Expression Profiling of T-Cell Lymphomas Differentiates Peripheral ...

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 Mele´ndez,1 Marta Cuadros,1 Javier Alvarez,5 Jose Maria Castrillo,6 Ana Ruiz de la Parte,6 Manuela Mollejo,7 Carmen Bellas,8 Ramon Diaz,2 Luis Lombardı´a,3 Fatima Al-Shahrour,2 Orlando Domı´nguez,4 Alberto Cascon,1 Mercedes Robledo,1 Carmen Rivas,1 and Javier Benitez1 1 Human Genetics Department, 2Bioinformatics Unit, 3Genomic Analysis Unit, and 4Genomics Unit, Centro Nacional de Investigaciones Oncologicas, Madrid; 5Pathology Department, Hospital La Paz, Madrid; 6Pathology and Internal Medicine Departments, Fundacio´n Jimenez Diaz, Madrid; 7Pathology Department, Hospital Virgen de la Salud, Toledo; and 8Pathology Department. Hospital Ramon y Cajal, Madrid, Spain

ABSTRACT Purpose: T-Cell lymphomas constitute heterogeneous and aggressive tumors in which pathogenic alterations remain largely unknown. Expression profiling has demonstrated to be a useful tool for molecular classification of tumors. Experimental Design: Using DNA microarrays (CNIOOncoChip) containing 6386 cancer-related genes, we established the expression profiling of T-cell lymphomas and compared them to normal lymphocytes and lymph nodes. Results: We found significant differences between the peripheral and lymphoblastic T-cell lymphomas, which include a deregulation of nuclear factor-␬B signaling pathway. We also identify differentially expressed genes between peripheral T-cell lymphoma tumors and normal T lymphocytes or reactive lymph nodes, which could represent candidate tumor markers of these lymphomas. Additionally, a close relationship between genes associated to survival and

Received 2/11/04; revised 4/22/04; accepted 4/28/04. Grant support: Comunidad Autonoma de Madrid Grants CAM 08.6/ 0005.1/2001 and CAM 08.1/0020.1/00. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Note: M. Cuadros is a fellow of the Colegio de Farmaceuticos and A. Cascon a fellow of the Madrid Council. Requests for reprints: Beatriz Martinez-Delgado, Human Genetics Department, Molecular Pathology Programme, Centro Nacional de Investigaciones Oncolo´gicas, Melchor Ferna´ndez Almagro 3, 28029 Madrid, Spain. Phone: 34-91-2246950; Fax: 34-91-2246923; E-mail: [email protected].

those that differentiate among the stages of disease and responses to therapy was found. Conclusions: Our results reflect the value of gene expression profiling to gain insight about the molecular alterations involved in the pathogenesis of T-cell lymphomas.

INTRODUCTION T-Cell lymphomas are tumors derived from different stages of maturation of T lymphocytes, which facilitates the separation of these tumors into two major groups: precursor lymphoblastic lymphomas derived from immature thymic lymphocytes and peripheral T-cell lymphomas arising from mature postthymic T cells. They are relatively uncommon tumors, representing ⬃10 – 20% of non-Hodgkin’s lymphomas in western countries, and show geographical variations in the incidence of the different subtypes. T-Cell lymphomas are considered as clinically aggressive tumors, generally demonstrating a much poorer response to treatment and a shorter survival than B-cell lymphomas. Moreover, they also manifest a great morphological variation within individual clinical subtypes, and in contrast to B-cell lymphomas, T-cell tumors have lack of correlation between morphology and prognosis. Other problems associated with the diagnosis of these tumors include the lack of specific immunophenotypic markers of clonality and scarce data regarding the genetic alterations involved in the tumor development. To date, knowledge surrounding the genetic abnormalities of T-cell lymphomas is still limited. Cytogenetic studies performed in peripheral T-cell lymphomas revealed some recurrent aberrations such us trisomy of chromosome 3, 5, 8, and X, deletions affecting 6q, 7q rearrangements, and monosomy 13 or del(13q14), occurring mainly in high-grade peripheral T-cell lymphoma rather than low grade (1–3). Genes involved in these abnormalities, however, have not been identified. Thus, specific 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, lymphoblastic T-cell leukemias and lymphomas have been frequently associated with rearrangements involving the chromosomal breakpoints 14q11 or 7q32-36 where the T-cell receptor genes (TCRA, TCRD, and TCRB) are located, leading to overexpression of different oncogenes (5, 6). Over the last 2 years, expression profiling using cDNA microarrays has proven to be a very useful tool to better classify tumors and also in identifying prognostic subgroups based on the presence of specific gene expression signatures (7–11). Using microarray experiments, new oncogenic pathways involving developmental genes have been discovered in T-cell acute lymphoblastic leukemia (12). However, little information about

4972 Expression Profiling of T-Cell Lymphomas

DNA microarray experiments performed with T-cell lymphomas has been reported. Only expression profiling of mycosis fungoides, a subtype of cutaneous T-cell lymphoma, has been recently published (13). Other genome-wide expression studies performed in T-cell lymphomas have used cell lines instead of primary tumors (14 –16), finding significant molecular heterogeneity within the groups of T-cell lymphomas. The goal of the present study was to establish the gene expression profiling of primary T-cell lymphomas, including the most common histological subtypes in western countries. Moreover, we also search for genes related to survival of patients and to other clinical parameters such as the stage of disease, proliferation index of tumors, or response to treatment, which traditionally have been associated to survival of T-cell lymphoma patients. Our results show that a large number of genes appeared clearly differentially expressed between the two major groups of T-cell lymphoma classification: lymphoblastic and peripheral lymphomas. Genes that better differentiate these subgroups include important immune response genes related with nuclear factor (NF)-␬B pathway. We also determine a good correlation among differentially expressed genes in patients with or without response to treatments and those associated to survival.

