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Keywords:

  • AML;
  • hMICL;
  • immunophenotyping;
  • LAIP;
  • MRD

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION AND CONCLUSION
  6. LITERATURE CITED

Background:

Stable flow cytometric markers for malignant myeloid cells are highly warranted. Based on data from stem cell research, we hypothesized that the human inhibitory C-type lectin like receptor (hMICL) might represent a novel diagnostic and prognostic vehicle in a standard flow cytometry (FCM) setting.

Methods:

Standard four-color FCM was employed to uncover the expression patterns of hMICL in bone marrow in a test set of 55 retrospectively collected diagnostic acute myeloid leukemia (AML) samples and in a set of 36 prospectively collected diagnostic AML samples.

Results:

Ninety-two percent of the AML patients stained positive for hMICL and in the otherwise poorly characterized CD34 negative patient group hMICL staining revealed a very homogenous expression profile in the blast cell compartment with a mean of 88% hMICL positive cells. Moreover, hMICL displayed significantly higher expression in AML as compared with normal donors as measured by median fluorescence intensity (MFI) ratios (P = 0.01). There was no difference in hMICL MFI ratios between the CD34 positive and the CD34 negative subgroups (P = 0.89). Importantly, there was no difference in MFI ratios between paired diagnostic and relapse samples (P = 0.76) in 23 cases studied, indicating stable expression of hMICL during the course of the disease. In contrast to the other stem cell associated antigens analyzed (CD34, CD96, CD117, and CD133), hMICL was expressed on myeloid blast cells only, revealing hMICL as a diagnostic marker in AML.

Conclusion:

These data identify hMICL as a myeloid leukemia-associated antigen and establishes its applicability for diagnosis and follow-up of AML patients in a standard FCM setting. © 2011 International Clinical Cytometry Society

Acute myeloid leukemia (AML) is a heterogeneous disease both in terms of clinical presentation and its response to cytoreductive therapy (1). Underlying this is a similar heterogeneity in its genomic and proteomic background (2, 3). With respect to the latter this is reflected in the immunophenotype (IP) of the leukemic blast cell, a feature which has been used for diagnosis by flow cytometry (FCM) of the patients (4). While not as powerful as molecular phenotyping for prognostic purposes, IP by FCM is nevertheless a very useful tool for fast AML identification and, by inference, exclude acute lymphoblastic leukemia (ALL) (5). On the other hand, in the absence of leukemia specific markers, the distinction between leukemic and normal immature cells relies on the expression of antigen combinations defining leukemia-associated IPs (LAIPs), which are absent or extremely infrequent in normal bone marrow (NBM) (6, 7). There is, however, evidence in the literature which has outlined that these LAIPs are very different from patient to patient and that they are not necessarily stable over the course of disease (8). Consequently, there is still a need for the identification of new antigens contributing to diagnostic and prognostic information, improving relapse detection, identification, and ideally eradication of leukemic stem cells (LSCs) through antibody mediated targeted therapy (9–12).

The human myeloid inhibitory C-type lectin-like receptor (hMICL) (also named CLL-1, KLR1, and CLEC12A) is present on monocytes and granulocytes in normal peripheral blood and bone marrow (BM), while absent in nonhematological tissues (13). The function of this heavily glycosylated transmembranal C-type lectin still has to be established. However, there is some evidence that hMICL is involved in the control of myeloid cell activation during inflammation (14). Van Rhenen et al. identified hMICL as an LSC-associated surface antigen expressed on a fraction of CD34+CD38− AML cells in CD34 positive (CD34+) AML (15, 16), and in fact the antigen has been proposed as a possible target in antibody mediated therapy (11, 17). Based on these data we decided to elucidate and determine the diagnostic impact and the applicability of hMICL in routine clinical FCM in the diagnosis of AML in both CD34+ and CD34 negative (CD34−) patients, with the latter being poorly characterized immunophenotypically and amounting for up to 25% of all AML cases (10, 18). Initially, we performed an analysis of 55 retrospectively collected BM samples and later the analysis was expanded with inclusion of 36 successively accrued patients resulting in a total of 91 diagnostic patient BM samples. Collectively, these data have established hMICL as a stable and reliable marker in immunophenotyping of AML, both at diagnosis and at relapse irrespective of CD34 expression.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION AND CONCLUSION
  6. LITERATURE CITED

Bone Marrow Samples

Two series of experiments were performed: in the first, cryopreserved mononuclear cells (MNCs) from BM from 55 retrospectively collected patients with newly diagnosed and untreated AML defined and classified as per WHO guidelines were immunophenotyped (19). All sampling was obtained after informed consent according to protocols approved by the local ethical committee. Diagnostic data of the retrospectively analyzed patients were based on morphology, immunohistochemistry, immunophenotyping, cytogenetics, and molecular aberrations in all cases. Patient characteristics are depicted in Table 1.

