Gene expression analysis identifies a genetic signature potentially associated with response to α-IFN in chronic phase CML patients

Leukemia Research 31 (2007) 931–938

Gene expression analysis identifies a genetic signature potentially associated with response to ␣-IFN in chronic phase CML patients Anette Hagberg a , Ulla Olsson-Str¨omberg b , Ulrika Wickenberg-Bolin a , Hanna G¨oransson c , Anders Isaksson a , Mats Bengtsson d , Martin H¨oglund b , Bengt Simonsson b , Gisela Barbany a,∗ a

Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University Hospital, SE-751 85 Uppsala, Sweden b Department of Medical Sciences, Hematology Section, Uppsala University Hospital, Uppsala, Sweden c Department of Engineering Sciences, Uppsala University, Sweden d Department of Oncology, Radiology and Clinical Immunology, Uppsala University Hospital, Uppsala, Sweden Received 25 July 2006; received in revised form 26 October 2006; accepted 12 November 2006 Available online 4 January 2007

Abstract Microarray-based gene expression analysis was performed on diagnostic chronic phase CML patient samples prior to interferon treatment. Fifteen patient samples corresponding to six cytogenetic responders and nine non-responders were included. Genes differentially expressed between responder and non-responder patients were listed and a subsequent leave-one-out cross validation (LOOV) procedure showed that the top 20 genes allowed the highest prediction accuracy. The relevant genes were quantified by real-time PCR that supported the microarray results. We conclude that it might be possible to use gene expression analysis to predict future response to interferon in CML diagnostic samples. © 2006 Elsevier Ltd. All rights reserved. Keywords: Chronic myeloid leukemia; Gene expression profiling; Interferon; Microarray; Drug response; RNASE2; CEBP

1. Introduction Interferon-␣ (IFN) was the first drug that could induce cytogenetic responses in CML, prolonging survival and delaying transformation to blast crisis in CML [1,2]. Not all patients will respond to IFN and the reasons for this heterogeneity are largely unknown. In an effort to identify patients that would do well on IFN therapy the group of European investigators in CML developed a prognostic score that proved highly relevant to identify a group of high risk patients, unlikely to benefit from IFN treatment [3]. The score is highly predictive with respect to survival probability, how∗ Corresponding author. Present address: Department of Molecular Medicine & Surgery, Karolinska University Hospital, Solna, S-17176 Stockholm, Sweden. Tel.: +46 8 517 73788; fax: +46 8 327734. E-mail address: [email protected] (G. Barbany).

0145-2126/$ – see front matter © 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.leukres.2006.11.015

ever, at the individual level, response to IFN is not predictable. With the introduction of Imatinib, IFN is no longer considered a drug of first choice in CML. Most patients treated with Imatinib will achieve a complete cytogenetic response and a significant molecular response [4,5]. Yet, not all CML patients respond to Imatinib and development of resistance is a concern [6,7]. Thus, IFN is still likely to play a role in therapeutic regimens in combination with other drugs, such as Imatinib and studies are currently underway to investigate a possible additive effect of IFN and Imatinib. Microarray gene expression studies allow the simultaneous analysis of the activity of thousands of genes in one single experiment. A number of studies have shown that gene profiling analysis can correctly classify leukemias [8,9] and distinguish between leukemias with recurrent chromosomal abnormalities [10]. Gene profiling studies have also identified a previously unknown subgroup of large B-cell lymphoma

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[11]. More recently a number of studies have revealed that it is possible to use microarray-based gene expression profiling to predict response to therapy [12] and clinical outcome in pediatric acute lymphoblastic leukemia [10], in adult T-cell acute lymphoblastic leukemia [13] and follicular lymphoma [14]. Global gene expression studies in CML have mainly focused on predicting response to imatinib treatment. However, the results have been partially contradictory and no consistent gene expression signature has been identified through the different studies, [15–17] while Crossman et al. were unable to find a particular gene expression pattern predictive of response to imatinib [18]. In the present study, we have used gene profiling to identify genes differentially expressed in CML chronic phase diagnostic samples from patients that achieve a cytogenetic response to IFN and those that do not. The results show a group of six differentially expressed genes between these two patient groups and that potentially allow prediction of response to IFN in CML patients at the time of diagnosis.

