CANCER STEM CELLS Molecular Profiling of CD34ⴙ Cells in Idiopathic Myelofibrosis Identifies a Set of Disease-Associated Genes and Reveals the Clinical Significance of Wilms’ Tumor Gene 1 (WT1) PAOLA GUGLIELMELLI,a ROBERTA ZINI,b COSTANZA BOGANI,a SIMONA SALATI,b ALESSANDRO PANCRAZZI,a ELISA BIANCHI,b FRANCESCO MANNELLI,a SERGIO FERRARI,b MARIE-CAROLINE LE BOUSSE-KERDILE` S,c ALBERTO BOSI,a GIOVANNI BAROSI,d ANNA RITA MIGLIACCIO,e ROSSELLA MANFREDINI,b ALESSANDRO M. VANNUCCHIa a
Department of Hematology, Azienda Ospedaliera-Universitaria Careggi, University of Florence, Florence, Italy; Department of Biomedical Sciences, Biological Chemistry Section, University of Modena and Reggio Emilia, Modena, Italy; cINSERM U602, University Paris 11, Institut Andre´ Lwoff, Villejuif Cedex, France; dUnit of Clinical Epidemiology, IRCCS Policlinico S. Matteo, Pavia, Italy; eDepartment of Hematology, Oncology and Molecular Medicine, Istituto Superiore Sanita`, Rome, Italy, and Department of Pathology, University of Illinois at Chicago, Illinois, USA, on behalf of the MPD Research Consortium
b
Key Words. Idiopathic myelofibrosis • CD34⫹ cells • Gene expression profiling • WT1 • JAK2V617F mutation
ABSTRACT This study was aimed at the characterization of a gene expression signature of the pluripotent hematopoietic CD34ⴙ stem cell in idiopathic myelofibrosis (IM), which would eventually provide novel pathogenetic insights and/or diagnostic/prognostic information. Aberrantly regulated genes were revealed by transcriptome comparative microarray analysis of normal and IM CD34ⴙ cells; selected genes were also assayed in granulocytes. One-hundred seventy four differentially expressed genes were identified and in part validated by quantitative polymerase chain reaction. Altered gene expression was corroborated by the detection of abnormally high CD9 or CD164, and low CXCR4, membrane protein expression in IM CD34ⴙ cells. According to class prediction analysis, a set of eight genes (CD9, GAS2, DLK1, CDH1, WT1, NFE2, HMGA2, and CXCR4) properly recognized IM from normal CD34ⴙ cells. These genes were
INTRODUCTION Myelofibrosis with myeloid metaplasia, also known as chronic idiopathic myelofibrosis (IM) [1], is a rare chronic myeloproliferative disorder (CMPD) characterized by the accumulation of abnormal megakaryocytes (Mks) in the bone marrow (BM), variable degrees of BM fibrosis, osteosclerosis and angiogenesis, immature myeloid and erythroid cells, and tear-drop erythrocytes in the peripheral blood (PB), and extramedullary hematopoiesis [2, 3]. Constitutive mobilization of CD34⫹ hematopoietic progenitor cells from the BM to PB distinguishes patients with IM [4, 5] from the other CMPDs [6] and is related to the severity of the disease and the risk of leukemic transformation [6 – 8]. The BM fibrosis is considered the response of local fibroblasts [9] to cytokines, such as transforming growth factor-1, released by the abnormal
aberrantly regulated also in IM granulocytes that could be reliably differentiated from control polycythemia vera and essential thrombocythemia granulocytes in 100% and 81% of cases, respectively. Abnormal expression of HMGA2 and CXCR4 in IM granulocytes was dependent on the presence and the mutational status of JAK2V617F mutation. The expression levels of both CD9 and DLK1 were associated with the platelet count, whereas higher WT1 expression levels identified IM patients with more active disease, as revealed by elevated CD34ⴙ cell count and higher severity score. In conclusion, molecular profiling of IM CD34ⴙ cells uncovered a limited number of genes with altered expression that, beyond their putative role in disease pathogenesis, are associated with patients’ clinical characteristics and may have potential prognostic application. STEM CELLS 2007;25: 165–173 Mks [10]. A myelofibrosis-like syndrome develops in mice with induced alterations of megakaryocytopoiesis, either because they overexpress the gene for thrombopoietin [11, 12] or because they lack Mk-specific regulatory sequences in the GATA-1 locus (GATA-1low mutation) [13]. Abnormal expression of cytokines and defective maturation of Mks have been demonstrated both in the murine models and in humans with IM, and an activating mutation of MPL was recently reported in a subset of IM patients [14]; on the other hand, although mutations of GATA-1 in IM patients are not known to occur, the cellular levels of GATA-1 were substantially reduced in IM Mks [15]. Furthermore, an acquired mutation in the negative autoregulatory JH2 pseudokinase domain of the JAK2 gene (V617F) has been detected in approximately 50%–70% of patients with IM; however, the mutation is not specific for IM, given that more than 90% of patients with polycythemia vera (PV) and more than 60% of those with
Correspondence: Alessandro M. Vannucchi, M.D., Department of Hematology, Azienda Ospedaliera-Universitaria Careggi, University of Florence, 50134 Florence, Italy. Telephone: ⫹39.055.7947-688; Fax: ⫹39.055.7947-688; e-mail:
[email protected] Received June 8, 2006; accepted for publication September 14, 2006; first published online in STEM CELLS EXPRESS September 21, 2006. ©AlphaMed Press 1066-5099/2007/$20.00/0 doi: 10.1634/stemcells.2006-0351
STEM CELLS 2007;25:165–173 www.StemCells.com