Table 1. Patient Characteristics in the Retrospectively Collected Test Set
CharacteristicValue
Patients (no)55
Sex 
 Male27
 Female28
Age at diagnosis (yrs) (median; range)62 (18–92)
Leukocyte count × 109/l (median; range)42.5 (3–290)
Bone marrow blasts at diagnosis, median (%, range)75 (25–99)
Peripheral blood blasts at diagnosis (median, range)56 (3–97)
Morphology 
 M03
 M14
 M27
 M32
 M47
 M53
 M60
 M70
 ND29
Karyotype 
 Favorable5
 Intermediate33
 Unfavorable10
 Unknown7
Molecular genetics 
 PML-RARA2
 AML1-ETO2
 CBFβ-MYH113
 DUP-MLL2
 FLT3-ITD/FLT3D83521
 NPM119
 WT-139

In the second data set, 36 BM samples from consecutively diagnosed AML patients were immunophenotyped immediately upon receipt. In addition, a total of 17 BM follow-up samples from different time points of six different patients treated with curative intent, were available for evaluation of MRD status. Twenty-three relapse samples (11 frozen BM MNCs and 12 fresh lysed BM samples) were analyzed for expression levels of hMICL. As controls, BM samples from 18 healthy volunteers and from 18 newly diagnosed ALL patients (nine B-ALL and nine T-ALL) were included.

Flow Cytometry

The murine anti-hMICL producing HB3 (IgG1) hybridoma was prepared as described in Ref.13. Hybridoma supernatant was prepared in Aarhus and purified on protein G columns and subsequently PE-conjugated by Dako, (Glostrup, Denmark). Specificity of the antibody was verified by Western blotting and reactivity was tested by FCM. The antibody was titrated against normal monocytes, granulocytes, and AML blasts and then used in a final concentration of 1.1 μg per 106 cells.

Besides our standard four-color diagnostic panel, all samples were stained with a second panel encompassing combinations of antibodies against stem cell related antigens in combination with the anti-hMICL antibody and used in the following combinations (and employing fluorochromes in the order of FITC, PE, APC, and PerCP-CY5.5): CD38/hMICL/CD34/CD45; CD117/hMICL/CD34/CD45; CD123/hMICL/CD34/CD45; CD123/hMICL/CD133/CD45. CD123 and CD133 were purchased from Miltenyi Biotech (Bergisch Gladbach, Germany), CD45 from BioLegend (San Diego, CA) while CD34, CD117, and CD38 were purchased from Dako (Glostrup, Denmark). The respective combinations of antibodies were added to 106 cells and incubated for 20 minutes at 17°C in dark. Data acquisition was performed on a Canto II (BD Biosciences, San José, CA (BD)) and analyzed using BD FACSDiva software (BD). Compensation setting was performed for each protocol using unlabeled, single labeled and isotype control for each antibody, and mixed antibodies. The unlabeled tube was used to set up baseline PMT gains. Compensation was calculated manually on the FACSDiva software. Seven-color SetUp beads (BD) were run daily to test detection and compensation stability.