MATERIALS AND METHODS Patients. Tumor samples from 42 primary T-cell lymphomas and cell lines were analyzed in this study to establish the expression profile of these tumors. Frozen sample materials were provided by different hospitals (La Paz, Ramo´ n y Cajal, Virgen de la Salud and Fundacio´ n Jimenez Diaz). They included five samples from precursor lymphoblastic T-cell lymphomas and 34 peripheral T-cell lymphomas. To increase the number of lymphoblastic T-cell tumors, three cell lines, Jurkat, Molt 16, and Karpas 45, derived from lymphoblastic T-cell lymphomas or leukemias, were also analyzed. Peripheral lymphomas included 19 peripheral not otherwise specified lymphomas, 5 anaplastic large cell lymphomas, 4 angioimmunoblastic lymphomas, 3 cutaneous T-cell lymphomas, and 3 NK lymphomas. All of the samples corresponded to samples obtained from patients at diagnosis, except two of the peripheral T-cell lymphomas and one anaplastic large cell lymphoma that were samples at relapse. All these tumors were diagnosed according to the WHO classification criteria. In most of the cases (31 cases), the tumor were localized in lymph nodes, although in three cases, the source material was skin and in other cases corresponded to testis, nose biopsy, bone marrow, and pleural effusion (Table 1). All of the samples were rapidly frozen in liquid nitrogen to avoid degradation of the RNA. The amount of tumoral cells was evaluated in each sample. Although T-cell lymphomas present different grades of cellular heterogeneity, they had in general a high proportion (60 –90%) of tumoral cells with the exception of cases diagnosed as mycosis fungoides. For normal controls to compare the gene expression pattern of tumors, we used magnetically isolated T lymphocytes obtained from a pooled peripheral blood of five anonymous donors, using either magnetic microbeads conjugated to monoclonal mouse antihuman CD3 and CD4 antibodies purchased

from Miltenyi Biotec, Inc. (Auburn, CA) or magnetic depletion of non-T cells with a mixture of antibodies using the Pan T Cell Isolation kit (Miltenyi Biotec, Inc.). Additionally, we obtained a pool of whole peripheral blood lymphocytes separated by Ficoll (Histopaque; Sigma Diagnostics). Five samples from reactive lymph nodes and two different normal thymus samples were also used as controls. Clinical Data. Tumor samples from T-cell lymphoma patients have been collected over the last 10 years. Complete clinical data regarding proliferation index of tumors, stage of disease, response to therapy, and overall survival from 25 patients was available. All peripheral T-cell lymphoma tumors appeared in adults and were treated similarly with standard polychemotherapy protocols. Lymphoblastic lymphomas occurred in young people and were treated as lymphoblastic leukemias. Microarray Experiments. Microarray experiments were performed by using the second version of the CNIO OncoChip (v1.1a), containing 7657 different cDNA clones (sequencevalidated I.M.A.G.E clones purchased from Research Genetics, Huntsville, AL) that correspond to 6386 known genes and expressed sequence tags corresponding to genes related with 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 the Oncochip was described elsewhere (17). The list of genes on the array can be found online.9 RNA Isolation and T7-Based Amplified RNA Preparation. Total RNA was extracted by combination of TriReagent kit (Molecular Research Center, Cincinnati, OH) and RNeasy kit (Qiagen, Inc.) purification. The quality of the RNA were evaluated after running in agarose gels. Those cases with an excessive RNA degradation were discarded. Five ␮g of total RNA were used to synthesize amplified RNA using the Superscript System for cDNA synthesis (Life Technologies, Inc.) and the T7 Megascript in vitro transcription kit (Ambion, Austin, TX). The amplified RNA was checked by electrophoresis and quantified. Labeling and Hybridizations. Five ␮g of the test or reference amplified RNAs were labeled with fluorescent Cy5 and Cy3, respectively. Hybridizations were performed at 42°C for 15 h as described previously (17). In all microarray experiments, each sample was cohybridized with a pool of amplified RNAs obtained from the Universal Human RNA (Stratagene, La Jolla, CA), used as reference. After washing, the slides were scanned in a Scanarray 5000 XL (GSI Lumonics, Kanata, Ontario, Canada). Images were then analyzed with the GenePix 4.0 program (Axon Instruments, Inc., Union City, CA). Six samples were hybridized twice to control possible variations in different hybridizations. Highly reproducible results were obtained in each duplicate experiment, with correlation coefficients between 0.73 and 0.81. Data Analysis. The Cy5/Cy3 ratios obtained in each experiment with the GenePix software were global median normalized. Before normalization, bad spots, or areas showing defects were manually flagged. Spots with intensities for both

9

Internet address: http://bioinfo.cnio.es/data/oncochip.

