Letters to the Editor
1338 Table 2
Clinical and laboratory features of MDS-AML showing expression of JAK2 V617F
Patient
Age/sex
1396 2704
51/M 52/M
WBC ( 109/l)
Hb (g/dl)
Platelets ( 109/l)
% blasts in BM
Myelofibrosis
1.6 12.6
5.6 7.9
8.0 115.0
85 75
F F
Surface markera
Karyotype
CD34,CD33,CD13,CD41,CD7 CD34,CD41
46,XY 46,XY
Splenomegaly F F
Abbreviations: BM, bone marrow; Hg, hemoglobin; M, male; WBC, white blood cells; F, not found. a Positive antigens on the blasts.
in eight cases with refractory anemia (RA) or 17 cases with refractory anemia with excess blasts (RAEB) (Table 1). In both cases with the mutation, most of fluorescent intensity at codon 617 was V617F and the wild type was only faintly visible, suggesting that most of the leukemic cells expressed V617F. A previous study found a high occurrence of the JAK2 V617F mutation in RA with ringed sideroblasts associated with marked thrombocytosis (71%),7 and this mutation has also been detected in MDS with myelofibrosis (33%).8 In our two cases with the mutation, neither thrombocytosis nor myelofibrosis was detected (Table 2). Interestingly, the blastic cells of these two cases were small with pale agranular cytoplasms and cytoplasmic blebs, and expressed CD41 antigen, suggesting that blastic cells have a megakaryoblastic nature. The progenitor cells of MDS are believed to undergo a multistep process during transformation into overt acute myelogenous leukemia. Several genetic abnormalities have been detected in both MDS and MSD-AML patients, which have suggested that genetic alterations are closely associated with disease progression of MDS.3 Therefore, MDS serves as a useful model for studying the abnormal genetic events that occur in leukemogenesis. We detected expression of JAK2 V617F in MDS-AML but not in RA or RAEB, suggesting that expression of JAK2 V617F may be one of the genetic factors involved in the progression of MDS to AML with a megakaryoblastic nature. In conclusion, detection of the expression of the JAK2 gene mutation in MDS-AML patients may enhance both the management of these patients and the application of adequate therapeutic strategies such as tyrosine kinase inhibitors.
Acknowledgements We thank Dr K Kita (Tokura Hospital, Kyoto, Japan) for providing the clinical data. This work was supported by research grants from the Mie Medical Research Fund (KN, 2004) Japan Leukemia
Research Fund (KN, 2004), and a Grant-in-Aid for Scientific Research (C, KAKENHI: KN, 17590992) from the Japan Society for the Promotion of Science (JSPS).
K Nishii, R Nanbu, F Lorenzo V, F Monma, K Kato, H Ryuu and N Katayama Division of Hematology and Oncology, Mie University School of Medicine, Tsu, Mie, Japan E-mail:
[email protected] References 1 Tefferi A, Pardanani A. Mutation screening for JAK2V617F: when to order the test and how to interpret the results. Leuk Res 2006; 30: 739–744. 2 James C, Ugo V, Le Couedic JP, Staerk J, Delhommeau F, Lacout C et al. A unique clonal JAK2 mutation leading to constitutive signaling causes polycythaemia vera. Nature 2005; 434: 1144–1148. 3 Lorenzo F, Nishii K, Monma F, Kuwagata S, Usui E, Shiku H. Mutational analysis of the KIT gene in myelodysplastic syndrome (MDS) and MDS-derived leukemia. Leuk Res 2006; 30: 1235–1239. 4 Papayannopoulou T, Yokochi T, Nakamoto B, Martin P. The surface antigen profile of HEL cells. Prog Clin Biol Res 1983; 134: 277–292. 5 Jelinek J, Oki Y, Gharibyan V, Bueso-Ramos C, Prchal JT, Verstovsek S et al. JAK2 mutation 1849G4T is rare in acute leukemias but can be found in CMML, Philadelphia chromosomenegative CML, and megakaryocytic leukemia. Blood 2005; 106: 3370–3373. 6 Frohling S, Lipka DB, Kayser S, Scholl C, Schlenk RF, Dohner H et al. Rare occurrence of the JAK2 V617F mutation in AML subtypes M5, M6, and M7. Blood 2006; 107: 1242–1243. 7 Renneville A, Quesnel B, Charpentier A, Terriou L, Crinquette A, Lai JL et al. High occurrence of JAK2 V617 mutation in refractory anemia with ringed sideroblasts associated with marked thrombocytosis. Leukemia 2006; 20: 2067–2070. 8 Ohyashiki K, Aota Y, Akahane D, Gotoh A, Miyazawa K, Kimura Y et al. The JAK2 V617F tyrosine kinase mutation in myelodysplastic syndromes (MDS) developing myelofibrosis indicates the myeloproliferative nature in a subset of MDS patients. Leukemia 2005; 19: 2359–2360.
