Expression Profiling of T-Cell Lymphomas Differentiates Peripheral and Lymphoblastic Lymphomas and Defines Survival Related Genes

Vol. 10, 4971–4982, August 1, 2004 Clinical Cancer Research 4971

Featured Article Expression Profiling of T-Cell Lymphomas Differentiates Peripheral and Lymphoblastic Lymphomas and Defines Survival Related Genes

Beatriz Martinez-Delgado,1 Barbara Meleńdez,1 those that differentiate among the stages of disease and Marta Cuadros,1 Javier Alvarez,5 responses to therapy was found. Jose Maria Castrillo,6 Ana Ruiz de la Parte,6 Conclusions: Our results reflect the value of gene ex- 7 8 2 pression profiling to gain insight about the molecular alter- Manuela Mollejo, Carmen Bellas, Ramon Diaz, ations involved in the pathogenesis of T-cell lymphomas. Luis Lombardıá,3 Fatima Al-Shahrour,2 Orlando Domıńguez,4 Alberto Cascon,1 INTRODUCTION Mercedes Robledo,1 Carmen Rivas,1 and 1 T-Cell lymphomas are tumors derived from different stages Javier Benitez of maturation of T lymphocytes, which facilitates the separation 1 2 3 Human Genetics Department, Bioinformatics Unit, Genomic of these tumors into two major groups: precursor lymphoblastic Analysis Unit, and 4Genomics Unit, Centro Nacional de Investigaciones Oncologicas, Madrid; 5Pathology Department, lymphomas derived from immature thymic lymphocytes and Hospital La Paz, Madrid; 6Pathology and Internal Medicine peripheral T-cell lymphomas arising from mature postthymic T Departments, Fundacioń Jimenez Diaz, Madrid; 7Pathology cells. They are relatively uncommon tumors, representing ϳ10– 8 Department, Hospital Virgen de la Salud, Toledo; and Pathology 20% of non-Hodgkin’s lymphomas in western countries, and Department. Hospital Ramon y Cajal, Madrid, Spain show geographical variations in the incidence of the different subtypes. ABSTRACT T-Cell lymphomas are considered as clinically aggressive tumors, generally demonstrating a much poorer response to treat- Purpose: T-Cell lymphomas constitute heterogeneous ment and a shorter survival than B-cell lymphomas. Moreover, they and aggressive tumors in which pathogenic alterations re- also manifest a great morphological variation within individual main largely unknown. Expression profiling has demon- clinical subtypes, and in contrast to B-cell lymphomas, T-cell strated to be a useful tool for molecular classification of tumors have lack of correlation between morphology and progno- tumors. sis. Other problems associated with the diagnosis of these tumors Experimental Design: Using DNA microarrays (CNIO- include the lack of specific immunophenotypic markers of clonality OncoChip) containing 6386 cancer-related genes, we estab- and scarce data regarding the genetic alterations involved in the lished the expression profiling of T-cell lymphomas and tumor development. compared them to normal lymphocytes and lymph nodes. To date, knowledge surrounding the genetic abnormalities Results: We found significant differences between the of T-cell lymphomas is still limited. Cytogenetic studies per- peripheral and lymphoblastic T-cell lymphomas, which in- formed in peripheral T-cell lymphomas revealed some recurrent clude a deregulation of nuclear factor-␬B signaling path- aberrations such us trisomy of chromosome 3, 5, 8, and X, way. We also identify differentially expressed genes between deletions affecting 6q, 7q rearrangements, and monosomy 13 or peripheral T-cell lymphoma tumors and normal T lympho- del(13q14), occurring mainly in high-grade peripheral T-cell cytes or reactive lymph nodes, which could represent can- lymphoma rather than low grade (1–3). Genes involved in these didate tumor markers of these lymphomas. Additionally, a abnormalities, however, have not been identified. Thus, specific close relationship between genes associated to survival and alterations have not been described for many of the T-cell neoplasms. One exception is anaplastic large cell lymphoma, which is heavily associated with the t(2;5) (4). Moreover, lym- Received 2/11/04; revised 4/22/04; accepted 4/28/04. phoblastic T-cell leukemias and lymphomas have been fre- Grant support: Comunidad Autonoma de Madrid Grants CAM 08.6/ quently associated with rearrangements involving the chromo- 0005.1/2001 and CAM 08.1/0020.1/00. somal breakpoints 14q11 or 7q32-36 where the T-cell receptor The costs of publication of this article were defrayed in part by the genes (TCRA, TCRD, and TCRB) are located, leading to over- payment of page charges. This article must therefore be hereby marked expression of different oncogenes (5, 6). advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Over the last 2 years, expression profiling using cDNA Note: M. Cuadros is a fellow of the Colegio de Farmaceuticos and microarrays has proven to be a very useful tool to better classify A. Cascon a fellow of the Madrid Council. tumors and also in identifying prognostic subgroups based on Requests for reprints: Beatriz Martinez-Delgado, Human Genetics the presence of specific gene expression signatures (7–11). Department, Molecular Pathology Programme, Centro Nacional de Investigaciones Oncolo´gicas, Melchor Fernańdez Almagro 3, 28029 Using microarray experiments, new oncogenic pathways involv- Madrid, Spain. Phone: 34-91-2246950; Fax: 34-91-2246923; E-mail: ing developmental genes have been discovered in T-cell acute [email protected]. lymphoblastic leukemia (12). However, little information about

