Clinical Experience With Modified, Single-Tube T-Cell Receptor Vβ Flow Cytometry Analysis for T-Cell Clonality

Analysis of flow cytometry data from ultrasound-guided lymph node biopsies with two types of needles

BACKGROUND

In this study, we combined two techniques, ultrasound-guided needle biopsy and flow cytometry (FCM), to explore their value in patients with enlarged lymph nodes.

METHODS

We compared the results of 198 needle biopsies on FCM and pathology. Forty-two were done by (fine needle aspiration, FNA), and the remaining 156 with (core needle biopsy, CNB), in 36 of 156 patients, a FNA was performed in the same lymph node after completion of the CNB. Except for five types of pathological entities, the rest were differentiated only detected or undetected tumours as the outcome distinction.

RESULTS

Among the 198 needle biopsies, 13 were inadequate specimens, while the remaining 185 had pathological findings, including 47 benign and 138 neoplastic findings. Thirty-six patients underwent puncture with both FNA and CNB, both needles produced identical results by FCM, but more cells were obtained by FNA. Among the pathologically positive results, there were 23 missed diagnoses in FCM, in contrast, evidence of tumours was observed in the FCM images of 15 needle biopsies that reported benign or findings that were inconsistent with pathology, and the final diagnosis was consistent with the FCM in 10 cases. FCM detected haematolymphoid tumours with a sensitivity of 87.8% and a specificity of 91.9%.

CONCLUSION

The combination of FCM and ultrasound-guided lymph node needle biopsy can quickly provide guidance for clinical decision-making. We recommend that all lymph node needle biopsies be sent for FCM, the specimen can be obtained by the last puncture with FNA.

Critical Role of Flow Cytometric Immunophenotyping in the Diagnosis, Subtyping, and Staging of T-Cell/NK-Cell Non-Hodgkin's Lymphoma in Real-World Practice: A Study of 232 Cases From a Tertiary Cancer Center in India

Background

T-cell/NK-cell non-Hodgkin’s lymphoma (T/NK-NHL) is an uncommon heterogeneous group of diseases. The current classification of T/NK-NHL is mainly based on histopathology and immunohistochemistry. In practice, however, the lack of unique histopathological patterns, overlapping cytomorphology, immunophenotypic complexity, inadequate panels, and diverse clinical presentations pose a great challenge. Flow cytometric immunophenotyping (FCI) is a gold standard for the diagnosis, subtyping, and monitoring of many hematological neoplasms. However, studies emphasizing the role of FCI in the diagnosis and staging of T/NK-NHL in real-world practice are scarce.

Methods

We included T-cell non-Hodgkin’s lymphoma (T-NHL) patients evaluated for the diagnosis and/or staging of T/NK-NHL using FCI between 2014 and 2020. We studied the utility of FCI in the diagnosis and subtyping of T/NK-NHL and correlated the FCI findings with the results of histopathology/immunohistochemistry. For correlation purposes, patients were categorized under definitive diagnosis and subtyping, inadequate subtyping, inadequate diagnosis, and misdiagnosis based on the findings of each technique.

Results

A total of 232 patients were diagnosed with T/NK-NHL. FCI findings provided definitive diagnoses in 198 patients and subtyping in 187/198 (95.45%) patients. The correlation between FCI and histopathological/immunohistochemistry results (n = 150) demonstrated an agreement on the diagnosis and subtyping in 69/150 (46%) patients. Of the remaining cases, the diagnosis and subtyping were inadequate in 64/150 (42.7%), and 14/150 (9.33%) were misdiagnosed on histopathology/immunohistochemistry results. FCI provided definitive diagnosis and subtyping in 51/64 (79.7%) patients. Among these, 13 patients diagnosed with peripheral T-cell lymphoma not-otherwise-specified were reclassified (angioimmunoblastic T-cell lymphoma (AITL)-11 and prolymphocytic leukemia-2) on FCI. It corrected the diagnosis in 14 patients that were misdiagnosed (6 B-cell NHL (B-NHL), 3 Hodgkin’s lymphoma, 1 acute leukemia, and 1 subcutaneous panniculitis-like T-cell lymphoma) and misclassified (3 T-NHL) on histopathological results. AITL was the commonest T-NHL misclassified on histopathological results. FCI also confirmed the definite involvement in 7/83 (8.4%) and 27/83 (32.5%) bone marrow (BM) samples reported as suspicious and uninvolved, respectively, on histopathological evaluation.

Conclusion

AITL was the most frequently diagnosed T/NK-NHL in this study. FCI provided a distinct advantage in detecting BM involvement by T/NK-NHL, especially in patients with low-level involvement. Overall, our study concluded that FCI plays a critical role in the diagnosis, subtyping, and staging of T/NK-NHL in real-world practice.

