Translational Applications of Flow Cytometry in Clinical Practice

Cancer diagnosis and prognosis

A growing body of literature supports the application of flow cytometry in disease prognostication and monitoring of patients with hematological malignancies (5, 6), as well as in clinical studies of immune status in patients with cancer, immune deficiency, and allogeneic stem cell and organ transplants. More than 50 y ago, when morphology was essentially the sole diagnostic modality, only a handful of distinct hematolymphoid neoplasms, such as Hodgkin lymphoma, was recognized. A critical component of the current classification of hematolymphoid neoplasms is determination of the best-fit, nonneoplastic immune cell lineage (e.g., B cell, T cell) and differentiation state (e.g., precursor, mature) to which the Ag expression profiles point. The current international classification of hematolymphoid neoplasms, with rare exceptions, requires that the immunophenotype be integrated with morphologic, cytogenetic, and molecular genetic data to render accurate diagnoses (7). More recent advances in identifying rare immune cell subsets, such as plasmacytoid dendritic cells, and in producing Abs to selectively detect these subsets have been translated to further refinements in disease classification (8).

Flow cytometry analysis can provide important diagnostic and prognostic information and identify with high accuracy the distinctive immunophenotype of malignancies and cell DNA content that are used to select specific therapies. For example, various CD20⁺ B cell malignancies are now commonly treated with anti-CD20 Abs (rituximab), and a subset of CD52-expressing T cell and B cell malignancies may be treated with anti-CD52 (alemtuzumab). Flow cytometry is used to distinguish mantle cell lymphoma, an aggressive disorder that requires more intensive therapy, from other more indolent mature CD20⁺ B cell lymphomas with similar morphology (9). Because the Ag-expression profiles of these B cell neoplasms can be subtly different, flow cytometry can facilitate diagnosis by identifying reproducible expression patterns of multiple Ags associated with specific neoplasms and relatively quantify Ag density on malignant cells (9). The power of the flow cytometer to detect minute quantities of specific cells in heterogeneous cell mixtures is used to identify residual malignant cells after therapy, known as minimal residual disease monitoring, in common malignancies for which disease persistence portends a worse prognosis (10). Lastly, flow cytometry is a rapid means of determining relative cell DNA content for acute lymphoblastic leukemia, the most common childhood blood cancer, which helps to guide treatment.

Immunologic diseases

The flow cytometer also serves as a principal clinical laboratory platform for assessing the immune status of patients, including determination of CD4⁺ T cell blood counts after HIV infection (11) and classification and prognostication of various primary immune deficiencies, such as autoimmune lymphoproliferative syndrome, severe combined immune deficiency, and Ab deficiencies (12). Moreover, flow cytometric quantification of specific immune cell subsets in an allogeneic stem cell graft and of immune reconstitution following allogeneic bone marrow transplantation serves as predictors of survival posttransplant (13, 14). Lastly, the flow cytometric quantification of extremely rare CD34⁺ endothelial progenitor cells in the blood represents a novel method to predict clinical outcomes in patients with peripheral arterial and cardiovascular disease (15, 16).

Cell therapy and transplantation

The flow cytometer has also been used as a research tool to purify specific cell populations for cell-based therapy. The identification of CD34⁺ as a marker for human hematopoietic stem and progenitor cells (17) led to interest in using flow cytometry to isolate and purify populations of phenotypically defined CD34⁺ cells for transplantation. The rationale for this approach was that patients with cancer can be treated with high-intensity chemotherapy and radiation, followed by autologous stem cell rescue to achieve remission of malignancies that relapsed after conventional chemotherapy or for which conventional chemotherapy was inadequate for long-term remissions. The clinical use of cytometry-based FACS for isolation of autologous CD34⁺ stem cells allowed the possibility of achieving a clinically significant depletion of contaminating tumor cells, reducing the risk for disease progression from the reinfusion of contaminating tumor cells in the stem cell graft. Protocols for the study of autologous transplantation of patients with breast cancer, non-Hodgkin’s lymphoma, and multiple myeloma using highly purified flow cytometry-sorted CD34⁺ progenitors were initiated (18–22) (Table II). The results of these studies established the “proof-of-concept” that transplantation of highly purified hematopoietic stem cells led to durable hematopoietic reconstitution and suggest that transplantation of autologous stem cell products with >5-log ex vivo tumor cell depletion may lead to durable remissions in patients with relapsed cancer (16). The small size of these studies and the lack of a control group precludes application for U.S. Food and Drug Administration approval. Although flow cytometry can identify other cellular subsets of therapeutic interest, such as FOXP3⁺ regulatory T cell staining (Fig. 1B), techniques that use fixation preclude isolation of viable cells for therapeutic administration by FACS (23).

