Chronic Lymphocytic Leukemia

Gene knockdown studies revealed CCDC50 as a candidate gene in mantle cell lymphoma and chronic lymphocytic leukemia

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The two B-cell non-Hodgkin's lymphoma entities, chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), show recurrent chromosomal gains of 3q25–q29, 12q13–q14 and 18q21–q22. The pathomechanisms affected by these aberrations are not understood. The aim of this study was to identify genes, located within these gained regions, which control cell death and cell survival of MCL and CLL cancer cells. Blood samples collected from 18 patients with CLL and 6 patients with MCL, as well as 6 cell lines representing both malignancies were analyzed by gene expression profiling. By a comparison of genomic DNA and gene expression, 72 candidate genes were identified. We performed a limited RNA interference screening with these candidates to identify genes affecting cell survival. CCDC50 (coiled coil domain containing protein 50), SERPINI2 and SMARCC2 mediated a reduction of cell viability in primary CLL cells as well as in cell lines. Gene knockdown and a nuclear factor kappa B (NFκB) reporter gene assay revealed that CCDC50 is required for survival in MCL and CLL cells and controls NFκB signaling.


Chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL) are B-cell non-Hodgkin's lymphoma subtypes that share patterns of genetic aberrations. The median survival time of patients with MCL was reported to be 3–5 years after diagnosis.1 A criterion for diagnosis of MCL is the translocation t(11;14)(q13;q32), resulting in the overexpression of the cyclin D1 gene.2, 3, 4, 5 In addition to the t(11;14), MCL carries a high number of secondary genetic alterations that may contribute to its pathogenesis. Several studies reported the high resolution detection of chromosomal imbalances in MCL using array comparative genomic hybridization,6 accurately defining the gained regions.7, 8, 9, 10, 11, 12, 13 However, these regions still contain too many genes to enable a reasonable selection of candidates, and it is not clear which genes have functional relevance for MCL. Recently, the incidence of genomic gains in t(11;14)(q13;q32)-positive MCL cases was assessed by fluorescence in situ hybridization analysis, revealing gains on 3q, 12q and 18q in 45, 17 and 14% of cases, respectively.14 Candidate genes within these chromosomal regions have been proposed by various groups on the basis of profiling data acquired on different microarray platforms (single-nucleotide polymorphism array, array comparative genomic hybridization, expression array).7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 Gains of genomic DNA may activate oncogenes through gene dosage effects,20, 22 such as CDK4 (cyclin-dependent kinase-4) on 12q13 and B-cell lymphoma-2 (BCL2) on 18q21 in MCL.1, 8, 11, 15, 18, 25 It was recently shown that highly proliferative and clinically aggressive variants of MCL have a complex karyotype with frequent gains on 3q and 12q.11, 22, 26Furthermore, the chromosomally gained region, 3q25–q29, shows an association with poor outcome in MCL patients.22

Chronic lymphocytic leukemia is the most common leukemia among adults of the western world, with a variable survival time between ca. 3 and 20 years. CLL is characterized by the accumulation of mature, but resting B cells in peripheral blood, bone marrow and lymphatic or extralymphatic tissues. The majority of leukemic CLL cells are arrested in the cell cycle, mainly in the G0/G1 phase.27, 28 Unlike other leukemias, there is only a small proportion of proliferating neoplastic cells that are localized in the so-called ‘pseudofollicular’ proliferation centers in the lymph nodes or are scattered in the bone marrow of the patients.29, 30 The most frequently recurring chromosomal gain, identified in CLL patients, is trisomy 12.27

The aim of this study was to identify genes with oncogenic potential in recurrently gained chromosomal regions of MCL and CLL. To this aim, gene expression profiling was performed, followed by cell survival and proliferation studies after silencing of candidate genes. First, we profiled the expression of 18 primary CLL, 6 primary MCL samples, as well as 6 cell lines, and compared all genes identified in the three gained regions (3q, 12q and 18q) with recently published data. Second, we investigated a set of 72 candidate genes derived from this analysis by the use of a small interfering RNA (siRNA)-mediated loss of function screen in a multiwell format in MCL cell lines. Third, we validated the observed changes in cell viability by gene knockdown in primary CLL cells and analyzed the downstream effects of the identified candidate gene, CCDC50 (coiled coil domain containing protein 50).

