Mantle Cell Lymphomas Express a Distinct Genetic Signature Affecting Lymphocyte Trafficking and Growth Regulation as Compared with Subpopulations of Normal Human B Cells¹

MCL³ is a heterogeneous subclass of malignant lymphomas having a poor prognosis and a median survival time of 3–5 years. At the time of diagnosis, most patients (70%) also have disseminated disease with a majority of cases exhibiting extranodal involvement in spleen, bone marrow, and/or gastrointestinal tract (1, 2). Because of the poor response of MCL to conventional treatment, a demand for better classification of this heterogeneous group has been raised (3, 4). To optimize treatment, it is essential to define subgroups of the disease that have differences in metastatic index, survival, and/or response to treatment.

MCLs are currently believed to originate from mature but naive B cells, i.e., B cells expressing rearranged immunoglobulin genes but not yet having encountered antigen (5). In addition, they express phenotypical markers, such as CD19, CD20, CD22 and CD79a/b, but lack CD10 and, generally, CD23. The most characteristic nuclear marker of MCL is the overexpression of cyclin D1 (CCND1), which is rarely seen in other types of non-Hodgkin lymphoma (6, 7), although CCND1 has been shown to be associated with several types of solid tumors (8). CCND1 promotes the G₁ to S-phase transition by binding to cyclin-dependent kinases and is probably one of the main features contributing to the malignant behavior of MCL. In most cases of MCL, the (11;14) translocation is also present, which leads to the rearrangement of CCND1/BCL-1/PRAD-1 and the overexpression of CCND1 by the heavy-chain promoter.

Normal human B cells go through several stages of differentiation. The mature B cells (IgD⁺/CD23⁻) that have not yet encountered antigen migrate from bone marrow into lymph nodes and primary B-cell follicles, in which the naive B cell makes initial antigenic contact in cognate interaction with T cells and interdigitating dendritic cells. These antigen-activated B cells (CD38⁻/CD23⁺) subsequently form a GC. The B cell populating the dark zone of a GC is the centroblast (CD38⁺/CD77⁺) that after a proliferative phase acquires the phenotype of a centrocyte (CD38⁺/CD77⁻), which are rescued from apoptosis by the FDCs. Finally, the B cells leave the GC differentiated into either a memory B cell or an antibody-secreting plasma cell. For several of these different stages, a malignant counterpart has been found that to some extent resembles the normal B-cell origin (5).

Today, much of the molecular staging of different cell populations is based on phenotypic characterization of surface-bound receptors, leading to a rather coarse categorization because of the few markers available. However, transcriptional profiling, using DNA microarrays, has made it possible to monitor the expression of thousands of genes in parallel (9, 10, 11) and, thus, to obtain a more exact classification. Microarrays have also made it possible to explore cellular markers and physiology in a much broader context (12) and to predict and discover new classes of specific malignancies (13, 14). In an attempt to further understand the etiology of the lymphoma and to identify genes potential useful for our ability to diagnose and treat MCLs, we have performed a comparative study in which MCL and five subpopulations of normal human B cells were transcriptionally analyzed, using high-density DNA microarrays. The MCLs were compared with both resting B cells (pre-GC B cells and memory B cells) as well as with GC-B-cell subpopulations from human tonsils and displayed a distinct genetic signature. Several hundred differentially expressed genes were discovered, categorized into different gene families based on a possible functional involvement. A number of these genes have not previously been described in MCL and seem to be involved in activities such as lymphocyte trafficking and differentiation (e.g., IL4R, CCR6, CXCR5, IL10R, and IL18) as well as neurotransmission.

Fresh MCL tumors (2 samples, designated MCL1 and MCL2) were cut into small pieces and suspended in RPMI 1640 containing 10% FCS (R10). Cells were filtered through a cell strainer (BD Labware, Franklin Lake, NJ) to remove tissue debris. The lymphocytes were then purified using Ficoll-Isopaque (Amersham Pharmacia Biotech, Uppsala, Sweden) and the T-cells were depleted, using CD3⁺ dynabeads (Dynal A.S, Oslo, Norway), leaving CD5⁺/CD19⁺ tumor cells with >95% purity. The cells were pelleted and lysed in Trizol (Life Technologies, Inc., Gaithersburg, MD). Frozen tumors (five samples, designated MCL 3–7) were homogenized (twice for 15 s each time) directly into Trizol, using an Ultra Turrax knife homogenizer (IKA-WERK; Tamro Med Lab, Mölndal, Sweden).

