need to be standardized, prospectively validated, and improved to be used for uniform clinical decision making and a potential surrogate end point for clinical trials testing novel treatment strategies. Important issues to be solved are time point of MRD measurement and threshold for MRD positivity. MRD is used for stem cell transplantation (SCT) selection in the large subgroup of patients with an intermediate risk profile. Patients who are MRD positive will benefit from allo-SCT. However, MRD-negative patients have a better chance of survival after SCT. Therefore, it is debated whether MRD-positive patients should be extensively treated to become MRD negative before SCT. Either way, accurate monitoring of potential residual or upcoming disease is mandatory. Tailoring therapy according to MRD monitoring may be the most successful way to provide appropriate specifically targeted, personalized treatment.

An R-Derived FlowSOM Process to Analyze Unsupervised Clustering of Normal and Malignant Human Bone Marrow Classical Flow Cytometry Data

Multiparameter flow cytometry (MFC) is a powerful and versatile tool to accurately analyze cell subsets, notably to explore normal and pathological hematopoiesis. Yet, mostly supervised subjective strategies are used to identify cell subsets in this complex tissue. In the past few years, the implementation of mass cytometry and the big data generated have led to a blossoming of new software solutions. Their application to classical MFC in hematology is however still seldom reported. Here, we show how one of these new tools, the FlowSOM R solution, can be applied, together with the Kaluza® software, to a new delineation of hematopoietic subsets in normal human bone marrow (BM). We thus combined the unsupervised discrimination of cell subsets provided by FlowSOM and their expert-driven node-by-node assignment to known or new hematopoietic subsets. We also show how this new tool could modify the MFC exploration of hematological malignancies both at diagnosis (Dg) and follow-up (FU). This can be achieved by direct comparison of merged listmodes of reference normal BM, Dg, and FU samples of a representative acute myeloblastic case tested with the same immunophenotyping panel. This provides an immediate unsupervised evaluation of minimal residual disease. © 2019 International Society for Advancement of Cytometry.