B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma

High proportion of anergic B cells in the bone marrow defined phenotypically by CD21(-/low)/CD38- expression predicts poor survival in diffuse large B cell lymphoma

Background

Diffuse large B cell lymphoma (DLBCL) is the commonest lymphoma that is highly aggressive where one-third of the patients relapse despite effective treatment. Interaction between the lymphoma cells and the non-clonal immune cells within the bone marrow microenvironment is thought to play a critical role in the pathogenesis of DLBCL.

Methods

We used flow cytometry to characterize the proportion of B cell subpopulations in the bone marrow (N = 47) and peripheral blood (N = 54) of 75 DLBCL patients at diagnosis and study their impact on survival.

Results

Anergic B cells in the bone marrow (BM), characterized as having CD21(−/low)/CD38- expression, influenced survival with high numbers (defined as > 13.9%) being associated with significantly shorter overall survival (59.7 months vs 113.6 months, p = 0.0038). Interestingly, low numbers of anergic B cells in the BM (defined as ≤13.9%) was associated with germinal center B cell type of DLBCL (p = 0.0354) that is known to have superior rates of survival when compared to activated B cell type. Finally, Cox regression analysis in our cohort of patients established that the inferior prognosis of having high numbers of anergic B cells in the bone marrow was independent of the established Revised International Prognostic Index (R-IPI) score.

Conclusions

High proportion of anergic B cells in the BM characterized by CD21(−/low)/CD38- expression predicts poor survival outcomes in DLBCL.

Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration

Flow cytometry is used in cell-based diagnostic evaluation for blood-borne malignancies including leukemia and lymphoma. The current practice for cytometry data analysis relies on manual gating to identify cell subsets in complex mixtures, which is subjective, labor-intensive, and poorly reproducible. This article reviews recent efforts to develop, validate, and disseminate automated computational methods and pipelines for cytometry data analysis that could help overcome the limitations of manual analysis and provide for efficient and data-driven diagnostic applications. It demonstrates the performance of an optimized computational pipeline in a pilot study of chronic lymphocytic leukemia data from the authors' clinical diagnostic laboratory.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery

Multi-color flow cytometry has become a valuable and highly informative tool for diagnosis and therapeutic monitoring of patients with immune deficiencies or inflammatory disorders. However, the method complexity and error-prone conventional manual data analysis often result in a high variability between different analysts and research laboratories. Here, we provide strategies and guidelines aiming at a more standardized multi-color flow cytometric staining and unsupervised data analysis for whole blood patient samples.

Adult pre B-cell acute lymphoblastic leukemia with unusually large proportion of bone marrow CD45 bright/high SSc blasts

BACKGROUND

We present a pre B-ALL patient with the rare clinical manifestation of extramedullary disease, and a normal hemogram. This patient's blasts expressed bright CD45 and high side scatter (SSc) placing the cells in the monocyte gate.

METHODS

Samples from peripheral blood and bone marrow (BM) aspirate from a 50-year-old female patient were immunophenotyped by multiparametric flow cytometry.

RESULTS

Flow cytometry studies of the BM aspirate showed a large monocyte gate with 90-95% of the cells expressing an abnormal B cell phenotype. Peripheral white blood cells count was normal and cytogenetic analysis of the BM revealed a normal karyotype.

CONCLUSION

It was not possible, based on CD45/SSc to identify a lymphoblast population in this pre B-ALL patient. Although bright expression of CD45 B-ALL blasts has been associated with poor prognosis to the best of our knowledge, the combination of bright CD45 blasts with high SSc has not been reported. As CD45 expression vs. SSc is routinely measured in the diagnostics of acute leukemias, a possible association between CD45 bright positivity and extramedullary disease or prognosis warrants further exploration. © 2015 International Clinical Cytometry Society.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

Flow cytometry bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

RchyOptimyx: cellular hierarchy optimization for flow cytometry

Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population.