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Flow cytometry (FC) has a well-established role in the diagnostic evaluation of mature B-cell neoplasms. Effective assessment for lineage associated antigens, aberrant antigen expression, and immunoglobulin light chain restriction requires a well-designed, optimized, and controlled FC assay. However, it is important for hematopathologists to know when flow cytometry has a more limited role, and other modalities, such as immunohistochemistry, cytogenetic and molecular testing, are more important. This review will discuss the features of an optimal FC assay for the evaluation of mature B-cell neoplasms, and the current role of FC in the diagnosis and sub-classification, prognostic assessment, identification of therapeutic targets, and assessment for disease response to therapy. © 2018 International Clinical Cytometry Society.

Flow cytometry is an important tool for identification of neoplastic T-cells, but immunophenotypic abnormalities are often subtle and must be distinguished from nonneoplastic subsets. Use of internal control (IC) T-cells in the evaluation for T-cell neoplasms was explored, both as a quality measure and as a reference for evaluating abnormal antigen expression.All peripheral blood specimens (3-month period), or those containing abnormal T-cells (29-month period), stained with CD45 V500, CD2 V450, CD3 PE-Cy7, CD7 PE, CD4 Per-CP-Cy5.5, CD8 APC-H7, CD56 APC, CD1657 FITC, were evaluated. IC T-cells were identified (DIVA, BD Biosciences) and median fluorescence intensity (MFI) recorded. Selected files were merged and reference templates generated (Infinicyt, Cytognos).IC T-cells were present in all specimens, including those with abnormal T-cells, but subsets were less well-represented. IC T-cell CD3 MFI differed between instruments (p = 0.0007) and subsets (p 0.001), but not specimen categories, and served as a longitudinal process control. Merged files highlighted small unusual IC-T subsets: CD2+(dim) (0.25% total), CD2- (0.03% total). An IC reference template highlighted neoplastic T-cells, but was limited by staining variability (IC CD3 MFI reference samples different from test (p = 0.003)).IC T-cells present in the majority of specimens can serve as positive and longitudinal process controls. Use of IC T-cells as an internal reference is limited by variable representation of subsets. Analysis of merged IC T-cells from previously analyzed patient samples can alert the interpreter to less-well-recognized non-neoplastic subsets. However, application of a merged file IC reference template was limited by staining variability. © 2016 Clinical Cytometry Society.

The Case Study Interpretation (CSI) cases presented at the 2014 International Clinical Cytometry Society (ICCS) meeting in Seattle illustrate the utility of state-of-the art multiparameter flow cytometry in the diagnosis of hematolymphoid neoplasms. Download the listmode files (Supporting Information) and test your analysis skills before reading the case reports, keeping in mind the following questions. How many separate abnormal mature B-cell populations can you identify, and how many of these represent different subtypes of B-cell neoplasm? How many separate abnormal mature T-cell populations can you identify, and do these represent different subtypes of T-cell neoplasm or phenotypic heterogeneity in one neoplasm? How many separate immature/blastic cell populations can you identify, and do they meet criteria for mixed phenotype leukemia? Is there a population of blasts that lacks T-cell, B-cell, and myeloid lineage defining antigens and if so, what entities should you consider and what additional antigens should you assess for?

Background: Although many clinical laboratories are adopting higher color flow cytometric assays, the approach to optimizing panel design and data analysis is often traditional and subjective. In order to address the question "What is the best flow cytometric strategy to reliably distinguish germinal center B-cell lymphoma (GC-L) from lymphoid hyperplasia (GC-H)?" we applied a computational tool that identifies target populations correlated with a desired outcome, in this case diagnosis. Design: Cases of GC-H and GC-L with a germinal center phenotype, evaluated by flow cytometric immunophenotyping using CD45, CD20, kappa, lambda, CD19, CD5, CD10, CD38, were analyzed with flowType and RchyOptimyx to construct cellular hierarchies that best distinguished the two diagnostic groups. Results: The population CD5-CD19+CD10+CD38- had the highest predictive power. Manual reanalysis confirmed significantly higher CD10+/CD38-B-cells in GC-L (median 12.44%, range 0.74 - 63.29, n=52) than GC-H (median 0.24%, 0.03 - 4.49, n=48, p=0.0001), but was not entirely specific. Difficulties encountered using this computational approach included the presence of CD10+ granulocytes, continuously variable B-cell expression of CD38, more variable intensity antigen staining in GC-L and inability to assess the contribution of light chain restriction. Conclusion: Computational analysis with construction of cellular hierarchies related to diagnosis helped guide manual analysis of high dimensional flow cytometric data. This approach highlighted the diagnostic utility of CD38 expression in the evaluation of B-cells with a CD10+ GC phenotype. In contrast to computational analysis of non-neoplastic cell populations, evaluation of neoplastic cells must be able to take into consideration increased variability in antigen expression. © 2013 Clinical Cytometry Society.