Materials and methods

Cell lines

Cell lines were obtained from DSMZ (Braunschweig, Germany) and from ATTC (American Type Culture Collection, Manassas, VA, USA). Granta (ACC 342), Mec-1 (ACC 497) and HEK-293T (Human embryonic kidney 293-T cells, CRL-1573) cells were cultured in DMEM (Dulbecco's modified Eagle's medium) (Invitrogen, Karlsruhe, Germany). JVM-2 (Human peripheral blood B-cell lymphoma cell line, ACC 12) and LCL-WEI (Human lympoblastoid B cells, ACC 218) were cultured in RPMI 1640 medium, including 2 mM L-glutamine (Invitrogen, Carlsbad, CA, USA). Both media were supplemented with 10% fetal calf serum and 1% penicillin/streptomycin.

Primary cells

Peripheral blood samples were collected from patients with CLL and MCL, as well as from healthy donors after informed consent was obtained from them (Supplementary Tables S1–S6). All cases matched standard diagnostic criteria.31 The human bone marrow stromal cell line, HS-5, was purchased from the ATCC. Cells were cultured and prepared as published earlier.32

Nucleofection of cell lines and primary CLL cells

Using the Human B-cell Nucleofector Kit and program U-015, 5 × 106 primary B cells were transfected with 500 nM siRNA (in 100 μl volume), according to the manufacturer's instructions (Lonza, Cologne, Germany). After nucleofection, primary cells were added to a sterile filtered conditioned medium, obtained from HS-5 cells, and cultured as published earlier.32 Transfections of cell lines were performed by nucleofection (solution T, program O-017), according to the manufacturer's instructions, using 2 × 106 cells and 2 μg DNA or 500 nM siRNA (in 100 μl volume). Cells were harvested 24, 48 and 72 h after transfection for RNA isolation and functional assays. Using 96-well Shuttle system with the 96-well Nucleofector Kit SF and program 96-DN-100, a total of 4 × 105 cells of the cell lines JVM-2 or Granta-519 were transfected with 500 nM siRNA (in 20 μl volume), according to the manufacturer's instructions (Lonza).

Transfection of HEK-293T cells

A total of 4 × 105 HEK-293T cells were transfected with 50 nM siRNA or 2.5 μg plasmid DNA using the TransIT transfection reagent, according to the manufacturer's instructions (Mirus Bio LLC, Madison, WI, USA).

Cloning of short hairpin coding plasmids

The plasmid pSUPERdL_Zeo was created by cloning the zeocin resistance gene, including an SV40 promoter and an SV40 polyA sequence, from the pTER plasmid33 into the NotI and BamHI sites of pSUPERdL.34 To obtain the PCR insert out of the pTER plasmid, the following primers were used: NotI_pTER_fw 5′-IndexTermcgattgcggccgctgatttaac-3′ and BamHI_pTER_rev 5′-IndexTermcggaggatccaagctctagcta-3′. The sequence for cloning of a short hairpin of CCDC50 into the pSUPERdL_Zeo plasmid was designed according to the siRNA sequence for CCDC50 (Ambion Inc., Austin, TX, USA; 129979). Short hairpin sequences specific for CCDC50 are sh_CCDC50_sense: IndexTermATCCCCCCCTATGCTGCATATACTTTCAAGAGAAGTATATGCAGCATAGAGGTTTTTGGAAA and sh_CCDC50_antisense: IndexTermAGCTTTTCCAAAAACCTCTATGCTGCATATACTTCTCTTGAAAGTATATGCA GCATAGAGG. Cloning was performed as described previously,34 and pSUPERdL_Zeo_sh_CCDC50 was sequenced. Stable transfection of the plasmid pSUPERdL_Zeo_sh_CCDC50 into the cell line Mec-1 and JVM-2 was performed by nucleofection. The transfected MCL cells were selected with 50 ng/ml zeocin.

