PANDA - Classification of Hematopoietic Malignancies

PANDA - Innovative Classification of Hematopoietic Malignancies

Giuseppe d'Onofrio, MD
Biographical Information

This is an excerpt from Bloodline Reviews, Volume 1, Issue 2-R, 2001, Diagnostic Advances in Hematology.

This coverage is supported by a grant from

Bayer automated hematology analyzers employ a complex and efficient method for leukocyte differential counting which is based on a laser light-based assessment of fundamental properties of the main components of cells, including 1) Cytoplasmic Peroxidase Activity (PA) in the peroxidase channel and 2) Nuclear Density (ND) in the basophil channel.

This method, indicated as PANDA (Peroxidase Activity and Nuclear Density Analysis), is especially fruitful for the analysis of any type of leukemic populations. When blood samples containing leukemic cells are analyzed by the Bayer hematology analyzers, very detailed information can be obtained on their nature, such as level of maturation and the direction of differentiation. The simple interpretation of PA and ND two-dimensional cytograms can be used to classify instances of leukemia in separate and distinct diagnostic categories correlated to FAB and WHO classifications. This subjective approach to the morphology of leukemic cell populations on PA and ND cytograms can be made objective using a very simple scoring system, which is the basis of a quick and reliable PANDA classification system.

Further, I will try to show you how just looking at a very simple instrument report - an apparently simple printout that can be obtained in less than one minute from a blood sample - you can have important information about the type of leukemia a patient has, and sometimes even more specific information, such as what types of chromosome abnormalities may be present.

Diagnosis of leukemia in a hematology laboratory is based on the presence of blast cells in the blood and other immature precursors of cells. Of course, diagnosis of leukemia is also made on bone marrow, but we are focusing on peripheral blood that we can look at with the microscope and analyze with automated instruments, like the Bayer ADVIA 120.

When we look at cells using all of these systems, what we want to know to make a diagnosis and classify a particular type of leukemia is to know the direction of differentiation of the leukemic cells and to obtain data on the level of this differentiation. To do this we have a number of methods, as stated previously. We have morphology, which provides us with information on the nuclear subcells. We also look at the cytoplasm of cells in which there are different structures that can give us information on the type of differentiation. Cytochemistry provides more information, and even more can be provided by important newer methods such as immunophenotyping, cytogenetics, and molecular biology.

It is possible, however, to obtain some of the same types of information as those from the aforementioned tests using an automated blood cell analyzer. Using analyzers we can obtain information such as the type of nuclear chromatin through the analyzer's basophil channel, also called the nuclear density channel. Information can also be obtained on the cytoplasmic structures through the peroxidase channel, which provides data on the content of the fundamental enzyme in diagnostic hematology: myeloperoxidase. These data are obtainable through the use of hematology analyzers because the position of each cell in the two-dimension space of analyzer-obtained cytograms is a direct visualization of cell properties.

Combined use of cytograms from the peroxidase (PA) and nuclear density (ND) channels of the Bayer ADVIA 120 hematology analyzer, indicated as PANDA (Peroxidase Activity and Nuclear Density Analysis), can be used to look at the shape of cell populations in order to understand the properties of the cells and to classify leukemias.

When we want to classify leukemia, the first step is to determine whether it is acute or chronic leukemia. In acute leukemia there are blast cells present with immature nuclei, while in chronic leukemia, differentiation is more advanced, so some nuclei are less immature. The second step in leukemia classification is to distinguish myeloid versus lymphoid leukemias. For this, the peroxidase enzyme is very important. We also have immunophenotyping, which is extremely important in cases without peroxidase. Once the two basic leukemia classification steps are completed, we can then move on to subclassify the different leukemia subtypes according to the FAB and the new WHO classifications by means of instrument printouts, morphology, and immunophenotyping.

The first classification step, that of determining acute versus chronic leukemia, frequently comes from the basophil, or nuclear density (ND), channel. Cytograms obtained using the ND channel show patterns of nuclear chromatin density, and can be classified into three groups: D0, D1, and D2.

We can define D0 as a case in which the shape of the mononuclear cell (MNC) cluster is not abnormal. A leukemic cell population that shows this pattern is generally indicative of a chronic leukemia.

Figure 1.

above: DO: Normal, rounded MNC cluster shape. There is no significant number of cells with immature chromatin, and the cytogram is blast flag negative. Indicative of: CLL and other CLD, CML, viral atypical lymphocytosis, ALL (very rarely), MPO deficiency, and AIDS neutrophils.