2. Materials and methods

response or failed to achieve a response were included in the study, whereas patients with an intermediate response were excluded. Responders were defined as patients achieving a complete or major cytogenetic response (<35% Ph+ cells) at 12 months or earlier after initiation of IFN. Conversely, patients that had no cytogenetic response at 6 months were considered non-responders and switched to another therapy [19]. In one case, the response was evaluated by monitoring the BCR–ABL fusion transcript by real-time PCR [20]. A six-fold reduction of BCR-ABL transcript was considered equivalent to no/minor cytogenetic response [21–24]. Blood samples, or alternatively leukapheresis cells, were collected at diagnosis and stored as mononuclear cell lysates in TRIzol® (Gibco-BRL, Rockville, MD, USA) at −70 ◦ C or as viable leukapheresis cells in liquid nitrogen. Mononuclear cells from two healthy blood donors were pooled and the extracted RNA was used as reference sample in all microarray assays. In addition, follow-up RNA samples from six patients that, after a therapy switch to Imatinib, later achieved a major molecular response and were analyzed by real-time PCR. The study was approved by the ethical committee at the Uppsala University Hospital.

2.1. Patient samples

2.2. Total RNA preparation and quality control

Peripheral blood samples were collected at diagnosis from 22 patients with chronic phase CML (Table 1). All but one patient carried the reciprocal t(9;22)(q34;q11) translocation, resulting in the Philadelphia chromosome as the sole cytogenetic abnormality, while one patient with a normal karyotype had a cryptic BCR–ABL fusion, detectable with fluorescent in situ hybridization (FISH) and RT-PCR. The majority of the patients received IFN in combination with hydroxyurea (HU) or AraC, while one patient received IFN alone (Table 1). Patients that achieved a major or complete cytogenetic response as well as patients that achieved only a minimal

Total RNA was extracted using TRIzol® preparation (Gibco-BRL). The yield of total RNA was measured with the Nano-drop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The RNA quality of all samples was analyzed with the RNA 6000 Nano Lab Chip kit in the Bioanalyzer 400 (Agilent Technologies, Palo Alto, CA, USA). 2.3. RNA labelling and hybridization The cDNA microarrays used were manufactured at the Microarray Core Facility, Rudbeck Laboratory (Uppsala

Table 1 Patients’ characteristics Sample

Procedure

Age at diagnosis

Karyotype

Treatment

Response

Hasford score

Sokal score

Best response Ph+ (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

F F F F L L F F F F F F F L L

66 41 22 55 20 19 60 32 51 65 46 52 47 34 44

46,XX,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11) 46,XY,ish t(9;22)(q34;q11) 46,XX, t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XY,t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11) 46,XX,t(9;22)(q34;q11)

IFN IFN + HU IFN + HU IFN + AraC IFN + HU IFN + HU IFN + HU IFN + HU IFN + HU IFN + AraC IFN + HU IFN + HU IFN + HU IFN + HU IFN + HU

R R R R R R nonR nonR nonR nonR nonR nonR nonR nonR nonR

I L I I H ND ND I I I I I ND L I

H I I I H ND I H I I H L H I I

0 34 0 18 33 34 Six-fold reductiona 100 100 100 100 100 100 100 100

Dg, diagnosis; F, ficoll separation; L, leukapheresis separation; Ph, philadelphia chromosome; IFN, interferon; HU, hydroxyurea; AraC, cytarabine; R, responder; nonR, non responder; L, low risk group; I, intermediate risk group; H, high risk group; ND, no data. a Detected by RQ-PCR.