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essential thrombocythemia (ET) share this acquired abnormality [16 –21]. The JAK2V617F is a constitutively activated tyrosine kinase that confers erythropoietin hypersensitivity and growth factor independence in transfected cell lines [16, 18, 19]; furthermore, mice transplanted with hematopoietic cells expressing the mutated JAK2 gene develop erythrocytosis [18] and, eventually, myelofibrosis [22, 23]. All these findings are consistent with a pathogenetic role of mutated JAK2 in the myeloproliferation of CMPD, but how a single mutation associates with phenotypically distinct diseases is still unresolved [24, 25]. With the aid of microarray technology [26], we have analyzed the gene expression profile in CD34⫹ cells purified from the PB of patients with IM. The knowledge that circulating CD34⫹ cells belong to the malignant clone [27] and circulate in high frequency in the PB [6] allowed us to study a cell population representative of the disease’s molecular aberrations, obviating difficulties in collecting BM aspirates because of dry tap due to fibrosis; these cells were compared with those obtained from the BM of healthy subjects. Characterization of the transcriptome of IM CD34⫹ cells allowed the identification of 174 genes that were aberrantly regulated and might putatively represent a molecular signature of the disease. Furthermore, a set of eight differentially expressed genes was characterized that, according to class prediction analysis, properly distinguished 100% of IM versus normal CD34⫹ cells. To evaluate whether the abnormal expression of these eight genes was also maintained in the mature progeny of CD34⫹ cells, we measured gene expression levels in the granulocytes and found a 100% discrimination power of IM from normal granulocytes; additionally, an 81% correct prediction was obtained when IM granulocytes were compared with those isolated from patients with PV or ET. Among the eight genes comprising the prediction set, the abnormal expression profile of HMGA2 and CXCR4 in IM granulocytes was associated with the presence of JAK2V617F mutation, whereas that of NFE2, CD9, CDH1, GAS2, WT1, and DLK1 was not. Finally, we found that the expression levels of DLK1 and CD9 were related to the platelet count and that high expression levels of WT1 identified patients with more active disease, as indicated by elevated number of circulating CD34⫹ cells and higher severity score.
MATERIALS
AND
METHODS
Subjects The diagnosis of IM was made according to the necessary criteria identified by the Italian Consensus Conference criteria (diffuse marrow fibrosis and absence of BCR-ABL rearrangement) and an algorithm based on variable combination of accessory criteria represented by splenomegaly, tear-drop erythrocytes, circulating immature myeloid cells and erythroblasts, and clusters of abnormal Mks in the BM [28]. According to the World Health Organization (WHO) classification, all patients were in a typical fibrotic stage of the disease, and all were primary forms of IM [1]; they were studied either at diagnosis or during follow-up, but chemotherapy had been eventually stopped from at least 3 months. Thirty-five patients each with PV or ET, diagnosed according to the WHO criteria [1], were also included for comparison; they were either at the diagnosis or during the follow-up, but all were chemotherapy-naı¨ve. Controls were 29 healthy BM donors in experiments involving purified CD34⫹ cells or 15 blood donors in assays involving granulocytes. IM patients were assigned to a prognostic score at the time of diagnosis according to the criteria of Dupriez et al. [29]. A “severity score” for IM patients was calculated, at the time of sampling, by indexing leukocytosis, thrombocytosis, and splenomegaly (“myeloproliferation” index; range 0 – 4) or anemia, leukopenia, and thrombocytopenia (“myelodepletion” index; range 0 – 4), as described by
Marchetti et al. [30]; the overall grading of “severity” score was 0 – 6. The study had received the approval from the local Ethical Committee, and informed consent was obtained from the subjects involved at the time of sample collection.
CD34ⴙ Cell Enumeration and Purification The number of CD34⫹ cells in the PB of IM subjects was determined using 50 l of EDTA-anticoagulated blood; cells were stained with CD45-fluorescein isothiocyanate (FITC)-/CD34-phycoerythrin (PE)-conjugated monoclonal antibodies (both from BD Biosciences, San Jose, CA, http://www.bdbiosciences.com) and propidium iodide for excluding dead cells. At least 200,000 events were acquired on a FACScan flow cytometer (BD Biosciences) and analyzed by CellQuest software; the percentage of positive cells was calculated according to the guidelines from the International Society of Hematotherapy and Graft Engineering [31]. The absolute number of circulating CD34⫹ cells per liter was calculated by normalizing to the total leukocyte count. CD34⫹ cells were purified from 30 –50 ml of PB collected from IM patients or from 5 ml of BM aspirates obtained in preservativefree heparin from healthy donors. Mononuclear cells were separated over a Ficoll-Paque gradient (Lymphoprep; Nycomed Pharma, Asker, Norway, http://www.nycomed.com) and processed through two sequential steps of immunomagnetic CD34⫹ selection (Miltenyi Biotec, Bergisch Gladbach, Germany, http://www.miltenyibiotec. com) [15]. Purity of the isolated CD34⫹ cell population was evaluated by flow cytometry after labeling with PE-HPCA2 anti-CD34 monoclonal antibody (BD Biosciences). Aliquots of CD34⫹ cells were immediately resuspended in lysis buffer for RNA purification.