The normal B-cell populations were derived from fresh tonsils and sorted using a FACSVantageSE (BD Immunocytometry Systems, San Jose, CA). The cells were suspended in R10 and purified, using Ficoll-Isopaque. The different B-cell populations were sorted out to a purity of >95%, by staining for different cell surface antigens (5). The naive B cells were sorted as IgD⁺, CD23⁻ cells, using two donors (donors no. 3 and 4). Preactivated B cells were sorted as IgD⁺, CD23⁺ cells, using three donors (donors no. 2, 3, and 4). The centroblast population were sorted as IgD⁻, CD38⁺, and CD77⁺ cells, using two donors (donors 1 and 2), whereas centrocytes were sorted as IgD⁻, CD38⁺, and CD77⁻ cells, using two donors (donors 1 and 2). Finally, the memory B cells were sorted as CD38⁻, IgD⁻ cells, using cells from two donors (donors 1 and 2). The purified cell subpopulations were all lysed in Trizol. The naive B cells and the preactivated B cells are together called the pre-GC population.

Cultured cells or freshly isolated cells were lysed in Trizol. The RNA was extracted from the cell lysate by adding 0.2 volumes of chloroform. The aqueous phase, containing the RNA, was separated, precipitated with isopropanol, and washed in 75% ethanol. The RNA pellet was dissolved in DEPC-H₂O and further purified with the RNeasy mini kit (Qiagen GmbH, Hilden, Germany). The total RNA content was assessed by spectroscopy at 260/280 nm (GeneQuant II; Pharmacia Biotech), and the quality of the RNA was analyzed by gel electrophoresis. After a second precipitation step in 2.5 volumes of ethanol and a subsequent wash, the RNA was resuspended in diethylene pyrocarbonate-H₂O. A minimum of 5 μg of total RNA from the preparations was used for the cDNA synthesis. The cDNA synthesis was performed according to the protocol supplied by Affymetrix, Inc. (Santa Clara, CA), using the SuperScript Choice System (Life Technologies, Inc., Paisley, United Kingdom). To monitor the reaction, a mixture of in vitro transcribed bacterial cRNAs (lysX, pheX, thrX, and trpnX; American Type Culture Collection, Manassas, VA) was added to the reaction mixture. The first-strand cDNA synthesis was performed at 42°C for 2 h, using a final concentration of 1× “first-strand synthesis” buffer (100 pmol T7-(dT)₂₄primer, 10 mm DTT, 500 μm dNTPs, and 200 units SuperScript II reverse transcriptase) per μg of RNA. The second-strand synthesis was performed at 16°C for 2 h, using a final concentration of 1× “second-strand synthesis” buffer (200 μm dNTP, 10 units of DNA ligase, 40 units of DNA polymerase I, and 2 units of RnaseH). Two units of T4 DNA polymerase was added to the reaction for 5 min at the end of the incubation. The double-stranded cDNA was extracted with 25:24:1 phenol:chloroform:isoamyl alcohol at pH 8.0 and precipitated in 0.5 vol. of 7.5 m NH₄OAc, 2.5 vol 99.7% ethanol, and 20 μg/ml glycogen with an immediate wash in 80% ethanol. The cDNA product was transcribed in vitro, using the Enzo BioArray HighYield RNA Transcript labeling kit (Enzo Diagnostics, Farmingdale, NY). Biotinylated CTP and UTP were incorporated into the cRNA product by a T7 RNA polymerase. The reaction was then performed at 37°C for 4–5 h according to the protocol supplied with the kit. The amplified cRNA was purified with the RNeasy mini kit (Qiagen GmbH) and eluted in RNase-free water. A quality assessment of the product was performed by spectroscopy at 260/280 nm. The cRNA was fragmented in 40 mm Tris-acetate (pH 8.1), 100 mm KOAc, 30 mm MgOAc at 94°C for 35 min.