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University, Sweden) and contained 15,552 spots representing 7458 cDNA clones and controls [25]. Three micrograms of patient RNA were labelled using the Micromax TSA Labelling and Hybridization Kit (Perkin-Elmer Life Sciences, Boston, MA, USA) mixed with the reference sample and hybridized to the array. Each patient sample was analyzed twice in a dye swap experiment. The slides were scanned in GenePix 4000B microarray scanner (Axon Instruments, Union City, CA, USA) at 10 ␮m resolution. 2.4. Image processing, normalization and filtering The procedure has been reported in more detail in Fryknas et al. [25]. Briefly the images were analyzed and raw data were extracted, using GenePix Pro software Version 4.0 (Axon Instruments) RNA expression values from spots which were not detected by the software or manually flagged as poor quality spots were removed from further analysis. Raw data was normalized using the SMA package (Statistics for Microarray Analysis, http://WWW.stat.berkeley.edu/ users/terry/zarray/Software/smacode.html). The LOWESS print tip normalization algorithm was used [26]. Genes with more than five and two missing expression values in the nonresponder (n = 9) and responder (n = 6) category, respectively, were discarded due to lack of data. The gene lists for both groups were matched resulting in 2916 genes, present on both lists, for further analysis. Hence, 2916 genes, out of 7458 genes, were used for supervised learning analysis and hierarchical clustering analysis [27]. 2.5. Supervised learning and feature selection The data were used to estimate the discriminative power of selected groups of genes, followed by a supervised classification procedure. The 15 available samples were used to train a classifier, in a conventional leave-one-out cross validation (LOOCV) procedure, where one sample at a time is held-out and not used for gene selection nor training of the classifier [28]. After each gene selection and classifier design, the sample held-out was classified and the procedure repeated for all samples. In each LOOCV step, the absolute difference in the median expression levels between the two sample categories is computed for each gene. In order to eliminate genes with small absolute differences, the genes were ordered according to the median differences. First the gene with the highest prediction accuracy is selected. The accuracy is defined as the error rate obtained in a LOOCV using classification with Fisher’s linear discriminant. Then the gene that in combination with the first one selected offers the best discrimination among all such pairs is selected. The pair of genes selected is then ignored and the all-pairs procedure is repeated until all genes have been included in gene pairs. In our series, the best discrimination between responder and non-responder was achieved by the top 20 genes. These 20 genes (with the largest median differences between the groups) are then used in an ‘all-pair’ selection

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procedure. The genes selected by means of the ‘all-pairs’ algorithm were finally used to design (train) Fisher’s linear discriminant. 2.6. Evaluation of significance of obtained error rates In order to verify that the error rates obtained with the data set were unlikely to have occurred by chance, a permutation test was performed. In a LOOCV, class labels for the training data were randomly permuted 1000 times and the data analysis was performed identically as described above. By counting how many times the observed LOOCV error rate estimate was equal or lower to the error rate obtained with the true class labels, the statistical significance of the results obtained with the true class labels was determined. 2.7. cDNA synthesis and RT-PCR analysis Reverse transcription of total RNA (2 ␮g) was performed in a volume of 20 ␮l using random hexamers and MMLV reverse transcriptase according to the protocol of the manufacturer (Invitrogen Life Technologies, Carlsbad, CA, USA). “Assay on demand” mixes for RNASE2, PRG2, NRGN, LTF, JARID1A and DEFA4 were purchased from (Applied Biosystems, Foster city, CA, USA). The real-time PCR reactions were performed in duplicate in 20 ␮l reaction volume, using 1 ␮l of each cDNA diluted 10 times and TaqMan Universal Master Mix (Applied Biosystems) with the ABI PRISM 7700 Sequence Detector instrument (Applied Biosystems). The expression level for each gene was normalized to GAPDH expression in each sample (n = 13) [23,29]. In addition, standard curves to calculate the relative concentrations were obtained by running dilution series of cDNA pooled from the patients.

3. Results 3.1. Patient samples All patients’ samples included in this study were collected at diagnosis from chronic phase CML patients. The samples were assigned to one of two categories, responder (R) and non-responder (nonR) according to the response to IFN treatment (Table 1). Response was determined as best cytogenetic response detected by bone marrow assessment of ≥25 metaphases and in one case by BCR-ABL quantification by real-time PCR [20]. Total RNA was isolated from blood mononuclear cells or leukapheresis cells. Initially, 22 patient samples were included in this study. However, most of the leukapheresis samples generated weak signals and as a consequence array quantification was not reliable and discrepant results were obtained from the dye swap experiments. Thus, seven samples did not pass the quality requirements and were excluded from further analysis due to discrepancies between the dye swap experiments. The four

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Fig. 1. Outcome of sample classification by hierarchical clustering of diagnostic CML samples using 2916 genes that passed the filtering criterion. L: samples collected by leukapheresis; F: samples processed by ficoll density centrifugation.

leukapheresis samples included in the analysis corresponded to two responder and two non-responder patients.

the genes according to the relative median expression between the two groups. The filter was set to select genes that separated the two groups with an expression level exceeding 1.8-fold (>1.8 and <−1.8) generating a list of 61 top genes (Supplementary Table 1 and Supplementary Fig. 1). Still, using these differentially expressed genes hierarchical clustering analysis was unable to separate both categories, and clustered R and nonR samples interspersed among each other. The four leukapheresis samples still clustered next to each other, but two of the mononuclear cell samples sorted in the same cluster (data not shown).