Clinical Cancer Research 4973

Table 1 No. 97G61 98G19 98G40 01G18 98G81 99G11 99G55 99G68 00G28 00G66 01G3 01G5 02T117 CNIO-02020070 CNIO-02020013 CNIO-02010124 CNIO-00001125 CNIO-0000867 CNIO-0000841 97G95 CNIO-0000819 CNIO-0000673 CNIO-00021624 97G52 98G13 98G83 00G33 99G63 CNIO-00021623 CNIO-0000715 CNIO-0000678 99G17 02T295 CNIO-0000792 02T9 02T322 02T291-G 02T291-DP 02M121

Age (yrs) Sex 22 72 56

F F M

62 74 71 66 86 74 24 21 41 – – – 68 81 90 60 77 – 87 71 12 64

M F M M M M M M M M M M M M M M M M F F M F

43 63 19 84 69 66 38 24

F M M M F F M F

26

F

10

M

Clinical features of the T-cell lymphoma patients

Sample

Diagnosis

Lymph node Lymph node Lymph node Lymph node Skin Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node nasal biopsy Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Lymph node Skin Skin Lymph node Lymph node Lymph node Testis Lymph node Lymph node Lymph node Pleural effusion Bone marrow

PTCL PTCL PTCL PTCL (Relapsed) PTCL PTCL PTCL PTCL (Relapsed) PTCL PTCL PTCL PTCL PTCL PTCL PTCL PTCL PTCL PTCL PTCL AIL AIL AIL AIL ALC ALC ALC ALC (Relapsed) ALC CTCL CTCL CTCL NK NK NK LB LB (Relapsed) LB

MIB1 (%) Stage 50 15

IB IA IV B

80 60 20 50 65 30 80 40 30

5 25 10 80 10 5 70 50 85 60 ⬍5 25 80 50 70 65 80

Treatment

FDS Actual Response (in months) Relapse status CR CR

48 27

No Yes

A A

IV A CHOP III B CHOP IV B CMOP VAPAC-Bleo III B Cycl ⫹ Pred IA RT ⫹ Steroids III B Cycl ⫹ Pred II A MACOP-B III A CHOP

PR PR CR NR NR CR CR CR

0 0 2 0 0 10 21 13

Yes Yes Yes No No No

D D D D D A A A

IV B IV III B IV B IIB

Steroids Steroids

NR NR NR PR PR

0 0 0 0 0

No No

D D D D D

III A III A IA II A

Steroids CHOP BFM 95 CMOP

NR CR CR CR

0 5 33 11

Yes No Yes

A D A D

CR

31

PR

0

No No No

A A A

NR NR CR – PR

0 0 8

Yes

D D A

0

No

A

CHOP ⫹ INF

IA CHOP ⫹ RT IV A IB Topic steroids IV B II A IV B IV B III A Induction therapy ALL II A Induction therapy ALL

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 backgrounds were also discarded. The Cy5/Cy3 ratios from tumoral samples were compared with those obtained in control samples. Genes were defined as significantly up-regulated or downregulated if the difference ratio was at least 2-fold. Data were firstly preprocessed. Log-transformation, averaging of replicated clones and filtering missing data were carried out using the Preprocessor tool (18), included in the Gene Expression Pattern Analysis Suite package (19).10 Hierarchical unsupervised clustering was performed using the SOTA program (20), also available in Gene Expression Pattern Analysis Suite. To find differentially expressed genes in groups of patients

presenting different clinical features, we applied Student t test corrected for multiple testing using the MaxT method of Westfall and Young (21), which provided us with adjusted P values corrected for multiple testing (for details, visit web site).11 Genes with values of adjusted P values ⬍ 0.05 were selected as genes differing between the classes. To obtain more information about the biological features of a specific signature and to check the biological coherence of the results obtained, we used the FatiGO program (22).12 FatiGO allows finding Gene Ontology (23) terms for biological processes or molecular functions of genes that are over- or underrepresented when comparing two lists of genes (e.g., genes with

11 10

Internet address: http://gepas.bioinfo.cnio.es/.

12

Internet address: http://bioinfo.cnio.es/cgi-bin/tools/multest/multest.cgi. Internet address: http://fatigo.bioinfo.cnio.es/.