Another emerging use of flow cytometry is the measurement of Abs to HLA molecules, potentially developed after exposure through blood transfusions, prior transplantation, or pregnancy, as well as determination of HLA genotypes for transplantation. Initially performed using cumbersome and insensitive serological assays, human histocompatibility testing has moved to the forefront in using multiplexing technology, especially in providing detailed HLA Ab analysis and molecular HLA typing. In 1983, a seminal publication (24) demonstrated that donor-directed HLA Abs detectable only by flow cytometry could pose a significant risk for early kidney allograft rejection and loss. Since then, numerous studies confirmed this observation, not only for recipients of kidney allografts but for other solid organ transplants as well (25, 26). In hematology, the ability to unambiguously identify HLA Abs directed against mismatched HLA Ags in unrelated stem cell transplantation led to the recognition that these Abs can cause engraftment failure (27, 28). In some instances, the identification of donor-specific HLA Abs may be a contraindication for transplantation, whereas, in other situations, Ab-reduction protocols prior to stem cell infusion can be instituted.

The clinical use of cytometry was accelerated by the development of a new approach to detect HLA Abs using small plastic spheres (microparticles) for interrogation by a flow cytometer. The use of microparticles displaying purified Class I or Class II HLA Ags conjugated on their surfaces markedly reduced false reactions due to non-HLA cell membrane proteins associated with using whole cells as targets. Recently, a new cytometry-based multiplexing platform (29) has emerged that can simultaneously identify 100 unique two-color fluorescent microparticles displayed as a two-dimensional virtual array. Fig. 3 shows an example of sophisticated gating strategies that permit the independent analysis of each individual microparticle within the array using a third (reporter) color. The reactivity of each bead, as determined by the intensity of the “reporter” fluorescence, determines whether a reaction is considered positive or negative. HLA Ag-coated microparticles can be used to identify the Class I and/or Class II Abs from the sera of sensitized patients. Compelling studies demonstrated a strong correlation between a positive flow cytometric cross-match due to microparticle proven donor-specific Ab and early rejection and graft loss (30). The implementation of this new technology led to significant changes in allocation of solid organs to sensitized recipients throughout the United States (31).

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FIGURE 3.

Generation of a virtual 10 × 10 data matrix using fluorescently labeled beads. Data are generated by a multiplexing, bead-based flow cytometry method. Individual beads are identified within a two-dimensional matrix by their unique and reproducible fluorescence signature derived from their incorporation of differing quantities of two classifier dyes (x- and y-axes). For a given combination of the classifier dyes (x, z), specific measurements of a test analyte are assessed by a “reporter” fluorescent molecule and are measured on the vertical or z-axis. The intensity of the fluorescence from the reporter molecule is proportional to the amount of specific binding of the “reporter” fluorescent probe to the bead. In this example, 10 beads have reporter fluorescence that is above the baseline background.

Interestingly, this multiplexing flow cytometry methodology can also be used for HLA genotyping by attaching individual DNA probes to microparticles and simultaneously testing for ∼100 HLA allele variations (32). Thus, high-volume HLA genotyping can now be performed easily in any HLA laboratory with a flow cytometer. In fact, this technology is sensitive and specific enough to accurately detect clinically significant single amino acid sequence differences predicted between HLA molecules for patients undergoing stem cell or solid organ transplantation (28, 32).

Bioinformatics: future area of growth

The use of multiplexed bead assays and DNA ploidy assays in high-throughput diagnostic or prognostic evaluations and studies involving detection of rare cell subsets can result in the acquisition of exceedingly large datasets. Progress in data sharing is hampered by the lack of standardization in data analysis and reporting, as well as a means of efficiently sharing large datasets for independent evaluation and collaboration across different sites. For example, data analysis tends to be intuitively based on serial evaluations of two-dimensional plots, which represents a significant source of variation that limits standardization and reproducibility in data interpretation. To maximize the potential of flow cytometry for clinical use and investigation, informatics systems, such as those applied in analyses of massive genomics and proteomics datasets, will need to be integrated with data-collection efforts.

The emerging field of “cytomics” represents an integrated, whole-cell–based description of cellular physiology that includes aspects of genomics (genes and regulatory processes) and proteomics (protein abundance and structure) endeavors to develop a more comprehensive picture of the biology of individual cells. The flow cytometer represents a logical platform upon which to build this field. However, a major challenge will be to develop standardized algorithms that allow combining and comparing datasets from different platforms to foster analytical reproducibility and allow informative meta-analyses.

Automated systems for flow cytometry data analysis are being developed that use standardized ontologies and methods that treat such datasets as statistical objects embedded in n-dimensional space or clustered distributions that may be compared with one another to foster objective data capture, evaluation, and reporting. New data standards and elements that incorporate variables of experimental protocols for data acquisition and methods used for postprocessing data analysis and interpretation will be required as well. In aggregate, these developments will advance the clinical use of flow cytometry and broaden its applications in the investigation of disease beyond hematologic malignancies and immune monitoring into a new age of clinical discovery and focused pathophysiology.