Quantification of ex vivo generated dendritic cells (DC) and leukemia-derived DC contributes to estimate the quality of DC, to detect optimal DC-generating methods or to optimize DC-mediated T-cell-activation-procedures ex vivo or in vivo
Leukemia (2007) 21, 1338–1341. doi:10.1038/sj.leu.2404639; published online 22 March 2007
There is a need for less intensive (post-remission) immunotherapies to maintain stable remissions in AML and at least stable diseases in MDS before or after stem cell transplantation Leukemia
(SCT). The significance of T cells to mediate cellular anti leukemic reactions has been demonstrated by donor-lymphocyte-infusion (DLI) therapy of relapsed AML,1 although not all of the patients treated respond to this therapy. Professional antigen-presenting cells like dendritic cells (DC) could be used to overcome this therapeutic resistance and (re)-activate antileukemia-directed allogeneic or autologous T cells.2 Leukemic
Letters to the Editor
1339 blasts from AML patients can be converted to ‘leukemia-derived DC’ (‘DCleu’), as already shown by others and us, giving rise to cells expressing ‘DC-typical markers’ together with the clonal or cell surface ‘blast’ marker of the AML.3,4 ‘MCM-MIMIC’ is a
standard method (GM-CSF, IL-4, TNF-a, IL-1b, IL-6, prostaglandin E) to generate DC. WT1-pulsed DC were successful in generating anti-leukemia directed, specific cytotoxic T-lymphocytes (CTL) ex vivo;5 minor
Figure 1 A (schematic) blast population before (left side) and after (right side) conversion to DC is given. (a) Convertibility of blasts to leukemia-derived DC (DCleu) can be demonstrated by FACS analyses and proportions of DCleu as well as of unconverted blasts or DC of nonleukemic origin should be quantified. A blast population can be characterized by the expression of patient-typical blast antigens (e.g. CD34, CD117, CD65), including also lineage-aberrant markers (e.g. CD56, CD7, CD19). The expression of DC markers (e.g. CD80, CD86, CD40, CD1a, CD83, CD206, CD1b, CD137L) has to be studied before culture and those markers that are not expressed on uncultured blasts of the patient (lower left) should be selected. After culture in DC medium cells gain an increased SSC and in addition express several costimulatory DC antigens not expressed on uncultured cells (upper right). A coexpression analysis of selected blast with DC markers allows a quantification of ‘leukemia-derived DC’, expressing both blast and DC markers, of blasts not converted. (b) Convertibility of blasts to leukemia-derived DC (DCleu) in the case of AMLM2. Case 1: AML-M2 with 80–90% blasts expressing CD33, CD117 and partially CD65, but CD80 negative blasts (left side). After 10 days culture in MCM-Mimic DC culture medium, cells had gained a higher side and forward scatter and in addition a positivity for CD80 (‘c’). However, the expression of the leukemia-associated antigen CD117 was not homogeneously expressed on this population: rather a population of CD117 þ cells without the high side scatter and not converted to DC could be detected (right side; ‘’’). (c) Convertibility of blasts to leukemia-derived DC (DCleu) in the case of AML-M1. Case 2: AML-M1 with 60–70% CD34 þ and CD15 þ , but CD80-negative blasts (left side). After conversion to DC in MCM-Mimic medium, a shift of the CD34/CD15 þ cell population towards a higher sideward scatter was seen. In addition, a gain of CD80 positivity of the cell population could be demonstrated (right side). The two dot plots given below demonstrate the gain of CD80 positivity on CD34 þ cells after culture. Calculating proportions of DC we could calculate 25–30% DC, expressing CD80, CD83 and CD1a. Combining CD80 and CD34 or CD80 with CD15, we could evaluate a 63–66 percentual convertibility of blasts to DCleu, resulting in, on average, 31% leukemiaderived DC in the DC fraction or 15% DCleu in the total cell suspension. Leukemia
Letters to the Editor