RNA isolation, synthesis of cDNA, quantitative real-time PCR and flow cytometry

RNA isolation of primary CLL cells and cell lines was performed using the RNeasy Mini Kit (Qiagen, Hilden, Germany). cDNA synthesis, quantitative real-time (qRT)-PCR and flow cytometry of cells were performed as published earlier.32, 33, 34, 35


The chemically synthesized siRNAs siCONTROL Non-Targeting siRNA Pool 2 (D-001206-14-05) and ON-TARGETplus siCONTROL Non-targeting Pool (D-001810-10-05) were obtained from Dharmacon Inc. (Chicago, IL, USA). The siRNAs for CCDC50 (siCCDC50 1:ID129977, siCCDC50 2: ID129978, siCCDC50 3: ID129979), Silencer Firefly Luc GL2/3 (AM4629), Negative Control 1 (AM4636) and Silencer Select negative control (4390844) were obtained from Ambion Inc. The negative control Control_AllStars_1 (SI03650318), as well as the 72 siRNA pools for the RNA interference screen, were obtained from Qiagen. Information about individual siRNA sequences used in the screening can be found in Supplementary Table S10.

Western blot analysis

Transfected CLL cells were harvested by centrifugation (800 r.p.m., 10 min, room temperature). Cell pellets were lysed and protein extracts were purified using the All Prep RNA/Protein Kit (Qiagen). Western blot analysis was carried out as published previously.34 After immunoblotting, polyvinylidene fluoride membranes were probed with primary antibodies specific for CCDC50 (HPA001336, Sigma Aldrich, St Louis, MO, USA) and GAPDH (glyceraldehyde-3-phosphate dehydrogenase) (CB1001, Calbiochem, Darmstadt, Germany). Secondary antibodies used were anti-rabbit HRP (horseradish peroxidase) and anti-mouse HRP (Cell Signaling Inc., Danvers, MA, USA).

Cell viability assay

The viability of transfected cells was assayed using the Cell Titer Glo Cell Viability Assay (Promega, Madison, WI, USA) in opaque-walled multiwell plates (Costar, Baar, Germany). At 24, 48 and 72 h after transfection, an aliquot of 100 μl of cell suspension was assayed for its adenosine triphosphate content according to the manufacturer's instructions. Each assay was carried out in triplicate. An integration time of 0.3 s per well was used.

NFκB reporter assay

The firefly luciferase plasmid containing six binding sites for nuclear factor kappa B (NFκB) and the renilla luciferase plasmid were obtained as generous gifts from Professor Bernd Doerken (Max Delbrueck Center, Berlin, Germany). Cells were transfected with 500 nM siRNAs targeting or 1.25 μg of the plasmids coding for CCDC50, together with 0.83 μg of firefly-luciferase reporter plasmid and 0.42 μg of renilla-luciferase reporter plasmid. At 24 h after transfection, tumor necrosis factor-α (TNFα) induction (50 ng/ml) was performed. Immediately (0 h), 3 and 6 h after TNFα induction, luciferase activities of firefly and renilla were assayed with a luminometer (LB-940 Mithras Multilabel Reader, Berthold Technologies, Bad Wildbach, Germany), using a Dual-Luciferase Reporter System (Promega).


Expression profiling revealed 72 overexpressed genes in frequently gained regions

Transcriptomes of 6 primary MCL and 18 primary CLL patient samples, as well as 6 cell lines, were profiled to identify overexpressed genes. The gene expression of cell lines was normalized to the lymphoblastoid non-tumor cell line LCL-WEI, whereas the gene expression of primary MCL and CLL cells was normalized to a pool of CD19+-sorted B cells from healthy donors. Results were compared with recent publications and revealed 37 novel and 35 predicted candidate genes (Supplementary Information, Supplementary Table S7). These genes are highly expressed and map to the respective gained chromosomal regions in MCL and CLL: 27 genes on chromosome bands 3q25–q29, 32 genes on bands 12q13–q14 and 13 genes on bands 18q21–q22 (Table 1).

