Abeloff's Clinical Oncology, 4th Edition
Part I – Science of Clinical Oncology
Section C – Diagnosing Cancer: Pathology and Laboratory Medicine
Chapter 17 – Flow Cytometry in Oncologic Diagnosis
Michael J. Borowitz
SUMMARY OF KEY POINTS
Lymphoma and Chronic Lymphoproliferative Disorders
Flow cytometry is used to study attributes of individual cells in a suspension. Over the past two decades, flow cytometry has become increasingly sophisticated; high-speed sorters and analyzers capable of detecting more than a dozen colors simultaneously have kept flow cytometry in the forefront as a tool for the fundamental investigation into cancer. At the same time, flow cytometry has matured from a research technology to one that is part of the routine clinical laboratory. This chapter focuses on diagnostic aspects of flow cytometry.
A flow cytometer analyzes large numbers of cells one at a time, making it an ideal tool for examination of the properties of populations of cells. It is complementary to imaging: although it does not provide as much detail about individual cells, it provides much better statistical measurements, and is able to characterize different groups of cells in heterogeneous mixtures. It also can physically sort specifc subpopulations of cells. Although a detailed discussion of the workings of a flow cytometer is outside the scope of this chapter, the chapter begins with a discussion of a few general principles.
A flow cytometer has three separate components: a fluidics system; an optical platform; and signal-processing electronics. The fluidic system aspirates the sample, mixes it with a sheath fluid to produce laminar flow, and conducts the cell suspension past the sensing zone where individual cells are examined. The optical platform consists of the laser light source(s), lenses to focus the light on the passing cell stream, band-pass filters to capture light of restricted wavelengths, and photomultiplier tubes (PMTs) that capture the emitted signals. The electronics convert photons to electrical signals in proportion to the total light contacting the PMTs, and amplify and scale the signals so that data can be readily analyzed. The value of each collected parameter is “quantitized” and assigned to a particular channel. Higher channels reflect brighter signals. Current software packages reprocess collected data electronically in a variety of formats; this flexibility of data analysis is a critical part of what makes this technology useful.
Electronically reprocessed data are displayed by software programs in the form of dot plots or histograms. The most useful displays usually correlate one parameter with another, with each dot representing a single event (i.e., cell) with the x- and y-channel values for the two chosen parameters. In addition to fluorescence, forward scatter and right-angle or side scatter can be displayed. Forward scatter is roughly proportional to the size of the cells, whereas side scatter is a measure of internal cellular complexity, which for hematopoietic cells usually means granularity. Typically, dot plots of either forward versus side scatter, or scatter versus fluorescence, are used to identify populations of interest in a process called gating, and additional displays show additional fluorescence or scatter characteristics of these populations. Because scatter measurements are made parallel to, and independent of, the fluorescence measurements, four-color flow cytometry, for example, is equivalent to six-parameter cytometry. The term multiparameter flow cytometry typically is used to define simultaneous analysis of five or more parameters on individual cells.
Fluorochromes and Fluorescence
Fluorochromes have spectral characteristics that allow them to absorb light of certain wavelengths and then to emit light at longer wavelengths. Emission, a specific characteristic of the compound, is not limited to a fixed wavelength, but, rather, constitutes a spectrum, with variable numbers of photons emitted at different wavelengths. Different fluorochromes, including fluorescein isothiocyanate (FITC) and phycoerythrin (PE) as well as others, all have the capacity of absorbing light at 488 nm but emit it at different wavelengths, thereby making it possible to perform multicolor flow cytometry with a single laser emitting at 488 nm. Tandem conjugates, which covalently couple two fluorochrome molecules, allow emitted light to be transferred from one molecule to the other and can greatly increase the number of colors that can be detected with a single laser. Additional dyes such as allophycocyanin, which emit longer wavelengths, cannot be excited by a 488-nm laser but require a second light source, most frequently one emitting light at 635 nm. The combination of new dyes, more tandem conjugates, and new instruments with even more lasers has made it possible to perform flow cytometry to detect more than a dozen colors, and 6- or even 8- to 10-color flow cytometry is now being performed routinely in clinical laboratories.
APPLICATIONS OF FLOW CYTOMETRY TO CLINICAL ONCOLOGY
Flow cytometry plays a significant role in the diagnosis, classification, and management of patients with acute leukemia, chronic lymphoproliferative disorders, and non-Hodgkin's lymphoma. The early promise of this technology in the management of patients with solid tumors has not been completely realized, but it still plays a role in certain areas.
Flow-cytometric immunophenotyping has become standard in the evaluation of new patients with acute leukemia. The most obvious role of flow cytometry is in distinguishing lymphoid from myeloid leukemia, but flow cytometry can help in the diagnosis and management of these patients in many ways ( Box 17-1 ).
APPLICATIONS OF FLOW CYTOMETRY IN HEMATOLOGIC NEOPLASIA
Lymphoma and Lymphoproliferative Disorders
ALL, acute lymphoblastic leukemia; CLL, chronic lymphocytic leukemia.
