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15 pages, 2154 KiB

Open AccessArticle

Usefulness of Flow Cytometry Monocyte Partitioning in the Diagnosis of Chronic Myelomonocytic Leukemia in a Real-World Setting

by Yijie Liu, Hamza Tariq, Lucy Fu, Juehua Gao, Taruna Jagtiani, Kristy Wolniak, Barina Aqil, Peng Ji, Yi-Hua Chen and Qing Ching Chen

Cancers 2025, 17(7), 1229; https://doi.org/10.3390/cancers17071229 - 5 Apr 2025

Viewed by 375

Abstract

Background: Based on CD14/CD16 expression, monocytes can be divided into the following three functionally distinct subsets: classical (MO1, CD14++/CD16-), intermediate (MO2, CD14+/CD16+) and non-classical (MO3, CD14^dim/CD16-). An expanded MO1 subset (cutoff, ≥94%) was found to be predictive of CMML. However, the [...] Read more.

Background: Based on CD14/CD16 expression, monocytes can be divided into the following three functionally distinct subsets: classical (MO1, CD14++/CD16-), intermediate (MO2, CD14+/CD16+) and non-classical (MO3, CD14^dim/CD16-). An expanded MO1 subset (cutoff, ≥94%) was found to be predictive of CMML. However, the utility of this test in routine practice has important limitations, with some reporting low sensitivity or a lack of correlation. Here, we sought to evaluate the practical usefulness of this test by using our routine antibody panel and a new gating strategy. Methods: Our study included 56 peripheral blood (PB) and 69 bone marrow (BM) samples. The PB cohort included 20 patients with CMML, 21 with no myeloid neoplasms (non-MN) and 15 with other myeloid neoplasms (non-CMML-MN). The BM cohort included 25 CMML, 16 non-MN and 28 non-CMML-MN cases. Taking advantage of an existing 8-color myelomonocytic tube routinely used in our lab, we conducted a retrospective monocyte subset analysis using a new sequential gating strategy. Results: The assay was able to distinguish CMML from non-CMML cases with high sensitivity (90.0%) and specificity (88.9%) in blood samples using a cutoff value of MO1 > 94%. For BM samples, a reduced MO3 < 1.24% was more closely associated with CMML with a sensitivity of 96.0% and a specificity of 79.5%. A side-by-side comparison of our assay with the original “monocyte assay” showed strong agreement. Conclusions: Our study demonstrates the utility of a practical and robust approach for monocyte subset analysis in the diagnosis of CMML. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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19 pages, 5787 KiB

Open AccessArticle

Measurable Residual Disease Analysis by Flow Cytometry: Assay Validation and Characterization of 385 Consecutive Cases of Acute Myeloid Leukemia

by Husam A. Jum’ah, Gregory E. Otteson, Michael M. Timm, Matthew J. Weybright, Min Shi, Pedro Horna, Dragan Jevremovic, Kaaren K. Reichard and Horatiu Olteanu

Cancers 2025, 17(7), 1155; https://doi.org/10.3390/cancers17071155 - 29 Mar 2025

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Abstract

Background/Objectives: Acute myeloid leukemia (AML) is a biologically heterogeneous malignancy with a variable prognosis. Despite many patients achieving complete remission, relapse remains common, underscoring the need for effective prognostic markers. Measurable residual disease (MRD) has emerged as a critical prognostic indicator, associated [...] Read more.

Background/Objectives: Acute myeloid leukemia (AML) is a biologically heterogeneous malignancy with a variable prognosis. Despite many patients achieving complete remission, relapse remains common, underscoring the need for effective prognostic markers. Measurable residual disease (MRD) has emerged as a critical prognostic indicator, associated with higher relapse risk and shorter survival. This study reports on our initial experience of MRD detection by flow cytometry in 385 bone marrow samples from 126 AML patients. Methods: The flow cytometry MRD assay, validated according to stringent consensus recommendations, consists of a 3-tube, 10-color panel incorporating a broad spectrum of lineage differentiation markers. Analytical specificity, sensitivity, precision, and reproducibility were evaluated, demonstrating the assay’s robustness. Results: The results reveal distinct immunophenotypic aberrancies in all AML cases, with consistent identification of aberrant immunophenotypes in follow-up specimens. AML MRD was detected in 32 out of 126 patients (25%) and in 77 out of 385 analyses (20%), with a median aberrant blast percentage of 1.87% (range, 0.01–12). A change in immunophenotype was documented in 21% of the MRD-positive cases. MRD positivity detected in the first sample studied was associated with reduced overall survival (HR: 5.153; p < 0.0001). Conclusions: Our findings support the integration of flow cytometric MRD analysis into routine clinical practice to enhance risk stratification and treatment planning for AML patients, as currently recommended by professional guidelines. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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Review