DNA microarray experiments performed with T-cell lympho- from Miltenyi Biotec, Inc. (Auburn, CA) or magnetic depletion mas has been reported. Only expression profiling of mycosis of non-T cells with a mixture of antibodies using the Pan T Cell fungoides, a subtype of cutaneous T-cell lymphoma, has been Isolation kit (Miltenyi Biotec, Inc.). Additionally, we obtained a recently published (13). Other genome-wide expression studies pool of whole peripheral blood lymphocytes separated by Ficoll performed in T-cell lymphomas have used cell lines instead of (Histopaque; Sigma Diagnostics). Five samples from reactive primary tumors (14–16), finding significant molecular hetero- lymph nodes and two different normal thymus samples were geneity within the groups of T-cell lymphomas. also used as controls. The goal of the present study was to establish the gene Clinical Data. Tumor samples from T-cell lymphoma pa- expression profiling of primary T-cell lymphomas, including the tients have been collected over the last 10 years. Complete clinical most common histological subtypes in western countries. More- data regarding proliferation index of tumors, stage of disease, over, we also search for genes related to survival of patients and response to therapy, and overall survival from 25 patients was to other clinical parameters such as the stage of disease, prolif- available. All peripheral T-cell lymphoma tumors appeared in eration index of tumors, or response to treatment, which tradi- adults and were treated similarly with standard polychemotherapy tionally have been associated to survival of T-cell lymphoma protocols. Lymphoblastic lymphomas occurred in young people patients. Our results show that a large number of genes appeared and were treated as lymphoblastic leukemias. clearly differentially expressed between the two major groups of Microarray Experiments. Microarray experiments were T-cell lymphoma classification: lymphoblastic and peripheral performed by using the second version of the CNIO OncoChip lymphomas. Genes that better differentiate these subgroups in- (v1.1a), containing 7657 different cDNA clones (sequence- clude important immune response genes related with nuclear validated I.M.A.G.E clones purchased from Research Genetics, factor (NF)-␬B pathway. We also determine a good correlation Huntsville, AL) that correspond to 6386 known genes and among differentially expressed genes in patients with or without expressed sequence tags corresponding to genes related with response to treatments and those associated to survival. cancer process or tissue specific genes. Some of the clones are duplicated to reach a total of 11,718 spots, which included 142 nonhuman species clones as negative controls. Construction of MATERIALS AND METHODS the Oncochip was described elsewhere (17). The list of genes on Patients. Tumor samples from 42 primary T-cell lym- the array can be found online.9 phomas and cell lines were analyzed in this study to establish RNA Isolation and T7-Based Amplified RNA Prepara- the expression profile of these tumors. Frozen sample materials tion. Total RNA was extracted by combination of TriReagent were provided by different hospitals (La Paz, Ramo´n y Cajal, kit (Molecular Research Center, Cincinnati, OH) and RNeasy Virgen de la Salud and Fundacio´n Jimenez Diaz). They included kit (Qiagen, Inc.) purification. The quality of the RNA were five samples from precursor lymphoblastic T-cell lymphomas evaluated after running in agarose gels. Those cases with an and 34 peripheral T-cell lymphomas. To increase the number of excessive RNA degradation were discarded. Five ␮g of total lymphoblastic T-cell tumors, three cell lines, Jurkat, Molt 16, RNA were used to synthesize amplified RNA using the Super- and Karpas 45, derived from lymphoblastic T-cell lymphomas script System for cDNA synthesis (Life Technologies, Inc.) and or leukemias, were also analyzed. Peripheral lymphomas in- the T7 Megascript in vitro transcription kit (Ambion, Austin, cluded 19 peripheral not otherwise specified lymphomas, 5 TX). The amplified RNA was checked by electrophoresis and anaplastic large cell lymphomas, 4 angioimmunoblastic lym- quantified. phomas, 3 cutaneous T-cell lymphomas, and 3 NK lymphomas. Labeling and Hybridizations. Five ␮g of the test or All of the samples corresponded to samples obtained from reference amplified RNAs were labeled with fluorescent Cy5 patients at diagnosis, except two of the peripheral T-cell lym- and Cy3, respectively. Hybridizations were performed at 42°C phomas and one anaplastic large cell lymphoma that were for 15 h as described previously (17). In all microarray exper- samples at relapse. All these tumors were diagnosed according iments, each sample was cohybridized with a pool of amplified to the WHO classification criteria. RNAs obtained from the Universal Human RNA (Stratagene, In most of the cases (31 cases), the tumor were localized in La Jolla, CA), used as reference. After washing, the slides were lymph nodes, although in three cases, the source material was scanned in a Scanarray 5000 XL (GSI Lumonics, Kanata, skin and in other cases corresponded to testis, nose biopsy, bone Ontario, Canada). Images were then analyzed with the GenePix marrow, and pleural effusion (Table 1). All of the samples were 4.0 program (Axon Instruments, Inc., Union City, CA). Six rapidly frozen in liquid nitrogen to avoid degradation of the samples were hybridized twice to control possible variations RNA. in different hybridizations. Highly reproducible results were The amount of tumoral cells was evaluated in each sample. obtained in each duplicate experiment, with correlation co- Although T-cell lymphomas present different grades of cellular efficients between 0.73 and 0.81. heterogeneity, they had in general a high proportion (60–90%) Data Analysis. The Cy5/Cy3 ratios obtained in each of tumoral cells with the exception of cases diagnosed as my- experiment with the GenePix software were global median cosis fungoides. normalized. Before normalization, bad spots, or areas showing For normal controls to compare the gene expression pattern defects were manually flagged. Spots with intensities for both of tumors, we used magnetically isolated T lymphocytes obtained from a pooled peripheral blood of five anonymous do- nors, using either magnetic microbeads conjugated to mono- clonal mouse antihuman CD3 and CD4 antibodies purchased 9 Internet address: http://bioinfo.cnio.es/data/oncochip.