Lineage Assignment in Acute Leukemia
Phenotypic analysis of a bone marrow that is completely replaced by blasts is an almost elementary problem. Multiparameter flow cytometry, however, can dissect and categorize all populations in bone marrow, and, most important, can distinguish leukemic cells from normal, even when the leukemic cells are not the majority population. The antibody panel used to study patients with acute leukemia typically contains representative markers of all lineages, with some redundancy to allow recognition and classifcation because many antigens may be aberrantly lost or acquired in leukemic cells.   No standard combinations of antibodies are used by all laboratories, but certain combinations have proved particularly useful not only for the most economical classification of leukemia, but also for their ability to demonstrate characteristic aberrant patterns that may be extremely useful for the detection of residual disease in follow-up samples, as discussed later in this chapter.
Diffculties encountered in the interpretation of flow cytometry data derive largely from two problems. First, in cases in which leukemic cells are not an obviously dominant population, it is important to ensure that the cells of interest are analyzed. The most useful general approach for this takes advantage of the fact that the common leukocyte antigen CD45 is differentially expressed on different types of hematopoietic cells, and, when combined with side scatter, produces a display in which blasts occupy a unique position not occupied by normal cells ( Fig. 17-1 ). Thus, combining CD45 in one color with multiple combinations of antibodies in additional colors allows detailed characterization of blast populations in marrow even when they are present only in low numbers.
Figure 17-1 CD45 gating in acute leukemia. Dual-parameter display of CD45 and side scatter of bone marrow containing increased blasts. These two parameters can readily separate lymphocytes (yellow), granulocytes (blue), monocytes (light blue), and nucleated red cells (pink). In normal marrow, a “hole” is found with few events in the low side scatter (SSC)/intermediate CD45 region, but in this case a distinct population can be identified. This distinction allows gating and analysis of antigen expression on just the blast population.
Failure to select the leukemic cell population for analysis, or including a mixture of leukemic and normal cells in a gate, may cause confusion in the reporting of flow cytometry results. It is best to identify the leukemic population visually and to provide a detailed description of the antigens expressed on the leukemic population, especially in myeloid leukemias, in which the dynamic patterns of maturation associated with morphologic variation in AML are reflected in changes in both light scatter and antigen expression as the leukemic cells mature. Tabular arrays of “percentage positive” are not recommended as part of a flow cytometry report, because they cannot reflect this complexity and may cause confusion.
The second problem in interpreting flow cytometry results in leukemia derives from the fact that most of the reagents used in classifying leukemia are only relatively rather than absolutely specific. Thus, correct classification requires not only a panel with some redundancy, but also an understanding of patterns of reactivity of the antibodies. Markers such as CD13 and CD33, which are considered myeloid antigens because they originally were produced against myeloid leukemia cells, are found in up to 50% of cases of lymphoid leukemia, and interpretation of leukemias positive for these markers continues to be a cause of confusion. Generally speaking, the most specific markers of a given lineage are not highly sensitive, and the most sensitive ones are not specific. This is true more of myeloid markers than of lymphoid markers, so that lymphoid leukemias usually can be recognized precisely, whereas poorly differentiated myeloid leukemias usually are defined by the presence of myeloid markers in the absence of specific lymphoid antigens.
Acute Leukemias of Indeterminate or Ambiguous Lineage
Although almost all cases of acute leukemia can be categorized easily regarding lineage, the lack of absolute specificity of most markers, and the promiscuity of their expression, implies that some cases cannot be resolved easily. Unfortunately, considerable controversy exists about the use of the terms mixed lineage and biphenotypic leukemia. Leukemias that express myeloid- and lymphoid-associated markers in combination represent a heterogeneous group of diseases. A scoring system has been proposed that assigns various point values to different antigens, with a diagnosis of “biphenotypic leukemia” rendered if the score is high for more than one lineage. Although this is an objective method of defining lineage, it mixes different kinds of cases with different molecular abnormalities. The scoring system as originally conceived also does not distinguish between cases in which blasts coexpress antigens of different lineages and those in which there are distinct leukemic blast populations. The latter situation is much less common, but can be thought of properly as true mixed-lineage leukemia. Certain of these, such as the combination of β-precursor ALL and AML in patients with the Philadelphia chromosome, represent distinct entities.
Association of Immunophenotype and Molecular Abnormalities
Many phenotypes in both ALL and AML are highly associated with characteristic cytogenetic abnormalities. In ALL, these include cases associated with MLL rearrangements, TEL-AML1 or E2A-PBX1. In AML, the most important link is in promyelocytic leukemia with the t(15;17). Whereas lack of HLA-DR is the best-known abnormality, only about half of cases of DR-negative AMLs turn out to be acute promyleocytic leukemia (APL), and other combinations of marker expression are much more sensitive and specific for APL. AML associated with the t(8;21) also shows a characteristic phenotype.
Minimal Residual Disease Detection in Acute Leukemia
Several recent studies have demonstrated that the presence of residual leukemic cells in the marrow of patients in clinical and morphologic remission is a very strong adverse prognostic factor.        Although the most extensive data exist in childhood ALL,    the principle has also been shown to apply to adult ALL and to AML.   