Gating Strategy and Flow Cytometric Evaluation

A minimum of 100,000 live CD45-positive events per tube was acquired into a FSC/SSC live gate. On a separate aliquot of AML cells, apoptotic and necrotic cells were excluded using the Annexin V-PE Apoptosis Kit I, (BD). Seven-AAD and Annexin double-negative cells were back-gated on a SSC/FSC plot to identify live leukocytes and this live leukocyte gate was subsequently applied to all tubes. CD45 was used in each tube to identify the blasts based on low expression of CD45 and low SSC properties (CD45low/SSClow) (20–22). Patients were assessed positive for a given antigen when more than 20% of the CD45low/SSClow cells stained positive for FCM analysis. The only exception was CD34, where true negativity was defined as <1% of the blast cells staining positive according to the rationale put forward by Taussig et al., van der Pol et al., and others (6, 18, 23, 24). The median fluorescence intensity (MFI) (25) and MFI ratio (antibody MFI/isotypic control MFI) was calculated for antigen positive cell populations to determine the expression level. A minimum of 500 events was acquired to allow for MFI calculations. An example of the gating strategy used is depicted in Figure 1. Since other CD34+ progenitors are hMICL− (data not shown), this gating strategy allows for unquicocal identification of myelomonocytic progenitors. In normal donors the SSC gate was set according to the extension of the neutrophil population in a hMICL/SSS plot where monocytes and neutrophils separated overtly. Back gating on CD34+ cells was performed in normal samples to exclude myelomonocytic differentiated cells. An example of hMICL expression in an AML sample is given in Figure 1.

thumbnail image

Figure 1. Gating strategy for determination of hMICL expression. Not shown is a live leukocyte gate acquired from a FSC/SSC plot and by exclusion of 7-AAD and Annexin V double negative cells. In normal bone marrow (AD), monocytes and granulocytes can be clearly distinguished (A). From a standard CD45low/SSC-A gate (B) Boolean gating of CD45low/SSClow cells reveals a minor fraction of CD34 positive/hMICL positive cells (C), which by back gating tightly clusters in the CD45low/SSClow area (D). In bone marrow from an AML patient with remaining neutrophils (E), a CD45low/SSClow subset can be distinguished (F). The blasts are hMICL positive, but CD34 negative (G), and also CD123 positive (H).

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Statistics

The Mann-Whitney test was used to compare expression levels of antigens between different groups. Calculation of the correlation coefficient according to Spearman's Rho test was used to describe associations between marker specific populations within groups. The Wilcoxon rank sum test was used to compare expression levels of hMICL at diagnosis and relapse. P values < 0.05 were regarded as statistically significant.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION AND CONCLUSION
  6. LITERATURE CITED

Validation of hMICL as a Myeloid Leukemia-Associated Marker

We first considered the hMICL expression in the retrospectively collected test set. As will be seen from Figure 2, hMICL was expressed in 50 of 55 (89%) of all cases with only five AML patients being hMICL negative (hMICL). In contrast, hMICL was absent in all 18 ALL cases. When we analyzed the expression level of hMICL and other purported LSC related antigens as evaluated by MFI ratios, we observed that the expression levels of CD34 and CD117 did not differ from the expression levels of their counterparts in normal BM. However, this does not preclude these antigens to be informative in subsets of patients. The latter is underscored by the bimodal distribution of fractions of positive cells. CD123 displayed a more uniform expression level in AML samples but the MFI ratios of AML patients did not differ from those of CD34+ CD123+ CD45low/SSClow cells in normal BM (P = 0.67), known as granulocyte/monocytes progenitors (GMPs). Like CD34 and CD117, CD123 may prove informative in a subset of patients. hMICL was expressed on a subset of CD34+ cells constituting a mean of 0.49% (range 0.19–0.72%) in normal BM. When comparing hMICL expression levels between AML blasts and normal CD34+ hMICL+ cells a significantly higher expression level was found in AML samples (P = 0.01) [Table 2, (I)].

thumbnail image

Figure 2. Antigen-positive cells in CD45low/SSClow blast in BM from 55 AML patients, 18 NBM samples, and 18 ALL BM samples. A: hMICL, B: CD34, C: CD117, D: CD123, E: CD133.

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Table 2. Comparisons of MFI Ratios Between (I) AML and NBM (II) CD34+ and CD34− AML
Antigen(I) P value AML vs. NBM(II) P value CD34+ vs. CD34− AML
CD340.100.002
CD1170.250.77
CD1330.880.001
CD1230.670.83
hMICL0.010.89

In the prospectively collected test set 34 of 36 (94%) patients stained hMICL positive (hMICL+) and there was no statistical significant difference in the MFI ratios of marker positive blasts cells between frozen MNCs and fresh lysed BM (P = 0.84). To assess the overall applicability of hMICL as a FCM tool in AML immunophenotyping, we studied hMICL expression in a consecutive series of six diagnostic BM samples from childhood AML patients and found all six to be hMICL+.

