Expression of Myeloid-Specific Genes in Childhood Acute Lymphoblastic

Home , ERG (gene), FGF6

Leukemia (2002) 16, 2213–2221  2002 Nature Publishing Group All rights reserved 0887-6924/02 $25.00 www.nature.com/leu Expression of myeloid-speciﬁc genes in childhood acute lymphoblastic leukemia – a cDNA array study

T Niini1, K Vettenranta2, J Hollme´n3, ML Larramendy1, Y Aalto1, H Wikman1, B Nagy1, JK Seppa¨nen3, A Ferrer Salvador1, H Mannila3, UM Saarinen-Pihkala2 and S Knuutila1

1Departments of Pathology and Medical Genetics, Haartman Institute and Helsinki University Central Hospital, University of Helsinki, Finland; 2Division of Hematology-Oncology and Stem Cell Transplantation, Hospital for Children and Adolescents, Helsinki University Central Hospital, Helsinki, Finland; and 3Laboratory of Computer and Information Science, Helsinki University of Technology, Espoo, Finland

Several specific cytogenetic changes are known to be associa- of hundreds to thousands of genes to be evaluated in a single ted with childhood acute lymphoblastic leukemia (ALL), and experiment, and the screening of genes with over- or under- many of them are important prognostic factors for the disease. Little is known, however, about the changes in gene expression expression. The method can be used to search for novel mol- in ALL. Recently, the development of cDNA array technology ecular changes that are associated with the development has enabled the study of expression of hundreds to thousands and/or prognosis of leukemia. In this study we used cDNA of genes in a single experiment. We used the cDNA array arrays to study gene expression profiles in the leukemic blast method to study the gene expression profiles of 17 children cells of 17 children with precursor-B ALL. with precursor-B ALL. Normal B cells from adenoids were used as reference material. We discuss the 25 genes that were most over-expressed compared to the reference. These included four genes that are normally expressed only in the myeloid lin- Materials and methods eages of the hematopoietic cells: RNASE2, GCSFR, PRTN3 and CLC. We also detected over-expression of S100A12, expressed Patients in nerve cells but also in myeloid cells. In addition to the myeloid-specific genes, other over-expressed genes included A total of 17 patients (6/11, M/F) with ALL were included in AML1, LCP2 and FGF6. In conclusion, our study revealed novel information about gene expression in childhood ALL. The data the study. Fourteen patients were analyzed at diagnosis and obtained may contribute to further studies of the pathogenesis three at relapse. Table 1 shows the key clinical data and the and prognosis of childhood ALL. karyotypes of the patients. The mean age of the children was Leukemia (2002) 16, 2213–2221. doi:10.1038/sj.leu.2402685 5.0 years (range 1.2–12.7 years) and the mean WBC was 38.6 Keywords: acute lymphoblastic leukemia; gene expression; × 109/l (range 1.2–272.4 × 109/l). All patients had an early myeloid-specific genes; cDNA array precursor-B phenotype, except patient 8, who had a precursor-B disease. In 16 of the 17 cases the blast population expressed the CD34 antigen, in 10 cases in conjunction with Introduction the expression of the myeloid antigens CD13 and CD33. Only in patient 8 was the blast cell population devoid of the Cytogenetic changes are important prognostic markers in expression of both CD34 and CD13/33. The patients were div- childhood acute lymphoblastic leukemia (ALL). For example, ided into standard, intermediate, and high-risk categories and the translocation t(12;21)/ETV6-AML1 is a marker of favorable treated according to the protocols of the Nordic Society of prognosis and most patients with this abnormality are best Pediatric Hematology and Oncology (NOPHO).3 Two of the treated with conventional chemotherapy. In contrast, patients patients analyzed at primary diagnosis have relapsed, but 12 with t(4;11)/MLL-MLLT2 and t(9;22)/BCR-ABL have a poor continue in first remission, one after allogeneic bone marrow prognosis, and high-dose chemotherapy with bone marrow transplantation from an unrelated donor. All three patients transplantation is advocated (for review see Ma et al1). Despite studied at relapse have received a bone marrow transplant, the extensive knowledge about chromosomal abnormalities in two from an unrelated donor and one from a matched sibling. ALL, little is known about the changes in gene expression in One of the three has died following a post-transplant relapse, the disease. but two remain in second complete remission. In addition to genetic markers, the prognostic tools in childhood ALL include white blood cell count (WBC) at diagnosis, age, gender, CNS/testicular involvement, response to primary Samples therapy and the phenotype of the blasts (mature B cell vs T cell vs precursor-B cell ALL). Groups of patients stratified using the Whole bone marrow specimens were diluted 1:10 in existing criteria remain, however, heterogeneous and result in RNA/DNA stabilization reagent for blood/bone marrow patients remaining in first complete remission and those (Boehringer Mannheim, Mannheim, Germany) for simul- relapsing either on or off therapy.2 A more comprehensive taneous cell lysis and stabilization of nucleic acids. The analysis of blast cell gene expression could provide us with samples were stored at −70°C until RNA isolation. novel prognostic tools as well as new insight into the pathogenesis of childhood ALL. The cDNA array methodology allows the expression levels Reference material