Both molecular and flow-based methods have been used to detect minimal residual disease (MRD). Flow-based assays of MRD are based on the principle that nearly all leukemias show a pattern of expression of antigens that is aberrant compared with the pattern seen in normal differentiation.       This aberrancy can take several forms. Some leukemic cells can abnormally express antigens of a different lineage or show loss of expression of a normal lineage marker. A more common finding is expression of normal differentiation antigens, but at an intensity that is different from that expected for a particular stage of differentiation. This latter attribute makes flow MRD analysis applicable to most cases of leukemia. Nonetheless, recognizing these deviations requires a clear understanding of patterns of maturation in normal differentiation, including marrow regeneration, as viewed in multiparameter space.
The pattern of antigen acquisition and loss during β-cell maturation in the bone marrow has been very well characterized, and certain markers are particularly useful for distinguishing normal and leukemic maturation. Consideration of markers including intensity of CD45, CD34, CD10, CD58 and CD38 or aberrant coexpression of myeloid or other unexpected antigens can allow detection of as few as 1 in 104 leukemic cells, even when normal β-cell precursors are present in significant numbers ( Fig. 17-2 ). Marrow T-ALL can also be distinguished from normal T cells, most readily by coexpression of cytoplasmic CD3 and TdT, which is never seen on any normal cell. Detection of myeloid MRD usually is a more elaborate process because of the greater phenotypic heterogeneity in AML. Certain aberrant combinations, including coexpression of CD34 and CD56, CD117 and CD15, or CD7 and myeloid antigens occur with sufficient frequency to be useful in a large number of cases.    To achieve 10-4 sensitivity in essentially all cases may require design of custom panels unique to a particular leukemia. Because abnormal populations at diagnosis may not persist at recurrence, monitoring patients with acute leukemia requires following more than one aberrant phenotype.
Figure 17-2 Minimal residual disease detection in acute lymphoblastic leukemia. Normal B cells have a fixed pattern of expression of different antigens as they mature. Leukemic cells depart from this normal pattern. In this case, an end induction marrow was stained by 6-color flow cytometry with the combination CD20-FITC/CD10-PE/CD38-PerCPCy5.5/CD58-APC/CD19-PE-Cy7/CD45-APCCy7, and the abnormal population revealed by sequential gating. Only selected correlated antigens are displayed. A, CD19+ B cells are gated, so that the great majority of the 750,000 events collected are not displayed (B, which only shows B cells). CD45-low, CD10+ cells are gated and displayed in the next panel. C, Normal B cell precursors (green) are recognized because they have relatively bright CD38 and relatively dim CD58, while the leukemic cells (blue) are bright 58 and dim 38. D, The few leukemic cells are superimposed on a CD45 vs SSC display of the entire sample. MRD accounted for 0.02% of the total cells.
Myelodysplasia and Chronic Myeloproliferative Disorders
Recently it has become apparent that flow cytometry can detect abnormalities in marrow disorders other than acute leukemia. Patients with chronic myeloproliferative disorders invariably show abnormalities in either blast phenotype or maturation pattern, although routine clinical phenotyping of MPDs is not performed in most cases. Interest in using flow cytometry as a primary diagnostic modality in myelodysplastic syndromes is increasing. In addition to finding phenotypically abnormal blasts, many studies have demonstrated characteristic phenotypic abnormalities in either myeloid or erythroid maturation in myelodysplastic syndrome (MDS), as well as describing abnormalities in granulocyte light scatter that correspond to the morphologic finding of hypogranularity.   Although a scoring system has been proposed to use flow cytometric characteristics to diagnose MDS, the sensitivity and specificity of these findings have not been rigidly established, and it is not clear whether flow cytometry significantly improves the accurate diagnosis of early MDS. Nevertheless, it is clear that the technique can add useful adjunctive information to the diagnosis and possibly the prognosis of MDS.
Chronic Lymphoproliferative Disorders and Lymphoma
Lymphoid Tissue Analysis
Flow-cytometric immunophenotyping plays a major role in the evaluation of a patient with lymphoma (see Box 17-1 ).    Lymphoid tissue first must be disaggregated to produce a cell suspension suitable for flow-cytometric analysis. Although this is readily accomplished in most low-grade lymphomas, high-grade lymphomas may give a nonrepresentative sample because of the greater fragility of the neoplastic cells compared with residual normal cells. Fibrosis also may make disaggregation difficult or result in disruption of neoplastic cells. Consequently, flow-cytometric analysis of lymphoma requires careful gating to ensure that the cells of interest are analyzed.
In contrast to the situation with acute leukemias, no single antibody serves as a useful surrogate for the neoplastic population. However, in β-cell malignancies, neoplastic B cells usually greatly outnumber normal cells (although there may be normal T cells), so that use of a pan-B-cell antibody such as CD19 or CD20 can help to isolate the neoplastic cells for analysis. In addition, forward scatter, as a marker of cell size, may be a very useful parameter to help distinguish frequently larger neoplastic cells from smaller normal counterparts.