hMICL and CD34 Expression

Since CD34 is considered to be the best surrogate marker for hematopoietic stem cells (HSCs), we next determined MFI ratios of CD117, CD133, CD123, and hMICL as a function of CD34 [Table 2, (II)]. CD133 was significantly higher expressed in CD34+ AML (P=0.002) as described in Ref.26 and predictably, a significant difference in MFI ratios of CD34 expression was observed between CD34+ and CD34− subgroups. Next, we looked at the fraction of hMICL+ as a function of CD34+. As can be seen from Figure 3, patients with CD34− AML displayed high percentages of hMICL+ blast cells (mean 88%, range 56–100%), whereas hMICL+ blasts revealed a wide spreading with increasing fractions of CD34+ cells. The negative correlation between fractions of hMICL+ cells and CD34+ cells was confirmed by a Spearman's Rho of −0.51, (P < 0.001). Hence, the CD34− AML patients displayed a peculiar homogenous staining pattern of hMICL. An example of hMICL staining in a CD34− patient is given in Figure 1. CD123, which was found highly positive in most cases analyzed, distributed equally between CD34+ and CD34− subgroups, as did CD117 with respect to positive fractions.

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Figure 3. Increasing spread of hMICL+ blast fractions as a function of CD34+ blast fractions from 91 AML patients at the time of diagnosis.

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Stability of hMICL Expression

Besides being absent or very infrequent in normal bone marrow, an essential parameter for a biomarker in AML is its stability during the course of the disease. To assess this, we compared paired diagnostic and relapse samples from 23 patients treated with curative intent. In these, MFI ratios were remarkably similar (P = 0.76) with no loss of antigen expression in any of the relapsed samples (data not shown).

DISCUSSION AND CONCLUSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION AND CONCLUSION
  6. LITERATURE CITED

In AML lack of specificity of antigen expression somewhat limits the applicability and the importance of IP by FCM as a routine tool. A separate issue of the IP in AML relates to the lack of constancy of antigen expression during the course of the disease (25, 27, 28). Added to this is the fact that antigen densities for many antigens are identical on leukemic blasts and normal progenitors. To obviate these issues, the LAIP concept has been applied and has proved helpful in decreasing some of the problems mentioned (9). Given the above caveats, there is, however, still a need for new markers for leukemic myeloid cells to be included in routine IP to increase the value of FCM not only as a diagnostic tool but also for prognosis, for monitoring of MRD, and for development of drugs for targeted therapy. Fortunately, seminal studies have recently suggested several surface antigens in the setting of identifying AML LSC from normal HSCs by FCM. These include CD123, CD96, CD44, CD47, CD32, CD25, and hMICL (11, 12). We aimed to characterize the blast compartment with respect to hMICL expression in a strictly diagnostic setting and specifically included CD34− AML cases, which have not been hMICL characterized. When analyzing for LAIPs suitable for MRD monitoring and in case of a heterogeneous blast populations LAIPs should be defined accordingly in each blast population.

From our hypothesis that hMICL expression might be both an important diagnostic tool, this series of experiments is noteworthy for the fact that hMICL was found to be restricted to the CD45low/SSClow population of AML cells, but absent on lymphoid blast cells in all cases studied, strongly suggesting that hMICL can be used to diagnose AML from ALL in a routine FCM setting. Several of our findings suggest that the routine use of hMICL can increase the value of FCM in AML. Firstly, 92% of the 91 patients studied stained hMICL+ with a significantly higher fluorescence intensity compared to NBM (P= 0.01), underscoring the specificity and wide applicability of hMICL (22). The inclusion of hMICL in pediatric AML samples highlights the validity of this antigen as a pan-AML marker, since six of six cases stained positive. Secondly, hMICL is uniformly present on the CD34− patients, who can be otherwise poorly lineage-characterized immunophenotypically and difficult to monitor for residual disease, since FCM approaches traditionally relies on LAIP positive CD34+ cells (29–31). The addition of hMICL to the standard IP panel at diagnosis and during follow up should increase both the diagnostic accuracy and the likelihood of upfront MRD marker identification by FCM in the CD34− subgroup. Thirdly, in a series of 23 relapsed AML patients treated with curative intent, we did not observe loss of hMICL expression and at overt relapse hMICL+ AML blasts were found with same fluorescence intensity as that of diagnosis (P = 0.76) indicating stable expression of hMICL expression during the course of the disease. These observations raise the question as to what extent hMICL-based IP can detect treatment failure, which unfortunately happens in the majority of AML patients (32). Needless to say, more data are needed to establish this notion.