Table 1 Overexpressed candidate genes on chromosome bands 3q25–q29, 12q13–q14 and 18q21–q22

Loss of function screen revealed 18 candidate genes

A total of 72 overexpressed genes were functionally analyzed in a transient RNA interference screen in MCL cell lines showing the respective chromosomal gain in 3q25–q29 (JVM-2), 12q13–q14 (JVM-2) or 18q21–q22 (Granta-519). For all functional assays, negative controls, such as scrambled siRNAs and siRNAs directed against firefly luciferase, were tested for unspecific effects on cell viability. Two assays were used as read out of the screen after functional gene knockdown: a luminescent-based cell viability assay (Figure 1) and FACS analysis (not shown). A direct correlation between cell viability and cell vitality was observed (not shown). The siRNAs directed against CCND1, KIF11, DUSP5, PLK1 and BCL2 served as positive controls. After the silencing of these genes, cell viability of MCL cell lines was reduced to 30–80%. For each of the 72 genes, a pool consisting of four siRNA sequences was transfected into MCL cell lines. After their transient knockdown, 18 genes revealed a reduction of cell viability when compared with negative controls.

Figure 1

Cell viability assay of the 18 candidate genes resulting from the small interfering RNA (siRNA) screening. Three independent replicates of the siRNA screening were performed in mantle cell lymphoma (MCL) cell lines. Genes located on chromosomes 3q25–q29 and 12q13–q14 were transfected into the MCL cell line JVM-2. MALT1 on chromosome 18q21 was transfected into Granta-519. Candidates are shown as white bars, positive controls as gray bars and the mean of three independent negative controls (siCONTROL Non-Targeting siRNA Pool 2, ON-TARGETplus siCONTROL Non-targeting Pool and Silencer-Firefly-LucGL2/3) as a black bar. Cell viability was measured 72 h after transfection. JVM-2 and Granta-519; Human peripheral blood B-cell lymphoma cell lines.

CCDC50, SERPINI2 and SMARCC2 affect cell survival

Validation of the 18 candidate genes was performed by transfecting up to four single siRNA molecules for each gene separately into the appropriate cell lines. Owing to the limited availability of primary cell material, pools of 2–4 siRNAs against the same gene were transfected into primary CLL cells. Validation of the candidate genes was scored positive if two or more siRNAs reduced cell viability in cell lines, and the pool of siRNAs reduced cell viability in primary CLL cells (Supplementary Information, Supplementary Figure S1). The single siRNA duplexes or pools of siRNAs showing the most effective reduction of cell viability are shown in Figure 2. For three genes, loss of viability was detected in cell lines (Figure 2a) as well as in primary cells (Figure 2b), namely CCDC50, SERPINI2 and SMARCC2. The most prominent reduction of cell viability was observed after gene knockdown of CCDC50 in primary CLL cells. This gene was selected for further analysis.

Figure 2

Validation of positive-scored candidate genes resulting from the small interfering RNA (siRNA) screening. (a) Cell survival was measured after transfection of single siRNA molecules targeting 14 candidate genes and was compared with negative control transfections at 24 h (light gray bar), 48 h (dark gray bar) and 72 h p.t.(black bar). Changes of cell viability after transfection of the negative control as well as positive controls CCND1, BCL2, KIF11 and PLK1 are shown. (b) Pools of siRNAs, consisting of 2–4 siRNA molecules were transfected into primary chronic lymphocytic leukemia (CLL) cells. Cell viability was measured 24, 48 and 72 h after transfection (dark gray, light gray and black bar). Candidate genes as well as positive controls (PLK1, BCL2 and KIF11) are shown. The negative controls used for normalization were SilencerFirefly-LucGL2/3, ON-TARGET-plus-siCONTROL, siControl Non-Targeting siRNA and siNegative Control 1.

CCDC50 is overexpressed in MCL and CLL

The CCDC50 transcript expression was analyzed in B-cell neoplasia cell lines, as well as in primary MCL and CLL cells. The quantitative realtime PCR (qRT-PCR) experiments that were conducted with 16 lymphoma cell lines, comprising Burkitt's lymphoma, Hodgkin's lymphoma, diffuse large BCL, MCL and CLL (Figure 3a), revealed an increased expression of CCDC50 only in MCL and CLL cells (ranging from 1.3- to 2.9-fold), except for the cell line Jeko-1. The qRT-PCR experiments with primary cells showed that 5 out of 8 MCL patients and 24 out of 28 CLL patients showed an upregulation of CCDC50 ranging from 1.5- to 10-fold, with a mean expression of 3.4-fold (MCL) and 3.5-fold (CLL) and a threshold of 1.5-fold overexpression, when compared with CD19+-selected B cells of healthy donors (Figure 3b).