RNA Extraction and Microarray Data Analysis Total RNA was extracted using Trizol (Invitrogen Ltd, Paisley, U.K., http://www.invitrogen.com). Disposable RNA chips (Agilent RNA 6000 Nano LabChip kit; Agilent Technologies, Waldbrunn, Germany, http://www.home.agilent.com) were used to determine the concentration and purity/integrity of RNA with the Agilent 2100 Bioanalyzer. To obtain enough RNA to perform the Affymetrix analysis, 0.6 g of RNA from CD34⫹ cells of five different normal donors or IM patients were pooled, obtaining three pools of 3 g of RNA each. The biotin-labeled target synthesis reactions, as well as the Affymetrix HG-U133A GeneChip arrays hybridization, staining, and scanning, were performed using Affymetrix standard protocols (Affymetrix, Santa Clara, CA, http://www.affymetrix.com). Briefly, biotin-labeled cRNA was purified using RNeasy spin columns (Qiagen Inc., Valencia, CA, http://www1.qiagen.com), and 20 g was fragmented following the Affymetrix GeneChip protocol. The assessment of cRNA concentration/quality and fragmentation was performed with Agilent RNA chips. Fragmented cRNA was then hybridized to an identical lot of Affymetrix HG-U133A GeneChip arrays for 16 hours. GeneChips were washed and stained using the instrument’s standard eukaryotic GE WS2v4 protocol, using antibody-mediated signal amplification. GeneChips were finally scanned using the GCS3000 Affymetrix GeneChip scanner [32]. The GeneChip Operating Software (GCOS) absolute analysis algorithm was used to determine the amount of transcript mRNA (signal) using the GCOS global scaling option. GCOS-generated data were uploaded onto GeneSpring software version 7.2 (Agilent Technologies). Two additional normalization procedures were performed. First, each signal was divided for the 50th percentile of all normalized above-10 signals in that sample and was then divided by the median value in all samples. A “low-level” filtering procedure was performed to reduce noise: genes showing an “absent” call in all conditions, as well as genes showing a normalized intensity between 0.5 and 2, were removed. The unsupervised analysis was performed on this “low-level filtered” gene list using the “condition tree” option and applying the Pearson correlation equation. For supervised analysis, the “low-level filtered” gene list was further processed to select only those genes that showed a fold change more than 2 or less than 0.5 between normal and IM samples. Furthermore, a Welch analysis of variance (ANOVA) test (parametric test, with variances not assumed equal, p value cutoff ⫽ .05), using the Benjamini and Hochberg method to control the family-wise error
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rate, was performed on the list of genes deriving from the twofold change filter analysis. Class prediction analyses were performed using the Support Vector Machine algorithm as implemented in GeneSpring (polynomial dot product [order one] kernel function; diagonal scaling factor: 0).
analysis was performed according to the SPSS software. The chosen level of significance from two-sided tests was p ⬍ .05.
Real-Time Quantitative Polymerase Chain Reaction
Patient Characteristics
cDNA was reverse-transcribed from total RNA (100 ng per sample) obtained from CD34⫹ cells using a High Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA, https://www2. appliedbiosystems.com). TaqMan polymerase chain reaction (PCR) was carried out with the TaqMan Universal PCR master mix, using either custom TaqMan low-density arrays or TaqMan gene expression assays (all reagents from Applied Biosystems), by means of an ABI Prism 7900 HT sequence detection system (Applied Biosystems). Assays were performed in quadruplicate. Gene expression profiling was achieved using the comparative cycle threshold (CT) method of relative quantitation using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as the housekeeping gene. To normalize data, ⌬⌬CT was calculated for each sample using the mean of its ⌬CT values subtracted from the mean ⌬CT value measured in the entire population of healthy subjects, considered as a calibrator; relative quantitation (RQ) value was expressed as 2⫺⌬⌬CT. Normalized ⌬⌬CT values were uploaded onto GeneSpring using the realtime data transformation. For the analysis of gene expression levels in granulocytes, PB cells were separated by differential centrifugation over a FicollPaque gradient, and after removal of contaminating red cells by hypotonic lysis, the cell pellet was resuspended in Trizol for RNA extraction; analysis of cytosmears showed that more than 98% of cells were granulocytes. cDNA was reverse-transcribed with random hexamers and MuLV (murine leukemia virus) reverse transcriptase (Applied Biosystems) and processed for real-time PCR as above, except that RNase P was used as the reference gene.
The hematologic and clinical characteristics of the 88 IM patients included in the study are presented in Table 1; for comparison, data concerning the 35 patients with PV or ET are shown. The median number of circulating CD34⫹ cells in IM patients was 60.0 ⫻ 106/l, significantly higher than in PV or ET (p ⬍ .001 for both). Six patients had an absolute CD34⫹ cell count higher than 300 ⫻ 106/l, a value that has been shown to harbor a greater risk of leukemic transformation [6]; however, they remained hematologically stable for at least 4 months after blood sampling for this study. According to the risk stratification scoring system of Dupriez [29], at diagnosis 68% and 30% of the patients were in the low- and intermediate-risk categories, respectively. The frequency of JAK2V617F mutation was 69%, 72%, and 45% in IM, PV, and ET patients, respectively; patients with IM were more frequently homozygous than those with either PV or ET (p ⬍ .05 and p ⬍ .001, respectively). IM patients were almost equally distributed between low (0 –2) and high (3– 6) severity score (48% and 52%, respectively) that was calculated at the time of blood sampling.