Collectively, our data add to the literature on the usefulness of hMICL in the management of AML patients by showing that it is an excellent diagnostic marker with preserved fluorescence intensity at relapse. Ongoing studies in the lab are aimed at evaluating hMICL as a tool for MRD quantification by FCM in AML by directly comparing its value to qPCR gold standard data.

LITERATURE CITED

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION AND CONCLUSION
  6. LITERATURE CITED
  • 1
    Löwenberg B. Acute myeloid leukemia: The challenge of capturing disease variety. Hematology Am Soc Hematol Educ Program 2008; 111.
  • 2
    Grimwade D, Hills RK. Independent prognostic factors for AML outcome. Hematology Am Soc Hematol Educ Program 2009; 385395.
  • 3
    Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, Harris NL, Le Beau MM, Hellström-Lindberg E, Tefferi A, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 2009; 114: 937951.
  • 4
    Craig FE, Foon KA. Flow cytometric immunophenotyping for hematologic neoplasms. Blood 2008; 111: 39413967.
  • 5
    Haycocks NG, Lawrence L, Cain JW, Zhao XF. Optimizing antibody panels for efficient and cost-effective flow cytometric diagnosis of acute leukemia. Cytometry B Clin Cytom 2011; 80: 221229.
  • 6
    Al-Mawali A, Gillis D, Hissaria P, Lewis I. Incidence, sensitivity, and specificity of leukemia-associated phenotypes in acute myeloid leukemia using specific five-color multiparameter flow cytometry. Am J Clin Pathol 2008; 129: 934945.
  • 7
    Olaru D, Campos L, Flandrin P, Nadal N, Duval A, Chautard S, Guyotat D. Multiparametric analysis of normal and postchemotherapy bone marrow: Implication for the detection of leukemia-associated immunophenotypes. Cytometry B Clin Cytom 2008; 74: 1724.
  • 8
    Voskova D, Schoch C, Schnittger S, Hiddemann W, Haferlach T, Kern W. Stability of leukemia-associated aberrant immunophenotypes in patients with acute myeloid leukemia between diagnosis and relapse: comparison with cytomorphologic, cytogenetic, and molecular genetic findings. Cytometry B Clin Cytom 2004; 62: 2538.
  • 9
    Ossenkoppele GJ, van de Loosdrecht AA, Schuurhuis GJ. Review of the relevance of aberrant antigen expression by flow cytometry in myeloid neoplasms. Br J Haematol 2011; 53: 421436.
  • 10
    Al-Mawali A, To L, Gillis D, Hissaria P, Mundy J, Lewis I. The presence of leukaemia-associated phenotypes is an independent predictor of induction failure in acute myeloid leukaemia. Int J Lab Hematol 2009; 31: 6168.
  • 11
    Majeti R. Monoclonal antibody therapy directed against human acute myeloid leukemia stem cells. Oncogene 2011; 0: 10091019.
  • 12
    Misaghian N, Ligresti G, Steelman LS, Bertrand FE, Bäsecke J, Libra M, Nicoletti F, Stivala F, Milella M, Tafuri A, et al. Targeting the leukemic stem cell: the Holy Grail of leukemia therapy. Leukemia 2009; 23: 2542.
  • 13
    Marshall ASJ, Willment JA, Lin H-H, Williams DL, Gordon S, Brown GD. Identification and characterization of a novel human myeloid inhibitory C-type lectin-like receptor (MICL) that is predominantly expressed on granulocytes and monocytes. J Biol Chem 2004; 279: 1479214802.
  • 14
    Marshall ASJ, Willment JA, Pyz E, Dennehy KM, Reid DM, Dri P, Gordon S, Wong SYC, Brown GD. Human MICL (CLEC12A) is differentially glycosylated and is down-regulated following cellular activation. Eur J Immunol 2006; 36: 21592169.
  • 15
    van Rhenen A, Moshaver B, Ossenkoppele GJ, Schuurhuis GJ. New approaches for the detection of minimal residual disease in acute myeloid leukemia. Curr Hematol Malig Rep 2007; 2: 111118.
  • 16