Two separate pools of CD19-positive B cells from human Correspondence: S Knuutila, Department of Medical Genetics, Haart- man Institute, PO Box 21, FIN-00014 University of Helsinki, Helsinki, adenoid samples from healthy children were used as reference Finland; Fax: +358–9–191 26788 material. One pool was from six individuals and the other Received 19 February 2002; accepted 31 May 2002 from five. The B cells were purified using microbeads conju- Myeloid-specific genes in childhood ALL J Niini et al 2214 Table 1 Clinical characteristics and karyotype data of 17 children with ALL

Patient Lab. code Sampling Sex Age Risk WBC Myeloid Myeloid Karyotype time dg (rel)b group dg (rel)c markers cells (%)

1 991506 Relapse F 1.8 (6.2) HR 99.4 (25.2) no 4 49,XX,+10,t(12;21)(p13;q22),+14,+21d 2 990663 Diagnosis F 4.2 HR 1.2 yes 5 64–66,XX,+X,+2,+3,+4,+5,+6,+8,+10,+11, +12,+14,+14,+16,+17,+18,+21,+21, +22,+2mar 3 981894 Diagnosis M 5.8 HR 4.0 yes 15 46,XY,t(12;21)(p13;q22)d 4 991170 Diagnosis M 3 IR 16.4 yes 10 46,XY,t(12;21)(p13;q22)d 5 GA99–17 Diagnosis F 8.9 SR 3.9 no 1 45–46,XX,-2,?t(2;14)(q?;q?),-12,-14,- 15,+mar,inc 6a 991477 Diagnosis F 4.7 HR 272.4 yes 2 47,XX,+21 7 GA00–10422 Diagnosis F 3.1 SR 4.9 no 15 46,XX 8 991843 Diagnosis M 12.7 IR 7.2 no 5 46,XY,del(9)(p11) 9 GA00–10526 Diagnosis F 3.2 IR 19.0 yes 5 55–57,XX,+X,+4,+6,+8,+8,+10,+14,+17, +18,+21,+21 10 981693 Diagnosis F 5.6 IR 26.3 no 10 48,XX,-20,+der21,+2mar 11a GA00–9977 Diagnosis F 3.8 HR 145.8 yes 2 44–45,X,-X,-9,-9,-11,-13,+3–4mar 12 GA00–9648 Relapse F 1.2 (3.5) IR 4.0 (14) no 15 46,XX,t(X;9)(q?;q11) 13 990710 Relapse M 4.7 (13.1) SR 7.6 (3.9) yes 10 55,XY,+X,1q+,+4,+6,+8,+14,+17,+18, +21,+21 14 GA00–9884 Diagnosis F 6.9 IR 10.6 yes 6 54–57,XX,+?X,+4,+6,+?14,+16,+21,+22 15 GA00–10058 Diagnosis F 3.1 SR 3.4 no 1 46,XX,-1,-1,+3mar,inc 16 GA00–10648 Diagnosis M 6.6 HR 7.0 no 3 54–55,XY,+X,+4,+6,+10,+14,+17,der(19) t(1;19)(q23;p13),+21,+21 17 GA00–10387 Diagnosis M 4.9 IR 22.5 yes 2 54,XY,+X,+Y,+6,+10,+14,+17,+21,+21

aThe patient has since relapsed. bAge at diagnosis (at relapse) in years. cWBC at diagnosis (at relapse) (×109/l). dThe t(12;21) was conﬁrmed by ﬂuoresence in situ hybridization. F, female; M, male; HR, high risk; IR, intermediate risk; SR, standard risk.

