Myeloid Leukemia

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Classification of AML by Monoclonal Antibody Microarray 243 entities, and these are incorporated in the WHO classification with morphological differences. The DotScan microarray also has considerable potential for identifying prognostic factors for leukemia subtypes (e.g., stable or progressive CLL), and whether a leukemia will be susceptible or refractory to a drug, but many more samples are required to define the consensus patterns of CD antigen expression that correlate with these attributes. When myeloid leukemias are analyzed using the DotScan microarray, human AB serum is included to eliminate nonspecific binding of leukocytes to immobilized antibodies via Fc receptors on cells (CD16, CD23, CD32, CD64, and CD89). Data of excellent quality have been obtained for AML samples from panels of patients, but more samples are required to define some of the rarer subtypes of this heterogeneous leukemia. 1.2. Immunophenotyping Techniques 1.2.1. Flow Cytometry Immunophenotyping of hematological malignancies is typically performed by flow cytometry with gating to identify the leukemic population and then study by fluorescence the antigen profile of these cells (7,8). The subjective placement of electronic gates to establish positive and negative antigen expression results in some variation between laboratories. Less commonly now, immunophenotyping may be performed using a slide-based immunofluorescence or cytochemical technique (9) as in cytochemistry. Immunophenotyping of hematological malignancies by flow cytometry typically involves testing for 15–20 antigens to determine cell lineage and leukemic classification. 1.2.2. DotScan Microarray We have devised a novel microarray of CD antibody dots (10 nL) bound to nitrocellulose on a glass slide, that enables testing for 80–100 antigens in a single, simple assay. The DotScan microarray has been successfully used to immunophenotype lymphoid (B- and T-) and myeloid leukemias. We have analyzed many B-lymphoid leukemias, using mononuclear leukocytes purified on Histopaque, and the results have been definitive, especially for B-cell chronic lymphocytic leukemia (B-CLL), where we have data for 100 patients and a characteristic immunophenotype (3,10). For these leukemias, an extensive immunophenotype (82 CD antigens) should be sufficient for diagnosis. We believe the microarray has considerable potential for identifying prognostic factors in B-CLL, but to date insufficient cases have been analyzed for meaningful correlation, and we are continuing to build the database. In general, there are insufficient data at present to enable discrimination between the rare forms of lymphoid leukemia, but this work is ongoing.
244 Christopherson et al. 1.2.3. Diagnosis Using an Extensive Immunophenotype The DotScan microarray is being used to accumulate an extensive patient database of immunophenotypes of myeloid leukemias to establish consensus patterns for the subtypes. It is likely that an extensive immunophenotype will be sufficient to classify subtypes of AML, and that expression patterns may also provide prognostic information not available from the limited immunophenotype currently obtained by flow cytometry. DNA microarrays determine mRNA levels, whereas an extensive immunophenotype determined with the DotScan microarray provides a direct extension of our knowledge of CD antigens and incorporates antibodies currently in diagnostic use. The DotScan microarray identifies cell-surface proteins that correlate directly with cellular function, whereas mRNA levels may not correlate directly with the leukemia. The antigens determined by flow cytometry in a standard immunophenotype for AML represent a narrow range of “lineage specific” markers (Table 1). A broader panel of myeloid antigens including adhesion molecules such as CD11(a-c), CD18, CD49(a-f), CD62L, activation markers such as CD71, and a broader panel of myeloid antigens are generally not studied by flow cytometry. The WHO classification includes five sub-types of AML with cytogenetic abnormalities: AML/ETO, inv16, CBF⇓/MYH11, PML-RARα, and MLL; this group will clearly expand with further investigation. These cytogenetic abnormalities may correlate with changes in expression of surface molecules observed as part of an extensive immunophenotype. For example, Munoz et al. (11) found differences between AML groups with and without internal tandem duplications (ITD) of the FLT3 gene. A diagnosis of FAB subtype AML-M5 and expression of monocytic markers CD36 and CD11b were more frequent in FLT3/ITD(+) patients, whereas stem cell markers CD34 and CD117 were less common. <strong>Myeloid</strong> leukemias characterized by specific immunophenotypes are uncommon, but CD13, CD33, and CD117 are expressed in AML-M0 (12,13), glycophorin A (CD235a) in AML-M6, and CD41 and CD61 in AML-M7. Furthermore, the extensive immunophenotype available from the DotScan microarray should result in additional patterns of antigen expression within these subtypes that increase the confidence of classification (14). Other subgroups of AML are likely to have characteristic consensus immunophenotypes. For example, Paietta (15) found diagnostic power for acute promyelocytic leukemia (APML) with three antigens—HLA-DR, CD11a, and CD18. Characteristic dot patterns or surface molecule profiles from the DotScan microarray for AMLs may allow classification and diagnosis of a wider range of subtypes. These immunological classifications may not necessarily correspond with previous morphological criteria, and a new classification system may evolve using surface molecule profiles from the DotScan microarray.