1340 antigens like HA-1 and HA-2, restrictedly expressed on hematopoetic cells, are useful peptides to develop anti-HA-1/2 directed CTL after stimulation with donor –DC pulsed with these antigens, although only useful in WT1 þ cases or in cases with a HA-1/HA-2 mismatch after SCT.5 Our approach is to develop a DC vaccine presenting multiple leukemic antigens of the patient’s AML rather than constructing, for example, an artificial leukemic peptide target expressed and useful in selected AML subtypes only (e.g. WT1 in HLA-A2-AML). It could be shown that DC could be generated from every AML or MDS sample.3,6 Some approaches have been made to vaccinate AML patients with ex vivo differentiated leukemia-derived DC to enhance the patients’ cellular immunity against naive blasts.5 Using DC pulsed with HLA-A2-restricted WT1 peptides, CTLs could be generated that lysed specifically leukemic but not healthy CD34 þ cells, thereby yielding the proof of principle that DC presenting a leukemic antigen are mediators of an antileukemia directed cytotoxic reaction.4 Overall results of Phase I/II clinical trials with autologous DC in AML patients were that vaccination with DC is feasible and safe, although not clinically effective in every patient.7,8 Obstacles of these clinical approaches are that clinically relevant numbers of efficient DC and CTL have to be prepared or a monitoring of the specificity of CTLs is necessary. So far, a quantification of DC was performed by microscopical counting of cells exhibiting DC characteristics (cell shape, dendrites), although differentiation of DC from cells with similar morphology (e.g. macrophages) is critical. Some authors quantify DC by flow cytometry by counting cells with the typical high forward and side scatter. Contaminating granulocytes or macrophages can contribute to false-positive DC counts, DC with a decreased forward and/or side scatter to false-negative DC counts. A quantification of DC from PB samples of patients with non-hemopoietic diseases is possible with a ‘Dendritic Value Bundle Kit’ (e.g. BD Bioscience, San Jose, CA, USA) using lineage markers (e.g. CD3, CD56, CD19, CD14) and markers expressed by DC (HLA-DR, CD11c, CD123).9 However, blasts of most AML cases express those DC markers;10 therefore, the inclusion of more specific DC markers in a FACS panel is necessary. Other DC markers (e.g. CD1a) are expressed to a lower degree after serum-free culture.11 Other authors confirm a successful generation of ‘leukemia-derived DC’ if more than 20% of cells in suspensions after DC generation express two or more of the DC markers CD40, CD80, CD86 or CD83;7 however, without studying the coexpression of these markers on uncultured AML cell: CD40, CD86 or CD80 can be expressed on AML blasts and are even associated with a worse prognosis.10 In a preliminary analysis of our group, we could show that up to seven DC antigens (CD1a, CD1b, CD1c, CD137L, CD206, CD40, CD83, CD86, CD80) can be expressed already on uncultured AML or MDS –MNC, although in 80% of the cases less than three ‘DC-markers’ are expressed on uncultured cells. This means that only those markers not expressed on naive blasts qualify for DC markers to quantify DC after culture: we could show that on average 30% DC can be generated from AML or MDS-MNC.12 In cases with a suitable marker the leukemic derivation of DC can be proven by FISH or Western blot analysis detecting the ‘leukemia-associated markers’ in DC.3 Here we describe a method that allows the proof of the leukemic derivation and a quantification of DCleu in every case with AML by combining blast- with suitable DC-markers not expressed on naı¨ve blasts: only ‘blast antigens’ that are not expressed on DC (e.g. CD117, CD56, Figure 1a–c left side) qualify for this strategy. Markers expressed on myeloid blasts and on DC (e.g. CD33) cannot be used to quantify DCleu, but to quantify the capacity of blasts to convert to DCleu by gain Leukemia
of other DC markers after culture (Figure 1a–c, right side). Owing to the higher fluorescence of DC, compared to blasts a quantification of both unconverted blasts and DC is not possible with only one reanalysis of the data. Our data analysis strategy is shown in Figure 1b: a ‘blast gate’ is defined surrounding the blast population before and after conversion to DCleu. Unconverted blasts can be identified and quantified with a ‘blast gate instrument setting’, especially regarding the lower isotype fluorescence before or after culture (Figure 1b, upper left/right). Regarding the higher isotype fluorescence of DC, the remaining cells are analyzed with a ‘DC-gate instrument setting’ (Figure 1b, upper right). This strategy allows quantification of DCleu and of blasts being converted or not converted to DCleu (Figure 1b and c). Apply this evaluation strategy, we are able to estimate the capability of different DC-generating methods to provide DC and especially their capability to differentiate leukemic blasts to ‘leukemia-derived DC’. In the case given in Figure 3, the DC were generated in MCM-Mimic. In parallel, the cells had been cultured in two other DC-differentiating media (Picibanil and Ca-Ionophore). Using the strategy described here, MCM-MIMIC