Lawrence W. Diamond

Cell surface markers on lymphoid cells from Warthin's tumors.

T‐ and B‐cell markers were studied on suspensions of lymphoid cells obtained from two Warthins tumors. Each tumor was composed predominantly of T‐lymphocytes and a smaller percentage of B‐lymphocytes with a polyclonal distribution of their surface immunoglobulin. These results do not differ from those obtained in normal or reactive lymph nodes. Together with the finding of normal lymph node structures in these tumors, our findings support the concept that the lymphoid component of Warthins tumor represents preexisting lymph node tissue. Cancer 44:580‐583, 1979.

Immunological markers and DNA content in a case of giant lymph node hyperplasia (Castleman's disease).

Little is known about the immunologic characteristics of the cells in giant lymph node hyperplasia (GLNH). For this reason, cell surface markers and intracytoplasmic immunoglobulins were determined on a case of GLNH. In addition, cellular DNA content was determined by flow analysis. A 59‐year‐old male underwent thoracotomy for a posterior madiastinal mass, which was entirely excised. Histologically, the mass was diagnosed as GLNH with features of both the hyaline‐vascular and plasma‐cell types. Preoperatively, the patient had a broad‐based hypergammaglobulinemia with an increase in serum IgG. Two months postoperatively, the serum protein electrophoresis had returned to normal. Surface immunoglobulins (SIg) were determined on fresh cells in suspension using a polyvalent antiserum and monospecific antisera against heavy and light chains. Ten percent of the cells had SIg. The distribution of SIg‐bearing cells was polyclonal. Intracytoplasmic immunoglobulin (CIg), as determined by immunofluorescence on ethanol‐fixed smears from the cell suspensions, showed 6% positive cells. The distribution of cytoplasmic immunoglobulin was similarly polyclonal. Ethanol‐fixed frozen sections also showed a polyclonal pattern when stained for CIg. Fifty‐two percent of fresh cells in suspension formed Erosettes. These immunologic characteristics do not differ from those observed in non‐neoplastic lymphoid tissues. A DNA content histogram was obtained by flow microfluorometry using ethanol‐fixed cells stained with propidium iodide following RNase treatment. The DNA content distribution was within the normal limits established by the study of non‐neoplastic lymphoid tissues.

FCM immunophenotyping and DNA analysis: Practical aspects that can affect data analysis and interpretation

In the optimal setting, the FCM lymphoma-leukemia immunophenotyping laboratory is an integral component of the diagnostic hematopathology service. Flow cytometric analysis involves three stages: preanalytical (specimen handling and processing, including antibody staining), analytical (running the sample through the flow cytometer and acquiring data), and postanalytical (data analysis and interpretation). The quality and performance of the preanalytical and analytical steps impact on the resulting fluorescence data and thereby the interpretation. Deficiencies such as suboptimal instrument performance, poor reagent quality (antibodies and/or fluorochromes), or poor specimen quality can all result in inadequate resolution of positive and negative immunofluorescence.

Approach to flow cytometry: General considerations

With the increased availability of antibodies and fluorochromes and the improvements in instrument hardware and software, flow cytometry (FCM) immunophenotyping has become a popular and useful diagnostic tool in the hematopathology laboratory. The utility of FCM immunophenotyping is multifold, as it facilitates (1) the distinction between neoplastic and benign conditions, (2) the diagnosis and classification of lymphomas and leukemias, (3) the assessment of other neoplastic and pre-neoplastic disorders such as plasma cell dyscrasias and myelodysplastic syndromes, and (4) the detection of minimal residual disease in patients with acute leukemia or chronic lymphoid leukemia.

FCM interpretation and reporting

The previous chapters focus on analyzing FCM data by evaluating the location, shape, density, and size of the cell clusters observed on the various FCM graphics produced by light scatter signals and antigenic expression of the cells analyzed. In this chapter, the focus is on the last step of FCM analysis (i.e., FCM data interpretation and result reporting) which requires the integration of FCM results and other relevant laboratory and clinical information, including the correlation of the FCM and morphologic data.

FCM data analysis on heterogeneous specimens

Most clinical specimens, whether normal/reactive or harboring neoplastic cells, are heterogeneous. The highest degree of heterogeneity is seen in bone marrow samples. Solid lymphoid tissues such as tonsils and lymph nodes are less heterogeneous because the granulocyte/monocyte component is usually insignificant. The benign nonpurulent effusion, when not overloaded with a large number of macrophages or mesothelial cells, is the least complicated type of specimen consisting of B- and T-cells in a similar proportion to that seen in the blood. The FCM 2D projections in heterogeneous samples are inherently complex, displaying multiple clusters, some of which may be overlapping. The task in analyzing these dot plots is twofold: first, to determine whether the sample is benign/reactive or contains a neoplastic population (which may or may not be evident at first glance) and, second, to characterize the tumor cells, if present. In some malignant conditions such as myelodysplastic syndrome (MDS), CML, or CMMoL, an overt neoplastic cluster (i.e., increased blasts) may not be present. The FCM graphics often demonstrate several features useful for recognizing these disorders however, such as an altered proportion of the granulocytic or monocytic component and qualitative antigenic abnormalities on the myeloid (e.g., altered maturation curves), monocytic, or erythroid elements.

FCM data analysis on nearly homogeneous samples

During data acquisition, the flow cytometer measures the light-scattering properties and fluorescence characteristics of each cell in sequence. The main purpose of the next step, data analysis, is to identify the cells of interest. This process involves distinguishing any abnormal cell population present from normal cell types and, when an abnormal population is found, determining its lineage, maturity, and other characteristics (cell size, antigens expressed, fluorescence pattern of the antigenic expression). The antigenic profile of the abnormal population may lead to a specific diagnosis.