Although some laboratories prefer tissue immunohistochemistry to flow cytometry for phenotyping lymphomas, the two techniques are complementary. The principle advantages of flow cytometry relate to its speed and its ability to identify and phenotype precisely the neoplastic elements in a heterogeneous sample. A complete phenotypic characterization, and, in many cases, a diagnosis can be achieved within a few hours from the time of biopsy, rather than the several days needed for histopathologic examination and immunostaining. Flow cytometry also is far better than immunohistochemistry for demonstrating light-chain restriction, and therefore clonality, in β-cell neoplasms.
The disadvantages of flow cytometry come from the architectural disruption created by making a cell suspension and the aforementioned possible loss of cells of interest. Hodgkin's lymphoma, in particular, is not diagnosed by routine flow cytometry, although recent studies have demonstrated that careful attention to the procedures used for phenotyping can overcome this drawback. Even in non-Hodgkin's lymphoma, grading may be difficult from flow-cytometric immunophenotyping alone, and precise classification can be accomplished only rarely; lymphoma classification still relies heavily on morphology, and this must be correlated with flow-cytometric information.
Classification of lymphoma and grading, in particular, can be improved by assessment of DNA content by flow cytometry (as discussed later in this chapter), because S-phase fraction is closely correlated with grade. Burkitt's lymphoma, which has the highest S fraction of any neoplasm, usually can be reliably identifed by flow cytometry. However, in lesions such as follicular lymphoma, grading systems still are based on morphologic criteria, and although there may be reason to expect that flow-based methods might be more objective, these have not been validated in large series to the point at which they are generally accepted.
One of the most fruitful applications of flow cytometry in the diagnosis of lymphoma is in the analysis of specimens from fine-needle aspiration (FNA).   Cells obtained by FNA already are in suspension, and flow-cytometric analysis contributes significantly to the cytopathologic diagnosis of lymphoma, which is notoriously difficult. Although FNA has been used for years in the evaluation of recurrent adenopathy in patients with known lymphoma, it can serve as a primary diagnostic modality in lymphoma when combined with flow cytometry. Most non-Hodgkin's lymphomas can be recognized and graded, and, importantly, lymphomas also can usually be excluded. Optimal application of this technology, however, requires close collaboration between the cytopathologist and flow cytometrist, a clear understanding of the strengths and limitations of the two techniques, and a willingness to revert to open biopsy in ambiguous cases.
Blood and Marrow Analysis
Limitations attendant on generating cell suspensions do not apply to patients with blood or marrow involvement by chronic lymphoproliferative disorders. Subclassification of these lesions relies heavily on flow cytometry.      Chronic lymphocytic leukemia (CLL) is essentially defined by its immunophenotypic characteristics. Other β-cell lymphoproliferative disorders have characteristic, if not always absolutely specific, phenotypes.      Mantle cell lymphoma often can be recognized in its leukemic phase, although the most specific marker, cyclin D1, is very difficult to detect by flow with current techniques. Hairy cell leukemia, conversely, has not only a characteristic but also a highly specific phenotype  ; occasionally patients with unexplained pancytopenia can be determined to have hairy cell leukemia when only a tiny number of blood cells with the classic phenotype are identified ( Fig. 17-3 ).
Figure 17-3 Flow-cytometric detection of hairy cell leukemia. A small population (arrows) accounts for less than 1% of this peripheral blood sample and can readily be recognized. Expression of CD19 and CD20 is brighter than the background normal B cells (blue), and the abnormal cells also express CD103 and CD25.
Recently, considerable attention has been paid to the use of flow cytometry to detect prognostic factors in CLL. Recognizing that CLL can be subdivided into a poor-prognosis type associated with nonmutated immunoglobulin V region genes and a better-prognosis mutated phenotype, several studies have attempted to identify flow surrogates. The first marker found to be prognostic was CD38, although the correlation with either mutational status or prognosis is imperfect.    Zeta-chain-associated protein kinase 70 (ZAP 70) appears to be a much better surrogate marker for either prognostication or V-region status.    However, even though this assay is widely available, particularly in commercial reference laboratories, there can be significant variability in how the assay is performed,   so that it is not always certain that a commercially available assay is the same as that in the published literature. For this reason, many researchers recommend caution in interpreting these results. Other attempts have been made to define prognostically significant subsets of patients with CLL using other combinations of markers, but these have not been adopted in routine practice.
The primary value of flow cytometry in chronic leukemias is its ability to demonstrate clonality in β-cell populations, based on restricted expression of one type of immunoglobulin light chain.   This feature is particularly valuable in the evaluation of patients with unexplained lymphocytosis   and also is useful for staging patients with β-cell non-Hodgkin's lymphoma,   because the presence of as little as 0.5% to 1% of a clonal β-cell population, or even less, can be demonstrated in the marrow or blood of some patients. The high sensitivity of flow cytometry for detecting clonal populations has, however, demonstrated small clonal β-cell populations in some patients without obvious lymphoma or leukemia. Thus, the finding of a small marrow clone, in the absence of other evidence of lymphoma, should be interpreted with caution, analogous to finding a monoclonal gammopathy in a patient without evidence of myeloma.
