How I Diagnose Mature T-Cell Proliferations by Flow Cytometry

Flow Cytometry for Non-Hodgkin and Hodgkin Lymphomas

Multiparametric 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 a general approach for the identification of abnormal lymphoid populations by flow cytometry, including B, T, NK, and Hodgkin lymphoma cells suitable for the clinical and research environment. Knowledge of the common patterns of antigen expression of normal lymphoid cells is critical to permit identification of abnormal populations at disease presentation and for minimal residual disease assessment. We highlight an overview of procedures for processing and immunophenotyping non-Hodgkin B- and T-cell lymphomas and also describe our strategy for the sensitive and specific diagnosis of classic Hodgkin lymphoma, nodular lymphocyte predominant Hodgkin lymphoma, and T-cell/histiocyte-rich large B-cell lymphoma.

Clustering Algorithm-Driven Detection of TRBC1-Restricted Clonal T-Cell Populations Produces Better Results than Manual Gating Analysis

Flow cytometric (FC) immunophenotyping and T-cell receptor (TCR) gene rearrangement studies are essential ancillary methods for the characterisation of T-cell lymphomas. Traditional manual gating and polymerase chain reaction (PCR)-based analyses can be labour-intensive, operator-dependent, and have limitations in terms of sensitivity and specificity. The objective of our study was to investigate the efficacy of the Phenograph and t-SNE algorithms together with an antibody specific for the TCR β-chain constant region 1 (TRBC1) to identify monoclonal T-cell populations. FC- and PCR-based clonality analyses were performed on 275 samples of T-cell lymphomas, B-cell lymphomas, and reactive lymphocytic proliferations. Monotypic T-cell populations were identified in 65.1% of samples by manual gating and 72.4% by algorithm-driven analysis, while PCR-based analysis detected clonal T cells in 68.0%. Of the 262 monotypic populations identified, 46.6% were classified as T-cell lymphomas and 53.4% as T-cell populations of uncertain significance (T-CUS). Algorithm-driven gating identified monotypic populations that were overlooked by manual gating or PCR-based methods. The study highlights the difficulty in distinguishing monotypic populations as T-cell lymphoma or T-CUS. Further research is needed to establish criteria for distinguishing between these populations and to improve FC diagnostic accuracy.

Flow Cytometric Assessment of Malignant Hematologic Disorders

Multiparameter flow cytometry (MPF) is an essential component of the diagnostic workup of hematologic malignancies. Recently developed tools have expanded the utility of MPF in detecting T-cell clonality and myelomonocytic dysplasia. Minimal/measurable residual disease analysis has long been established as critical in the management of B-lymphoblastic leukemia and is emerging as a useful tool in myeloid malignancies. With the continued increased complexity of MPF assays, emerging tools for data collection and analysis will allow users to take full advantage of MPF in the diagnosis of hematologic disease.

Recommendations for using artificial intelligence in clinical flow cytometry

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

TRBC1 in flow cytometry: Assay development, validation, and reporting considerations

The assessment of T-cell clonality by flow cytometry has long been suboptimal, relying on aberrant marker expression and/or intensity. The introduction of TRBC1 shows much promise for improving the diagnosis of T-cell neoplasms in the clinical flow laboratory. Most laboratories considering this marker already have existing panels designed for T-cell workups and will be determining how best to incorporate TRBC1. We present this comprehensive summary of TRBC1 and supplemental case examples to familiarize the flow cytometry community with its potential for routine application, provide examples of how to incorporate it into T-cell panels, and signal caution in interpreting the results in certain diagnostic scenarios where appropriate.

Dual T-cell constant β chain (TRBC)1 and TRBC2 staining for the identification of T-cell neoplasms by flow cytometry

The diagnosis of leukemic T-cell malignancies is often challenging, due to overlapping features with reactive T-cells and limitations of currently available T-cell clonality assays. Recently developed therapeutic antibodies specific for the mutually exclusive T-cell receptor constant β chain (TRBC)1 and TRBC2 isoforms provide a unique opportunity to assess for TRBC-restriction as a surrogate of clonality in the flow cytometric analysis of T-cell neoplasms. To demonstrate the diagnostic utility of this approach, we studied 164 clinical specimens with (60) or without (104) T-cell neoplasia, in addition to 39 blood samples from healthy donors. Dual TRBC1 and TRBC2 expression was studied within a comprehensive T-cell panel, in a fashion similar to the routine evaluation of kappa and lambda immunoglobulin light chains for the detection of clonal B-cells. Polytypic TRBC expression was demonstrated on total, CD4+ and CD8+ T-cells from all healthy donors; and by intracellular staining on benign T-cell precursors. All neoplastic T-cells were TRBC-restricted, except for 8 cases (13%) lacking TRBC expression. T-cell clones of uncertain significance were identified in 17 samples without T-cell malignancy (13%) and accounted for smaller subsets than neoplastic clones (median: 4.7 vs. 69% of lymphocytes, p < 0.0001). Single staining for TRBC1 produced spurious TRBC1-dim subsets in 24 clinical specimens (15%), all of which resolved with dual TRBC1/2 staining. Assessment of TRBC restriction by flow cytometry provides a rapid diagnostic method to detect clonal T-cells, and to accurately determine the targetable TRBC isoform expressed by T-cell malignancies.

19-color, 21-Antigen Single Tube for Efficient Evaluation of B- and T-cell Neoplasms

Non-Hodgkin lymphoma (NHL) is a heterogeneous disease, encompassing a wide variety of individually distinct neoplastic entities of mature B-, T-, and NK-cells. While they constitute a broad category, they are the most common hematologic malignancies in the world. The distinction between different neoplastic entities requires a multi-modal approach, such as flow cytometric immunophenotyping, which can exclude a neoplastic proliferation and help narrow the differential diagnosis. This article describes a flow cytometric test developed at Memorial Sloan Kettering Cancer Center to assess B-, T-, and NK-cells in a single tube, 21-antibody, 19-color assay. The assay can identify most B- and T-cell NHLs with high specificity and sensitivity and significantly narrow the differential when a specific diagnosis cannot be made. The basic protocol provides a detailed operational procedure for sample processing, staining, and cytometric acquisition. The support protocol provides typical steps and caveats for data analysis in lymphoproliferative disorders and in discriminating a variety of specific disease entities from each other and normal lymphoid populations. © 2023 Wiley Periodicals LLC. Basic Protocol: Processing, staining, and cytometric analysis of samples for B- and T-cell assessment Support Protocol: Analysis and interpretation of the B- and T-cell lymphocyte assay.