3.2. Unsupervised hierarchical clustering analysis 3.3. Identification of marker genes Unsupervised hierarchical clustering analysis was performed using the Genesis software (http://genome.tugraz.at/ Software/Genesis/Genesis.html) [27]. The analysis was based on gene expression values from the 2916 genes passing the filtering procedure. The samples clustered in two distinct categories according to the sample processing procedure used before RNA extraction; ficoll density sedimentation or leukapheresis separation (Fig. 1) reflecting the different cell populations analyzed with the two methods. A list of the genes that showed differential expression between R group and nonR group was generated by ranking

In order to identify genes that potentially could predict response to IFN we performed a LOOV procedure, leaving one sample out at a time when training the classifier. The LOOCV analysis was based on the data after the filtering process that generated a list of 2916 genes. One sample at a time was held-out and was neither used for feature selection nor for training. Best discrimination power was obtained with a cut off of 20, i.e. the 20 top ranked genes (with the largest median differences) were subsequently used in an ‘all-pair’ selection procedure. The genes most frequently utilized in

Table 2 LOOCV selected genes Gene name

Symbol

Chromosome band localization

UniGene Accession number

Times used in LOOV classification

Ribonuclease, RNase A family 2 (eosinophil-derived neurotoxin) Proteoglycan 2 (eosinophil granule major basic protein) Neurogranin (protein kinase C substrate, RC3) Lactotransferrin Jumonji, AT rich interactive domain 1A (RBBP2-like) Defensin, alpha 4, (corticostatin) Serine (or cysteine) proteinase inhibitor clade B (ovalbumin), member 1 Reticulon 3 Cathelicidin antimicrobial peptide Mediterranean fever Chromosome 20 open reading frame 3 Proteoglycan 1, secretory granule V-myc myelocytomatosis viral oncogene homolog Bactericidal/permeability-increasing protein Metallothionein 1F Carcinoembryonic antigen-related cell adhesion molecule 5 2 Chondroitin sulfate proteoglycan (versican) T-cell receptor beta chain V3-D-J2.7-C2 region Cystatin F (leukocystatin) In multiple clusters

RNASE2a,b,c

14q24-q31d

NM 002934

15/15

PRG2a,b,c

11q12e

BX395608

12/15

NRGNa,b LTFa,c JARID1Aa

11q24e 3q21-q23d 12p11e,f

BM701366 AK093852 NM 005056

9/15 8/15 8/15

DEFA4a SERPINB1

8p23e 6p25e

BU616655 M93056

8/15 5/15

RTN3 CAMP MEFVc C20orf3 PRG1 MYC

11q13d 3p21.3d 16p13.3e 20p11.22-p11.21e 10q22.1d,e 8q24.12-q24.13e,f

NM 201428 BM71211 NM 000243 NM 020531 CD359027 NM 002467

5/15 4/15 3/15 2/15 2/15 2/15

BPI MT1F CEACAM5

20q11.23-q12e,c 16q13e,c 19q13.1-q13.2d,e

BC040955 BU742440 M29540

1/15 1/15 1/15

CSPG2 TCR-β CST7c –

5q14.3e 7q35-q36e 20p11.21e –

NM 004385 AA994097 BQ051877 –

1/15 1/15 1/15 1/15

a b c d e f

Validated with RQ-PCR. Statistical significant difference between R and nonR (p < 0.05). Reported as chromosomal segment involved rearrangement in CML. Genes reported as amplified/deleted in CML due to an extra/missing chromosome. Genes with a cis element for C/EBP. Genes indirectly coupled to C/EBP.