4974 Expression Profiling of T-Cell Lymphomas

a specific signature versus the remainder ones). FatiGO provides adjusted P values for multiple testing (24). For the clinical variables stage of disease and response to treatment, we used a two-sample t test, comparing tumors in advanced (A) stages (stages III or IV) versus tumors in initial (I) stages (stages I or II), and patients that did not respond (NR) versus patients that responded to the treatments (R) even with a partial or complete remission. We used Welch’s two-sample t test, which does not require the assumption of equal variances in the two groups. The comparison-wise or genewise P values were obtained using permutation tests, with 200,000 random permutations. The adjusted P values were obtained using the false discovery rate approach (25) on the comparison-wise P values. For survival, we fitted a Cox model with each individual gene. The values we show in the figures are the t-statistics of the ␤ coefficients (i.e., the coefficient divided by its SE). For proliferation, we fitted a linear regression model with each gene, in turn, as the independent variable and percentage proliferation as the dependent variable. The values we show in the figures are the t-statistics for the slope coefficients (coefficient divided by its SE). As before, for both the Cox model and linear regression, genewise P values were obtained using random permutations, with 200,000 random permutations, and adjusted P values were obtained with the false discovery rate procedure. All these analyses were carried out using our publicly available program Pomelo.13 Quantitative Reverse Transcription-PCR. To validate microarray experiment data, real-time quantitative reverse transcription-PCR was performed. Seven genes, UBD, JAK2, LYN, MAP3K14 (NIK), CTSB, SIRT1, and NKTR, which represented some those that clearly differentiate between the lymphoblastic and peripheral lymphomas, were chosen for this validation. Assays-on-Demand Taqman MGB probes (Applied Biosystems) of these genes were used. All PCRs were performed under the conditions recommended by the manufacturers using the ABI prism 7900 system (Applied Biosystems). A standard curve was constructed with at least four different concentrations in triplicate using a control cDNA, for both the control gene (B-actin) and the genes of interest. These seven genes were analyzed in 25 T-cell lymphoma samples: 17 peripheral lymphomas and 8 lymphoblastic lymphomas. Some of these tumors were cases not included in microarray experiments. Expression was quantified after the analysis of two different dilutions of the cDNAs (1/20 and 1/100) in triplicate. Differences in gene expression among peripheral and lymphoblastic samples were estimated using Student t tests. Electrophoretic Mobility Shift Assay (EMSA). Activity of NF-␬B factor was analyzed by EMSA in three lymphoblastic cell lines (Molt16, Karpas 45, and KE37) and in a cutaneous T-cell lymphoma-derived cell line, Hut78. Nuclear protein extracts were obtained by standard methods and quantified by the Bradford method. Ten ␮g of protein extracts were incubated with a ␥-ATP end-labeled consensus NF-␬B-specific probe, 5⬘-AGTTGAGGGGACTTTCCCAGGC-3⬘ in a binding

13

Internet address: http://pomelo.bioinfo.cnio.es.

buffer containing 10 mM HEPES (pH 7.8), 60 mM KCl, 4% Ficoll, 1 mM DTT, 1 mM EDTA (pH 8), 5% glycerol, and 0.5 ␮g of unspecific inhibitor poly(deoxyinosinic-deoxycytidylic acid) in a reaction volume of 20 ␮l (26). Samples were incubated for 30 min on ice and electrophoresed in 10% nondenaturing polyacrylamide gels for 1 h. The complexes were visualized by autoradiography and quantitation was performed by Phosphorimager.

RESULTS Gene Expression Profiles of Primary T-Cell Lymphomas. To establish the expression profiles of T-cell lymphomas, we analyzed 42 tumor samples representing some of the most frequent subtypes of these lymphomas in Spain. For control samples, we chose a pool of normal T lymphocytes from peripheral blood (CD3⫹ and CD4⫹), and because most tumors occurred in lymph nodes, reactive lymph nodes were also used for comparison. Unsupervised hierarchical clustering analysis of normal and tumoral samples with 2969 clones more significantly expressed (ratios 3-fold), in at least one of the samples, high similarity among normal samples that appeared clearly differentiated from tumoral samples. All samples representing normal T lymphocytes were grouped together as also occurred with reactive lymph node samples. However, the reactive lymph nodes samples are enclosed among the tumors in this general clustering. The thymus appeared related to lymphoblastic samples (Fig. 1A). All peripheral T-cell lymphoma tumors were grouped together and all lymphoblastic T-cell lymphomas and the three lymphoblastic cell lines defined the other branch (Fig. 1B). Two of the lymphoblastic lymphomas (02T322 and 02T291DP), which clearly differ from the others, corresponded to pleural effusion instead of tissue samples. Thus, peripheral T-cell lymphoma shows a gene expression profile markedly different from precursor T-cell lymphomas, with a high number of genes differentially expressed between these two groups. Despite the morphological diversity of peripheral T-cell lymphoma, we found a very similar general expression pattern. Differential Gene Expression between Lymphoblastic and Peripheral Lymphomas. A supervised method was then used to find the more significant (adjusted P ⬍ 0.05) differentially expressed genes among peripheral T-cell lymphomas and lymphoblastic T-cell lymphomas (see supplementary data online).14 We found 184 clones representing 160 genes that were differentially expressed between these two classes (Fig. 2). An important fraction of them (35 genes) corresponded to genes involved in the immune response such as different interleukin receptors, cytokines, or complement components (Table 2). Interestingly, we found an important number of these genes involved in the NF-␬B-signaling pathway. NF-␬B has been defined as a central regulator of the immune response, in general promoting cell proliferation and inhibition of apoptosis, in response to different external and internal stress signals (27). Activation of this factor allows it to enter the cell nucleus and

14

Internet address: http://bioinfo.cnio.es/data/profiling_lymphomaT/.