The protocol in the technical manual provided by Affymetrix, Inc. was followed. Briefly, a hybridization cocktail was prepared with the biotinylated and fragmented cRNA at 50 μg/ml; 50 pm control oligonucleotide B2; 0.1 mg/ml herring sperm DNA; 0.5 mg/ml acetylated BSA; 100 mm 4-morpholinepropanesulfonic acid (MES); 20 mm EDTA; 0.01% Tween 20; and bacterial cRNA controls, BioB, BioC, BioD, and cre at 1.5, 5.0, 25, and 100 pm, respectively. The hybridization cocktail was heated to 99°C for 5 min and to 45°C for 5 min, and was briefly centrifuged before hybridization. The prewet Gene Chip probe array cartridge (U95 Array; Affymetrix, Inc.) was filled with the hybridization cocktail and incubated on rotation, 60 rpm at 45°C for 16–18 h. The cartridge was then subjected to an automated washing procedure, using the Gene Chip Fluidics Station 400 and 10 cycles at 25°C with nonstringent wash buffer, containing 6× saline, sodium, phosphate, EDTA (SSPE), 0.01% Tween 20, 0.005% Antifoam. A final 10-cycle wash with stringent buffer, containing 100 mm MES-sodium salt and 0.01% Tween 20 was performed. The probe array was then stained for 10 min at 25°C with a solution of 2 mg/ml acetylated BSA, 10 μg/ml streptavidin R-phycoerythrin (Molecular Probes, Eugene, OR) in stain buffer, containing 100 mm sodium-MES and 0.05% Tween 20 and 0.005% Antifoam, followed by 10 cycles at 25°C with nonstringent wash buffer. A secondary stain for 10 min at 25°C was performed with a solution of 2 mg/ml acetylated BSA, 0.1 mg/ml normal goat IgG (Sigma Chemical, St. Louis, MO) and 3 μg/ml biotinylated goat antistreptavidin antibody (Vector Laboratories, Burlingame, CA) in stain buffer. A third staining step with streptavidin R-phycoerythrin was performed as described above, before a final 15 cycles with nonstringent wash buffer at 30°C. The probe arrays were scanned with the Gene Array Scanner (Affymetrix, Inc.) and controlled by the software Micro Array Suite 4.0. All of the experiments were performed using the human U95 chip, containing 12,700 genes and expressed sequence tag (Affymetrix, Inc.).

Statistical and Database Methods. The expression level for each probe set is given as an average difference value by the Micro Array Suite 4.0 software, provided by Affymetrix, Inc. The Average Difference values were scaled in Micro Array Suite 4.0, against a target value of 500 to enable different arrays to be compared. The Average Difference values were then imported into Gene Spring (Silicon Genetics, Redwood City, CA) for further data analysis. Hierarchical clustering was performed, in which the minimum distance was set to 0.001 and the separation ratio to 0.95. Comparison files were created using the batch analysis tool in Micro Array Suite 4.0. The comparison files were created for all of the MCL files (7 samples) compared with all of the B-cell populations (11 samples) yielding 77 “chp files.” The comparison was also made for the MCL files compared with the pre-GC B-cell populations (naive B cells and preactivated B cells) yielding 35 chp files. The data mining tools (Affymetrix, Inc.) were used to sort out all of the genes that had a fold change of more than 2 in at least 80% of the comparison files. A Mann-Whitney t test was then performed using the data mining tool software, and the genes with Ps below 0.01 were considered to be significantly different. The genes were called up- or down-regulated in the MCL samples compared with all of the B-cell populations if the P for the two groups were <0.01. If P was >0.01 for the MCL compared with all of the B-cell populations, but P < 0.01 compared with the pre-GC populations the gene was called up- or down-regulated compared with this latter population. To be certain that no genes from adjacent cells, such as T-cells and FDCs, were included in these lists, the gene had to be present in at least one of the purified (>95%) samples (MCL1 or MCL2). This efficiently eliminated contributions from cells other than the tumor cells.