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Fig. 2. Expression level of RNASE2, PRG2, NRGN, JARID1A, DEFA4 and LTF transcripts relative to GAPDH expression determined by real-time PCR in pre-treatment CML samples (n = 13). Filled circles represent nonR and open circles represent R patients. * Statistically significant.

this selection procedure were RNASE2, PRG2, NRGN, LTF, JARID1A and DEFA4 (Table 2). Classification of the held-out samples based on six genes resulted in an error rate of 13%. A permutation test showed that it is unlikely to obtain equal or more successful classification by chance (p = 0.004). 3.4. Real-time PCR quantification of selected classifier genes Real-time PCR was performed with the same set of samples to verify the microarray expression results. The expression levels as determined by real-time PCR correlated well with the data generated by microarray analysis (R2 = 0.7). The RQ-data indicate that RNASE2, PRG2, JARID1A and DEFA4 showed a higher expression in nonresponders, whereas NRGN and LTF showed a lower expression in this group (Fig. 2). RNASE2, PRG2 and NRGN showed a statistical significant difference among R and nonR (p < 0.05). The data generated by real-time PCR from the 15 diagnostic samples were subsequently used in a principal component analysis (PCA) (Fig. 3). PCA separated the samples into two clusters corresponding to R and nonR with one exception,

Fig. 3. Principal component analysis using data generated by real-time PCR quantification of RNASE2, PRG2, NRGN, LTF, JARID1A and DEFA4 transcripts. Filled circles: nonR patients (n = 7), open circles: R patients (n = 6), filled squares: patients in major molecular remission after a therapy switch)(n = 6). The arrow indicates the patient that relapsed in blast crisis, despite having rapidly achieved a complete cytogenetic response to IFN.

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one R patient (#3) clustered among the nonR. In addition, six samples derived from patients, that after a therapy switch to Imatinib achieved a major molecular remission and had undetectable or very low level of BCR-ABL mRNA, were included in the analysis and these samples grouped into a third cluster (Fig. 3), suggesting that the gene expression signature was specific to the leukemia cells. These results support the interest of the classifier genes as predictors of response to IFN.

4. Discussion Using microarray-based gene expression analysis on diagnostic samples derived from chronic phase CML patients, we have identified a set of genes that can possibly predict response to IFN treatment in CML patients. Patients were selected based on their cytogenetic responses to IFN and the availability of good quality RNA. Only patients that achieved a major or complete cytogenetic response were included in the R group whereas only patients with no or only minimal cytogenetic response were included in the nonR group in order to maximize the differences between the groups. Unsupervised hierarchical clustering analysis using 2916 genes separated the samples according to the method used for cell isolation prior to RNA preparation. This implies that the largest differences in gene expression in the sample cohort are derived from the differences in cell populations in the samples studied. Similarly, Crossman et al. could not identify a molecular signature associated with response to Imatinib by hierarchical clustering when analyzing a mixed sample population consisting of both blood and bone marrow samples from two different institutions [18]. These problems illustrate the importance to have adequate samples with respect to RNA quality and purity as well as samples processed uniformly. The present samples cohort included two leukapheresis samples in each group and it is not possible to exclude a bias in the selection of genes due to the fact that these four samples consisted of a different cell population. Nonetheless, the genes most frequently used for samples classification were still highly ranked in the list of differentially expressed genes, when the leukapheresis samples were omitted from the analysis. Some of the gene expression studies performed on CML samples have used CD34 selected cells [30–33] in order to achieve homogeneous patient samples. Previous studies exemplify the importance of homogeneously processed patient samples, however, in some instances individual differences in phenotypic distribution in patient samples may be related to leukemia biology and thus important information may be lost by enriching the samples for a particular subpopulation. In particular in the present study, the analysis of CD34+ cells did not seem optimal, since IFN is an endogenous cytokine that probably exerts its effect through a constitutive immunomodulatory mechanism involving other cells. Using an optimized set of 20 differentially expressed genes in a LOOV procedure we identified six genes that poten-