gated to a monoclonal CD19 antibody (Miltenyi Biotec, cDNA array hybridization Bergisch Gladbach, Germany). CD19 is expressed from earl- iest recognizable B-lineage cells to activated B cell blasts, but Total RNA (3–4 ␮g) was converted into cDNA and labeled expression is lost on maturation to plasma cells. The pro- with 33P-dATP using the Atlas pure total RNA labeling system portion of T lymphocytes was analyzed in one of the pools, (Clontech) according to the manufacturer’s instructions. and it was less than 5%, indicating that 95% of the isolated Probes were puriﬁed and hybridized to the arrays according cells were B lymphocytes. to the manufacturer’s instructions. The arrays were exposed to an imaging plate (BAS-MP 2040S; Fuji, Kanagawa, Japan) for 3–7 days followed by scanning of the plate with a phos- Total RNA extraction phorimager (Bio-Imaging Analyzer, BAS-2500; Fuji). Two separate reference hybridizations were performed from the two distinct pools of B cells. Nucleic acids were extracted using an mRNA isolation kit (Boehringer Mannheim) and following steps 1–6 in the manufacturer’s instructions. The DNA was removed according to Quantitative real-time reverse transcriptase polymerase Clontech’s (Palo Alto, CA, USA) protocol for DNase treatment chain reaction (RT-PCR) of total RNA, with the exception that RNA precipitation was carried out overnight at −70°C. The quality of the total RNA was checked by gel electrophoresis. The RNA was stored at In order to validate the cDNA array results, real-time RT-PCR −70°C until used. was performed on seven genes over-expressed in the array (ERG, AML1, ENG, ETV6, MLLT2, DAPK1 and MYC) and on one that was under-expressed (BAX). The real-time RT-PCR was only performed on samples from 15 patients because not Atlas Human Hematology/Immunology cDNA array enough RNA was available from the other two. From each patient sample and from one of the B cell pools (reference), The study was performed using Atlas Human Hematology/ 500 ng RNA was converted into cDNA using the ﬁrst strand Immunology cDNA expression arrays (Clontech). Each array cDNA synthesis kit for RT-PCR (AMV) (Roche Diagnostics,

contains duplicate cDNA spots of 415 known and sequence- Indianapolis, IN, USA). Oligo-p(dT)15 primer was used. RT- veriﬁed genes, which have been found to be expressed in nor- PCRs were performed simultaneously for all the samples in mal hematopoietic cells, hematopoietic cell lines, or hematol- order to minimize variation in the amount and quality of ogical disorders (for information about the genes, see cDNA between the samples. http://atlasinfo.clontech.com/bioinfo/). The cDNA spots are The real-time PCR was performed in a rapid thermal cycler immobilized on a nylon membrane. system (LightCycler; Roche Diagnostics, Mannheim,

Leukemia Myeloid-speciﬁc genes in childhood ALL J Niini et al 2215 Germany). The primers were designed and prepared by TIB pal component, each gene is given a score, which represents Molbiol (Berlin, Germany) (Table 2). The PCRs were perfor- the collective difference in its expression between the patient med in a 10 ␮l volume with 1 ␮l ‘Hot Start’ reaction mix from samples and the reference. In order to assign abnormalities the LightCycler-FastStart DNA Master SYBR Green I kit (Roche in gene expression to individual patients, we used lower and