Some controversy exists about the applicability of flow cytometry to patients with myeloma. Whereas plasma cells have a characteristic phenotype, including very bright expression of CD38 and expression of CD138,   plasma cells are underrepresented on marrow aspirates studied by flow compared with the prevalence of cells on films or in biopsies. Nevertheless, the unique phenotype of plasma cells makes them easy to recognize, and with the use of membrane-permeabilization techniques, it is easy to demonstrate cytoplasmic immunoglobulin (Ig) light-chain restriction. It is of more significance that neoplastic plasma cells usually have abnormal phenotypes. Although no single specific phenotypic abnormality permits distinction between benign monoclonal gammopathy and myeloma, the relative proportion of abnormal versus normal plasma cells has been suggested to be a predictor of behavior. Moreover, flow cytometric detection of circulating plasma cells or persistence of an abnormal phenotype after therapy is predictive of outcome in patients with myeloma.    
Clonality of T cell populations also can be demonstrated by flow cytometry, although the method is more complex and is not as widely available as that used for B cells. This technique is based on demonstration of restriction of V-beta gene use in T-cell leukemias.   T-cell malignancies also often show abnormal T-cell phenotypes, most often characterized by loss of a normal pan-T antigen, or expression of a T-cell antigen at abnormal intensity.   Because certain small, unusual, and even clonal T-cell populations may be seen in small numbers in non-neoplastic conditions, this technique has more limited sensitivity than demonstration of a clonal β-cell population. However, when abnormal T cells account for more than a very small percentage of cells, multiparameter flow cytometry easily demonstrates them and plays a significant role in categorizing these uncommon tumors.
Residual Disease Detection in Chronic Lymphoproliferative Disorders
As with acute leukemia, MRD can be detected by flow cytometry in chronic lymphoproliferative disorders. The most work has been done with CLL. Although detection of light-chain-restricted clones is difficult when the level is much below 0.1%, aberrant phenotypes that allow detection with a sensitivity of at least 1 in 10-4 can be detected in most cases of CLL.   Just as in acute leukemia, the presence of MRD in patients considered to be in remission by standard criteria is associated with an adverse prognosis, whereas clearance of MRD after therapy is associated with a good outcome.
Solid Tumors: Analysis for DNA Content
Numerous fluorescent dyes bind stoichiometrically to cellular DNA and thus can provide an accurate assessment of DNA content of cells. In a single-parameter fluorescence histogram, tumors with abnormal numbers of chromosomes show a distinct peak separate from the normal G0/G1 peak of diploid cells. Such tumors are referred to as aneuploid. Moreover, cells progressing through the cell cycle show incrementally increased levels of DNA while they are in S phase, and those in G2 or in mitosis show exactly twice the amount of DNA as that represented by the G0/G1 population. Thus, integration of the area under the curve between the G0/G1 and G2/M peaks gives the proportion of cells in S phase, commonly referred to as S-phase fraction. A variety of software packages are available to calculate S-phase fraction from these histograms.
DNA content analysis in tumors was one of the earliest applications of flow cytometry in tumors. Many studies have attempted to define the prognostic significance of either ploidy or S-phase fraction in a large number of different solid tumors. This proliferation of studies was made possible by the finding that fixed, paraffin-embedded tissue sections could be used for DNA analysis by flow, which resulted in many retrospective studies on archival material for which outcome was already known. However, the literature is confusing and contradictory, and the early promise of this measurement as an important diagnostic and prognostic marker in cancer has not been realized. Although some studies have demonstrated prognostic significance to measurements of ploidy, and especially S-phase fraction in a number of tumors—most specifically bladder, prostate, and breast cancer—many studies conflict, with the result that this technology has not been widely embraced in clinical oncology. In some practices, it is used occasionally to help manage certain subgroups of patients with some cancers. For example, some practices use S-phase fraction to help manage patients with early-stage, node-negative breast cancer, whereas others use ploidy of superficial bladder cancers to help identify patients whose tumors might progress. One reason for the lack of acceptance of this measurement is the difficulties that have been encountered in standardization. S-phase fraction, in particular, has shown very poor interlaboratory reproducibility. The ultimate effect of these technical problems is that it is very difficult for an individual laboratory to offer clinicians a test result that helps them decide how to manage a particular patient. The inability of this technology to make significant inroads in the clinic is unfortunate, because some more recent studies, by using highly sophisticated analytic methods, suggest that in breast cancer, at least, both ploidy and S-phase fraction are powerful independent prognostic markers. A detailed summary of all the controversies is outside the scope of this chapter, but several reviews have been published.  
FUTURE OF FLOW CYTOMETRY IN CLINICAL ONCOLOGY
Flow cytometry in the clinical laboratory is at something of a crossroads. On the one hand, improvements in standardization and the technology itself have made it possible to develop easy-to-operate instruments that fit well with the model of the clinical laboratory. On the other hand, with the notable exception of MRD assessment, no significant growth has occurred in the applicability of this technology to cancer. Empirical classification of leukemia and lymphoma with new markers has not, in general, been fruitful. At the same time, technologic advances in cancer diagnosis currently seem to be focusing on methods of assessing genetic lesions in cancer. However, although genetic abnormalities clearly produce cancer, they do so through production of abnormal proteins, and detection of a number of different proteins in specific cell populations is what flow cytometry does best.