tially can classify the patients as likely R or nonR. The genes identified as possible predictors of IFN response seem to be biologically relevant. Browsing published data revealed that at least 12 out of the 20 genes (60%) selected for the LOOCV procedure have either a cis element for the C/EBP transcription factor family in the promoter region (RNASE2, PRG2, LTF, MEFV, BPI, MT1F and CST7), or are indirectly coupled to this transcription factors, e.g. MYC that antagonizes the transcription factor C/EBP (Table 2). Previous studies have shown that C/EBPα is expressed early in the myeloid differentiation [34] and that C/EBPε is essential for terminal differentiation of committed granulocyte progenitor cells. This finding suggests that the ability to respond to IFN might be associated with the capacity to differentiate in CML cells. C/EBP is negatively regulated by BCR-ABL [35] and induction of C/EBPβ has been shown to inhibit proliferation and promote differentiation of BCR-ABL expressing cells [36]. No significant difference in median BCR-ABL mRNA expression was found between R and nonR at diagnosis in the present study (data not shown). RNASE2 or eosinophil-derived neurotoxin (EDN) gene and PRG2, also known as major basic protein (MBP), are primarily expressed in eosinophils and showed a higher expression in the nonR group. There was no significant difference in eosinophil counts between the two patient groups that could explain why RNASE2 and PRG2 were differentially expressed. Interestingly these two genes have been identified among a set of 34 genes that distinguish CD34+ cells derived from chronic phase and blast crisis CML samples [32]. The PRG2 gene has also been found to be over-expressed in CD34+ cells from CML patients with a more indolent disease [33]. Two further genes were found to have a higher expression in patients that did not respond to IFN, JARID1A and DEFA4. DEFA4 is highly expressed in neutrophils, and similarly to eosinophil counts, there was no significant difference in neutrophil counts among R and nonR. Several of the genes reported in this study as differentially expressed between the two categories of patients, have been reported in other gene expression studies on CML cells. In addition to DEFA4, NRGN, CAMP, BPI, LTF, MYC and CD44 was found to be deregulated in CML cells as compared to normal controls [37]. The microarray expression data were confirmed with real-time PCR analysis. Univariate analysis of the RQ-PCR data showed that only RNASE2, PRG2, NRGN and DEFA4 expression level was significantly different in the two patient groups. The expression data from the six most interesting genes clustered the samples into two categories as illustrated by PCA. These two categories overlapped with the R and nonR groups with one exception. One responder patient clustered among the nonR. Interestingly, this patient rapidly progressed to blast crisis, despite the good initial response to IFN. In conclusion, we found different gene expression profiles in pre-treatment samples from chronic phase CML patients that responded to INF or not. Given the small size of our sample cohort we cannot, however, exclude that the differ-

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ences observed are random or due to different cell populations being analyzed as a result of two different sample processing methods. The genes differentially expressed are mainly involved in immune surveillance and response to pathogens. Moreover, a large proportion of the genes that discriminated between the two groups are connected to the transcription factor CEBP, important in myeloid differentiation. Taken together these findings suggest that response to INF in CML patients might be related to differentiation capacity of the leukemic cells perhaps superimposed on individual differences in immune response competence. Larger studies with uniformly processed samples are warranted in order to confirm our findings.

Acknowledgements This study was supported by grants from Swedish Cancer foundation, Lions Cancer foundation and Uppsala University Hospital Cancer funds. We would like to express our gratitude towards Maria Ryd˚aker and Niclas Olsson at the Uppsala Expression Array Platform, Rudbeck Laboratory, Uppsala University for technical assistance and to Hanna G¨oransson and M˚arten Frykn¨as for useful suggestions. Contributions. Anette Hagberg contributed to the conception and design of the study, data acquisition, analysis and interpretation and provided the initial draft of the manuscript. Ulla Olsson-Str¨omberg contributed to the conception and design of the study, and the writing of the manuscript. Ulrika Wickenberg Bolin contributed with acquisition and analyzing data, and revision of the manuscript. Anders Isaksson contributed to the conception of the study, and revision of the manuscript. Mats Bengtsson contributed with data acquisition. Martin H¨oglund contributed to the conception and design of the study and the revision of the manuscript. Bengt Simonsson contributed to the conception and design of the study and the revision of the manuscript. Gisela Barbany contributed to the conception and design of the study, interpretation of data and contributed to the initial draft of the manuscript.

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12] [13]

[14]

[15]

[16]

Appendix A. Supplementary data

[17]

Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.leukres. 2006.11.015.

[18]

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