Figure 3

Expression of coiled-coil domain containing protein 50 (CCDC50) in B-cell lines and primary mantle cell lymphoma (MCL) and chronic lymphocytic leukemia (CLL) cells was measured by quantitative real-time (qRT)-PCR. (a) The expression of CCDC50 was analyzed by qRT-PCR in 16 cell lines: five Burkitt's lymphoma (Burkitt), three diffuse large B-cell lymphoma (DLBCL), two Hodgkin's lymphoma (HL), three MCL and three CLL cell lines. Expression levels were first normalized by the two housekeeping genes PGK1 and DCTN2. Each sample was then compared with the lymphoblastoid non-tumor B-cell line, LCL-WEI. (b) CCDC50 expression was analyzed in 28 primary CLL and 8 primary MCL samples. Results were normalized to CD19+ B cells obtained from healthy donors (n=10). M=median expression, T=Threshold of 1.5-fold, overexpressed after normalization.

Silencing of CCDC50 inhibits survival

To validate the reduction of cell viability after transient CCDC50 gene knockdown, we determined the silencing effect of three independent siRNAs (siCCDC50 1, siCCDC50 2 and siCCDC50 3) in the cell line JVM-2. All siRNAs that were tested showed a significant RNA knockdown of 35–83% at 48 h post transfection (p.t.). The siCCDC50 2 and 3 revealed a decrease in cell viability to 51 and 44% at 72 h p.t. (Supplementary Figure S2). We generated cell lines of the CLL and MCL origin (Mec-1 and JVM-2), in which CCDC50 was stably silenced by the genomic introduction of a short hairpin RNA coding vector. As shown by the results obtained from qRT-PCR experiments, the wild-type JVM-2 and Mec-1 cell lines showed a 2.9- and a 2.0-fold CCDC50 overexpression, respectively (Figure 3a). CCDC50 RNA levels were reduced after stable CCDC50 silencing by 50% (JVM2+sh_CCDC50 1 and 3) and 80% (Mec1+sh_CCDC50 1) when compared with their parental cell lines (Figure 4a), accompanied by reduced protein levels (Figure 4b) and reduced cell viability as shown for JVM-2 (Figure 4c). To investigate the impact of CCDC50 on primary CLL cells, we transiently transfected siCCDC50 3 into cells of five different CLL patients. The qRT-PCR experiments revealed a 50–60% RNA knockdown of CCDC50 in all patients (Figure 5a), correlating with decreased CCDC50 protein levels (Figure 5b) as well as a 40–80% reduction of cell viability (Figure 5c).

Figure 4

Functional analyses of stably silenced coiled-coil domain containing protein 50 (CCDC50) cell lines. (a) quantitative real-time (qRT)-PCR analysis showed that CCDC50 expression was reduced in stable clones JVM-2+sh_CCDC50 1, JVM-2+sh_CCDC50 3 and Mec1+sh_CCDC50 1. LCL-WEI was used as a reference cell line because of a low CCDC50 expression level. (b) Western blot analysis revealed a CCDC50 protein decrease in stable cell lines JVM-2+sh_CCDC50 3 and Mec-1+sh_CCDC50 1 in comparison with the loading control glyceraldehyde-3-phosphate dehydrogenase (GAPDH). (c) Cell viability analysis was carried out by seeding 3 × 106 JVM-2 cells and measuring adenosine triphosphate (ATP)-dependent cell viability at 24 h intervals for 96 h.

Figure 5

Functional analyses of primary chronic lymphocytic leukemia (CLL) cells after chronic lymphocytic leukemia (CCDC50) silencing. (a) Quantitative real-time (qRT)-PCR measuring CCDC50 expression in CLL samples 48, 49 and 50. Values were normalized by CCDC50 expression after transfection of a small interfering RNA (siRNA) non-template control (NTC). (b) Western blot analysis showed CCDC50 knockdown in CLL cells from patient 49 at 48 and 72 h after transfection compared with transfection with a non-template control siRNA (siNTC) and with mock transfection (Mock). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a loading control. (c) A cell viability assay was carried out 72 h p.t. in primary CLL cells after transient transfection of siCCDC50. Values were normalized to transfection with a siNTC set to 100% viability.