Lymph nodes were processed for routine histology by fixation in PBS-buffered 4% paraformaldehyde followed by paraffin embedding and sectioning. Sections were stained with Erlich′s eosin for microscopic examination. For analysis of cyclin D1 expression, the paraffin-embedded sections of lymph nodes were processed according to standard protocols (15) before immunostaining. Briefly, the glass slides with the fixed tissue sections of lymph nodes were first heated in a microwave oven before incubation with the primary mouse-anti-Cyclin D1 antibody (Oncogene, Cambridge, MA), according to the manufacturer’s instructions. After washing, the tissue sections were probed with a biotinylated goat antimouse antibody, as secondary antibody (DAKO, Carpinteria, CA). Subsequently, peroxidase/3-3′diamino-benzidine (DAKO) was used as a substrate to visualize the presence of cyclin D1 protein in individual cell nuclei, or CD20 (data not shown) on the surface of individual cell membranes.

To obtain a differential expression analysis of MCLs and normal subpopulations of human B cells, we analyzed seven MCLs and five different B-cell subpopulations. The strategy for differential gene expression analysis was dual. First, the MCL were compared with all five B subpopulations as one group, to display genes specifically expressed by only the MCL cells. Secondly, the MCL were compared with the pre-GC B cells, i.e., naive B cells and preactivated B cells as one group, to display genes that differed between the malignant cells and their extrafollicular normal counterparts (5). The MCLs included in the present study demonstrated either a nodular growth morphology (MCL 1–2; Fig. 1,A) or a more diffuse growth morphology (MCL 3–7; not shown). The overexpression of the cyclin D1 gene, a hallmark of MCL, was clearly demonstrated in all of the samples, as compared with the normal human subpopulations of B cells (Fig. 1, B and C).

Homology of the global gene expression patterns of normal and malignantly transformed B cells was studied by creating a hierarchical tree. This revealed that the tumor samples could be divided into two distinct clusters (Fig. 2). Thus, four of the MCL samples were highly homologous to the gene pool of pre-GC B cells and memory cells, i.e., the resting B-cell populations, whereas the three remaining MCL samples clustered as one distinct population, exhibiting less similarities with the resting B-cell gene pool (Fig. 2). The subgrouping of the MCL samples also correlated with the bone marrow involvement of the patients. Patients (MCL sample) 4, 6, and 7 had bone marrow involvement (data not shown) and clustered together as one group in the hierarchical tree (Fig. 2). Patients 1, 3, and 5, who lacked bone marrow involvement, clustered separately with patient 2, where no information about the spread of disease was available (Fig. 2). A subsequent cluster analysis was then performed comparing MCL samples from patients 4, 6, and 7 (bone marrow involvement) with those of patients 1, 3, and 5 (lack of bone marrow involvement). Genes that were transcriptionally active only in patients with bone marrow involvement included the regulatory NF-IL6β gene and elongation factor EF1A as well as the neural adhesion factor CHL1. These findings are now being validated in a larger sampling of tumor material.

The global differential gene expression analysis showed, furthermore, that a few hundred genes, mostly with known function, were differentially expressed in the MCLs compared with the normal B-cell population. From these genes, we have selected 90 annotated genes based on their possible involvement in the lymphoma etiology and diagnostic/therapeutic potential for MCLs (Table 1). The genes in Table 1 were represented as groups of genes having similar functions and/or working in concert in signaling pathways or other biological processes, and the grouping is not absolute.

Genes involved in trafficking and differentiation, such as CXCR5, CCR6, IL4R, CD23, and IgD were down-regulated in MCLs (Table 1, part 1). The absence of CD23 and a low expression of IgD have been reported previously and are common negative markers for MCL (6), whereas CXCR5, CCR6, and IL4R have not been identified in this context before. CXCR5, which directs normal B cells into the primary B-cell follicles (16), are, together with CCR6, down-regulated compared with the normal pre-GC B cells. The expression of CCR6 is normally up-regulated on mature B-cells but gets down-regulated when the B cells encounter antigens (17). However, the expression of CCR7, which homes the cells to the lymph node (16), was similar both in MCL and in normal B-cells.