Thus it would appear that flow cytometry is well suited to validate, and to translate into clinical practice, many of the exciting findings that derive from genomics. One of the areas that has seen significant attention is the flow cytometric detection of phosphorylated signaling molecules. These studies have greatly added to our understanding of the operation of signaling pathways, can be used to demonstrate alterations in signaling in leukemia, and are ideally suited to use as surrogate markers to validate the action of new drugs.   While these measurements have not yet been incorporated into routine clinical practice, they are already being used in clinical trials, and it is likely that the most robust and informative of them will soon find their way into the routine laboratory.
- Braylan RC, Orfao A, Borowitz MJ, et al: Optimal number of reagents required to evaluate hematolymphoid neoplasias: results of an international consensus meeting. Cytometry2001; 46:23-27.
- Stewart CC, Behm FG, Carey JL, et al: U.S.–Canadian Consensus recommendations on the immunophenotypic analysis of hematologic neoplasia by flow cytometry: selection of antibody combinations. Cytometry1997; 30:231-235.
- Borowitz MJ, Guenther KL, Shults KE, Stelzer GT: Immunophenotyping of acute leukemia by flow cytometric analysis: use of CD45 and right angle light scatter to gate on leukemic blasts. Am J Clin Pathol1993; 100:534-540.
- Braylan RC, Atwater SK, Diamond L, et al: U.S.–Canadian Consensus recommendations on the immunophenotypic analysis of hematologic neoplasia by flow cytometry: data reporting. Cytometry1997; 30:245-248.
- Khalidi HS, Chang KL, Medeiros LJ, et al: Acute lymphoblastic leukemia: survey of immunophenotype, French-American-British classification, frequency of myeloid antigen expression, and karyotypic abnormalities in 210 pediatric and adult cases. Am J Clin Pathol1999; 111:467-476.
- Bene MC, Castoldi G, Knapp W, et al: Proposals for the immunological classification of acute leukemias: European Group for the Immunological Characterization of Leukemias (EGIL). Leukemia1995; 9:1783-1786.
- Hrusak O, Porwit-MacDonald A: Antigen expression patterns reflecting genotype of acute leukemias. Leukemia2002; 16:1233-1258.
- Orfao A, Chillon MC, Bortoluci AM, et al: The flow cytometric pattern of CD34, CD15 and CD13 expression in acute myeloblastic leukemia is highly characteristic of the presence of PML-RARalpha gene rearrangements. Haematologica1999; 84:405-412.
- Khoury H, Dalal BI, Nevill TJ, et al: Acute myelogenous leukemia with t(8;21)—identification of a specific immunophenotype. Leuk Lymphoma2003; 44:1713-1718.
- Campana D, Coustan-Smith E: Minimal residual disease studies by flow cytometry in acute leukemia. Acta Haematol2004; 112:8-15.
- Dworzak MN, Froschl G, Printz D, et al: Prognostic significance and modalities of flow cytometric minimal residual disease detection in childhood acute lymphoblastic leukemia. Blood2002; 99:1952-1958.
- Borowitz MJ, Pullen DJ, Shuster JJ, et al: Minimal residual disease detection in childhood precursor-B-cell acute lymphoblastic leukemia: relation to other risk factors. A Children's Oncology Group Study. Leukemia2003; 17:1566-1577.
- Vidriales MB, Orfao A, San-Miguel JF: Immunologic monitoring in adults with acute lymphoblastic leukemia. Curr Oncol Rep2003; 5:413-418.
- Kern W, Schoch C, Haferlach T, Schnittger S: Monitoring of minimal residual disease in acute myeloid leukemia. Crit Rev Oncol Hematol2005; 56:283-309.
- Venditti A, Maurillo L, Buccisano F, et al: Multidimensional flow cytometry for detection of minimal residual disease in acute myeloid leukemia. Leuk Lymphoma2003; 44:445-450.
- San Miguel JF, Vidriales MB, Lopez-Berges C, et al: Early immunophenotypical evaluation of minimal residual disease in acute myeloid leukemia identifies different patient risk groups and may contribute to postinduction treatment stratification. Blood2001; 98:1746-1751.
- Kussick SJ, Wood BL: Four-color flow cytometry identifies virtually all cytogenetically abnormal bone marrow samples in the workup of non-CML myeloproliferative disorders. Am J Clin Pathol2003; 120:854-865.
- Ogata K, Nakamura K, Yokose N, et al: Clinical significance of phenotypic features of blasts in patients with myelodysplastic syndrome. Blood2002; 100:3887-3896.
- Stetler-Stevenson M, Arthur DC, Jabbour N, et al: Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood2001; 98:979-987.
- Wells DA, Benesch M, Loken MR, et al: Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood2003; 102:394-403.
- Stetler-Stevenson M, Braylan RC: Flow cytometric analysis of lymphomas and lymphoproliferative disorders. Semin Hematol2001; 38:111-123.
- Echeverri C, Fisher S, King D, Craig D: Immunophenotypic variability of β-cell non-Hodgkin lymphoma: a retrospective study of cases analyzed by flow cytometry. Am J Clin Pathol2002; 117:615-620.