Flow Cytometry Analysis of CD9, CXCR4 (CD184), and CD164 Expression on Circulating CD34ⴙ Cells The cell surface expression of CD9, CXCR4 (CD184), and CD164 was analyzed by flow cytometry (fluorescence-activated cell sorting [FACS]) on fresh (within 3 hours from drawing) PB samples from normal subjects or patients with IM, PV, or ET. Samples were incubated for 30 minutes at 4°C with FITC-conjugated anti-CD34, PerCyp-conjugated anti-CD45, and PE-conjugated anti-CD9, -CXCR4, or -CD164 antibodies (all from BD Biosciences), followed by red-cell lysis and washing. Appropriate isotope controls were used for each sample; cellular debris was excluded in a side scatter/forward scatter plot. A minimum of 200,000 events were acquired. Results were expressed both as the percentage of CD45⫹/ CD34⫹ cells coexpressing CD9, CXCR4, or CD164 and as the ratio of geometric mean fluorescence intensity (MFI) by dividing the value of specific antibodies with the corresponding isotope control antibody.
Analysis of JAK2V617F Mutation The analysis of JAK2 mutation was performed by an allelespecific PCR, starting from 75 ng DNA purified from granulocytes using the QIAmp DNA blood Kit (Qiagen) [17]. To evaluate whether the mutation was carried in the homozygous or heterozygous status, digestion of PCR products with BsaXI restriction enzyme (New England Biolabs Ltd., Hitchin, U.K., http://www.neb.uk.com) was performed as described [17].
Statistical Analysis Comparison between groups was performed by the Mann-Whitney U or Fisher test; associations between clinical characteristics and experimental data (logarithmically transformed) were assessed by Spearman’s or Wilcoxon-Mann-Whitney test, as appropriate, using the SPSS (StatSoft, Inc., Tulsa, OK, http://www.statsoft.com) or GraphPad InStat software (GraphPad Software, Inc., San Diego, http://www.graphpad.com) for computation. Logistic regression
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RESULTS
Gene Expression Profile of CD34ⴙ Cells from IM Patients We screened for abnormally regulated genes in pooled CD34⫹ cells purified from 15 IM subjects and 15 healthy controls (three distinct pools each). Purity of CD34⫹ cells was always more than 98%. All of the microarray analysis data have been deposited in the Gene Expression Omnibus MIAME-compliant public database at http://www.ncbi.nlm.nih.gov/geo (supplemental online Table S1). Microarray data analysis showed that mRNA complexity was comparable in normal and IM samples, given that the number of sequences called “present” by Affymetrix GCOS absolute analysis algorithm was 9,821 and 10,946 in normal and IM CD34⫹ cells, respectively. An unsupervised clustering analysis, performed on samples that had been managed using a low-level filtered gene approach, paired the transcript profiles of CD34⫹ cells from IM patients or normal subjects, respectively (supplemental online Fig. S1). Using the filtering procedure and the ANOVA analysis described in Materials and Methods, we finally identified 218 probesets, corresponding to 174 wellcharacterized genes, that showed significant differences in expression levels between the two kinds of cell samples. Functional analysis revealed that several of these 174 genes might be putatively involved in megakaryocytic commitment, BM fibrosis, oncogenesis, or hematopoietic stem cell adhesion and migration; a list of the 77 best-characterized genes under this respect is presented in supplemental online Fig. S2 (see also Discussion). To validate array data, we designed a TaqMan lowdensity array containing 47⫹GAPDH TaqMan gene expression assays (supplemental online Table S2) that had been selected based on either the statistically significant difference in gene expression levels or their putative pathogenetic role in IM. TaqMan assays were carried out in an independent cohort of CD34⫹ cells from eight IM patients and five healthy subjects and allowed to validate 36/47 genes (77%) (Fig. 1).
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Table 1. Clinical characteristics, at the time of sample collection, of the patients with IM and the other chronic myeloproliferative disorders included in the study Characteristic
Age, years Median Range Hemoglobin, g/dl Median Range WBC count, ⫻ 109/l Median Range Platelet count, ⫻ 109/l Median Range PB CD34⫹, ⫻ 106/l Median Range Dupriez score 0 (low risk) 1 (intermediate risk) 2 (high risk) JAK2 V617F Patient number (percentage) G/G G/T T/T Severity score Patient number (percentage) 0–2 3–6
IM (n ⴝ 88)
PV (n ⴝ 35)
ET (n ⴝ 35)
64 35–87
60 32–84
51 24–89
11.8 7.0–14.7
16.5 12.4–21.0
14.4 10.2–16.6
12.0 4.6–71.0
10.7 5.0–36.0
8.2 2.8–23.5
353 38–1,851
355 113–1,050
657 496–1,700
60.0 11–449
8.9 0–31 Not applicable
4.1 0–18 Not applicable
10 (28%) 18 (52%) 7 (20%) Not applicable
19 (55%) 15 (42%) 1 (3%) Not applicable
60 (68%) 26 (30%) 2 (2%)
27 (31%) 32 (36%) 29 (33%) 42 (48%) 46 (52%)
G/G is the wild-type sequence, G/T is the heterozygous sequence, and T/T is the homozygous mutation. Abbreviations: ET, essential thrombocythemia; IM, idiopathic myelofibrosis; PB, peripheral blood; PV, polycythemia vera; WBC, white blood cell.