Furthermore, IL4R is heavily down-regulated in MCL together with the downstream effector genes such as CD23 and MHC class II genes (Table 1, part 5; Refs. 18, 19). The IL4R is normally well expressed in pre-GC B cells, a process that is induced by IL-4 and STAT6. STAT3, which is involved in IL-4 signaling, was down-regulated 2-fold (Table 1, part 2).

Several genes involved in cell proliferation had an altered expression in MCL. The IL10R, which can act as a growth factor and induce proliferation of MCL (20), was up-regulated, whereas JAB, an inhibitor of the JAK signaling pathway (21), was down-regulated, which promoted the ability of MCL cells to proliferate. Furthermore, several genes with IFN- or TGF-β-related activities, such as IFNγR, IFNαR, STAT1, IL-6, TGFβ2, and SMAD3, all of them down-regulated, were present in this group together with an almost 7-fold up-regulated IL18 gene (Table 1, part 2). Of note, IFNα has been used in the therapy of indolent lymphomas (22, 23) and TGF-β and MAD-related proteins have previously also been shown to act as tumor suppressors in, for example, T-cell lymphomas (24). MNDA, which is involved in growth regulation and was previously reported to be overexpressed in MCL (25), and MARCKS (26), which suppresses proliferation in cancer cells, were up-regulated 12–15 times.

Several oncogenes were also found to have altered gene expression in MCL compared with the normal B-cell populations. Cyclin D1, which is one of the main markers for MCL, was together with Bcl-2 heavily up-regulated. Bcl-2 has been shown to be involved in the blockage of apoptosis (27). It has previously been shown that the overexpression of cyclin D1 alone is not enough to induce tumor formation but that other, thus-far-unknown, factors are involved (28). Finally, the MERTK oncogene, previously unknown in this context, was up-regulated (Table 1, part 3).

Most of the patients diagnosed with MCL have involvement of the bone marrow and/or the lymphoid tissue of the gastrointestinal tract (MALT; Refs. 1 and 2). Several genes, previously reported to be involved in metastasis in other types of malignancies, were shown to be up-regulated in MCL. Interestingly, RECK, which is a negative regulator for MMP-9 (29), is down-regulated, and we could consequently demonstrate that MMP-9 was heavily up-regulated (Table 1, part 4). However, some of the genes involved in metastasis (Table 1, part 4) were most heavily up-regulated in the nonpurified samples (MCLs 3–7), whereas the expression was lower or absent in the purified tumor cells (MCLs 1–2). This could indicate that genes such as MMP-9, NMB, ATX, and Cystatin C are expressed by adjacent cells and not by the actual tumor cells. CD107b, which together with CEACAM1 is significantly up-regulated in all of the MCL samples, has previously also been shown to be involved in the metastatic program of tumor cells (30).

Genes involved in TNFR-mediated signaling and the regulation of NFκβ-2 are differentially regulated in the MCLs (Table 1, part 6). The members of the TNFR superfamily are known to be important for cell growth, differentiation, and apoptosis (30). Among the genes that seem to be supporting an antiapoptotic program are the up-regulated IEX-1L, an inhibitor of apoptosis (31); CDw150, a receptor that has been shown to enhance CD95-mediated apoptosis (32); and DRAK2, which, together with CDw150 and TNF-β, is down-regulated in the MCL samples. However, CD27 (member of the TNFR family) is up-regulated and was previously reported to be involved in apoptosis (33) and to be overexpressed in several B-cell malignancies (34, 35), which again points to a complex dysregulation of the apoptotic program in MCLs.

Several receptors signaling through neurotransmitters, such as those coding for the β2-adrenergic receptor and the CRS, were up-regulated, whereas genes coding for the serotonin receptor and the purinergic receptor were down-regulated in the MCL compared with all of the B-cell populations (Table 1, part 7). Norepinephrine, a sympathetic neurotransmitter and the ligand for the β2-adrenergic receptor, is known to stimulate the activity level of T and B lymphocytes (36). Cannabinoids, which bind the CRS, have previously been reported to stimulate B-cell growth (37), but the in vivo implication of this receptor in the immune system is not clear (38). Furthermore, the Midkine gene expression goes from absent in normal B cells to present in MCL and has been shown to be expressed in solid tumors, but not previously in non-Hodgkin lymphoma (39), and has been shown to be involved in angiogenesis and neurogenesis (40).