Diagnostics), 2.6 mM MgCl2, 0.5 mM each primer (0.25 mM upper thresholds to determine under- and over-expression, in the case of MYC gene), and 1 ␮l of diluted cDNA (1:5, respectively. We obtained these thresholds by calculating 1:10 or 1:50; Table 2). the10th and 90th percentiles of the pooled expression arrays. The LightCycler run was started with an initial denaturation On the scale of normalized intensity differences, these thresh- at 95°C for 7 min. The target DNA was amplified by per- olds were −5555 and 4005 for under- and over-expression, forming 45 cycles of denaturation at 95°C for 15 s, annealing respectively. at 58–66°C for 5 s (Table 2), and elongation at 72°C for 10 s. To verify the amplification specificity, melting curve analyses were performed using an initial denaturation at 95°C for The effect of myeloid cells on the cDNA array results 10 s, followed by 20 s at 55°C, and then slow heating of the samples to 95°C at the rate of 0.1°C/s with continuous We wanted to confirm that the over-expression of five fluorescence detection. myeloid-specific genes, S100A12, RNASE2, GCSFR, PRTN3 Each patient sample was run in parallel with the reference and CLC, was not caused by the myeloid cells in the samples. B cell sample. A negative control without cDNA was included In order to do this, we studied whether the proportion of in each run. In addition, standard curves to calculate the rela- myeloid cells had a non-random effect on the expression lev- tive concentrations were obtained by running a dilution series els of these genes. The patients were divided into two groups of the ␤ globin gene (LightCycler-Control Kit DNA; Roche according to the proportion of myeloid cells in their bone Diagnostics) in each assay according to the manufacturer’s marrow samples, six patients with Ն10% of myeloid cells to instructions. The Second Derivative Maximum method pro- one group, and the remaining 11 with Ͻ10% to another group vided by LightCycler software was used to calculate the (Table 1). To test the degree of accuracy with which gene concentration values for the PCR product of each sample. expression could predict the group assignment, we calculated the receiver operating characteristic (ROC) curve and the area under it.5,6 To check whether this diagnostic accuracy is stat- Data analysis istically significant, we performed a randomization experiment by randomly permuting the group assignment and cDNA array analysis repeating the original calculation for the diagnostic accuracy 10 000 times.7 The brightness of the hybridization images was reduced so that the level of background intensity was approximately the same in all images. Then, the intensities of the gene spots were Comparison between the results of cDNA array and obtained using the Atlas Image 1.5 software (Clontech). The real-time RT-PCR intensity of each spot on the patient array was compared with the intensity of the corresponding spot in the reference array. From the real-time RT-PCR results, the logarithm of the ratio First, the background intensity of the hybridization image was between the patient and the reference concentrations for each subtracted from all intensity values. Then, the intensity of each gene was calculated. Similarly, for the cDNA array results, the gene spot in the reference array was subtracted from the corre- logarithm of the ratio between the patient and the reference sponding intensity in the patient array to get the intensity dif- intensities for each gene was calculated. The logarithmic PCR ference to represent the difference in the expression of a given ratios were plotted against the corresponding logarithmic gene between the patient sample and the reference. The data array ratios. were globally normalized by subtracting the average of the intensity differences of the genes in one array from each intensity difference value. This standardizes the sample average in Results all arrays to zero. In addition, the variance was standardized to one by dividing each of the normalized intensity differences Over-and under-expressedgenes of an array by the standard deviation. Principal component analysis (PCA) was applied to nor- PCA was used to give each of the 415 genes in the array a malized differences.4 By projecting the data on the first princi- score according to differences in expression between the

Table 2 The sequences of the primers, annealing temperatures and cDNA dilutions used in real-time RT-PCR

Gene Forward primer Reverse primer T (°C) cDNA dilution

ERG CCACAgggTCAggTAAgAgATg TgTCgTgTgTCTTTggCTg 58 1:5 AML1 AAgTCgCCACCTACCACAg gCATCTgACTCTgAggCTgAg 58 1:10 ENG AgCCCAgTgAAgCCTCTg gTTggTgCTgCTgCTCTC 58 1:10 ETV6 TTATCAggAAggAgCCAggA AAgTgTCCCTgCCATTTCTg 63 1:50 MLLT2 TAACACAggCAgCATTCACC AgATTTgggACACATgCgT 62 1:50 DAPK1 CAgTgTTgTTgCTCTAggAAg gggACTgCCACAAATgATgAg 66 1:5 MYC AggAgggTTTggAAgCCAgT TAAgggCTgCTTgTCTCgTT 58 1:50 BAX TgCTTCAgggTTTCATCCAg ggCggCAATCATCCTCTg 60 1:5

Leukemia Myeloid-specific genes in childhood ALL J Niini et al 2216 patient and the reference samples. The genes with the highest mic PCR ratio was positive). Accordingly, the PCR results and lowest scores were considered to be over- and under- agreed well with the cDNA array results. expressed, respectively (Figure 1). On the basis of PCA projection, 25 genes (6%; marked in Figure 1) ranked as the most over-expressed ones compared to the reference, were chosen for detailed analysis. Table 3 lists these genes, their functions, and the type of hematopoietic cells in which they are normally expressed. The expression pattern of these genes in all the patients is shown in Figure 2. Table 3 also shows for each gene the number of patients in whom it was over-expressed, ranging from seven to 17 patients. Among the 25 most over- expressed were genes encoding myeloid-specific proteins (RNASE2, GCSFR, PRTN3 and CLC), neuropeptide also expressed in myeloid cells (S100A12), proteins involved in the rearrangement of immunoglobulin genes (TDT and RAG1), cell surface proteins of B cell precursors (CD34, SPN, CD9 and CALLA), transcription factors (ERG, AML1, ETV6, MLLT2, MLLT1 and MYC), adapter protein (LCP2), and fibroblast growth factor (FGF6). Table 4 lists the 20 most under-expressed genes. Most of these genes are known to be expressed in mature B cells, some of them also during normal B cell development.