Differential Expression of CD9, CXCR4 (CD184), and CD164 by FACS Analysis on CD34ⴙ Cells from Patients with IM Among the products of the 36 validated genes described above, we selected CD9, CXCR4, and CD164 for cytofluorimetric analysis because these proteins might play some role in the pathogenesis of IM. All of these membrane receptors are possibly involved in hematopoietic stem/progenitor cell adhesion and migration; in particular, CD9 is a tetraspanin [33] that also regulates the commitment and maturation of cells along the Mk lineage, CD164 belongs to the sialomucin family [34], and CXCR4 [35], the receptor for SDF-1, intervenes in the mechanisms of CD34⫹ cell homing and migration. According to gene expression analysis, the levels of CD9 and CD164 mRNA were increased in IM CD34⫹ cells, whereas those of CXCR4 were decreased, as compared with controls. The results of the FACS analysis on circulating CD34⫹ cells from IM patients (n ⫽ 20), normal healthy subjects (n ⫽ 15), and patients with PV or ET (n ⫽ 20 each) are shown in Figure 2. The fraction of gated CD45⫹/CD34⫹ cells coexpressing CD9 in IM patients was significantly higher than in healthy subjects and PV or ET patients (p ⬍ .001 for all) (Fig. 2); an upper cutoff limit of 40% PB CD45⫹/CD34⫹ cells coexpressing CD9 allowed the discrimination of 98% of IM patients from both controls and PV or ET patients. Also, the amount of CD9 molecules, measured as the MFI, was significantly increased in IM CD34⫹ cells as compared with healthy subjects (p ⫽ .009) or patients with PV (p ⫽ .03). On the other hand, the percentage of PB CD45⫹/CD34⫹ cells coexpressing CXCR4 was significantly lower in IM patients than in controls (p ⬍ .001) and PV or ET patients
(p ⬍ .01). CD34⫹ cells from IM patients also expressed significantly less CXCR4 molecules than controls (p ⬍ .01), whereas the difference with PV or ET patients was not statistically significant (Fig. 2). Finally, the percentage of CD34⫹ cells coexpressing CD164 was significantly greater in IM patients than in both controls and PV or ET patients (p ⬍ .01 for all), as well as the MFI (p ⬍ .01 vs. controls, and p ⬍ .05 vs. PV or ET patients).
IM-Derived CD34ⴙ Cells Can Be Reliably Distinguished from Normal Ones Based on TaqMan Low-Density Array Using a Set of Eight Genes With the aim of identifying a smaller set of genes that might successfully distinguish IM from normal CD34⫹ cells, we selected 8 genes among the 36 TaqMan validated genes on the basis of both their abnormal expression levels and their putative pathogenetic role in IM (listed in supplemental online Table S3). The performance of this set of genes was then assessed by employing class prediction analysis with the Support Vector machine using 15 samples (5 controls and 10 IM) as the training set and 27 samples (4 controls and 23 IM) as the test set. The results of the cross-validation of the training set as well as the outcome of test prediction are detailed in supplemental online Table S4; we found no incorrect prediction in the cross-validation of the training set or in the test set (27/27 ⫽ 100% validation).
The Eight Genes Comprising the Prediction Set in CD34ⴙ Cells Are also Abnormally Expressed in IM Granulocytes and Allow Differentiation of IM from Normal, PV, or ET Granulocytes We then asked whether abnormal expression of the eight gene markers considered above could also be demonstrated in gran-
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Figure 1. Clustering analysis of the 36 genes representing the “validation set.” Thirty-six genes, from a total of 47 chosen from the 174 abnormally regulated genes in IM CD34⫹ cells, were validated using a quantitative reverse transcription-polymerase chain reaction technology on a new set of eight IM patients and five healthy controls. GeneSymbol is listed on the right side of dendrogram. Clustering has been performed applying the “gene tree” and “condition tree” clustering algorithms provided by GeneSpring and the standard correlation equation. For each gene, the columns marked in dark blue refer to the results originally obtained from microarray analysis of the three pools of IM patients and controls, whereas those marked in turquoise refer to TaqMan data. Gene coloring was based on normalized signals, as shown at the bottom. Abbreviations: CTR, control; IM, idiopathic myelofibrosis.