In this study, we show that the gene expression of MCL clusters with the resting B-cell populations and not with GC B-cells and that an altered gene expression is detected when MCL populations are compared with all of the B-cell populations. MCLs are malignant lymphomas with a relatively slow proliferation rate, presently having a naive B cell as the proposed normal counterpart (5, 41). This correlates only in part with our hierarchical cluster analysis and the experimental tree shown in Fig. 2, in which some MCL samples and the resting B-cell populations (pre-GC B cells and memory B cells) cluster together, whereas the activated GC populations (centroblasts and centrocytes) cluster separately. Although the number of MCL samples in this study is too small to find new and statistically valid diagnostic subgroups, it is interesting to note that the spread of disease to the bone marrow was diagnosed in three of seven cases and that these samples clustered together. The other four patients, not showing any bone marrow involvement (three cases; data not available for one case), clustered separately, together with the pre-GC B-cell populations. Because bone marrow involvement is a feature that seems to predict a shorter survival time (3), genes with potential association to metastasis are of particular interest. From Table 1, part 4, it was also evident that several genes involved in metastasis are up-regulated in the MCL samples compared with the normal B-cell populations. Two genes that most certainly are involved in the spread of MCL, but that have not been reported in that context before, are RECK and MMP-9 (Table 1, part 4). RECK (29), a negative regulator for MMP-9, was 3-fold down-regulated in MCL, whereas MMP-9, a known tissue-remodeling enzyme, seems to be highly expressed. However, because the highest expression was seen in the unpurified MCL samples, it could indicate that MMP-9 was mainly expressed in cells surrounding the tumor cells. In fact, Bergers et al. (42), recently showed that MMP-9 was indicated in angiogenesis, but that the protein was produced by nontumor cells in proximity to the vasculature and not in the tumor cells themselves. CEACAM1, which exhibit angiogenic properties, was also up-regulated in all of the MCL samples and has been suggested as a target for the inhibition of angiogenesis (43).

Although previous studies have suggested a naïve B cell (5, 41) as the normal counterpart for the MCLs, the expression of CXCR5, CCR6, IL4R, and IgD suggests otherwise. CCR6 and IgD are up-regulated on normal B cells going from the immature to the mature B-cell stage after which CCR6 is down-regulated because of antigenic encounter (17). In contrast, they are markedly down-regulated in the lymphoma cells. The down-regulation of CCR6 and IgD gene transcription in MCLs compared with naive B cells indicates thus a more differentiated B-cell origin for the MCLs than previously suggested. The IL4R transcript is normally also up-regulated in pre-GC B cells, a process that is induced by IL-4 (44), but in all of the samples from MCL patients the IL4R transcript was down-regulated. This also probably contributed to the impaired up-regulation of downstream effector genes such as CD23, MHC class II, and STAT3 (18, 19, 45). Furthermore, the importance of chemokines and their receptors for the migration of cells involved in the immune system has become increasingly evident in recent years. The numbers of known chemokines involved in the migration of B cells are still rather few but each one holds an important function in the trafficking of B cells to the secondary lymphoid organs and into the B-cell follicles. The expression of CCR7 (16), which homes B cells to the lymph node, is the same in the MCL B cells as in their normal counterparts. However, the transcriptional levels of CXCR5 (16, 46), which initially directs the B cell into the primary follicle, was completely absent in MCL compared with normal pre-GC B cells. CXCR5 has been found to be down-regulated in proliferating T cells and in the more differentiated T and B cells found in GCs (47, 48). The absence of CXCR5 transcripts in MCL could thus indicate that the malignant transformation occurs during the transition from a primary to a secondary follicle (GC), which again is at a later stage than previously suggested. Current findings demonstrate that FDCs secrete chemokine ligands in the follicles (16), thus providing B cells with essential survival signals, such as CD21L (49, 50). FDCs are also often seen as an extensive network in MCL tumors (6) and could contribute to an antiapoptotic program in the tumor cells by providing survival signals (51). FDCs have also been implicated in the growth of follicular lymphomas (52).

