Computational analysis optimizes the flow cytometric evaluation for lymphoma

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 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.

High throughput automated analysis of big flow cytometry data

“…After gating, the next step in the pipeline is biomarker discovery step, with the goal to identify cell populations that are significantly associated with an outcome of interest, in the case of our example dataset cell populations that are at different proportions in KO mice versus WT. The approach is generalizable to any case where samples can be divided into two groups based on some outcome of interest such as different disease types [32], time to onset of symptoms [33], or different sample types [34]. The approach can be based on any approach to identify cell populations but we recommend basing this on flowDensity (discussed in Section 3.3) as the final results will be easily interpretable by the biologist.…”

Issue highlights—January 2014

“…As Craig et al (this issue, page 18) describe, that computational aid in identifying cellular hierarchies have a high predictive power in a model to differentiate between germinal center lymphoma and reactive germinal center cells, but there is still a need to optimize these gating strategies. Nevertheless, such statistical strategies are more than welcome not only to define germinal center lymphoma but also in the detection of minimal residual disease or to identify clonal plasma cells in multiple myeloma .…”

Issue Highlights: November 2018

“…A quick Pub Med search for immunophenotyping of canine lymphoma reveals less than two dozen reports in the literature, so one can easily see that this is a relatively new field with limited numbers of monoclonal antibodies. Needless to say, the state of the art in using immunophenotyping to characterize human lymphomas is far more mature as evidenced by several recent studies in this journal (18)(19)(20). Hopefully, reading the current study will foster more interactions with our colleagues in veterinary medicine and help to advance the diagnostic efforts in that field.…”