Table 1 Clinical features of the T-cell lymphoma patients Age MIB1 FDS Actual No. (yrs) Sex Sample Diagnosis (%) Stage Treatment Response (in months) Relapse status 97G61 22 F Lymph node PTCL 50 I B 98G19 72 F Lymph node PTCL IA CR 48 No A 98G40 56 M Lymph node PTCL 15 IV B CHOP ϩ INF CR 27 Yes A 01G18 Lymph node PTCL (Relapsed) 98G81 62 M Skin PTCL 80 IV A CHOP PR 0 Yes D 99G11 74 F Lymph node PTCL 60 III B CHOP PR 0 Yes D 99G55 71 M Lymph node PTCL 20 IV B CMOP CR 2 Yes D 99G68 66 M Lymph node PTCL (Relapsed) 50 VAPAC-Bleo NR 0 D 00G28 86 M Lymph node PTCL 65 III B Cycl ϩ Pred NR 0 D 00G66 74 M Lymph node PTCL 30 I A RT ϩ Steroids CR 10 No A 01G3 24 M Lymph node PTCL 80 III B Cycl ϩ Pred CR 21 No A 01G5 21 M Lymph node PTCL 40 II A MACOP-B CR 13 No A 02T117 41 M Lymph node PTCL 30 III A CHOP CNIO-02020070 – M nasal biopsy PTCL CNIO-02020013 – M Lymph node PTCL CNIO-02010124 – M Lymph node PTCL CNIO-00001125 68 M Lymph node PTCL 5 IV B NR 0 D CNIO-0000867 81 M Lymph node PTCL 25 IV NR 0 D CNIO-0000841 90 M Lymph node PTCL 10 III B NR 0 D 97G95 60 M Lymph node AIL 80 IV B Steroids PR 0 No D CNIO-0000819 77 M Lymph node AIL 10 IIB Steroids PR 0 No D CNIO-0000673 – M Lymph node AIL 5 CNIO-00021624 87 F Lymph node AIL III A Steroids NR 0 A 97G52 71 F Lymph node ALC 70 III A CHOP CR 5 Yes D 98G13 12 M Lymph node ALC 50 I A BFM 95 CR 33 No A 98G83 64 F Lymph node ALC 85 II A CMOP CR 11 Yes D 00G33 Lymph node ALC (Relapsed) 99G63 43 F Lymph node ALC 60 I A CHOP ϩ RT CR 31 No A CNIO-00021623 63 M Skin CTCL IV A No A CNIO-0000715 19 M Skin CTCL Ͻ5 I B Topic steroids PR 0 No A CNIO-0000678 84 M Lymph node CTCL 25 IV B 99G17 69 F Lymph node NK 80 II A 02T295 66 F Lymph node NK 50 IV B NR 0 D CNIO-0000792 38 M Testis NK 70 IV B NR 0 D 02T9 24 F Lymph node LB 65 III A Induction therapy CR 8 Yes A 02T322 Lymph node LB (Relapsed) ALL – 02T291-G 26 F Lymph node LB 80 II A Induction therapy PR 0 No A 02T291-DP Pleural effusion ALL 02M121 10 M Bone marrow T-ALL Abbreviations: PTCL, peripheral T-cell lymphoma; CHOP, cyclophosphamide, doxorubicin, vincristine, prednisone; IFN, interferon; CMOP, cyclophosphamide, vincristine, procarbazine prednisone; Bleo, belomicine; Cycl, cyclophosphamide; Pred, prednisone; RT, radiotherapy; MACOP-B, metothrexate, doxorubicin cyclophosphamide, vincristine, bleomycin, prednisone; PUVA, ultraviolet light A; CR, complete remission; PR, partial remission; NR, no response to treatment; FDS, Free disease survival; A, alive; D, death.

channels (sum of medians) lower than the sum of mean back- presenting different clinical features, we applied Student t test grounds were also discarded. The Cy5/Cy3 ratios from tumoral corrected for multiple testing using the MaxT method of West- samples were compared with those obtained in control samples. fall and Young (21), which provided us with adjusted P values Genes were defined as significantly up-regulated or down- corrected for multiple testing (for details, visit web site).11 regulated if the difference ratio was at least 2-fold. Genes with values of adjusted P values Ͻ 0.05 were selected as Data were firstly preprocessed. Log-transformation, aver- genes differing between the classes. aging of replicated clones and filtering missing data were car- To obtain more information about the biological features of ried out using the Preprocessor tool (18), included in the Gene a specific signature and to check the biological coherence of the Expression Pattern Analysis Suite package (19).10 Hierarchical results obtained, we used the FatiGO program (22).12 FatiGO unsupervised clustering was performed using the SOTA pro- allows finding Gene Ontology (23) terms for biological program (20), also available in Gene Expression Pattern Analysis cesses or molecular functions of genes that are over- or under- Suite. represented when comparing two lists of genes (e.g., genes with To find differentially expressed genes in groups of patients

11 Internet address: http://bioinfo.cnio.es/cgi-bin/tools/multest/multest.cgi. 10 Internet address: http://gepas.bioinfo.cnio.es/. 12 Internet address: http://fatigo.bioinfo.cnio.es/.