- DiGiuseppe JA, Borowitz MJ: Clinical utility of flow cytometry in the chronic lymphoid leukemias. Semin Oncol1998; 25:6-10.
- Fromm JR, Kussick SJ, Wood BL: Identification and purification of classical Hodgkin cells from lymph nodes by flow cytometry and flow cytometric cell sorting. Am J Clin Pathol2006; 126:764-780.
- Dong HY, Harris NL, Preffer FI, Pitman MB: Fine-needle aspiration biopsy in the diagnosis and classification of primary and recurrent lymphoma: a retrospective analysis of the utility of cytomorphology and flow cytometry. Mod Pathol2001; 14:472-481.
- Nicol TL, Silberman M, Rosenthal DL, Borowitz MJ: The accuracy of combined cytopathologic and flow cytometric analysis of fine-needle aspirates of lymph nodes. Am J Clin Pathol2000; 114:18-28.
- Garcia DP, Rooney MT, Ahmad E, Davis BH: Diagnostic usefulness of CD23 and FMC-7 antigen expression patterns in β-cell lymphoma classification. Am J Clin Pathol 115:258–265.
- Xu Y, McKenna RW, Kroft SH: Assessment of CD10 in the diagnosis of small β-cell lymphomas: a multiparameter flow cytometric study. Am J Clin Pathol2002; 117:291-300.
- Sanchez ML, Almeida J, Vidriales B, et al: Incidence of phenotypic aberrations in a series of 467 patients with B chronic lymphoproliferative disorders: basis for the design of specific four-color stainings to be used for minimal residual disease investigation. Leukemia2002; 16:1460-1469.
- Frater JL, McCarron KF, Hammel JP, et al: Typical and atypical chronic lymphocytic leukemia differ clinically and immunophenotypically. Am J Clin Pathol2001; 116:655-664.
- Cornfield DB, Mitchell Nelson DM, Rimsza LM, et al: The diagnosis of hairy cell leukemia can be established by flow cytometric analysis of peripheral blood, even in patients with low levels of circulating malignant cells. Am J Hematol2001; 67:223-226.
- Durig J, Naschar M, Schmucker U, et al: CD38 expression is an important prognostic marker in chronic lymphocytic leukaemia. Leukemia2002; 16:30-35.
- Del Poeta G, Maurillo L, Venditti A, et al: Clinical significance of CD38 expression in chronic lymphocytic leukemia. Blood2001; 98:2633-2639.
- Ibrahim S, Keating M, Do KA, et al: CD38 expression as an important prognostic factor in β-cell chronic lymphocytic leukemia. Blood2001; 98:181-186.
- Crespo M, Bosch F, Villamor N, et al: ZAP-70 expression as a surrogate for immunoglobulin-variable-region mutations in chronic lymphocytic leukemia. N Engl J Med2003; 348:1764-1775.
- Rassenti LZ, Huynh L, Toy TL, et al: ZAP-70 compared with immunoglobulin heavy-chain gene mutation status as a predictor of disease progression in chronic lymphocytic leukemia. N Engl J Med2004; 351:893-901.
- Del Principe MI, Del Poeta G, Buccisano F, et al: Clinical significance of ZAP-70 protein expression in β-cell chronic lymphocytic leukemia. Blood2006; 108:853-861.
- Best OG, Ibbotson RE, Parker AE, et al: ZAP-70 by flow cytometry: a comparison of different antibodies, anticoagulants and methods of analysis. Cytometry B Clin Cytom2006; 70:235-241.
- Gibbs G, Bromidge T, Howe D, et al: Comparison of flow cytometric methods for the measurement of ZAP-70 expression in a routine diagnostic laboratory. Clin Lab Haematol2005; 27:258-266.
- Zucchetto A, Bomben R, Dal Bo M, et al: A scoring system based on the expression of six surface molecules allows the identification of three prognostic risk groups in β-cell chronic lymphocytic leukemia. J Cell Physiol2006; 207:354-363.
- Rawstron AC, Green MJ, Kuzmicki A, et al: Monoclonal B lymphocytes with the characteristics of “indolent” chronic lymphocytic leukemia are present in 3.5% of adults with normal blood counts. Blood2002; 100:635-639.
- Wang C, Amato D, Rabah R, et al: Differentiation of monoclonal B lymphocytosis of undetermined significance (MLUS) and chronic lymphocytic leukemia (CLL) with weak CD5 expression from CD5(-) CLL. Leuk Res2002; 26:1125-1129.
- Sah SP, Matutes E, Wotherspoon AC, et al: A comparison of flow cytometry, bone marrow biopsy, and bone marrow aspirates in the detection of lymphoid infiltration in B cell disorders. J Clin Pathol2003; 56:129-132.
- Duggan PR, Easton D, Luider J, Aver IA: Bone marrow staging of patients with non-Hodgkin lymphoma by flow cytometry: correlation with morphology. Cancer2000; 88:894-899.
- Kussick SJ, Kalnoski M, Braziel RM, Wood BL: Prominent clonal β-cell populations identified by flow cytometry in histologically reactive lymphoid proliferations. Am J Clin Pathol2004; 121:464-472.