ulocytes, which would represent a more convenient source for analysis than CD34⫹ cells. Using real-time RT-PCR, we found that CD9, NFE2, GAS2, DLK1, WT1, HMGA2, and CDH1 were all significantly increased in IM granulocytes compared with healthy controls, whereas CXCR4 was reduced (Fig. 3). Abnormally high expression levels of CD9, GAS2, CDH1, and NFE2 were also measured in the granulocytes from patients with PV or ET, whereas the levels of CXCR4, DLK1, WT1, and HMGA2 in these patients did not differ significantly from controls. This same set of genes was used to perform class prediction analysis on IM and control granulocytes, using 17 samples as a training set and 23 samples as a test set. As detailed in supplemental online Table S5, no incorrect prediction for either IM patients or controls was made in the training or test prediction set. This analysis was also carried out on the granulocytes obtained from patients with the different CMPDs; 30 patients (10 each with IM, PV, or ET) were used as a training set and 55 patients (16 IM, 19 PV, and 20 ET) www.StemCells.com
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Figure 2. Fluorescence-activated cell sorting analysis of the expression of CD9, CXCR4 (CD184), and CD164 on CD34⫹ cells from patients with IM, PV, or ET and healthy controls. Panels on the left depict the percentage of electronically gated CD45⫹/CD34⫹ cells that coexpress CD9, CD184, or CD164, whereas the MFI level is represented in the panels on the right. All of these analyses were performed on PB CD34⫹ cells; data are representative of IM, PV, or ET patients (20 each) and 15 healthy controls. PE-labeled antibodies. Boxes represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, and bars show the range of values. Data obtained in IM patients were compared with both controls and PV or ET patients for calculation of significance level. ⴱ, p ⬍ .05; ⴱⴱ, p ⬍ .01; ⴱⴱⴱ, p ⬍ .001. Abbreviations: Ctr, control; ET, essential thrombocythemia; IM, idiopathic myelofibrosis; MFI, mean fluorescence intensity; PE, phycoerythrin; PV, polycythemia vera.
as a test set. The results of class prediction analysis (shown in supplemental online Table S6) demonstrated 100% correct prediction in the cross-validation of the training set, whereas three incorrect predictions were made in the test set prediction (52/55 ⫽ 95% validation of all cases), and concerned three IM patients out of 16 (19%) that were misclassified as PV/ET.
Effect of the Presence of JAK2V617F Mutation on Gene Marker Expression Levels To evaluate whether abnormal expression of any of the eight gene markers correlated with the presence of the JAK2V617F mutation, IM patients were grouped according to their mutational status and the respective levels of gene expression were considered (Fig. 4). We found that abnormal expression of HMGA2 and CXCR4 was associated with the presence of JAK2V617F; in the case of CXCR4, the expression levels were significantly lower in homozygotes than in heterozygotes, suggesting dependence of gene expression on the number of mutated alleles. On the other hand, the expression levels of CD9, DLK1, GAS2, CDH1, NFE2, and WT1 were not significantly different in JAK2V617F-mutated patients as compared with wild-type.
Association of CD9 and DLK1 Expression Levels with Platelet Count in IM Patients To establish possible correlations of the aberrantly regulated genes comprised within the eight-gene set with hematologic
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Figure 3. Expression levels of the eight genes comprising the prediction set in the granulocytes from patients with chronic myeloproliferative disorder and healthy controls. Gene expression levels were measured by real-time reverse transcription-polymerase chain reaction starting from granulocyte RNA and were expressed as RQ log10. Boxes represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, and bars show the range of values. Data are representative of 25 IM, 30 PV, and 30 ET patients (except for WT1, CD9, DLK1, and HMGA2, in which the number of IM patients examined comprised between 45 and 60). The significance levels of the differences among patient categories are detailed in the table inside. Abbreviations: Ctr, control; ET, essential thrombocythemia; IM, idiopathic myelofibrosis; n.s., not significant; PV, polycythemia vera; RQ, relative quantitation.
phenotype of IM patients, expression levels in granulocytes were correlated with hemoglobin, white blood cell, and platelet counts. We found that CD9 and DLK1 expression levels were directly (r ⫽ .56, p ⬍ .001) and inversely (r ⫽ ⫺.58, p ⬍ .001) related, respectively, to the platelet count (Fig. 5A, 5B); of interest, no correlation was observed in PV or ET patients. No additional correlation was found between any of the eight genes considered and red or white blood cell indexes, except for a trend between higher WT1 expression levels and lower hemoglobin (Pearson correlation coefficient ⫽ ⫺.36, p ⫽ .059) or an inverse relationship between HMGA2 levels and white blood cell counts (coefficient value ⫽ ⫺.41, p ⫽ .04).
Association of WT1 Expression Levels with Disease Severity To address whether abnormal gene expression was associated with clinical characteristics of IM patients, we used the Dupriez
Gene Expression Profiling in IM CD34⫹ Cells
Figure 4. Effects of JAK2V617F mutation on the expression of the genes comprising the prediction set in idiopathic myelofibrosis granulocytes. Gene expression levels were measured by real-time reverse transcription-polymerase chain reaction starting from granulocyte RNA and were expressed as RQ log10. Boxes represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, and bars show the range of values. Patients were categorized according to the absence (WT) or the presence of JAK2V617F mutation (V617F) and whether the mutation was found in the heterozygous or homozygous form. ⴱ, p ⬍ .05; ⴱⴱ, p ⬍ .01. Abbreviations: Hetero, heterozygous; Homo, homozygous; RQ, relative quantitation; WT, wild-type.
score (at diagnosis) as a prognostic index and the CD34⫹ cell count and the severity score (both calculated at the time of blood sampling for this study) as indexes of disease severity. In univariate analysis, we found a strong association of increased expression of WT1 with both the CD34⫹ cell count (r ⫽ .61, p ⬍ .001) (Fig. 5C) and the severity score (Fig. 5D). In this regard, patients were arbitrarily divided into low (score 0 –2) and high (score 3– 6) severity score groups, whose median RQ values were ⫺0.91 and 5.67, respectively (p ⬍ .001). On the other hand, WT1 was not associated with the prognostic Dupriez score (p ⫽ .10). We then performed a multivariate analysis that included the expression levels of CD9, CXCR4, HMGA2, and WT1, and the clinical variables considered in Table 1, in a total of 66 IM patients. The results of this statistical approach showed that the only parameter associated with the severity score was represented by the levels of WT1 RNA (confidence interval 1.23–2.80, p ⫽ .003). Thus, we conclude that WT1 represents an independent factor associated with disease severity.