a specific signature versus the remainder ones). FatiGO provides buffer containing 10 mM HEPES (pH 7.8), 60 mM KCl, 4% adjusted P values for multiple testing (24). Ficoll, 1 mM DTT, 1 mM EDTA (pH 8), 5% glycerol, and 0.5 ␮g For the clinical variables stage of disease and response to of unspecific inhibitor poly(deoxyinosinic-deoxycytidylic acid) treatment, we used a two-sample t test, comparing tumors in in a reaction volume of 20 ␮l (26). Samples were incubated for advanced (A) stages (stages III or IV) versus tumors in initial (I) 30 min on ice and electrophoresed in 10% nondenaturing poly- stages (stages I or II), and patients that did not respond (NR) acrylamide gels for 1 h. The complexes were visualized by versus patients that responded to the treatments (R) even with a autoradiography and quantitation was performed by Phosphor- partial or complete remission. We used Welch’s two-sample t imager. test, which does not require the assumption of equal variances in the two groups. The comparison-wise or genewise P values RESULTS were obtained using permutation tests, with 200,000 random Gene Expression Profiles of Primary T-Cell Lymphomas. permutations. The adjusted P values were obtained using the To establish the expression profiles of T-cell lymphomas, we false discovery rate approach (25) on the comparison-wise P analyzed 42 tumor samples representing some of the most values. frequent subtypes of these lymphomas in Spain. For control For survival, we fitted a Cox model with each individual samples, we chose a pool of normal T lymphocytes from pe- gene. The values we show in the figures are the t-statistics of the ripheral blood (CD3ϩ and CD4ϩ), and because most tumors ␤ coefficients (i.e., the coefficient divided by its SE). For occurred in lymph nodes, reactive lymph nodes were also used proliferation, we fitted a linear regression model with each gene, for comparison. Unsupervised hierarchical clustering analysis of in turn, as the independent variable and percentage proliferation normal and tumoral samples with 2969 clones more signifi- as the dependent variable. The values we show in the figures are cantly expressed (ratios 3-fold), in at least one of the samples, the t-statistics for the slope coefficients (coefficient divided by high similarity among normal samples that appeared clearly its SE). As before, for both the Cox model and linear regression, differentiated from tumoral samples. All samples representing genewise P values were obtained using random permutations, normal T lymphocytes were grouped together as also occurred with 200,000 random permutations, and adjusted P values were with reactive lymph node samples. However, the reactive lymph obtained with the false discovery rate procedure. All these nodes samples are enclosed among the tumors in this general analyses were carried out using our publicly available program clustering. The thymus appeared related to lymphoblastic sam- 13 Pomelo. ples (Fig. 1A). Quantitative Reverse Transcription-PCR. To validate All peripheral T-cell lymphoma tumors were grouped to- microarray experiment data, real-time quantitative reverse tran- gether and all lymphoblastic T-cell lymphomas and the three scription-PCR was performed. Seven genes, UBD, JAK2, LYN, lymphoblastic cell lines defined the other branch (Fig. 1B). Two MAP3K14 (NIK), CTSB, SIRT1, and NKTR, which represented of the lymphoblastic lymphomas (02T322 and 02T291DP), some those that clearly differentiate between the lymphoblastic which clearly differ from the others, corresponded to pleural and peripheral lymphomas, were chosen for this validation. effusion instead of tissue samples. Thus, peripheral T-cell lym- Assays-on-Demand Taqman MGB probes (Applied Biosys- phoma shows a gene expression profile markedly different from tems) of these genes were used. All PCRs were performed under precursor T-cell lymphomas, with a high number of genes the conditions recommended by the manufacturers using the differentially expressed between these two groups. Despite the ABI prism 7900 system (Applied Biosystems). A standard curve morphological diversity of peripheral T-cell lymphoma, we was constructed with at least four different concentrations in found a very similar general expression pattern. triplicate using a control cDNA, for both the control gene Differential Gene Expression between Lymphoblastic (B-actin) and the genes of interest. These seven genes were and Peripheral Lymphomas. A supervised method was then analyzed in 25 T-cell lymphoma samples: 17 peripheral lym- used to find the more significant (adjusted P Ͻ 0.05) differen- phomas and 8 lymphoblastic lymphomas. Some of these tumors tially expressed genes among peripheral T-cell lymphomas and were cases not included in microarray experiments. Expression lymphoblastic T-cell lymphomas (see supplementary data on- was quantified after the analysis of two different dilutions of the line).14 We found 184 clones representing 160 genes that were cDNAs (1/20 and 1/100) in triplicate. Differences in gene ex- differentially expressed between these two classes (Fig. 2). An pression among peripheral and lymphoblastic samples were important fraction of them (35 genes) corresponded to genes estimated using Student t tests. involved in the immune response such as different interleukin Electrophoretic Mobility Shift Assay (EMSA). Activ- receptors, cytokines, or complement components (Table 2). ␬ ity of NF- B factor was analyzed by EMSA in three lympho- Interestingly, we found an important number of these genes blastic cell lines (Molt16, Karpas 45, and KE37) and in a involved in the NF-␬B-signaling pathway. NF-␬B has been cutaneous T-cell lymphoma-derived cell line, Hut78. Nuclear defined as a central regulator of the immune response, in general protein extracts were obtained by standard methods and quan- promoting cell proliferation and inhibition of apoptosis, in re- ␮ tified by the Bradford method. Ten g of protein extracts were sponse to different external and internal stress signals (27). ␥ ␬ incubated with a -ATP end-labeled consensus NF- B-specific Activation of this factor allows it to enter the cell nucleus and probe, 5Ј-AGTTGAGGGGACTTTCCCAGGC-3Ј in a binding

13 Internet address: http://pomelo.bioinfo.cnio.es. 14 Internet address: http://bioinfo.cnio.es/data/profiling_lymphomaT/.

Fig. 1 Unsupervised hierarchical cluster analysis of gene expression data of T-cell lymphomas. Clustering with all 42 tumoral samples and 13 different control samples are shown in A. Peripheral T-cell lymphoma tumors are in white and lymphoblastic lymphomas in black. Reactive lymph nodes are marked in vertical lines box, and the normal T lymphocytes samples are marked in gray. Thy1 and Thy2 represent the two normal thymus samples. C1, pooled samples of normal T lymphocytes obtained by magnetic depletion of non-T cells. C2, CD3ϩ normal T lymphocytes positively extracted with magnetic microbeads. C3 and C4 are normal lymphocytes from peripheral blood. C5, CD4ϩ subpopulation of T-lymphocytes. B, clustering analysis of tumoral samples with 2853 clones more significantly expressed (ratios 3-fold) in at least one of the samples provided two major clusters corresponding to lymphoblastic tumors (in black) and peripheral T-cell lymphomas (in white).