Low NFκB inducibility in CCDC50-silenced cells

CCDC50, also known as Ymer, was previously identified in HeLa cells as a suppressor of NFκB activity through its interaction with the NFκB inhibitor, tumor necrosis factor, alpha-inducing protein 3 (TNFAIP3)36, 37 To elucidate a possible downstream effect of CCDC50 expression, NFκB luciferase reporter assays were carried out. Therefore, we used HEK-293T cells because they combine the following three major advantages: (1) a high transfection efficiency, (2) a high inducibility of the NFκB reporter by TNFα (sevenfold) and (3) a 9.3-fold lower CCDC50 expression level than that of JVM-2 cells. NFκB reporter plasmids were transfected together with either a siRNA targeting CCDC50 or an expression construct coding for CCDC50. Twenty-four hours after transfection, NFκB activity was induced by TNFα and measured immediately, as well as 3 and 6 h after induction. After normalizing the activity of the firefly luciferase to the activity of the renilla luciferase, our results showed a direct correlation between inducibility and CCDC50 expression. High CCDC50 expression levels revealed a 16-fold NFκB induction by TNFα, whereas low CCDC50 expression levels only revealed a 6- to 7-fold induction (Figure 6).

Figure 6

Nuclear factor kappa B (NFκB) reporter assay in HEK-293T cells after chronic lymphocytic leukemia (CCDC50) gene modulation. Tumor necrosis factor-α (TNFα) induction in HEK-293T cells was performed 24 h after transfection of the two reporter plasmids. NFκB reporter activity was measured before (0 h), as well as 3 and 6 h after TNFα induction. Normalization was performed when comparing the activity of the firefly luciferase with the activity of the renilla luciferase. Finally, fold induction was calculated as a correlation between TNFα-induced cells and uninduced cells. HEK-293T; human embryonic kidney 293-T cell line.

Genome-wide expression changes after CCDC50 modification

To investigate genome-wide gene expression changes, CCDC50 was transiently silenced by siCCDC50 3 or overexpressed by the plasmid pCMV_CCDC50 in MCL cell lines, JVM-2 and Granta-519. Seventy-two hours after transfection, the expression changes were analyzed using the Illumina expression array platform (Illumina Inc, San Diego, CA, USA) (Supplementary Figure S3). Expression array results were normalized to transfection of cells with negative controls, such as siRNA non-template control or plasmid pCMV. Expression profiling in the cell line JVM-2 revealed a total of 28 deregulated genes (17 upregulated and 11 downregulated) and 58 aberrantly expressed genes (39 upregulated and 19 downregulated) in the cell line Granta-519 (Supplementary Tables S8 and S9). Among the deregulated genes, a majority of genes were involved in p53 signaling pathways, as well as in the control of cell cycle progression and apoptosis. In JVM-2 and Granta-519, the genes TP53I3, CDKN1A and FDXR showed inverse expression levels when compared with CCDC50. Among the differentially expressed genes that were observed only in the cell line JVM-2 were PRODH, LAMP3, FAM129A, MAD4 and SENS1, which are involved in the control of cell cycle progression and apoptosis, whereas genes found differentially expressed only in Granta-519 were PHLDA3, GADD45A, BAX and RARRES3. Among the downregulated genes in JVM-2 after CCDC50 silencing were BNIP3L and LGMN, as well as BRWD1 and MDM4 in the cell line Granta-519. We identified TP53I3 as a significantly regulated gene in JVM-2 and Granta-519, showing an inverse expression status compared with that of CCDC50.These results indicate a role of CCDC50 in p53 signaling pathways.


The expression analysis of genes located in commonly gained chromosomal regions 3q25–q29, 12q13–q14 and 18q21–q22 revealed 72 overexpressed genes in MCL and CLL. Silencing of these genes in a limited RNA interference screen identified 18 candidates that affect cell survival in the MCL cell line, JVM-2. Out of these, CCDC50, SERPINI2 and SMARCC2 controlled cell viability also in primary CLL cells. As CCDC50 showed the most prominent effect on patient cells, it was further functionally characterized. NFκB reporter assays and genome-wide expression profiling studies showed an effect of CCDC50 on NFκB and p53 signaling pathways.