DISCUSSION To investigate pathogenetic mechanisms of IM, and possibly to identify molecular markers associated with the disease and/or with clinical aspects, we have characterized the transcriptome profile of CD34⫹ cells purified from the PB of IM patients. We obtained CD34⫹ cells from the BM of healthy donors as controls, because their very low number in steady-state PB pre-
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Figure 5. Clinical correlates of CD9, DLK1, and WT1 gene expression levels in patients with idiopathic myelofibrosis (IM). Gene expression levels were measured by real-time reverse transcription-polymerase chain reaction starting from granulocyte RNA and were expressed as RQ log10. (A, B): Gene expression levels were plotted against platelet count at the time of blood sampling in 62 and 54 IM patients, respectively, for CD9 (A) and DLK1 (B). In plots (C) and (D), WT1 expression levels were plotted against CD34⫹ cell count (n ⫽ 46) and clinical severity score (n ⫽ 60), respectively, calculated at the time of blood sampling. Boxes in (D) represent the interquartile range that contains 50% of the subjects, the horizontal line in the box marks the median, and bars show the range of values. Twenty-seven and 33 patients were in the low and high score groups, respectively. Abbreviation: RQ, relative quantitation.
cluded cell enrichment in amounts sufficient for microarray analysis. Although some differences in the global gene profile between these two sources of cells may be expected [36], in the case of normal subjects we preferred to use unstimulated BM rather than granulocyte cell-stimulating factor (G-CSF)-primed PB to avoid the effects of exogenous growth-factor stimulation on gene expression. To obtain the necessary amount of RNA from CD34⫹ cells and to minimize the interindividual variability, we performed microarray analysis on three pools prepared from five different subjects each, rather than use in vitro amplification of RNA from single donors, because these technologies might potentially bias for low-abundance RNAs [26]. Gene profiling analysis identified 218 transcripts that correspond to 174 well-characterized genes and show aberrant expression in IM versus normal CD34⫹ cells; these transcripts, based on their known function(s), are possibly involved in some pathogenetic steps of this disorder (supplemental online Fig. S2) and would deserve further investigation. Among those that are involved in the control of the development of Mks, which represent the most obviously involved cell lineage in IM, are CD9, which influences Mk differentiation [37], vWF, a distinctive marker for early Mks, and NF-E2, which acts in late stages of megakaryocytopoiesis and in platelet release. Other abnormally upregulated genes encode for proteins possibly involved in the development of fibrosis (such as TIMP1 or TIMP3, and DLK1) or in the regulation of cell migration (such as CD164, TM4SF1, ENPP2, ADAM8, and ADAM28). Consistently, the decreased expression in IM CD34⫹ cells of CXCR4 (the SDF-1 chemokine receptor), CD38, protease inhibitors (CSTA, CST7, CST3, and SERPINB10), and cadherins (such as CDH1 and CDH2) might facilitate the abnormal constitutive mobilization of IM hematopoietic/stem progenitor cells in the circulation through the generation of a proteolytic marrow microenvironwww.StemCells.com
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ment [5]. Of interest for IM pathogenesis, the expression of some oncogenes and neoplastic marker genes, such as ETS2 (already reported as upregulated in PV), DLK1, MYC, LEPR, WT1, PDZK3, PIM, TEM6, GRB10, TNKS, PLAG1, and TPBG, was upregulated, whereas tumor-suppressor genes AIM-1, DP-1, and BRCA1 were downregulated. Finally, it is worth mentioning the activation of genes that are not normally expressed in the hematopoietic system, such as KLRG1, GLS, BTNA-3 and -2, HFL1, KRT18, and CKB (for a list of pertinent references, please refer to supplemental online Table S7). Gene profiling analysis in IM CD34⫹ cells has been reported by Jones et al. [38]; in spite of the different technical approach (we prepared pools of unmanipulated RNA from 15 patients rather than performing a double in vitro transcription starting from nanogramscale RNA of eight IM subjects) and the Affymetrix GeneChips used (the HUG133A chip we used comprises 22,238 probesets vs. the 12,625 in the HGU95Av2 chip), we found some overlap in the genes found to be differentially expressed, including DLK1, CDH1, and N-MYC (see below). However, our analysis required a smaller set of genes, only 8 genes as compared with 75 [38], for the discrimination of IM versus control CD34⫹ cells according to class prediction analysis. This novel gene set includes CD9, CDH1, CXCR4, GAS2, NFE2, DLK1, HMGA2, and WT1. The possibility that abnormal gene expression found in CD34⫹ cells was also maintained in their mature progeny was addressed by studying PB granulocytes, which would also represent a more accessible source of cells for further analysis. Indeed, the eight-gene set that properly distinguished IM from normal CD34⫹ cells also proved to be aberrantly regulated in the granulocytes of patients with IM and, with some differences (Fig. 3), in PV or ET granulocytes. According to the results of class prediction analysis using this eight-gene set, IM granulocytes could be efficiently distinguished from either control or PV and ET granulocytes in 100% and 81% of cases, respectively. A notable exception was the CDH1 gene, which showed opposite changes from CD34⫹ cells (where it was downregulated) to granulocytes (where expression levels were significantly higher than controls). Reduced expression of E-cadherin, the product of CDH1, influences