Clinical Cancer Research 4975

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, CD3⫹ normal T lymphocytes positively extracted with magnetic microbeads. C3 and C4 are normal lymphocytes from peripheral blood. C5, CD4⫹ 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 FatiGO program to find differences in the assigned function of genes that differentiate peripheral T-cell lymphoma and lymphoblastic T-cell lymphomas from the rest of genes, we obtained statistically significant overrepresentation of genes involved in response to external stimulus (P ⫽ 0.0001) and stress response (P ⫽ 0.0002; see supplementary figure online).14 This result supports the idea that immune response and NF-␬B pathway genes were present in the subset of genes that distinguished between peripheral T-cell lymphoma and lymphoblastic T-cell lymphoma tumors. Those genes related with NF-␬B pathway appeared in general more expressed in peripheral T-cell lymphoma samples than in lymphoblastic T-cell lymphomas. This is the case, for example, of NFKB1, one of the components of the NF-␬B complex, the MAP3K14 (NIK), or some target genes regulated by this factor such as VCAM1, MMP9. In most of the cases those genes appeared overexpressed in tumors compared with normal T-lymphocytes, suggesting a deregulation of this pathway in peripheral T-cell lymphoma. Additionally, determination of NF-␬B activity by EMSA resulted in a clear distinction in the level of activity of this factor in lymphoblastic cell lines compared with the activity showed by the cutaneous T-cell lymphoma cell line Hut-78. Quantitation of this difference allows us to confirm an 8 –12-fold overactivation of NF-␬B in the peripheral T-cell lymphoma cell line (Fig. 3). Confirmation of Differential Gene Expression by Quantitative Reverse Transcription-PCR. To confirm microarray experiments data, we analyzed the expression of seven differentially expressed genes between peripheral T-cell lymphoma and lymphoblastic T-cell lymphomas, UBD, JAK2, LYN, CTSB, NIK, SIRT1 and NKTR, by real-time quantitative reverse tran-

scription-PCR. For this validation 25 tumoral samples were analyzed. We also compared the expression of these genes in tumors with the expression in control samples. Highly concordant results were obtained for all these genes with statistically significant differences between these two groups of lymphomas (Fig. 4). Also these results allowed us to corroborate the up-regulation of NIK gene, which is one of the main kinases involved in the phosphorylation of the NF-␬B inhibitors, in peripheral T-cell lymphoma versus lymphoblastic T-cell lymphomas. Peripheral T-cell lymphoma lymphomas showed higher expression of NIK than normal T lymphocytes, suggesting that NF-␬B pathway could be up-regulated in these lymphomas. Differentially Expressed Genes between Peripheral TCell Lymphoma and Normal Samples. We tried to identify those genes that better differentiate peripheral T-cell lymphoma tumors and both normal T lymphocytes and reactive lymph nodes. Firstly, differently expressed genes between normal (CD3⫹) samples and peripheral T lymphomas were obtained using supervised methods. A set of 17 genes with a significant (adjusted P ⬍ 0.05) different expression between tumors and normal T cells were found (Fig. 5). Some of these genes represented immune response proteins such as different MHC genes, HLA-DM or HLADRB3, interleukin receptors such us IL1R1 and IL7R, and oncogenes such us LYN. Primary T-cell lymphomas constitute heterogeneous tumors, presenting different cell types accompanying the tumoral cells such as B cells or histiocytes. For this very reason, we also compared the expression of peripheral T-cell lymphoma with reactive lymph nodes as a control lymphoid tissue. In this case, we found 35 genes where the expression differs significantly

4976 Expression Profiling of T-Cell Lymphomas

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 ⬍ 0.05) are represented. Red color represents up-regulation in the gene expression and blue means underexpression. (see also supplementary data online).14

Clinical Cancer Research 4977

Fig. 3 Determination of active NF-␬B by EMSA. EMSA results performed in three lymphoblastic cell lines, and the comparison to Hut78 cell line derived from a peripheral T-cell tumor. A significant increase in the NF-␬B activity is shown in Hut78 versus the lymphoblastic cell lines. In the top, different amounts (in ␮g) of unspecific competitor poly(deoxyinosinic-deoxycytidylic acid) [poly (dI:dC)] are shown for the Hut78 cell line. Line 7 is a standard binding reaction but includes an unlabelled DNA fragment identical to the probe. In this case, NF-␬Bprobe complex disappeared because of the specific competition. A2 represent a nonspecific DNA-protein interaction. Numbers at the bottom mean the ratio of intensities in the NF-␬B complex bands and A2 bands.

Fig. 4 Quantitative reverse transcription-PCR analysis of seven genes differentially expressed between peripheral Tcell lymphoma (PTL) and lymphoblastic T-cell lymphoma (LB). Box plots represent the expression values of the percentiles 25 and 75 for each group of tumors, and the extremes of vertical lines represent the maximum and minimum expression values. Statistically significant differences were found for each gene.

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 survival of patients, appearing response to treatment and survival more strongly associated (correlation coefficient: 0.8534) than stage of disease and survival (correlation coefficient: 0.7215). However, genes related to proliferation of tumors were not similar to those more associated to survival, thus indicating that proliferation were not correlated with survival and neither of the other variables, stage, or response to treatment. We then tried to identify those genes that contribute more to distinguish between lengths of survival, and we compared them to genes with significantly different expression among the other clinical parameters. Among genes that contribute more to

4978 Expression Profiling of T-Cell Lymphomas

Table 2

Differentially expressed genes (160 genes) between lymphoblastic and peripheral T-cell lymphomas