It was very clear from our data in Table 1, part 6, that the apoptotic program is dysregulated. Alterations in apoptosis pathways have previously been reported (53), although direct comparisons are difficult to make because of differences in the purity of the samples as well as usage of different control B-cell populations. Of note, the Bcl-2 gene, which is involved in the blockage of apoptosis (27), was overexpressed 8-fold in the MCL samples compared with normal B cells. Furthermore, the MERTK oncogene, not reported in this context before, was also overexpressed and may, together with bcl-2, enhance the apoptotic block. The most distinct marker for MCL is the overexpression of cyclin D1, promoting the G₁ to S-phase transition. This characteristic overexpression was found in all of the seven MCL samples analyzed (Fig. 1 C), although the overexpression of cyclin D1 alone is not enough to induce tumor formation (28). Another pathway involved in the G₁ to S-phase transition is funneled through the pRb, which blocks proliferation when in a hypophosphorylated state. TGF-β activate pRb by blocking its phosphorylation in a number of ways. Interestingly, TGF-β was down-regulated in the MCL samples, which together with the down-regulation of Smad3, which transduces signals from the TGF-β receptor to downstream targets (54), indicates that the antiproliferative block normally exerted by pRb has been lifted in MCL. Consequently, our findings support a more complex view in which the overexpression of cyclin D1, together with oncogenes such as bcl-2 and MERTK, and insensitivity to antigrowth signals contribute to the malignant behavior of MCLs.

Neuroimmunology is a recent area that deals with the cross-talk between the nervous system and the immune system. Here we report a differential expression of several neurotransmitter receptors that may be involved in the pathology of MCL. It is known that B cells are stimulated by norepinephrine (36), and the 7-fold up-regulation of the gene coding for the β2-adrenergic receptor in the MCL in this study may, thus, indicate that norepinephrine is involved in the growth of MCL. Another up-regulated neurotransmitter receptor is the CRS. Cannabinoids have also previously been reported to stimulate B-cell growth (37), although the in vivo relationship of the endogenous ligands (anandamide and 2-arachidonolyl-glycerol, which are present in both central and peripheral tissue) to CRS is not clear (38). On the other hand, the serotonin receptor (5-HTR3A), normally expressed on B cells (55), was here shown to be down-regulated in the MCL. The effect of its ligand (serotonin) on B cells is not yet understood (55). IL-6, which has also been reported to be involved in the communication with the nervous system and to interact with serotonin (56), was dramatically (>28-fold) down-regulated in MCL compared with pre-GC B cells.

As mentioned above, MCLs are incurable, and new strategies for treatment are of highest relevance. The monoclonal anti-CD20 antibody Rituximab has been used in clinical trials against different B-cell lymphomas. It has been particularly successful in the treatment of follicular lymphomas but demonstrated a significantly lower efficacy in the treatment of MCL (57). On the other hand, MCLs show an increase in proliferation after stimulation with IL-10 and a decrease in proliferation after blocking the IL-10 receptor (IL-10R; Ref. 20). Interestingly, the IL-10R is overexpressed in the MCL samples compared with normal B cells and could be a possible therapeutic target, as was recently suggested for IL-13/IL-13R in Hodgkin lymphomas (58). Other potential novel targets for antibody-based therapy could be the β2-adrenergic receptor or IL-18, both up-regulated almost 7-fold. IL-18 has previously been implicated in angiogenesis (59) and T-cell polarization as well as in B-cell differentiation (60), and the β2-adrenergic receptor can be involved in B-cell differentiation (61). The β2-adrenergic receptor is, furthermore, internalized in complex with the epidermal growth factor receptor (62), a characteristic port of entry into tumor cells.

In summary, the altered transcriptional expression profile that we have demonstrated for MCLs, compared with normal human B-cell populations, supports a dysregulation in B-cell trafficking and differentiation, which results in a malignant transformation after initial B-cell activation. Furthermore, several regulatory networks involved in growth regulation were demonstrated to be altered in the lymphoma cells, which indicates possible points for therapeutic intervention. Finally, the genetic clustering indicated that subtypes of this disease could exist that are related to underlying parameters involved in the malignant spread of tumor cells.

We gratefully acknowledge the expert technical assistance of Ann-Charlotte Olsson.