Conﬁrmation of the results

The results obtained from the cDNA arrays were validated by real-time RT-PCR for seven of the over-expressed genes (ERG, AML1, ENG, ETV6, MLLT2, DAPK1, and MYC) and for one under-expressed gene (BAX). Figure 3 shows the relationship between the cDNA array and the real-time RT-PCR results for seven of the genes for which PCR was performed. ERG was not expressed in the reference sample at all, and it was excluded from the figure because the PCR concentrations could not be calculated. For most of the over-expressed genes, PCR gave much higher ratios than the cDNA array, probably because of the higher sensitivity of the method. Of the results with over-expression in the arrays (normalized intensity difference Ͼ4005), 99.7% were confirmed by PCR (ie the logarith- Figure 2 Gene expression profiles of the 25 most over-expressed genes in the 17 patients. Patient numbers are at the top. The more intense the red color, the higher is the over-expression. The figures on the color scale refer to the normalized intensity differences (see Methods, Data analysis, cDNA array analysis).

Figure 3 Correspondence between the cDNA array results and the Figure 1 Projection of gene expression scores calculated by prin- real-time RT-PCR results for seven genes in 15 patients. The ratios of cipal component analysis. The scores are sorted in ascending order patient to reference concentrations from the PCRs (y-axis) are plotted for the 415 genes in the array. Under-expressed genes are on the left against the ratios of patient to reference intensities in the arrays (x- hand side of the ﬁgure and over-expressed genes on the right. The axis). Both ratios are on logarithmic scales. The cases for which dashed vertical line marks the limit of the 25 most over-expressed there is correspondence appear in the upper-right quadrant genes that were chosen for more detailed analysis. (over-expression) and the lower-left quadrant (under-expression).

Leukemia Myeloid-speciﬁc genes in childhood ALL J Niini et al 2217 Table 3 The 25 most over-expressed genes, their function, the hematopoietic cells in which the gene is known to be expressed, and the number of patients with over-expression of the gene

Rank Gene Protein function Hematopoietic cells Patients with with expression over-expressiona

1 TDT terminal Template-independent DNA B and T lymphoid precursors 17 deoxynucleotidyltransferase polymerase 2 ERG v-ets avian erythroblastosis virus Hematopoietic transcriptional Expression higher in B cell 17 E26 oncogene related activator precursors than in mature B cells, T cell precursors, myeloid cells 3 AML1 acute myeloid leukemia 1, Hematopoietic transcription factor B lymphoid, T lymphoid and 16 aml1 oncogene; RUNX1 myeloid progenitors, mature B cells, B and T cell lines 4 S100A12 S100 calcium-binding Regulator of cell cycle and Myeloid cells, T cells 15 protein A12 calgranulin C; CGRP differentiation 5 RNASE2 ribonuclease, RNase A Ribonuclease Eosinophils 15 family, 2; EDN 6 BCR breakpoint cluster region Serine/threonine kinase; GTPase- Myeloid and B lymphoid cells; 16 activating protein for p21rac Stage-speciﬁcity not known 7 CD34 CD34 antigen Trans-membrane protein involved Hematopoietic progenitor cells 15 in cell-to-cell adhesion 8 SPN sialophorin; CD43 Trans-membrane protein involved Most leucocytes including B cell 12 in T cell activation and repulsion precursors, NOT in most resting B between cells lymphocytes 9 GCSFR granulocyte colony- Growth factor receptor important Various myeloid cells 12 stimulating factor receptor in the proliferation and differentiation of myeloid cells 10 RAG1 recombination activating gene Activates the rearrangement of B and T cell precursors 12 1 immunoglobulin genes 11 LCP2 lymphocyte cytosolic protein 2; Adaptor protein in T cell receptor T cells, monocytic cell lines, B cell 15 SLP76 mediated signalling lines 12 PRTN3 proteinase 3 Serine protease that regulates myelomonocytic cells 11 growth and differentiation of myeloid cells 13 ENG endoglin Component of the transforming Fetal pre-B cells (NOT adult 13 growth factor beta (TGFB) receptor pre-B cells), erythroid precursors, complex activated monocytes and tissue macrophages 14 CD9 CD9 antigen Cell-surface protein involved in Platelets, variety of other 10 platelet activation and cell adhesion hematopoietic cells including early B cells, not in mature B cells 15 CALLA common acute lymphocytic Cell-surface protein important in Early B and T lymphoid precursors 8 leukaemia antigen; CD10 the destruction of opioid peptides 16 ETV6 ets variant gene 6; TEL Hematopoietic transcriptional Expression higher in B cell 10 regulator precursors than in mature B cells 17 FGF6 ﬁbroblast growth factor 6 Growth factor with strong Expression not reported in normal 12 mitogenic and angiogenic hematopoietic cells properties 18 MLLT2 myeloid/lymphoid or mixed- Transcription activator critical for Expression higher in B cell 12 lineage leukemia; translocated to, 2; lymphocyte development precursors than in mature B cells; AF4 thymocytes, myeloid precursors 19 CLC Charcot-Leyden crystal protein May have lysophospholipase and Eosinophils and basophils 11 carbohydrate-binding activities 20 ILF1 interleukin enhancer binding Binds IL2 promoter T cells 11 factor 1 21 TCF7 transcription factor 7; TCF-1 Transcription activator involved in T lymphoid cells 10 T cell differentiation 22 MLLT1 myeloid/lymphoid or mixed- Transcription activator ? 12 lineage leukemia; translocated to, 1; ENL 23 DAPK1 death-associated protein Protein kinase involved in Pre-B and B cell lines 7 kinase 1 interferon-gamma-induced cell death 24 MYC v-myc avian myelocytomatosis Transcription factor important in Expression seems to be higher in B 11 viral oncogene homologue controlling cell growth and vitality cell precursors than in resting mature B cells 25 TM4SF2 transmembrane 4 Cell surface protein with unknown T cell acute lymphoblastic cells 10 superfamily member 2; TALLA-1 function aNumber of patients with over-expression out of the 17 patients studied.