cell-cell adhesion interactions and may facilitate cancer metastasis [39]; we hypothesize that it might also play a role in the constitutive mobilization of CD34⫹ cells in IM patients. On the other hand, the mechanisms leading to CDH1 gene upregulation in the granulocytes of CMPD patients are unclear, but hypothetically they might reflect their activated status [40] given that, for example, CDH1 levels were found to be increased in CD14⫹ cells after G-CSF treatment of normal donors [41]. The three RNA pools prepared from IM patients and used for microarray analysis contained an almost 50% mixture of JAK2V617F-mutated (n ⫽ 7) and -nonmutated (n ⫽ 8) patients, mirroring the overall incidence of the mutation in this disease; therefore, the list of differentially expressed genes actually reflects the abnormal transcriptome of IM CD34⫹ cells including both JAK2V617F-dependent and -independent genes. Further analyses, using cells prepared from either mutated or wild-type IM patients, might help in identifying differences of gene expression attributable to the mutation per se; meanwhile, based on the results of gene expression in granulocytes, HMGA2 and CXCR4 should be added to the growing list of genes whose expression is affected by the presence of JAK2V617F mutation [42– 44]. WT1, the Wilms’ tumor 1 gene, deserves particular interest because high WT1 expression levels in IM cells were found to be associated with signs of disease activity, such as the number of CD34⫹ cells in the PB or the severity score (Fig. 5C, 5D). Although WT1 is considered a tumor-suppressor gene, the WT1 wild-type form is overexpressed in a variety of human tumors,
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including myelodysplasia (MDS) and leukemias [45], in which high WT1 mRNA levels correlate with poor prognosis and/or advanced disease [46, 47]. It has been demonstrated that the growth of WT1-expressing leukemic cells is inhibited by treatment with WT1 antisense oligomers [48]; consistently, constitutive overexpression of wild-type WT1 in normal myeloid cells promoted proliferation and blocked differentiation [48, 49]. It is also worth mentioning that constitutive expression of WT1 was found to induce morphological changes in cancer cell lines, allowing cancer cells to acquire a more invasive phenotype [50]. Thus, we hypothesize that WT1 might play a role in the pathogenesis of IM through two different mechanisms, first by favoring proliferation of CD34⫹ cells at the expense of differentiation, second by promoting the constitutive mobilization of CD34⫹ cells in the PB, a phenomenon that would also be favored by the downregulation of CXCR4 and CDH1 (Fig. 6). Additionally, the overexpression of DLK1 might contribute, on one side, to the abnormal regulation of CD34⫹ cell proliferation and differentiation (as suggested by the effects of its forced expression in the K562 cell line and by the findings of DLK1 overexpression in the CD34⫹ cells of most MDS patients [51, 52]) and, on the other, to the development of fibrosis, a mechanism postulated to occur in the liver fibrogenesis associated with biliary atresia [53] (Fig. 6). Overall, our observations in IM are consistent with other neoplastic conditions in which upregulation of WT1 negatively impacts on survival; however, the design of the present study does not allow us to correlate WT1 levels with the major clinical endpoints (i.e., survival and/or leukemia transformation); that would require a larger series of patients and a longer follow-up. It would also be of interest to prospectively study newly diagnosed patients to evaluate any change in WT1 levels which depends on the disease activity and/or the effects of therapy. Finally, the abnormal activation of WT1 in IM may have potential therapeutic perspectives, because the WT1 protein is an attractive target antigen for immunotherapy; clinical responses have been reported in a recent phase I clinical study of WT1 peptide-based immunotherapy in patients with MDS or acute myeloid leukemia [54]. Collectively, the results of this study led to the characterization of a limited set of genes specifically associated with both the CD34⫹ cells and mature granulocytes of IM patients. These genes, beyond being potentially implicated in some pathogenetic aspects of the disease and amenable to future studies, have intriguingly been found to associate with some clinical characteristics; in particular, it is anticipated that determination of WT1 expression levels in IM granulocytes might suitably employ WT1 as the first molecular marker of disease activity and, prospectively, prove useful to evaluate response to therapy [55].
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ACKNOWLEDGMENTS We thank all colleagues who referred patients, and we also thank patients for their willingness to contribute to the study. Dr. G. Longo helped with statistical analysis. This study was supported by the Italian Ministry of Health (Progetti di Ricerca di Interesse Nazionale) and The National Stem Cell Project; Associazione Italiana per la Ricerca sul Cancro, Milano; Ente Cassa di Risparmio di Firenze; Grants RBNE0189JJ 006/ RBNE01SP72 003 from the Italian Ministry of Industrial and University Research; institutional funds from the University of Illinois; the Association pour la Recherche contre le Cancer (ARC) number 9806, the Groupement d’Interet Scientifique (GIS)-Institut des Maladies Rares number 03/GIS/PB/SJ/n°35. A.P. was the recipient of a fellowship from Associazione Italiana per le Leucemie Firenze. R. M. and A.M.V. contributed equally to the study.
DISCLOSURES The authors indicate no potential conflicts of interest.
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