could be defined as the optimal method in this case of AML to generate DC/DCleu. Moreover, the application of our evaluation strategy allows us to set up ex vivo or in vivo stimulation experiments using defined amounts of DCleu to stimulate for example, T or NK cells. In addition, the in vivo occurrence of DCleu in individual patients can be monitored. A variation of this method could be used in the DC generation from healthy or leukemic MNC by quantifying and differentiating mature (e.g. CD14-negative, DC-marker positive) from immature (CD14-positive, DC-marker positive) DC. We think that the method described here allows not only a detailed characterization of the antigen expression profile of DC, but especially contributes to study the quality of the DC generated, especially by quantifying proportions of DCleu. This might help to identify those DC-generating methods that allow optimal DCleu amounts for vaccination of AML patients or to set up T/NK-cell stimulations ex vivo to generate specific antileukemia directed cytotoxic cells. Thereby, individual immunotherapeutic approaches for individual AML patients could be found.13
HM Schmetzer, A Kremser, J Loibl, T Kroell and H-J Kolb Department for Hematopoetic Transplantations, Med III, Klinikum Grosshadern, University of Munich, Munich, Germany E-mail:
[email protected] References 1 Schmid C, Schleuning M, Schwerdtfeger R, Hertenstein B, Mischak-Weisinger E, Bunjes D et al. Long-term survival in refractory acute myeloid leukaemia after sequential treatment with chemotherapy and reduced-intensity conditioning for allogeneic stem cell transplantation. Blood 2006; 108: 1092–1099. 2 Choudhury A, Liang JC, Thomas EK, Flores-Romo L, Xie QS, Agusala K et al. Dendritic cells derived in vitro from acute myelogenous leukaemia cells stimulate autologous, anti-leukemic T-cell responses. Blood 1999; 93: 780–786. 3 Kufner S, Kroell T, Pelka-Fleischer R, Schmid C, Zitzelsberger H, Salih H et al. Serum-free generation and quantification of functionally active leukaemia-derived dendritic cells is possible from malignant blasts in acute myeloid leukaemia (AML) and myelodysplastic syndromes (MDS). Cancer Immunol Immunther 2005; 54: 953–970. 4 Mutis T, Verdijk R, Schrama E, Esendam B, Brand A, Goulmy E. Feasibility of immunotherapy of relapsed leukaemia with ex-vivo generated cytotoxic T lymphocytes specific for hematopoietic system –restricted minor histocompatibility antigens. Blood 1999; 93: 2336–2341.
Letters to the Editor
1341 5 Gao L, Bellantuono I, Elsasser A, Marley SB, Gordon MY, Goldman JM et al. Selective elimination of leukemic CD34(+) progenitor cells by cytotoxic T lymphocytes specific for WT1. Blood 2000; 95: 2198–2203. 6 Roddie PH, Horton Y, Turner M. Primary acute myeloid leukaemia blasts resistant to cytokine-induced differentiation to dendritic like leukaemia cells can be forced to differentiate by the addition of bryostatin-1. Leukemia 2002; 16: 84–93. 7 Roddie H, Klammer M, Thomas C, Thomson R, Atkinson A, Sproul A et al. Phase I/II study of vaccination with dendritic-like leukaemia cells for the immunotherapy of acute myeloid leukaemia. Brit J Haemotol 2006; 133: 152–157. 8 Li L, Giannopoulos K, Reinhardt P, Tabarkiewicz J, Schmitt A, Greiner J et al. Immunotherapy for patients with acute myeloid leukemia using autologous dendritic cells generated from leukemic blasts. Int J Oncol 2006, 855–861. 9 Womer K, Peng R, Patton P, Murawski MR, Bucci M, Kaleem A et al. The effect of renal transplantation on peripheral blood dendritic cells. Clin Transplant 2005; 19: 659–667.
10 Graf M, Reif S, Hecht K, Pelka-Fleischer R, Kroell T, Pfister K et al. High expression of costimulatory molecules correlates with low relapsefree-survival-probability in Acute Myeloid Leukemia (AML). Ann Hematol 2005; 84: 287–297. 11 Pietschmann P, Stoeckl J, Draxler S, Majdic O, Knapp W. Functional and phenotypic characteristics of dendritic cells genereated in human plasma supplemented medium. Scand J Immunol 2000; 51: 377–383. 12 Loibl J, Kremser A, Schmid C, Zitzelsberger H, Kroell T, Scholl N et al. Surface marker expression profiles have to be evaluated before and after the generation of dendritic cells (DC) from blasts in acute myeloid leukaemia (AML) and myelodysplastic syndromes (MDS) to characterize and quantify DC in experimental settings. 2007, submitted for publication. 13 Houtenbos I, Westers G, Ossenkoppele GJ, van de Loosdrecht AA. Leukemia-derived dendritic cells: towards clinical vaccination protocols in acute myeloid leukaemia. Haematologica 2006; 91: 348–355.