Leukemia Myeloid-speciﬁc genes in childhood ALL J Niini et al 2218 Table 4 The 20 most under-expressed genes, their function, and the number of patients with under-expression of the gene

Rank Gene Protein function Patients with under- expressiona

1 ACP5 acid phosphatase 5, tartrate resistant Acid phosphatase 17 2 MS4A1 membrane-spanning 4-domains, subfamily May have a role in B cell proliferation and 17 A, member 1 differentiation 3 LCP1 lymphocyte cytosolic protein 1 Not known 14 4 CD83 CD83 antigen May play a role in antigen presentation and 17 lymphocyte activation 5 LYN v-yes-1 Yamaguchi sarcoma viral related Functions in B cell development and likely in B-cell 15 oncogene homolog antigen receptor-mediated signaling 6 SPIB Spi-B transcription factor Essential for antigen-dependent expansion of B 15 cells 7 CR2 complement component receptor 2 Membrane protein of B cell with unknown function 17 8 BTG1 B-cell translocation gene 1, anti-proliferative Negatively regulates cell proliferation 13 9 CD48 CD48 antigen Might facilitate interaction between activated 16 lymphocytes 10 RAC2 ras-related C3 botulinum toxin substrate 2 Could play a role in control of growth and death of 13 T cells; may function in leukocyte adhesion 11 ETS1 v-ets erythroblastosis virus E26 oncogene Transcription factor; functions in angiogenesis 14 homolog 1 12 LMO2 LIM domain only 2 Functions in the regulation of red blood cell 10 development 13 SYK spleen tyrosine kinase Plays a role in lymphocyte activation 11 14 CD22 CD22 antigen Mediates B cell-B cell interactions; may be involved 12 in the localization on B cells 15 BLR1 Burkitt lymphoma receptor 1, GTP binding Essential in B cell migration and localization; 10 protein Candidate for cell–cell interactions and activation of B cells 16 RGS2 regulator of G-protein signalling 2, 24kD Could function in the control of growth and death of 13 T cells or in leukocyte adhesion 17 MCL1 myeloid cell leukemia sequence 1 Repressor of apoptotic cell death 11 18 RAC1 ras-related C3 botulinum toxin substrate 1 Plasma membrane-associated protein, which could 9 (rho family, small GTP binding protein Rac1) regulate secretory processes 19 CD37 CD37 antigen Membrane protein of B cells; may have a role in T 7 cell/B cell interactions 20 UBC ubiquitin C Not known 10

aNumber of patients with under-expression out of the 17 patients studied.