activate the transcription of thousand of genes (28). Using the scription-PCR. For this validation 25 tumoral samples were FatiGO program to find differences in the assigned function of analyzed. We also compared the expression of these genes in genes that differentiate peripheral T-cell lymphoma and lym- tumors with the expression in control samples. Highly concord- phoblastic T-cell lymphomas from the rest of genes, we ob- ant results were obtained for all these genes with statistically tained statistically significant overrepresentation of genes in- significant differences between these two groups of lymphomas volved in response to external stimulus (P ϭ 0.0001) and stress (Fig. 4). response (P ϭ 0.0002; see supplementary figure online).14 This Also these results allowed us to corroborate the up-regu- result supports the idea that immune response and NF-␬B path- lation of NIK gene, which is one of the main kinases involved in way genes were present in the subset of genes that distinguished the phosphorylation of the NF-␬B inhibitors, in peripheral T-cell between peripheral T-cell lymphoma and lymphoblastic T-cell lymphoma versus lymphoblastic T-cell lymphomas. Peripheral lymphoma tumors. Those genes related with NF-␬B pathway T-cell lymphoma lymphomas showed higher expression of NIK appeared in general more expressed in peripheral T-cell lym- than normal T lymphocytes, suggesting that NF-␬B pathway phoma samples than in lymphoblastic T-cell lymphomas. This is could be up-regulated in these lymphomas. the case, for example, of NFKB1, one of the components of the Differentially Expressed Genes between Peripheral T- NF-␬B complex, the MAP3K14 (NIK), or some target genes Cell Lymphoma and Normal Samples. We tried to identify regulated by this factor such as VCAM1, MMP9. In most of the those genes that better differentiate peripheral T-cell lymphoma cases those genes appeared overexpressed in tumors compared tumors and both normal T lymphocytes and reactive lymph with normal T-lymphocytes, suggesting a deregulation of this nodes. Firstly, differently expressed genes between normal pathway in peripheral T-cell lymphoma. Additionally, determi- (CD3ϩ) samples and peripheral T lymphomas were obtained nation of NF-␬B activity by EMSA resulted in a clear distinc- using supervised methods. A set of 17 genes with a significant tion in the level of activity of this factor in lymphoblastic cell (adjusted P Ͻ 0.05) different expression between tumors and lines compared with the activity showed by the cutaneous T-cell normal T cells were found (Fig. 5). Some of these genes repre- lymphoma cell line Hut-78. Quantitation of this difference al- sented immune response proteins such as different MHC genes, lows us to confirm an 8–12-fold overactivation of NF-␬Binthe HLA-DM or HLADRB3, interleukin receptors such us IL1R1 and peripheral T-cell lymphoma cell line (Fig. 3). IL7R, and oncogenes such us LYN. Confirmation of Differential Gene Expression by Quan- Primary T-cell lymphomas constitute heterogeneous tu- titative Reverse Transcription-PCR. To confirm microarray mors, presenting different cell types accompanying the tumoral experiments data, we analyzed the expression of seven differ- cells such as B cells or histiocytes. For this very reason, we also entially expressed genes between peripheral T-cell lymphoma compared the expression of peripheral T-cell lymphoma with and lymphoblastic T-cell lymphomas, UBD, JAK2, LYN, CTSB, reactive lymph nodes as a control lymphoid tissue. In this case, NIK, SIRT1 and NKTR, by real-time quantitative reverse tran- we found 35 genes where the expression differs significantly

Fig. 2 Differentially expressed genes between peripheral T-cell and lymphoblastic T-cell lymphomas. The 160 genes showing statistically significant different expression between peripheral T-cell lymphoma and lymphoblastic T-cell lymphoma (adjusted P Ͻ 0.05) are represented. Red color rep- resents up-regulation in the gene expression and blue means underexpression. (see also supplementary data online).14

between reactive lymph nodes and peripheral T-cell lymphomas. Eight of these genes constitute unknown genes. Among the other genes, we found interesting genes such as EMS1 or HOXC13 and kinases such us MAPK81P1 or STK17. These results indicate that although different subtypes of peripheral T-cell lymphoma were included, they could share a common pattern of expression relative to normal T cells and lymph nodes. Survival-Related Genes in T-Cell Lymphomas. We analyzed the behavior of some different clinical parameters traditionally associated with bad prognosis in these tumors such us stage of disease (stages I–II versus III–IV), proliferation index of tumors, and response to treatment. Survival is a complex variable that could be affected by many other parameters. For this reason, we compared if genes more strongly associated to survival were also those more associated to stage of disease, proliferation of tumors, or response to treatments. The genes obtained being associated to these variables can be found in the supplementary material.14 We performed correlation analysis among genes more related to survival and those genes associated to stage of disease, proliferation index, and response to treatments (Fig. 6). We found that genes associated to response to treatment as well as to stage of disease were highly correlated to those associated to Fig. 3 Determination of active NF-␬B by EMSA. EMSA results per- survival of patients, appearing response to treatment and sur- formed in three lymphoblastic cell lines, and the comparison to Hut78 vival more strongly associated (correlation coefficient: 0.8534) cell line derived from a peripheral T-cell tumor. A significant increase than stage of disease and survival (correlation coefficient: in the NF-␬B activity is shown in Hut78 versus the lymphoblastic cell 0.7215). However, genes related to proliferation of tumors were ␮ lines. In the top, different amounts (in g) of unspecific competitor not similar to those more associated to survival, thus indicating poly(deoxyinosinic-deoxycytidylic acid) [poly (dI:dC)] are shown for the Hut78 cell line. Line 7 is a standard binding reaction but includes an that proliferation were not correlated with survival and neither unlabelled DNA fragment identical to the probe. In this case, NF-␬B- of the other variables, stage, or response to treatment. probe complex disappeared because of the specific competition. A2 We then tried to identify those genes that contribute more represent a nonspecific DNA-protein interaction. Numbers at the bottom to distinguish between lengths of survival, and we compared mean the ratio of intensities in the NF-␬B complex bands and A2 bands. them to genes with significantly different expression among the other clinical parameters. Among genes that contribute more to

Fig. 4 Quantitative reverse transcription-PCR analysis of seven genes differentially expressed between peripheral T- cell lymphoma (PTL) and lymphoblastic T-cell lymphoma (LB). Box plots represent the expression values of the per- centiles 25 and 75 for each group of tumors, and the ex- tremes of vertical lines represent the maximum and mini- mum expression values. Sta- tistically significant differences were found for each gene.