    van Rhenen A, Moshaver B, Kelder A, Feller N, Nieuwint AWM, Zweegman S, Ossenkoppele GJ, Schuurhuis GJ. Aberrant marker expression patterns on the CD34+CD38- stem cell compartment in acute myeloid leukemia allows to distinguish the malignant from the normal stem cell compartment both at diagnosis and in remission. Leukemia 2007; 21: 17001707.
  • 17
    Becker MW, Jordan CT. Leukemia stem cells in 2010: Current understanding and future directions. Blood Rev 2011; 25: 7581.
  • 18
    van der Pol MA, Feller N, Roseboom M, Moshaver B, Westra G, Broxterman HJ, Ossenkoppele GJ, Schuurhuis GJ. Assessment of the normal or leukemic nature of CD34+ cells in acute myeloid leukemia with low percentages of CD34 cells. Haematologica 2003: 88; 983993.
  • 19
    Swerdlow HS. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Lyon: International Agency for Research on Cancer, World Health Organization; 2008. pp 439.
  • 20
    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 in three-color analysis. Am J Clin Pathol 1993; 100: 534540.
  • 21
    Lacombe F, Durrieu F, Briais A, Dumain P, Belloc F, Bascans E, Reiffers J, Boisseau MR, Bernard P. Flow cytometry CD45 gating for immunophenotyping of acute myeloid leukemia. Leukemia 1997; 11: 18781886.
  • 22
    Béné MC, Nebe T, Bettelheim P, Buldini B, Bumbea H, Kern W, Lacombe F, Lemez P, Marinov I, Matutes E, et al. Immunophenotyping of acute leukemia and lymphoproliferative disorders: A consensus proposal of the European LeukemiaNet Work Package 10. Leukemia 2011; 25: 567574.
  • 23
    Taussig DC, Vargaftig J, Miraki-Moud F, Griessinger E, Sharrock K, Luke T, Lillington D, Oakervee H, Cavenagh J, Agrawal SG, et al. Leukemia-initiating cells from some acute myeloid leukemia patients with mutated nucleophosmin reside in the CD34(-) fraction. Blood 2010; 115: 19761984.
  • 24
    Goardon N, Marchi E, Atzberger A, Quek L, Schuh A, Soneji S, Woll P, Mead A, Alford KA, Rout R, et al. Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia. Cancer Cell 2011; 19: 138152.
  • 25
    Tomová A, Babusíková O. Shifts in expression of immunological cell markers in relapsed acute leukemia. Neoplasma 2001; 48: 164168.
  • 26
    Vercauteren SM, Sutherland HJ. CD133 (AC133) expression on AML cells and progenitors. Cytotherapy 2001; 3: 449459.
  • 27
    Langebrake C, Brinkmann I, Teigler-Schlegel A, Creutzig U, Griesinger F, Puhlmann U, Reinhardt D. Immunophenotypic differences between diagnosis and relapse in childhood AML: Implications for MRD monitoring. Cytometry B Clin Cytom 2005; 63: 19.
  • 28
    Li X, Du W, Liu W, Li X, Li H, Huang S-A. Comprehensive flow cytometry phenotype in acute leukemia at diagnosis and at relapse. APMIS 2010; 118: 353359.
  • 29
    Kern W, Bacher U, Haferlach C, Schnittger S, Haferlach T. The role of multiparameter flow cytometry for disease monitoring in AML. Best Pract Res Clin Haematol 2010; 23: 379390.
  • 30
    Al-Mawali A, Gillis D, Lewis I. The use of receiver operating characteristic analysis for detection of minimal residual disease using five-color multiparameter flow cytometry in acute myeloid leukemia identifies patients with high risk of relapse. Cytometry B Clin Cytom 2009; 76: 91101.
  • 31
    Björklund E, Gruber A, Mazur J, Mårtensson A, Hansson M, Porwit A. CD34+ cell subpopulations detected by 8-color flow cytometry in bone marrow and in peripheral blood stem cell collections: application for MRD detection in leukemia patients. Int J Hematol 2009; 90: 292302.
  • 32
    Hokland P, Ommen HB. Towards individualized follow-up in adult acute myeloid leukemia in remission: What is lacking before we can apply minimal residual disease as an integrated tool in clinical decision-making? Blood 2011; 117: 25772584.