Exclusion of the effect of myeloid cells Out of the 20 most under-expressed genes, at least 16, including LCP1, CD83, LYN, SPIB and CD48,areknowntobe Because the patient samples were from whole bone marrow, expressed in mature B cells.9–13 Although some of the under- we wanted to find out whether the over-expression of expressed genes are also known to be expressed during the myeloid-specific genes was caused by the presence of development of B cells, the under-expression of most of them myeloid cells in the samples. We focused on those of the 25 may be a consequence of the use of mature B cells as reference most over-expressed genes that are normally expressed in for the precursor-B cell type of leukemia. Thus, we focused our myeloid cells but not in B lymphoid cells, ie S100A12, more detailed analysis on the 25 most over-expressed genes. RNASE2, GCSFR, PRTN3 and CLC. Four of the 25 most over-expressed genes that are usually The ROC values for the genes ranged from 0.286 to 0.657 considered to be expressed specifically in the myeloid lin- and the P values from 0.134 to 0.457. Thus, we were unable eage: GCSFR, PRTN3, RNASE2 and CLC.14–17 Since we com- to demonstrate a statistically significant non-random effect pared whole bone marrow to a B cell reference, it was poss- between the proportion of myeloid cells and the expression ible that the over-expression originated from the myeloid cells of any of the five genes. We concluded that the small amount in the patient samples. However, by calculating an area under of myeloid cells did not have a significant effect on the a ROC curve and performing a randomization experiment, we expression level seen in the myeloid-specific genes. showed that the small amount of myeloid cells (1–15%) did not affect the results. Thus, we conclude the over-expression of these myeloid genes to be a consequence of their up-regu- Discussion lation in the leukemic blasts. Variable degrees of phenotypic expression of myeloid mark- In this study we performed gene expression analysis by cDNA ers can be detected in many cases with B-lineage ALL using arrays comparing gene expression of leukemic blasts to nor- flow cytometry.18 The same was found in our immunopheno- mal mature B cells. Recently, the authors of an extensive gene type studies, in which the blast population showed expression expression profiling study performed gene clustering analyses of CD34 and CD13 antigens in 16 and 10 patients, respect- but they did not compare expression of leukemic cells to any ively. Importantly, our array studies showed over-expression references.8 Thus their results cannot be compared to ours. of CD34 and CD13 in 15 and six patients, respectively.