In the RNA interference screen, 18 candidate genes have been identified, of which 12 were novel and 6 have previously been associated with MCL or CLL: CCDC50,22, 38, 39 ECT2,11, 14, 25 SERPINI2,10 PAK2,22 KLHL622 and ITGB7.40 In all, 14 of the 18 candidates could be confirmed in the validation experiment. DDIT3 was shown to reduce cell viability only in primary CLL cells. It has been reported to show higher expression levels in progressive CLL than in clinically stable CLL cells.41 Other previously predicted genes such as GLI1,14 Timeless23 and TCF421 did not show an effect on cell viability in the focused siRNA screen. Validation of the candidates by transfecting single siRNAs targeting the same gene separately revealed four genes with a false-positive effect (Supplementary Figure S1c). Off-target effects of single siRNA molecules, as well as technical limitations, may have contributed to the false positives and negatives, wherein the loss of cell viability could not be validated in the majority of tested individual siRNA sequences in cell lines. Such effects are key limitations in respective screening approaches, as siRNAs may produce a ‘signature’ of inhibited transcripts in addition to the intended targets.42, 43, 44

CCDC50 was earlier identified to be overexpressed in MCL cells in comparison with other lymphomas22, 39and with benign reactive lymph node tissue of MCL patients.38 We showed that MCL and CLL cell lines with a stable CCDC50 knockdown revealed 75% less proliferation than the parental cell lines (Figure 4a). Recent publications reported an involvement of CCDC50 in NFκB and epidermal growth factor receptor signaling pathways.45, 36, 37CCDC50 is phosphorylated and ubiquitinated on epidermal growth factor stimulation and inhibits epidermal growth factor receptor downregulation.44 On the basis of investigations conducted in HeLa cells, CCDC50 was postulated as a negative regulator of the NFκB pathway.36, 37 CCDC50 was found to interact with TNFAIP3,46 which acts as a negative feedback regulator of NFκB and decreases NFκB signaling.46, 47 Interestingly, our data showed a survival-stimulating effect of CCDC50 in MCL and CLL cells, which might be mediated by enhanced NFκB signaling and implicate that the influence of CCDC50 on cell viability might be cell-type specific. Recent literature reported that TNFα induced NFκB activation is leading to the survival of MCL and CLL cells.46, 47, 48 In this study, we showed that the TNFα stimulation of the NFκB reporter plasmid revealed a 56% less inducibility in human embryonic kidney 293-T (HEK-293T) cells, transiently silenced for CCDC50, compared with cells with CCDC50 overexpression (Figure 6). These findings, as well as cell viability assays carried out in MCL and CLL cells, support our hypothesis that CCDC50 has a survival-stimulating effect. Low CCDC50 expression levels correlated with reduced cell viability, which may be caused by low NFκB inducibility.

Genome-wide expression changes after CCDC50 modulation discovered genes mainly involved in the p53 signaling pathway (BAX, CDKN1A, FDXR, GADD45A, LAMP3, PRODH, PHLDA3, SESN1 and TP53I3), as well as those involved in the inhibition of cell cycle progression and the induction of apoptosis (FAM129A, MAD4 and RARRES3).49, 50, 51, 52, 53, 54, 55 BAX, GADD45A, CDKN1A and SESN1 were previously reported as direct p53 target genes.56 Among the downregulated genes were BNIP3L and LGMN (JVM-2), as well as MDM4 and BRWD1 (Granta-519). These genes have previously been identified in cell cycle progression and p53 pathways.49, 50, 51, 52, 53, 54, 55 TP53I3 is described as an oxidoreductase that acts in p53-mediated apoptosis. MCL and CLL cells show, after CCDC50 silencing, increased TP53I3 expression levels and reduced cell viability. Increased inducibility of TP53I3 could be protective against cancer because cells might more readily undergo apoptosis after stress. Further studies revealed mir-34a as a novel target of p53 in primary CLL cells.24, 57 Reduction of mir-34a expression was associated with increased cell viability after DNA damage. Upregulation of mir-34a induced the expression of BAX and p21.24, 57 As we identified an overexpression of BAX and p21, as well as a reduction of cell viability after CCDC50 silencing, we postulated an involvement of CCDC50 in p53 signaling pathways.