Differential diagnosis and identification of prognostic markers for peripheral T-cell lymphoma subtypes based on flow cytometry immunophenotype profiles

We compared the differential expression of 15 markers in PTCL (Peripheral T-cell lymphoma) subtypes and T-CUS (T-cell clones of uncertain significance), and summarized the specific immunophenotype profiles of each subtype and its impact on prognosis. PD-1 and CD10 are diagnostic markers for AITL (angioimmunoblastic T-cell lymphoma). To avoid confusion with T-CUS of benign clones, it is recommended to define AITL as bounded by PD-1+%>38.01 and/or CD10+%>7.46. T cell-derived ENKTL-N (extranodal NKT cell lymphoma) specifically expresses CD56. ALCL (anaplastic large cell lymphoma) characteristically expresses CD30 and HLA-DR. PTCL-NOS (peripheral T-cell lymphoma unspecified) still lacks a relatively specific phenotype and is prone to loss of basic lineage markers CD3, CD5, and CD7. The determination of T-CUS can be verified by the overall assessment of the bone marrow and a certain period of follow-up. The clustering results showed that the expression of 8 specific markers was significantly different among the 5 groups, suggesting that a combination of related markers can be analyzed in the identification of PTCLs subtypes. The study explores the advantages of TRBC1 combined with CD45RA/CD45RO in detecting T cell clonality, which can efficiently and sensitively analyze multiple target T cell populations at the same time. The sensitivity of PB to replace BM to monitor the tumor burden or MRD (minimal residual disease) of PTCLs is as high as 85.71%, which can relieve the huge pressure of clinical sampling and improve patient compliance. CD7, CD38, and Ki-67 are prognostic indicators for AITL. CD3 and CD8 on PTCL-NOS, and CD56 and HLA-DR on ENKTL-N have prognostic role. This study supports and validates the current classification of PTCL subtypes and establishes an immunophenotypic profile that can be used for precise diagnosis. The important clinical value of PTCLs immunophenotype in routine classification diagnosis, clonality confirmation, prognosis prediction, and treatment target selection was emphasized.

Emerging Role of T-cell Receptor Constant β Chain-1 (TRBC1) Expression in the Flow Cytometric Diagnosis of T-cell Malignancies

T-cell clonality testing is integral to the diagnostic work-up of T-cell malignancies; however, current methods lack specificity and sensitivity, which can make the diagnostic process difficult. The recent discovery of a monoclonal antibody (mAb) specific for human TRBC1 will greatly improve the outlook for T-cell malignancy diagnostics. The anti-TRBC1 mAb can be used in flow cytometry immunophenotyping assays to provide a low-cost, robust, and highly specific test that detects clonality of immunophenotypically distinct T-cell populations. Recent studies demonstrate the clinical utility of this approach in several contexts; use of this antibody in appropriately designed flow cytometry panels improves detection of circulating disease in patients with cutaneous T-cell lymphoma, eliminates the need for molecular clonality testing in the context of large granular lymphocyte leukemia, and provides more conclusive results in the context of many other T-cell disorders. It is worth noting that the increased ability to detect discrete clonal T-cell populations means that identification of T-cell clones of uncertain clinical significance (T-CUS) will become more common. This review discusses this new antibody and describes how it defines clonal T-cells. We present and discuss assay design and summarize findings to date about the use of flow cytometry TRBC1 analysis in the field of diagnostics, including lymph node and fluid sample investigations. We also make suggestions about how to apply the assay results in clinical work-ups, including how to interpret and report findings of T-CUS. Finally, we highlight areas that we think will benefit from further research.

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 classical Hodgkin lymphoma and nodular lymphocyte predominant Hodgkin lymphoma.

PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data

BACKGROUND

Flow cytometric identification of neoplastic T-cell populations is complicated by the wide range of phenotypic abnormalities in T-cell neoplasia, and the diverse repertoire of reactive T-cell phenotypes. We evaluated whether a recently described clustering algorithm, PhenoGraph, and dimensionality-reduction algorithm, viSNE, might facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data.

METHODS

We applied PhenoGraph and viSNE to peripheral blood mononuclear cells labeled with a single 8-color T/NK-cell antibody combination. Individual peripheral blood samples containing either a T-cell neoplasm or reactive lymphocytosis were analyzed together with a cohort of 10 normal samples, which established the location and identity of normal mononuclear-cell subsets in viSNE displays.

RESULTS

PhenoGraph-derived subpopulations from the normal samples formed regions of phenotypic similarity in the viSNE display describing normal mononuclear-cell subsets, which correlated with those obtained by manual gating (r²  = 0.99, P < 0.0001). In 24 of 24 cases of T-cell neoplasia with an aberrant phenotype, compared with 4 of 17 cases of reactive lymphocytosis (P = 1.4 × 10^-7 , Fisher Exact test), PhenoGraph-derived subpopulations originating exclusively from the abnormal sample formed one or more distinct phenotypic regions in the viSNE display, which represented the neoplastic T cells, and reactive T-cell subpopulations not present in the normal cohort, respectively. The numbers of neoplastic T cells identified using PhenoGraph/viSNE correlated with those obtained by manual gating (r²  = 0.99; P < 0.0001).

CONCLUSIONS

PhenoGraph and viSNE may facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data. © 2017 Clinical Cytometry Society.

Flow cytometry: Surface markers and beyond

Flow cytometry is a routinely available laboratory method to study cells in suspension from a variety of human sources. Application of this technology as a clinical laboratory method has evolved from the identification of cell-surface proteins to characterizing intracellular proteins and providing multiple different techniques to assess specific features of adaptive and innate immune function. This expanded menu of flow cytometric testing approaches has increased the utility of this platform in characterizing and diagnosing disorders of immune function.