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15 pages, 2822 KiB

Open AccessReview

The Flow Cytometric Evaluation of B- and T-Lymphoblastic Leukemia/Lymphoma

by David M. Dorfman

Cancers 2025, 17(7), 1111; https://doi.org/10.3390/cancers17071111 - 26 Mar 2025

Viewed by 478

Abstract

Lymphoblastic leukemia/lymphoma, a neoplasm of precursor B or T lineage lymphoid cells, usually involves the bone marrow and peripheral blood, and may involve nodal and/or extranodal sites. The diagnosis is based on morphologic assessment, immunophenotypic analysis, usually by flow cytometry, and genetic analysis, [...] Read more.

Lymphoblastic leukemia/lymphoma, a neoplasm of precursor B or T lineage lymphoid cells, usually involves the bone marrow and peripheral blood, and may involve nodal and/or extranodal sites. The diagnosis is based on morphologic assessment, immunophenotypic analysis, usually by flow cytometry, and genetic analysis, including cytogenetics and FISH analysis, as well as molecular diagnostic analysis. This review will focus on the flow cytometric immunophenotypic findings in B- and T-lymphoblastic leukemia/lymphoma, which include expressions of early B or T cell markers, low-level expressions of CD45, as well as expressions of terminal deoxynucleotidyl transferase (TdT), and, in many cases, stem/progenitor cell marker CD34. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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24 pages, 9077 KiB

Open AccessReview

Acute Leukemia of Ambiguous Lineage: Diagnosis and Evaluation by Flow Cytometry

by Franklin Fuda and Weina Chen

Cancers 2025, 17(5), 871; https://doi.org/10.3390/cancers17050871 - 3 Mar 2025

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Abstract

Acute leukemia of ambiguous lineage (ALAL) includes mixed-phenotype acute leukemia (MPAL), which exhibits immunophenotypic evidence of differentiation along more than one cell lineage, and acute undifferentiated leukemia (AUL), which lacks sufficient immunophenotypic differentiation along any cell lineage. This review provides an overview of [...] Read more.

Acute leukemia of ambiguous lineage (ALAL) includes mixed-phenotype acute leukemia (MPAL), which exhibits immunophenotypic evidence of differentiation along more than one cell lineage, and acute undifferentiated leukemia (AUL), which lacks sufficient immunophenotypic differentiation along any cell lineage. This review provides an overview of ALAL, emphasizing the central role of flow cytometric analysis in its diagnostic workflow. It primarily focuses on MPAL, addressing updated classification and diagnostic criteria by the WHO-HEM5 and the ICC, including both genetically defined and phenotypically defined MPAL. The article provides a detailed review of the MPAL lineage assignment criteria with an illustrative description of a series of MPAL cases. Future studies are needed to reconcile the different criteria used in these two classifications. Continuously expanded molecular studies are expected to provide a genomic and lineage-associated framework for the classification of ALAL with clinical relevance in the diagnosis and therapy selection. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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25 pages, 18556 KiB

Open AccessReview

Flow Cytometry for B-Cell Non-Hodgkin and Hodgkin Lymphomas

by David C. Gajzer and Jonathan R. Fromm

Cancers 2025, 17(5), 814; https://doi.org/10.3390/cancers17050814 - 26 Feb 2025

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Abstract

Multi-parametric flow cytometry is a powerful diagnostic tool that permits rapid assessment of cellular antigen expression to quickly provide immunophenotypic information suitable for disease classification. This chapter describes the classification of B-cell non-Hodgkin lymphoma (B-NHL) by flow cytometry suitable for the clinical and [...] Read more.

Multi-parametric flow cytometry is a powerful diagnostic tool that permits rapid assessment of cellular antigen expression to quickly provide immunophenotypic information suitable for disease classification. This chapter describes the classification of B-cell non-Hodgkin lymphoma (B-NHL) by flow cytometry suitable for the clinical and research environment. In addition to describing the immunophenotypic patterns of the most common B-NHL (including examples of common B-NHL), the effect of anti-CD19, -CD20, and -CD38 therapies on the evaluation of flow cytometric data is also discussed. Over the last 15 years, our laboratory has developed flow cytometry combinations that can immunophenotype classic Hodgkin lymphoma (CHL), nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), and T-cell/histocyte-rich large B-cell lymphoma (THRLBCL) and the use of these assays will be presented. The CHL assay combination is also particularly well suited to immunophenotype primary mediastinal large B-cell lymphoma (PMLBCL) and our experience immunophenotyping PMLBCL by flow cytometry will be discussed. Finally, an approach to the evaluation of the reactive infiltrate of CHL, NLPHL, and THRLBCL that can provide diagnostic information will also be provided. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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26 pages, 1536 KiB