Table 2 Differentially expressed genes (160 genes) between lymphoblastic and peripheral T-cell lymphomas Immune response CSF1R colony stimulating factor 1 receptor, McDonough APOL3 Apolipoprotein L, 3 feline sarcoma viral (v-fms) BTK Bruton agammaglobulinemia tyrosine kinase JUNB jun B proto-oncogene C1S complement component 1, s subcomponent PIM2 pim-2 oncogene C7 complement component 7 RAB31 RAB31, member RAS oncogene family CCR7 chemokine (C-C motif) receptor 7 RAB9 RAB9, member RAS oncogene family CD4 CD4 antigen (p55) RASSF2 Ras association (RalGDS/AF-6) domain family 2 HLA-DMA major histocompatibility complex, class II, DM ␣ SPI1 spleen focus forming virus (SFFV) proviral HLA-DOB major histocompatibility complex, class II, DO ␤ integration oncogene spi1 HLA-DRB3 major histocompatibility complex, class II, DR ␤ 3 USP6 ubiquitin specific protease 6 (Tre-2 oncogene) HLA-E major histocompatibility complex, class I, E DOC1 downregulated in ovarian cancer 1 ICSBP1 interferon consensus sequence binding protein 1 FAT FAT tumor suppressor (Drosophilia) homolog IFI30 interferon, ␥-inducible protein 30 LYN v-yes-1 Yamaguchi sarcoma viral related oncogene IFI75 interferon-induced protein 75, 52 kDa homolog IL10RA interleukin 10 receptor, ␣ PTEN phosphatase and tensin homolog (mutated in IL13RA1 interleukin 13 receptor, ␣ 1 multiple advanced cancers 1) IL15RA interleukin 15 receptor, ␣ RBBP7 retinoblastoma-binding protein 7 IL18BP interleukin 18 binding protein RBL1 retinoblastoma-like 1 (p107) IL18R1 interleukin 18 receptor 1 RELB v-rel avian reticuloendotheliosis viral oncogene IL2RB interleukin 2 receptor, ␤ homologue B IL7R interleukin 7 receptor Signal transduction IRF2 interferon regulatory factor 2 BLR1 Burkitt lymphoma receptor 1, GTP-binding protein ISGF3G interferon-stimulated transcription factor 3, ␥ (48 kD) HCK hemopoietic cell kinase LTB lymphotoxin ␤ (TNF superfamily, member 3) KDR kinase insert domain receptor (a type III receptor NFKB1 nuclear factor of ␬ light polypeptide gene enhancer tyrosine kinase) in B-cells 1 (p105) MAP3K14 mitogen-activated protein kinase kinase kinase 14 NFKBIA nuclear factor of ␬ light polypeptide gene enhancer PLCG2 phospholipase C, ␥ 2 (phosphatidylinositol-specific) in B-cells inhibitor PTPNS1 protein tyrosine phosphatase, non-receptor type SCYA18 small inducible cytokine subfamily A (Cys-Cys), substrate 1 member 18 TACSTD2 tumor-associated calcium signal transducer 2 SCYA19 small inducible cytokine subfamily A (Cys-Cys), CREBL2 cAMP responsive element binding protein-like 2 member 19 JAK2 Janus kinase 2 (a protein tyrosine kinase) SCYA3 small inducible cytokine A3 (homologous to mouse JPO1 c-Myc target JPO1 Mip-1a) LTBP1 latent transforming growth factor beta binding SCYA4 small inducible cytokine A4 (homologous to mouse protein 1 Mip-1b) PRKCD protein kinase C, ␦ SCYB11 small inducible cytokine subfamily B (Cys-X-Cys), SSI-3 STAT induced STAT inhibitor 3 member 11 TGFB1 transforming growth factor, ␤ 1 TNFAIP2 tumor necrosis factor, ␣-induced protein 2 Cell cycle TNFSF11 tumor necrosis factor (ligand) superfamily, member 11 CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) TRAF1 TNF receptor-associated factor 1 RAD54B RAD54, S. cerevisiae, homolog of, B TYROBP TYRO protein tyrosine kinase binding protein TERF1 telomeric repeat binding factor (NIMA-interacting) 1 Apoptosis RPA1 replication protein A1 (70 kDa) BCL2A1 BCL2-related protein A1 P12 DNA polymerase ⑀ p12 subunit BIRC3 baculoviral IAP repeat-containing 3 HDAC6 histone deacetylase 6 CFLAR CASP8 and FADD-like apoptosis regulator MSH5 mutS (E. coli) homologue 5 PIK3CD phosphoinositide-3-kinase, catalytic, ␦ polypeptide LIG1 ligase I, DNA, ATP-dependent TNFSF11 tumor necrosis factor (ligand) superfamily, member 11 Transcription Factors Oncogenes and tumor suppressor genes BTF3 basic transcription factor 3 BCL7A B-cell CLL/lymphoma 7A ID2 inhibitor of DNA binding 2, dominant negative helix-loop-helix protein

differentiate between cases that respond to treatment (with par- there are genes associated to survival not found among the genes tial or complete remission) versus patients that did not respond related to these other variables such as HOXC5, PIG11,or to treatment, we found that an important number of them were STK15. also found to be associated to survival, but they were not found related to stage of disease. These genes indicated interesting genes such as an EBV-induced gene, EBI3, a cytokine receptor, DISCUSSION CCRL2, the thyroid hormone receptor interactor 4, and insulin- The molecular alterations involved in the development of like growth factor 1 receptor. However, we also found genes T-cell lymphomas are largely unknown. Expression profiling more differently expressed between initial or advanced stages of studies in tumors could be considered as the first step for a disease, which seem not to be associated to survival such as molecular diagnosis of cancer, allowing a better subclassifica- JUNB. Moreover, although a good correlation exists among tion of tumors, identification of undiscovered oncogenic path- survival, response to therapy, and stage of disease of the tumors, ways, or prediction of outcome (9–12, 29). Microarray experi-