Immune response APOL3 Apolipoprotein L, 3 BTK Bruton agammaglobulinemia tyrosine kinase C1S complement component 1, s subcomponent C7 complement component 7 CCR7 chemokine (C-C motif) receptor 7 CD4 CD4 antigen (p55) HLA-DMA major histocompatibility complex, class II, DM ␣ HLA-DOB major histocompatibility complex, class II, DO ␤ HLA-DRB3 major histocompatibility complex, class II, DR ␤ 3 HLA-E major histocompatibility complex, class I, E ICSBP1 interferon consensus sequence binding protein 1 IFI30 interferon, ␥-inducible protein 30 IFI75 interferon-induced protein 75, 52 kDa IL10RA interleukin 10 receptor, ␣ IL13RA1 interleukin 13 receptor, ␣ 1 IL15RA interleukin 15 receptor, ␣ IL18BP interleukin 18 binding protein IL18R1 interleukin 18 receptor 1 IL2RB interleukin 2 receptor, ␤ IL7R interleukin 7 receptor IRF2 interferon regulatory factor 2 ISGF3G interferon-stimulated transcription factor 3, ␥ (48 kD) LTB lymphotoxin ␤ (TNF superfamily, member 3) NFKB1 nuclear factor of ␬ light polypeptide gene enhancer in B-cells 1 (p105) NFKBIA nuclear factor of ␬ light polypeptide gene enhancer in B-cells inhibitor SCYA18 small inducible cytokine subfamily A (Cys-Cys), member 18 SCYA19 small inducible cytokine subfamily A (Cys-Cys), member 19 SCYA3 small inducible cytokine A3 (homologous to mouse Mip-1a) SCYA4 small inducible cytokine A4 (homologous to mouse Mip-1b) SCYB11 small inducible cytokine subfamily B (Cys-X-Cys), member 11 TNFAIP2 tumor necrosis factor, ␣-induced protein 2 TNFSF11 tumor necrosis factor (ligand) superfamily, member 11 TRAF1 TNF receptor-associated factor 1 TYROBP TYRO protein tyrosine kinase binding protein Apoptosis BCL2A1 BCL2-related protein A1 BIRC3 baculoviral IAP repeat-containing 3 CFLAR CASP8 and FADD-like apoptosis regulator PIK3CD phosphoinositide-3-kinase, catalytic, ␦ polypeptide TNFSF11 tumor necrosis factor (ligand) superfamily, member 11 Oncogenes and tumor suppressor genes BCL7A B-cell CLL/lymphoma 7A

CSF1R

colony stimulating factor 1 receptor, McDonough feline sarcoma viral (v-fms) JUNB jun B proto-oncogene PIM2 pim-2 oncogene RAB31 RAB31, member RAS oncogene family RAB9 RAB9, member RAS oncogene family RASSF2 Ras association (RalGDS/AF-6) domain family 2 SPI1 spleen focus forming virus (SFFV) proviral integration oncogene spi1 USP6 ubiquitin specific protease 6 (Tre-2 oncogene) DOC1 downregulated in ovarian cancer 1 FAT FAT tumor suppressor (Drosophilia) homolog LYN v-yes-1 Yamaguchi sarcoma viral related oncogene homolog PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) RBBP7 retinoblastoma-binding protein 7 RBL1 retinoblastoma-like 1 (p107) RELB v-rel avian reticuloendotheliosis viral oncogene homologue B Signal transduction BLR1 Burkitt lymphoma receptor 1, GTP-binding protein HCK hemopoietic cell kinase KDR kinase insert domain receptor (a type III receptor tyrosine kinase) MAP3K14 mitogen-activated protein kinase kinase kinase 14 PLCG2 phospholipase C, ␥ 2 (phosphatidylinositol-specific) PTPNS1 protein tyrosine phosphatase, non-receptor type substrate 1 TACSTD2 tumor-associated calcium signal transducer 2 CREBL2 cAMP responsive element binding protein-like 2 JAK2 Janus kinase 2 (a protein tyrosine kinase) JPO1 c-Myc target JPO1 LTBP1 latent transforming growth factor beta binding protein 1 PRKCD protein kinase C, ␦ SSI-3 STAT induced STAT inhibitor 3 TGFB1 transforming growth factor, ␤ 1 Cell cycle CDKN1A cyclin-dependent kinase inhibitor 1A (p21, Cip1) RAD54B RAD54, S. cerevisiae, homolog of, B TERF1 telomeric repeat binding factor (NIMA-interacting) 1 RPA1 replication protein A1 (70 kDa) P12 DNA polymerase ⑀ p12 subunit HDAC6 histone deacetylase 6 MSH5 mutS (E. coli) homologue 5 LIG1 ligase I, DNA, ATP-dependent Transcription Factors BTF3 basic transcription factor 3 ID2 inhibitor of DNA binding 2, dominant negative helix-loop-helix protein

differentiate between cases that respond to treatment (with partial or complete remission) versus patients that did not respond to treatment, we found that an important number of them were 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, CCRL2, the thyroid hormone receptor interactor 4, and insulinlike growth factor 1 receptor. However, we also found genes more differently expressed between initial or advanced stages of disease, which seem not to be associated to survival such as JUNB. Moreover, although a good correlation exists among survival, response to therapy, and stage of disease of the tumors,

there are genes associated to survival not found among the genes related to these other variables such as HOXC5, PIG11, or STK15.

