Computational analysis optimizes the flow cytometric evaluation for lymphoma

Authors

  • Fiona E. Craig,

    Corresponding author
    1. Division of Hematopathology, Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
    • Correspondence to: Fiona E. Craig, MD, Professor, Division of Hematopathology, Department of Pathology, University of Pittsburgh School of Medicine, 200 Lothrop Street, Suite G300, Pittsburgh, PA 15213-2582, USA. E-mail: craigfe@upmc.edu

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  • Ryan R. Brinkman,

    1. Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
    2. Medical Genetics, University of British Columbia, British Columbia, Canada
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  • Stephen Ten Eyck,

    1. Division of Hematopathology, Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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  • Nima Aghaeepour

    1. Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
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Abstract

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 germinal center 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 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 International Clinical Cytometry Society

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