Dispersion of the ND pattern down and to the left of the main area of the regular, rounded shape of the MNC cell cluster on the cytogram (indicating cells with immature chromatin and/or very loose chromatin) points toward a diagnosis of an acute leukemia, either myeloid or lymphoid. This is the pattern when blast cells are present.

Figure 2.

above: D1: Down/leftward shift of the MNC cluster, indicating the presence of cells with immature chromatin. The cytogram is blast flag positive. Indicative of: ALL, AML, blast crisis of CML, and high-grade NHL.

An upward shift of the main cluster in cytograms from the ND channel is indicative of viral atypical lymphocytosis, especially infectious mononucleosis. This pattern indicates the presence of large cells with heterogeneous chromatin, and is usually blast flag-negative.

Figure 3.

above: D2: Upward shift of the MNC main cluster.

Following the differentiation between chronic and acute leukemias, the next classification stage is to distinguish between myeloid and lymphoid leukemias, for which cytograms obtained via the peroxidase channel are used. The distribution patterns found on peroxidase channel cytograms are much more heterogeneous, breaking up into at least six categories that reflect the different levels of leukemic cell differentiation according to their peroxidase activity. These categories range from P0 (the absence of peroxidase activity) to P6 (extremely high levels of peroxidase activity). Figure 4 shows the peroxidase channel cytogram areas indicative of each category, P0-P6.

Figure 4.

above: Approximate areas corresponding to levels of leukemic cell differentiation according to the peroxidase cytogram.

Category P0 shows an absence of peroxidase, along with no myeloid differentiation. This pattern type is indicative of ALL L1- L3, CLL, leukemic NHL, viral atypical lymphocytosis, total MPO deficiency, and AML subtypes M0, M5a, M6, and M7.

Figure 5.

above: P0: Absence of peroxidase staining.

Category P1 shows a scattering at the top of the main cell cluster, indicating the presence of a low number of cells with peroxidase activity and either early or partial myeloid differentiation. This pattern is indicative of AML subtypes M1, M2, M5a, and M5b.

Figure 6.

above: P1: Rightward scattering of the LUC peak.

Category P2 shows a more or less homogeneous cluster of cells separated from the LUC area, spreading across the monocyte area, and beginning to enter the neutrophil area of the cytogram. This pattern is indicative of AML subtypes M1, M2, M4, M5a and M5b, and partial MPO deficiency.

Figure 7.

above: P2: Low-to-medium peroxidase activity.

Category P3 shows moderate-to-strong peroxidase activity and homogenous cell size. This pattern is indicative of AML subtypes M2 and M4.

Figure 8.

above: P3: Homogenous size and moderate-to-strong peroxidase activity

Category P4 shows strong and heterogeneous peroxidase activity, and is indicative of CML and AML subtypes M2 and M4.

Figure 9.

above: P4: Strong and heterogeneous peroxidase activity.

Category P5 shows strong peroxidase activity in cells that are very large, and is indicative of AIDS neutrophils, MDS, CML, and AML subtype M3v.

Figure 10.

above: P5: Very strong peroxidase activity in large cells.

Category P6 shows extremely high levels of peroxidase activity, and is indicative of AML subtype M3.

Figure 11.

above: P6: Extremely high peroxidase activity.

Combined use of the PA and ND scoring systems can be used to create a simple grid to aid in the classification of leukemias.

Table 1.

above: PANDA classification grid.

At this point the PANDA leukemia classification scheme remains only an exercise, and is not the way we make a diagnosis. However, many times the report from a Bayer ADVIA 120 provides enough information to classify a leukemia prior to microscope examination of the peripheral blood smear.

An exercise conducted several years ago using the H*3 analyzer - the precursor to the ADVIA - showed that, without further investigation, the use of cytogram patterns was accurate in categorizing various types of leukemia.

Table 2.

above: Efficiency of PANDA classification.

In conclusion, the PANDA method of classifying hematopoietic malignancies provides distinct cell distribution patterns that can include any single case in a separate and distinct diagnostic category correlated to FAB and WHO leukemia classifications. The method can be used as a screening prior to microscope morphologic examination, and could potentially aid in the selection of the most appropriate CD antibodies for leukemia immunophenotyping.

Finally, the numerical PANDA classification method has the potential to be automated and improved using computer recognition of leukemic cell cluster distributions, leading to an expert system for PANDA classification.

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