Use of internal control T-cell populations in the flow cytometric evaluation for T-cell neoplasms

Pandemic H1N1 influenza virus triggers a strong T helper cell response in human nasopharynx-associated lymphoid tissues

The pH1N1 belongs to influenza A family that is sometimes transmitted to humans via contact with pigs. Human tonsillar immune cells are widely used as in vitro models to study responses to influenza viruses. In the current study, human memory (M) and naïve (N) T cells responses in mononuclear cells of tonsil (TMCs) and peripheral blood (PBMCs) were stimulated by pH1N1/sH1N1, and then stained for estimation of T cells proliferation index. Individuals with an anti-pH1N1 hemagglutination (HA) inhibition (HAI) titer of forty or greater exhibited stronger HA-specific M-CD4+ T cells responses to pH1N1 in TMCs/PBMCs than those with an HAI titer of less than forty (P < 0.01). In addition, a positive correlation was observed between proliferation indices of M-CD4+ T cells induced by exposure to sH1N1/pH1N1 (p < 0.01). Moreover, a strong correlation (p < 0.001) was detected between subjects' age and their HA-specific M-CD4+ T cells induced by pH1N1 exposure, indicating that this response was age-dependent. Finally, stimulation of TMCs with pH1N1-HA resulted in a significant M-CD8+ T cells response (p < 0.05). In conclusion, pH1N1 HA elicits a strong M-CD4+ T cells response in TMCs. Additionally, this response correlates with the response to sH1N1 suggesting cross-reactivity in T cells epitopes directed against HAs of both viral strains.

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring

Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.

A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data

OBJECTIVES

Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance.

METHODS

Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification.

RESULTS

High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties.

CONCLUSIONS

Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.

International guidelines for the flow cytometric evaluation of peripheral blood for suspected Sézary syndrome or mycosis fungoides: Assay development/optimization, validation, and ongoing quality monitors

Introducing a sensitive and specific peripheral blood flow cytometric assay for Sézary syndrome and mycosis fungoides (SS/MF) requires careful selection of assay design characteristics, and translation into a laboratory developed assay through development/optimization, validation, and continual quality monitoring. As outlined in a previous article in this series, the recommended design characteristics of this assay include at a minimum, evaluation of CD7, CD3, CD4, CD8, CD26, and CD45, analyzed simultaneously, requiring at least a 6 color flow cytometry system, with both quantitative and qualitative components. This article provides guidance from an international group of cytometry specialists in implementing an assay to those design specifications, outlining specific considerations, and best practices. Key points presented in detail are: (a) Pre-analytic components (reagents, specimen processing, and acquisition) must be optimized to: (i) identify and characterize an abnormal population of T-cells (qualitative component) and (ii) quantitate the abnormal population (semi/quasi-quantitative component). (b)Analytic components (instrument set-up/acquisition/analysis strategy and interpretation) must be optimized for the identification of SS/MF populations, which can vary widely in phenotype. Comparison with expert laboratories is strongly encouraged in order to establish competency. (c) Assay performance must be validated and documented through a validation plan and report, which covers both qualitative and semi/quasi-quantitative assay components (example template provided). (d) Ongoing assay-specific quality monitoring should be performed to ensure consistency.

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.