Table 2 Continued TCFL5 transcription factor-like 5 (basic helix-loop-helix) PSMB10 proteasome (prosome, macropain) subunit, ␤ type, 10 TFAP2C transcription factor AP-2 ␥ (activating enhancer- PRG1 proteoglycan 1, secretory granule binding protein 2 PRF1 perforin 1 (pore forming protein) TFDP2 transcription factor Dp-2 (E2F dimerization partner 2) PDE4B phosphodiesterase 4B, cAMP-specific Miscellaneous (dunce (Drosophila)-homolog CRIP2 cysteine-rich protein 2 UBD diubiquitin CPSF2 cleavage and polyadenylation specific factor 2, 100 TMEFF1 transmembrane protein with EGF-like and two kDa subunit follistatin-like domains 1 CHAF1B chromatin assembly factor 1, subunit B (p60) VCAM1 vascular cell adhesion molecule 1 ARPC2 actin related protein 2/3 complex, subunit 2 (34 Unknown genes kDa) C9orf5 chromosome 9 open reading frame 5 APOC1 apolipoprotein C-I Hs.278222 Homo sapiens cDNA FLJ14885 fis, clone PLACE ANXA5 annexin A5 1003711 AGTRL1 angiotensin receptor-like 1 Hs.22546 Homo sapiens cDNA: FLJ21300 fis, clone ACTN4 actinin, ␣ 4 COL02062 ITGAX integrin, ␣ X (antigen CD11C (p150), ␣ Hs.131493 EST, Highly similar to 3-7 gene product [H.sapiens] polypeptide) Hs.319825 Homo sapiens, clone IMAGE:3616574, mRNA, INHBB inhibin, ␤ B (activin AB ␤ polypeptide) partial cds GYG2 glycogenin 2 Hs.119779 EST GS3686 hypothetical protein, expressed in osteoblast Hs.109438 Homo sapiens clone 24775 mRNA sequence FUCA1 fucosidase, ␣-L- 1, tissue Hs.58643 ESTs, Highly similar to JAK3B [H.sapiens] FRZB frizzled-related protein Hs.332567 EST FGF7 fibroblast growth factor 7 (keratinocyte growth Hs.46531 Homo sapiens mRNA; cDNA DKFZp434C1915 factor) Hs.204692 Human Chromosome 16 BAC clone CIT987SK-A- F10 coagulation factor X 735G6 ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 Hs.11210 ESTs, Moderately similar to Z137_HUMAN ZINC ENPP2 ectonucleotide pyrophosphatase/phosphodiesterase 2 FINGER PROTEIN 13 (autotaxin) Hs.23540 ESTs OR2I6 olfactory receptor, family 2, subfamily I, member 6 Hs.22869 ESTs, Moderately similar to KIAA1395 protein MS4A1 membrane-spanning 4-domains, subfamily A, [H.sapiens] member 1 Hs.83071 ESTs MMP9 matrix metalloproteinase 9 (gelatinase B, 92 kDa Hs.343214 Homo sapiens, clone MGC: 19762 gelatinase, 92 kDa type IV IMAGE:3636045, mRNA MP12 matrix metalloproteinase 12 (macrophage elastase) Hs.16954 ESTs MIR myosin regulatory light chain interacting protein Hs.94953 Homo sapiens, Similar to complement component 1 LYZ lysozyme (renal amyloidosis) IMAGE:3703434 LRP2 low density lipoprotein-related protein 2 IMAGE:262938 LR8 LR8 protein IMAGE:260922 K6HF cytokeratin type II IMAGE:46536 SYNE-1B synaptic nuclei expressed gene 1 IMAGE:2969161 SPARCL1 SPARC-like 1 (mast9, hevin) IMAGE:898035 SNX9 sorting nexin 9 FLJ23231 hypothetical protein FLJ10392 SIGLEC7 sialic acid binding Ig-like lectin 7 FLJ22690 hypothetical protein FLJ10956 SERPING1 serine (or cysteine) proteinase inhibitor, clade G (C1 FLJ22490 hypothetical protein FLJ13213 inhibitor), member 1 FLJ13855 hypothetical protein FLJ13855 SELPLG selectin P ligand FLJ13213 hypothetical protein FLJ22490 RXRG retinoid X receptor, ␥ FLJ10956 hypothetical protein FLJ22690 RARRES3 retinoic acid receptor responder (tazarotene induced) 3 FLJ10392 hypothetical protein FLJ23231 PSMB9 proteasome (prosome, macropain) subunit, ␤ type, 9 KIAA1181 KIAA1181 protein (large) KIAA0053 KIAA0053 gene product MGC5618 hypothetical protein MGC5618

ments on T-cell malignancies, however, have only been carried phoblastic T-cell lymphoma and peripheral T-cell lymphoma. out for T-cell acute lymphoblastic leukemias (12) along with These subtypes constitute very different entities arising from some studies on expression profiling using cell lines derived different stages of maturation of T-lymphocytes, and it is then from T-cell malignancies (14–16), but expression profiling of possible that a large amount of genes contribute to differentiate primary T-cell lymphomas has only been explored for specific between them. Peripheral T-cell lymphomas appeared as a rel- subtypes (13). atively homogeneous group at least in relation to lymphoblastic The results reported here show the general expression T-cell lymphomas. Given the variable morphology and clinical patterns of T-cell lymphomas. The gene expression of these outcomes among peripheral T-cell lymphoma, it is surprising tumors was compared with normal T-lymphocytes and normal the similarity in the gene expression profiles. We maintain that lymph node samples to extract those relevant genes character- much fewer genes, compared with those that differentiate pe- izing the tumors. Clustering analysis of tumoral samples easily ripheral T-cell lymphoma and lymphoblastic T-cell lymphomas, identify the two major subgroups of T-cell lymphomas: lym- might be distinguishing among the different subtypes of periph-

Fig. 5 Differentially expressed genes between peripheral T-cell lymphoma (PTCL) tumors and normal samples. In the top, the 19 genes with a significant different expression (adjusted P Ͻ 0.05) between PTCL and normal CD3ϩ lymphocytes are shown. In the bottom, 35 differentially expressed genes that better differentiate between PTCL and reactive lymph nodes.