Open AccessReview

Machine Learning Methods in Clinical Flow Cytometry

by Nicholas C. Spies, Alexandra Rangel, Paul English, Muir Morrison, Brendan O’Fallon and David P. Ng

Cancers 2025, 17(3), 483; https://doi.org/10.3390/cancers17030483 - 1 Feb 2025

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Abstract

This review will explore the integration of machine learning (ML) techniques to enhance the analysis of increasingly complex and voluminous flow cytometry data, as traditional manual methods are insufficient for handling this data. We attempt to provide a comprehensive introduction to ML in [...] Read more.

This review will explore the integration of machine learning (ML) techniques to enhance the analysis of increasingly complex and voluminous flow cytometry data, as traditional manual methods are insufficient for handling this data. We attempt to provide a comprehensive introduction to ML in flow cytometry, detailing the transition from manual gating to computational methods and emphasizing the importance of data quality. Key ML techniques are discussed, including supervised learning methods like logistic regression, support vector machines, and neural networks, which rely on labeled data to classify disease states. Unsupervised methods, such as k-means clustering, FlowSOM, UMAP, and t-SNE, are highlighted for their ability to identify novel cell populations without predefined labels. We also delve into newer semi-supervised and weakly supervised methods, which leverage partial labeling to improve model performance. Practical aspects of implementing ML in clinical settings are addressed, including regulatory considerations, data preprocessing, model training, validation, and the importance of generalizability, and we underscore the collaborative effort required among pathologists, data scientists, and laboratory professionals to ensure robust model development and deployment. Finally, we show the transformative potential of ML in flow cytometry in uncovering new biological insights through advanced computational techniques. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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25 pages, 15916 KiB

Open AccessReview

Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry

by Feras Ally and Xueyan Chen

Cancers 2024, 16(22), 3855; https://doi.org/10.3390/cancers16223855 - 17 Nov 2024

Cited by 2 | Viewed by 3865

Abstract

With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the [...] Read more.

With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the integration of immunophenotypic, cytogenetic, and molecular data, alongside clinical and morphologic findings, for accurate diagnosis, prognostication, and guiding therapeutic strategies in AML. Flow cytometry offers rapid and sensitive immunophenotyping through a multiparametric approach and is a pivotal laboratory tool for the classification of AML, identification of therapeutic targets, and monitoring of measurable residual disease (MRD) post therapy. The association of immunophenotypic features and recurrent genetic abnormalities has been recognized and applied in informing further diagnostic evaluation and immediate therapeutic decision-making. Recently, the evolving role of machine learning models in assisting flow cytometric data analysis for the automated diagnosis and prediction of underlying genetic alterations has been illustrated. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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11 pages, 4061 KiB

Open AccessReview

Flow Cytometry Profiling of Plasmacytoid Dendritic Cell Neoplasms

by Siba El Hussein and Wei Wang

Cancers 2024, 16(11), 2118; https://doi.org/10.3390/cancers16112118 - 1 Jun 2024

Cited by 5 | Viewed by 2595

Abstract

In this review, we aim to provide a summary of the diverse immunophenotypic presentations of distinct entities associated with plasmacytoid dendritic cell (pDC) proliferation. These entities include the following: (1) blastic plasmacytoid dendritic cell neoplasm (BPDCN); (2) mature pDC proliferation (MPDCP), most commonly [...] Read more.

In this review, we aim to provide a summary of the diverse immunophenotypic presentations of distinct entities associated with plasmacytoid dendritic cell (pDC) proliferation. These entities include the following: (1) blastic plasmacytoid dendritic cell neoplasm (BPDCN); (2) mature pDC proliferation (MPDCP), most commonly seen in chronic myelomonocytic leukemia (CMML); and (3) myeloid neoplasms with pDC differentiation, in which pDCs show a spectrum of maturation from early immature pDCs to mature forms, most commonly seen in acute myeloid leukemia (pDC-AML). Our aim is to provide a flow cytometry diagnostic approach to these distinct and sometimes challenging entities and to clarify the immunophenotypic spectrum of neoplastic pDCs in different disease presentations. In this review, we also cover the strategies in the evaluation of residual disease, as well as the challenges and pitfalls we face in the setting of immune and targeted therapy. The differential diagnosis will also be discussed, as blasts in some AML cases can have a pDC-like immunophenotype, mimicking pDCs. Full article

(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)

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