DISCUSSION The molecular alterations involved in the development of T-cell lymphomas are largely unknown. Expression profiling studies in tumors could be considered as the first step for a molecular diagnosis of cancer, allowing a better subclassification of tumors, identification of undiscovered oncogenic pathways, or prediction of outcome (9 –12, 29). Microarray experi-

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Table 2 Continued TCFL5 TFAP2C

transcription factor-like 5 (basic helix-loop-helix) transcription factor AP-2 ␥ (activating enhancerbinding protein 2 transcription factor Dp-2 (E2F dimerization partner 2)

TFDP2 Miscellaneous CRIP2 cysteine-rich protein 2 CPSF2 cleavage and polyadenylation specific factor 2, 100 kDa subunit CHAF1B chromatin assembly factor 1, subunit B (p60) ARPC2 actin related protein 2/3 complex, subunit 2 (34 kDa) APOC1 apolipoprotein C-I ANXA5 annexin A5 AGTRL1 angiotensin receptor-like 1 ACTN4 actinin, ␣ 4 ITGAX integrin, ␣ X (antigen CD11C (p150), ␣ polypeptide) INHBB inhibin, ␤ B (activin AB ␤ polypeptide) GYG2 glycogenin 2 GS3686 hypothetical protein, expressed in osteoblast FUCA1 fucosidase, ␣-L- 1, tissue FRZB frizzled-related protein FGF7 fibroblast growth factor 7 (keratinocyte growth factor) F10 coagulation factor X ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 ENPP2 ectonucleotide pyrophosphatase/phosphodiesterase 2 (autotaxin) OR2I6 olfactory receptor, family 2, subfamily I, member 6 MS4A1 membrane-spanning 4-domains, subfamily A, member 1 MMP9 matrix metalloproteinase 9 (gelatinase B, 92 kDa gelatinase, 92 kDa type IV MP12 matrix metalloproteinase 12 (macrophage elastase) MIR myosin regulatory light chain interacting protein LYZ lysozyme (renal amyloidosis) LRP2 low density lipoprotein-related protein 2 LR8 LR8 protein K6HF cytokeratin type II SYNE-1B synaptic nuclei expressed gene 1 SPARCL1 SPARC-like 1 (mast9, hevin) SNX9 sorting nexin 9 SIGLEC7 sialic acid binding Ig-like lectin 7 SERPING1 serine (or cysteine) proteinase inhibitor, clade G (C1 inhibitor), member 1 SELPLG selectin P ligand RXRG retinoid X receptor, ␥ RARRES3 retinoic acid receptor responder (tazarotene induced) 3 PSMB9 proteasome (prosome, macropain) subunit, ␤ type, 9 (large)

ments on T-cell malignancies, however, have only been carried out for T-cell acute lymphoblastic leukemias (12) along with some studies on expression profiling using cell lines derived from T-cell malignancies (14 –16), but expression profiling of primary T-cell lymphomas has only been explored for specific subtypes (13). The results reported here show the general expression patterns of T-cell lymphomas. The gene expression of these tumors was compared with normal T-lymphocytes and normal lymph node samples to extract those relevant genes characterizing the tumors. Clustering analysis of tumoral samples easily identify the two major subgroups of T-cell lymphomas: lym-

PSMB10 PRG1 PRF1 PDE4B UBD TMEFF1

proteasome (prosome, macropain) subunit, ␤ type, 10 proteoglycan 1, secretory granule perforin 1 (pore forming protein) phosphodiesterase 4B, cAMP-specific (dunce (Drosophila)-homolog diubiquitin transmembrane protein with EGF-like and two follistatin-like domains 1 vascular cell adhesion molecule 1

VCAM1 Unknown genes C9orf5 chromosome 9 open reading frame 5 Hs.278222 Homo sapiens cDNA FLJ14885 fis, clone PLACE 1003711 Hs.22546 Homo sapiens cDNA: FLJ21300 fis, clone COL02062 Hs.131493 EST, Highly similar to 3-7 gene product [H.sapiens] Hs.319825 Homo sapiens, clone IMAGE:3616574, mRNA, partial cds Hs.119779 EST Hs.109438 Homo sapiens clone 24775 mRNA sequence Hs.58643 ESTs, Highly similar to JAK3B [H.sapiens] Hs.332567 EST Hs.46531 Homo sapiens mRNA; cDNA DKFZp434C1915 Hs.204692 Human Chromosome 16 BAC clone CIT987SK-A735G6 Hs.11210 ESTs, Moderately similar to Z137_HUMAN ZINC FINGER PROTEIN 13 Hs.23540 ESTs Hs.22869 ESTs, Moderately similar to KIAA1395 protein [H.sapiens] Hs.83071 ESTs Hs.343214 Homo sapiens, clone MGC: 19762 IMAGE:3636045, mRNA Hs.16954 ESTs Hs.94953 Homo sapiens, Similar to complement component 1 IMAGE:3703434 IMAGE:262938 IMAGE:260922 IMAGE:46536 IMAGE:2969161 IMAGE:898035 FLJ23231 hypothetical protein FLJ10392 FLJ22690 hypothetical protein FLJ10956 FLJ22490 hypothetical protein FLJ13213 FLJ13855 hypothetical protein FLJ13855 FLJ13213 hypothetical protein FLJ22490 FLJ10956 hypothetical protein FLJ22690 FLJ10392 hypothetical protein FLJ23231 KIAA1181 KIAA1181 protein KIAA0053 KIAA0053 gene product MGC5618 hypothetical protein MGC5618

phoblastic T-cell lymphoma and peripheral T-cell lymphoma. These subtypes constitute very different entities arising from different stages of maturation of T-lymphocytes, and it is then possible that a large amount of genes contribute to differentiate between them. Peripheral T-cell lymphomas appeared as a relatively homogeneous group at least in relation to lymphoblastic T-cell lymphomas. Given the variable morphology and clinical outcomes among peripheral T-cell lymphoma, it is surprising the similarity in the gene expression profiles. We maintain that much fewer genes, compared with those that differentiate peripheral T-cell lymphoma and lymphoblastic T-cell lymphomas, might be distinguishing among the different subtypes of periph-