Gene expression overlap affects karyotype prediction in pediatric acute lymphoblastic leukemia
Leukemia(2007) 21, 1341–1344. doi:10.1038/sj.leu.2404640; published online 5 April 2007
Leukemia is the most common childhood malignancy in the United States.1 Acute lymphoblastic leukemia (ALL) accounts for 75% of new leukemia cases in children. Although the outcome for children with ALL has improved dramatically over the past three decades, 25% of children with ALL still develop recurrent disease. Current risk classification schemes in pediatric ALL use clinical and laboratory parameters such as age and initial white blood cell count, as well as the presence of specific ALL-associated cytogenetic or molecular genetic abnormalities. Stratification based on cytogenetic analysis and molecular genetic detection consider B precursor ALL translocations such as t(12;21)(TEL-AML1), t(1;19)(E2A-PBX1) and t(9;22)(BCRABL), as well as numerical imbalances such as hyperdiploidy, specific chromosome trisomies or hypodiploidy. Despite such efforts, current diagnosis and risk classification schemes in pediatric ALL remain imprecise. In particular, it is likely that a significant number of higher-risk children are currently overtreated and could be cured with less intensive regimens, resulting in fewer toxicities and long-term side effects. Conversely, a significant number of children in lower-risk categories still relapse and precise means to prospectively identify them have remained elusive. The advent of deoxyribonucleic acid based microarray technology raises the possibility of improving our understanding of the pathogenesis and treatment of leukemia. There have been efforts at genome-wide studies of leukemia classification. Both Yeoh et al.2 and Ross et al.3 have reported that gene expression profiling can identify the known prognostic subtypes of ALL. Yeoh et al. have further determined that T-ALL cases can be divided into intrinsic biological clusters, with risk groupings similar to B precursor ALL. We report the results of another gene expression experiment using a different cohort of pediatric ALL cases. Our cohort is more typical of pediatric ALL at presentation in that it includes patients with the above well-defined karyotypes as well as patients with ambiguous cytogenetic abnormalities. Using a variety of machine learning techniques,
we have validated the results of Yeoh et al., but have also discovered that expression profiles can overlap when considering cohorts containing a mixture of patients with and without well-defined cytogenetic abnormalities. This overlap results in difficulties predicting karyotype from gene expression and can be seen in both supervised and unsupervised machine learning approaches, revealing complexity and novel biologic clusters not precisely correlated with the known abnormalities. Our 311 patient cohort samples were collected with informed consent by the Pediatric Oncology Group in therapeutic trials ALinC15 and ALinC16 (8602, 9005, 9006, 9201, 9405, 9406 and 9605). The details of these clinical trials have been reported previously.4–6 The diagnosis of ALL was made on morphologic evaluation of bone marrow or peripheral blood and was confirmed by central review. Gene expression profiling was performed using Qiagen RNA isolation (www.qiagen.com), amplification and hybridization to Affymetrix HG_U95Av2 oligonucleotide microarrays (www.affymetrix.com). The HG_U95Av2 chips were scanned and analyzed following the Affymetrix Microarray Suite (MAS) Version 5.0 Software. Quality control criterion related to percent leukemic blasts, sample quantity, RNA quality, oligonucleotide staining, array hybridization and amplification were applied to eliminate 57 of the original 311 samples, ultimately leaving a cohort of 254. All Affymetrix microarray signal and CEL data, together with covariate clinical, cytogenetic and annotated experimental information is available at the National Cancer Institute Cancer Array Informatics website (http://caarraydb.nci.nih.gov/caarray/: Experiment ID 1015897590271440). A detailed description of experimental protocol and quality control criterion can be found in the Supplementary Information. The 254 patient data set was first preprocessed by the removal of control probe sets (AFFX accession IDs), and probe sets with no ‘present’ calls, as determined by the Affymetrix MAS 5.0 statistical software. After this process, 8943 of the original 12 625 Affymetrix HG_U95Av2 probe sets were retained for analysis. Next a base-10 logarithmic transformation of the gene expression data was performed. Finally, the 254 patient data set was divided into a 167 patient training set and an 87 patient test set. The training set was selected at random, but was balanced Leukemia