eral T-cell lymphoma. Using different T-cell lines, significant MAP3K14 (NIK), or genes that are targets of the transcriptional heterogeneity in the expression profiles has been previously activity of NF-␬B such us VCAM1, BIRC3, JUNB,orMMP9. reported, although not with a complete correlation to the clini- This finding completely confirms our results obtained using the copathologically related categories (14). In contrast, the com- FatiGO program regarding the overrepresentation of response to parison of peripheral T-cell lymphoma with normal samples external stimulus and response to stress genes in the set of genes revealed a subset of 17 and 35 significantly differentially ex- contributing to distinguish lymphoblastic T-cell lymphoma and pressed genes between all peripheral T-cell lymphoma tumors peripheral T-cell lymphoma tumors. As a whole, we found that and normal T lymphocytes or reactive lymph nodes, respec- NF-␬B pathway is not activated in lymphoblastic T-cell lym- tively (see Fig. 5). Some of these genes represented immune phomas, although it seems to be hyperactivated in peripheral response proteins, and some of them could represent tumoral T-cell lymphoma tumors. Constitutive activation of NF-␬B markers characterizing T-cell lymphomas. seems to be a common feature in some leukemias and lympho- On the basis of genes that are differentially expressed mas (30). NF-␬B deregulation in oncogenesis may occurs both between lymphoblastic T-cell lymphoma and peripheral T-cell as a result of activation of different upstream signals by ampli- lymphoma tumors, we identified genes related with NF-␬B- fication, overexpression or rearrangements (31–33), or by inac- signaling pathway, both proteins necessary for the activation of tivating mutations in NF-␬B inhibitors (34). Moreover, evidence this factor as could be some interleukin receptors such us IL2RB, of constitutive activation of NF-␬B in a cutaneous T-cell lym- LTB, tumor necrosis factor-induced proteins, PRKCD, RELB,or phoma cell line, Hut78, although not in a lymphoblastic T-cell

Fig. 6 Correlation analysis among genes associated to clinical features. Relationship of the coefficients for each gene with each of the dependent variables is represented. Dots in red are gene with a false discovery rate-adjusted P Ͻ 0.1 for response to treatment, and dots in blue are genes with an FDR-adjusted P Ͻ 0.1 for proliferation.

lymphoma cell line, Jurkat, has been reported (35). Recent response to therapy has an additional effect on survival (data not expression study of mycosis fungoides found deregulation of shown). genes involved in the tumor necrosis factor-signaling pathway In summary, our studies explore the molecular alterations with some up-regulated genes inducible by NF-␬B (13). An that take place in T-cell lymphomas. Expression profiling of increased activity of NF-␬B factor comparing to the activity these tumors showed wide differences between peripheral T-cell shown by lymphoblastic T-cell lymphoma cell lines was con- lymphomas and lymphoblastic T-cell lymphomas, the two ma- firmed by EMSA. Our results suggest that the up-regulation of jor subtypes of these tumors, which involved NF-␬B pathway NF-␬B-signaling pathway is a common event in peripheral deregulation. Moreover, the comparison of expression profiles T-cell lymphoma tumors that differentiate them from T-cell of the tumors to those obtained in normal T lymphocytes and lymphoblastic T-cell lymphomas. lymph nodes allowed the identification of genes that could Aggressiveness is one of the most important features char- contribute to the formation of these neoplasms. Finally, genes acterizing T-cell lymphomas, with Ͻ30% 5-year overall sur- associated to the response to therapy are strongly correlated to vival in peripheral T-cell lymphomas. The one exception is survival of T-cell lymphomas. anaplastic large cell lymphomas, which showed the best prognosis. The fact that T-cell lymphomas respond poorly to therapy ACKNOWLEDGMENTS and that many T-cell neoplasms are at an advanced stage of disease, which also confers a poor prognosis, prompted us to We thank Javier Herrero for their help with statistics and microarray analysis tools. We also thank Victoria Fernandez and Alicia Barroso search for genes that differentiate between these clinical param- for their excellent technical assistance, Esteban Ballestar for his help eters. The correlation analysis revealed that the response to with EMSA experiments, and Amanda Wren for kindly reviewing the therapy is the factor more strongly associated to survival of manuscript. We also thank to the CNIO Tumor Bank for providing T-cell lymphomas (P ϭ 0.00016), although the stage of the tumor samples. tumor showed also a good correlation (P ϭ 0.013). The fact that the genes associated to stage of disease were less correlated to REFERENCES survival suggests that adverse outcome related with the stage of the disease is influenced by different genes. However, genes 1. Lepretre S, Buchonnet G, Stamatoullas A, et al. Chromosome abnormalities in peripheral T-cell lymphoma. Cancer Genet Cytogenet more strongly associated to the proliferation index of tumors 2000;117:71–9. were not coincident with those related to survival. Then, the 2. Schlegelberger B, Himmler A, Bartles H, Kuse R, Sterry W, Grote response to therapy is a very important feature determining W. Recurrent chromosome abnormalities in peripheral T-cell lympho- survival of patients in T-cell lymphomas. As the majority of our mas. Cancer Genet Cytogenet 1994;78:15–22. cases were adults and were treated similarly, it is not likely that 3. Schlegelberger B, Himmler A, Godde E, Grote W, Feller AC, variations of treatment in elderly patients contributed signifi- Lennert K. Cytogenetic findings in peripheral T-cell lymphomas as a basis for distinguishing low-grade and high-grade lymphomas. Blood cantly in the response to therapy of this group of patients. 1994;83:505–11. Moreover, we found statistically significant differences in sur- 4. Morris SW, Kirstein MN, Valentine MB, et al. Fusion of a kinase vival curves of responders versus no responders by age, both in gene, ALK, to a nucleolar protein gene, NPM, in non-Hodgkin’s lym- patients younger or older than 50 years, suggesting that the phoma. Science (Wash. DC) 1994;263:1281–4.

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Beatriz Martinez-Delgado, Barbara Meléndez, Marta Cuadros, et al.

Clin Cancer Res 2004;10:4971-4982.

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