4980 Expression Profiling of T-Cell Lymphomas

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 ⬍ 0.05) between PTCL and normal CD3⫹ 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 heterogeneity in the expression profiles has been previously reported, although not with a complete correlation to the clinicopathologically related categories (14). In contrast, the comparison of peripheral T-cell lymphoma with normal samples revealed a subset of 17 and 35 significantly differentially expressed genes between all peripheral T-cell lymphoma tumors and normal T lymphocytes or reactive lymph nodes, respectively (see Fig. 5). Some of these genes represented immune response proteins, and some of them could represent tumoral markers characterizing T-cell lymphomas. On the basis of genes that are differentially expressed between lymphoblastic T-cell lymphoma and peripheral T-cell lymphoma tumors, we identified genes related with NF-␬Bsignaling pathway, both proteins necessary for the activation of this factor as could be some interleukin receptors such us IL2RB, LTB, tumor necrosis factor-induced proteins, PRKCD, RELB, or

MAP3K14 (NIK), or genes that are targets of the transcriptional activity of NF-␬B such us VCAM1, BIRC3, JUNB, or MMP9. This finding completely confirms our results obtained using the FatiGO program regarding the overrepresentation of response to external stimulus and response to stress genes in the set of genes contributing to distinguish lymphoblastic T-cell lymphoma and peripheral T-cell lymphoma tumors. As a whole, we found that NF-␬B pathway is not activated in lymphoblastic T-cell lymphomas, although it seems to be hyperactivated in peripheral T-cell lymphoma tumors. Constitutive activation of NF-␬B seems to be a common feature in some leukemias and lymphomas (30). NF-␬B deregulation in oncogenesis may occurs both as a result of activation of different upstream signals by amplification, overexpression or rearrangements (31–33), or by inactivating mutations in NF-␬B inhibitors (34). Moreover, evidence of constitutive activation of NF-␬B in a cutaneous T-cell lymphoma cell line, Hut78, although not in a lymphoblastic T-cell

Clinical Cancer Research 4981

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 ⬍ 0.1 for response to treatment, and dots in blue are genes with an FDR-adjusted P ⬍ 0.1 for proliferation.

lymphoma cell line, Jurkat, has been reported (35). Recent expression study of mycosis fungoides found deregulation of genes involved in the tumor necrosis factor-signaling pathway with some up-regulated genes inducible by NF-␬B (13). An increased activity of NF-␬B factor comparing to the activity shown by lymphoblastic T-cell lymphoma cell lines was confirmed by EMSA. Our results suggest that the up-regulation of NF-␬B-signaling pathway is a common event in peripheral T-cell lymphoma tumors that differentiate them from T-cell lymphoblastic T-cell lymphomas. Aggressiveness is one of the most important features characterizing T-cell lymphomas, with ⬍30% 5-year overall survival in peripheral T-cell lymphomas. The one exception is anaplastic large cell lymphomas, which showed the best prognosis. The fact that T-cell lymphomas respond poorly to therapy and that many T-cell neoplasms are at an advanced stage of disease, which also confers a poor prognosis, prompted us to search for genes that differentiate between these clinical parameters. The correlation analysis revealed that the response to therapy is the factor more strongly associated to survival of T-cell lymphomas (P ⫽ 0.00016), although the stage of the tumor showed also a good correlation (P ⫽ 0.013). The fact that the genes associated to stage of disease were less correlated to survival suggests that adverse outcome related with the stage of the disease is influenced by different genes. However, genes more strongly associated to the proliferation index of tumors were not coincident with those related to survival. Then, the response to therapy is a very important feature determining survival of patients in T-cell lymphomas. As the majority of our cases were adults and were treated similarly, it is not likely that variations of treatment in elderly patients contributed significantly in the response to therapy of this group of patients. Moreover, we found statistically significant differences in survival curves of responders versus no responders by age, both in patients younger or older than 50 years, suggesting that the

response to therapy has an additional effect on survival (data not shown). In summary, our studies explore the molecular alterations that take place in T-cell lymphomas. Expression profiling of these tumors showed wide differences between peripheral T-cell lymphomas and lymphoblastic T-cell lymphomas, the two major subtypes of these tumors, which involved NF-␬B pathway deregulation. Moreover, the comparison of expression profiles of the tumors to those obtained in normal T lymphocytes and lymph nodes allowed the identification of genes that could contribute to the formation of these neoplasms. Finally, genes associated to the response to therapy are strongly correlated to survival of T-cell lymphomas.

ACKNOWLEDGMENTS We thank Javier Herrero for their help with statistics and microarray analysis tools. We also thank Victoria Fernandez and Alicia Barroso for their excellent technical assistance, Esteban Ballestar for his help with EMSA experiments, and Amanda Wren for kindly reviewing the manuscript. We also thank to the CNIO Tumor Bank for providing tumor samples.

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