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M E T H O D S I N M O L E C U L A R
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M E T H O D S I N M O L E C U L A R
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© 2006 Humana Press Inc. 999 River
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vi Preface comprehensive review of
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viii Contents 11 Detection of the F
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x Contributors D. GARY GILLILAND
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RNA and DNA From Leukocytes 1 1 Iso
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RNA and DNA From Leukocytes 3 mine
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RNA and DNA From Leukocytes 5 late
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RNA and DNA From Leukocytes 7 9. Us
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RNA and DNA From Leukocytes 9 16. C
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RNA and DNA From Leukocytes 11 3. I
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Cytogenetic and FISH Techniques 13
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Real-Time RT-PCR Strategies 27 3 Ov
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Real-Time RT-PCR Strategies 29
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Real-Time RT-PCR Strategies 31 cifi
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Real-Time RT-PCR Strategies 33 2.1.
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Real-Time RT-PCR Strategies 35 Fig.
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Real-Time RT-PCR Strategies 37 2.1.
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Real-Time RT-PCR Strategies 39 the
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Real-Time RT-PCR Strategies 43 duri
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MyiQ single color 400 nm 585 nm 96
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Real-Time RT-PCR Strategies 47 side
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Table 2 Real-Time Quantitative Poly
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Real-Time RT-PCR Strategies 51 AML-
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Real-Time RT-PCR Strategies 53 the
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Real-Time RT-PCR Strategies 55 plas
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Real-Time RT-PCR Strategies 57 betw
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Real-Time RT-PCR Strategies 59 Ct C
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Real-Time RT-PCR Strategies 63 asse
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Real-Time RT-PCR Strategies 65 21.
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Real-Time RT-PCR Strategies 67 50.
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Quantitative PCR for Monitoring CML
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Detection of BCR-ABL Mutations 93 5
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Detection of BCR-ABL Mutations 95 F
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Detection of BCR-ABL Mutations 97 2
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Detection of BCR-ABL Mutations 99 o
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Detection of BCR-ABL Mutations 101
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Deletion of Derivative Chromosome 9
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Diagnosis of PML-RARA-Positive APL
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Duplexed QZyme RT-PCR for APL Analy
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Diagnosis of AML1-MTG8 (ETO)-Positi
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Diagnosis of CBFB-MYH11-Positive AM
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165 Table 1 Primers for CBFB-MYH11
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FIP1L1-PDGFRA in Hypereosinophilic
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FLT3 Mutations in AML 189 12 FLT3 M
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FLT3 Mutations in AML 191 3.1.1. DN
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Page 266 and 267: Detecting JAK2 V617F Mutation in MP
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Page 288 and 289: Chimerism Analysis 275 18 Chimerism
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Chimerism Analysis 293 Table 2 Comm
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Chimerism Analysis 295 12. Maas, F.
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298 Index management, 128 AML, see
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300 Index and AML, 213, 236, 238, 2
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302 Index chimerism analysis in sex
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304 Index minimal residual disease
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306 Index Stem cell transplantation
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Myeloid Leukemia

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