- Chilosi M, Adami F, Lestani M, et al: CD138/syndecan-1: a useful immunohistochemical marker of normal and neoplastic plasma cells on routine trephine bone marrow biopsies. Mod Pathol1999; 12:1101-1106.
- Chang CC, Schur BC, Kampalath B, et al: A novel multiparametric approach for analysis of cytoplasmic immunoglobulin light chains by flow cytometry. Mod Pathol2001; 14:1015-1021.
- Sezer O, Heider U, Zavrski I, Possinger K: Differentiation of monoclonal gammopathy of undetermined significance and multiple myeloma using flow cytometric characteristics of plasma cells. Haematologica2001; 86:837-843.
- Dingli D, Nowakowski GS, Dispenzieri A, et al: Flow cytometric detection of circulating myeloma cells before transplantation in patients with multiple myeloma: a simple risk stratification system. Blood2006; 10:3384-3388.
- San Miguel JF, Almeida J, Mateo G, et al: Immunophenotypic evaluation of the plasma cell compartment in multiple myeloma: a tool for comparing the efficacy of different treatment strategies and predicting outcome. Blood2002; 99:1853-1856.
- Rawstron AC, Davies FE, DasGupta R, et al: Flow cytometric disease monitoring in multiple myeloma: the relationship between normal and neoplastic plasma cells predicts outcome after transplantation. Blood2002; 100:3095-3100.
- Nowakowski GS, Witzig TE, Dingli D, et al: Circulating plasma cells detected by flow cytometry as a predictor of survival in 302 patients with newly diagnosed multiple myeloma. Blood2005; 106:2276-2279.
- Schwab C, Willers J, Niederer E, et al: The use of anti-T-cell receptor-Vbeta antibodies for the estimation of treatment success and phenotypic characterization of clonal T-cell populations in cutaneous T-cell lymphomas. Br J Haematol2002; 118:1019-1026.
- Langerak AW, van Den BR, Wolvers-Tettero IL, et al: Molecular and flow cytometric analysis of the Vbeta repertoire for clonality assessment in mature TCRalphabeta T-cell proliferations. Blood2001; 98:165-173.
- Gorczyca W, Weisberger J, Liu Z, et al: An approach to diagnosis of T-cell lymphoproliferative disorders by flow cytometry. Cytometry2002; 50:177-190.
- Matutes E: Chronic T-cell lymphoproliferative disorders. Rev Clin Exp Hematol2002; 6:401-420.
- Garcia VJ, Delgado I, Benito L, et al: CD79b expression in B cell chronic lymphocytic leukemia: its implication for minimal residual disease detection. Leukemia1999; 13:1501-1505.
- Rawstron AC, Kennedy B, Evans PA, et al: Quantitation of minimal disease levels in chronic lymphocytic leukemia using a sensitive flow cytometric assay improves the prediction of outcome and can be used to optimize therapy. Blood2001; 98:29-35.
- Moreton P, Kennedy B, Lucas G, et al: Eradication of minimal residual disease in β-cell chronic lymphocytic leukemia after alemtuzumab therapy is associated with prolonged survival. J Clin Oncol2005; 23:2971-2979.
- Hedley DW, Friedlander ML, Taylor IW, et al: Method for analysis of cellular DNA content of paraffin-embedded pathological material using flow cytometry. J Histochem Cytochem1983; 31:1333-1335.
- Coon JS, Paxton H, Lucy L, Homburger H: Interlaboratory variation in DNA flow cytometry: results of the College of American Pathologists' Survey. Arch Pathol Lab Med1994; 118:681-685.
- Bagwell CB, Clark GM, Spyratos F, et al: DNA and cell cycle analysis as prognostic indicators in breast tumors revisited. Clin Lab Med2001; 21:875-895.
- Wenger CR, Clark GM: S-phase fraction and breast cancer: a decade of experience. Breast Cancer Res Treat1998; 51:255-265.
- Ross JS, Linette GP, Stec J, et al: DNA ploidy and cell cycle analysis in breast cancer!. Am J Clin Pathol2003; 120(Suppl):S72-84.
- Perez OD, Nolan GP: Phospho-proteomic immune analysis by flow cytometry: from mechanism to translational medicine at the single-cell level. Immunol Rev2006; 210:208-228.
- Ricciardi MR, McQueen T, Chism D, et al: Quantitative single cell determination of ERK phosphorylation and regulation in relapsed and refractory primary acute myeloid leukemia. Leukemia2005; 19:1543-1549.
- Chow S, Hedley D, Grom P, et al: Whole blood fixation and permeabilization protocol with red blood cell lysis for flow cytometry of intracellular phosphorylated epitopes in leukocyte subpopulations. Cytometry A2005; 67:4-17.
- Tong FK, Chow S, Hedley D: Pharmacodynamic monitoring of BAY 43–9006 (Sorafenib) in phase I clinical trials involving solid tumor and AML/MDS patients, using flow cytometry to monitor activation of the ERK pathway in peripheral blood cells. Cytometry B2006; 70:107-114.