Leukemia Myeloid-specific genes in childhood ALL J Niini et al 2219 Two of the myeloid-specific genes, GCSFR and PRTN3, absolute amount of AML1 mRNA, the relative amounts of dif- encode proteins that are known to regulate cell proliferation, ferent transcripts are important for the proper functioning of their over-expression thus possibly having some role in leuke- the gene.44,45 Because the primers that we used recognize a mogenesis. GCSFR encodes the receptor for G-CSF, a myeloid number of different AML1 mRNAs, we cannot determine their growth factor.19 GCSFR has been found to be up-regulated individual contribution. in precursor-B ALL by the t(1;19)-specific oncoprotein E2A- We also detected over-expression of LCP2, a gene encoding PBX1.20 Only one patient (patient 16) with this translocation an adapter protein involved in precursor T and T cell receptor was included in our study. This patient was one of several signaling (reviewed in Rudd46). LCP2 is expressed in some B patients with over-expression of GCSFR, suggesting that the cell lines and normal splenic B cells.47,48 Over-expression of up-regulation of GCSFR is not restricted to patients with LCP2 has been detected in a Burkitt’s lymphoma B cell line t(1;19). PRTN3 is a gene known to be up-regulated by after B cell receptor cross-linking, and the protein may have G-CSF.21 The PRTN3 protein is a serine protease involved in a role in B cell receptor signaling as well.48,49 Interestingly, the regulation of cell growth and differentiation, and the LCP2 was also found to be over-expressed in CLL by the constitutive over-expression of the gene causes G-CSF-inde- cDNA array method.26 No involvement of the gene in ALL pendent growth of myeloid progenitors, the G-CSF receptor has been reported so far. One of the most interesting findings being essential for this process.15,21 Involvement of PRTN3 in of this study was the over-expression of a gene encoding a ALL has not been reported before. fibroblast growth factor, FGF6, not normally expressed in In addition to ‘true’ myeloid-specific genes, we detected bone marrow. Expression of FGFR1 and FGFR4, encoding over-expression of the gene encoding neuropeptide S100A12 receptors of FGF6, has been detected in several leukemic cell which, in addition to being released from sensory nerve end- lines including precursor B cells, and the expression of FGFR1 ings to the bone marrow, is known to be synthesized in has also been detected in purified mature B cells.50–52 The myeloid cells.22–24 The S100A12 protein has numerous func- FGF6 protein in turn has been shown to up-regulate the tions, one of which is to inhibit B cell development.25 We expression of FGFR1 in myoblasts.53 Interestingly, we found previously also found over-expression of S100A12 in CLL over-expression of FGFR1 in five out of the 12 patients show- using arrays.26 This gene has not been associated with ALL ing over-expression of FGF6, whereas that was not detected before. in any patient without over-expression of FGF6. FGFR1 is By using mature B cells as the reference for precursor-B cell involved in several 8p12 translocations leading to constitutive ALL, over-expression was likely to be detected in those genes tyrosine kinase activity of FGFR1 in a stem cell myeloprolifer- that are distinctly more highly expressed in the early stages of ative disorder that generally progresses to AML.54–56 Further- B cell development. In agreement with this, we found over- more, we have previously demonstrated FGFR1 over- expression of the genes involved in the rearrangement of expression in AML cases without these translocations, using immunoglobulin genes (TDT and RAG1) and the genes enco- cDNA arrays.57 No association of either FGF6 or FGFR1 with ding surface proteins known to be expressed only during the ALL has been reported. In conclusion, we obtained novel data early stages of lymphoid differentiation (CD34, SPN, CD9 and about gene expression in childhood ALL using cDNA arrays. CALLA) (see Table 3 for functions). In addition, expression of Among the most over-expressed were several ‘myeloid-spe- murine homologues of ERG, ETV6 and MLLT2 is higher in B cific’ genes (S100A12, RNASE2, GCSFR, PRTN3 and CLC) and cell precursors than in mature B cells.27,28 These genes encode their over-expression was found to derive from the leukemic transcription factors, which are known to be involved in the blasts. Other most over-expressed genes included AML1, recurrent translocations found in acute leukemias, ERG in LCP2 and FGF6. Although the use of normal B cells as a refer- t(16;21)(p11;q22), ETV6 in t(12;21)(p13;q22), and MLLT2 in ence would pick up genes both on the basis of differentiation t(4;11)(q21;q23).29–31 For these genes, all or at least most of specificity as well as of tumor specificity, both may be the over-expression detected is probably related to differen- important in identifying pathways for future therapeutic inter- tiation. ventions. Further studies using more patient material and dif- The over-expression of the well-known proto-oncogene ferent techniques are needed to investigate which of the genes MYC would be expected given that MYC expression correlates are involved in the leukemogenic process and whether any of with the stage of differentiation and the proliferative activity the findings can be used as prognostic indicators for ALL. of B cells.32 Yet, MYC is up-regulated by translocations in Bur- kitt’s lymphoma and mature B cell and T cell ALL, and by amplification or other mechanisms in AML and in several Acknowledgements solid tumors.29,33–36 Like ETV6 (see above), its partner in the t(12;21) translo- This work was supported by grants from the Finnish Cancer cation, AML1, was found to be over-expressed. The translo- Institution Foundation, Nona and Kullervo Va¨re Foundation, cation t(12;21)(p13;q22), resulting in the production of a Biomedicum Helsinki Foundation, Sigrid Juselius Foundation chimeric ETV6-AML1 protein, is present in about 25% of chil- and the research funds of Helsinki University Central Hospital, dren with B-lineage ALL.30,31,37–39 The level of AML1 over- in Finland. expression in our material was very high and several pieces of evidence suggest that its over-expression may cause leukemia. We previously found a high-level amplification of AML1 References in two out of 112 children with ALL, and AML1 is located in chromosome 21, which is gained in about 25% of children 1 Ma SK, Wan TS, Chan LC. Cytogenetics and molecular genetics with ALL.40,41 In addition, up-regulation of the gene shortens of childhood leukemia. Hematol Oncol 1999; 17: 91–105. the G1 phase of the cell cycle in myeloid progenitor cells and 2 Schrappe M, Camitta B, Pui CH, Eden T, Gaynon P, Gustafsson 42,43 G, Janka-Schaub GE, Kamps W, Masera G, Sallan S, Tsuchida M, leads to neoplastic transformation in fibroblasts. It has to Vilmer E. Long-term results of large prospective trials in childhood be noted, however, that AML1 encodes a variety of alternative acute lymphoblastic leukemia. Leukemia 2000; 14: 2193–2194. mRNA forms with different functions and, in addition to the 3 Gustafsson G, Kreuger A, Clausen N, Garwicz S, Kristinsson J, Lie

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