In summary, the reduction of cell viability after CCDC50 silencing might be explained by the overexpression of apoptosis-stimulating genes involved in the p53 signaling pathways as well as by the downregulation of apoptosis-protecting genes. Moreover, the involvement of CCDC50 in NFκB signaling pathways may have major effects on cell survival.


Expression profiling of primary MCL and CLL cells identified 72 upregulated genes in recurrently gained regions 3q, 12q and 18q. Knockdown of these genes applying a siRNA screen revealed for CCDC50, SERPINI2 and SMARCC2 a reduction of cell viability in primary CLL cells as well as in cell lines. Stable silencing of CCDC50 inhibited cell proliferation in MCL and CLL cell lines. Furthermore, our data indicated that CCDC50 has an important function in TNFα-induced NFκB signaling.

Conflict of interest

The authors declare no conflict of interest.


  1. 1

    Campo E, Raffeld M, Jaffe ES . Mantle-cell lymphoma. Semin Hematol 1999; 36: 115–127. unbedingt noch neuere Referenzen dazu.

  2. 2

    Erikson J, Finan J, Tsujimoto Y, Nowell PC, Croce CM . The chromosome 14 breakpoint in neoplastic B cells with the t(11;14) translocation involves the immunoglobulin heavy chain locus. Proc Natl Acad Sci USA 1984; 81: 4144–4148.

  3. 3

    Tsujimoto Y, Finger LR, Yunis J, Nowell PC, Croce CM . Cloning of the chromosome breakpoint of neoplastic B cells with the t(14;18) chromosome translocation. Science 1984; 226: 1097–1099.

  4. 4

    Bentz M, Plesch A, Bullinger L, Stilgenbauer S, Ott G, Muller-Hermelink HK et al. t(11;14)-positive mantle cell lymphomas exhibit complex karyotypes and share similarities with B-cell chronic lymphocytic leukemia. Genes Chromosomes Cancer 2000; 27: 285–294.

  5. 5

    Schaffner C, Idler I, Stilgenbauer S, Dohner H, Lichter P . Mantle cell lymphoma is characterized by inactivation of the ATM gene. Proc Natl Acad Sci USA 2000; 97: 2773–2778.

  6. 6

    Solinas-Toldo S, Durst M, Lichter P . Specific chromosomal imbalances in human papillomavirus-transfected cells during progression toward immortality. Proc Natl Acad Sci USA 1997; 94: 3854–3859.

  7. 7

    Monni O, Oinonen R, Elonen E, Franssila K, Teerenhovi L, Joensuu H et al. Gain of 3q and deletion of 11q22 are frequent aberrations in mantle cell lymphoma. Genes Chromosomes Cancer 1998; 21: 298–307.

  8. 8

    de Leeuw RJ, Davies JJ, Rosenwald A, Bebb G, Gascoyne RD, Dyer MJ et al. Comprehensive whole genome array CGH profiling of mantle cell lymphoma model genomes. Hum Mol Genet 2004; 13: 1827–1837.

  9. 9

    Kohlhammer H, Schwaenen C, Wessendorf S, Holzmann K, Kestler HA, Kienle D et al. Genomic DNA-chip hybridization in t(11;14)-positive mantle cell lymphomas shows a high frequency of aberrations and allows a refined characterization of consensus regions. Blood 2004; 1: 104 (3): 795-801.

  10. 10

    Tagawa H, Karnan S, Suzuki R, Matsuo K, Zhang X, Ota A et al. Genome-wide array-based CGH for mantle cell lymphoma: identification of homozygous deletions of the proapoptotic gene BIM. Oncogene 2005; 24: 1348–1358.

  11. 11

    Rubio-Moscardo F, Climent J, Siebert R, Piris MA, Martin-Subero JI, Nielander I et al. Mantle-cell lymphoma genotypes identified with CGH to BAC microarrays define a leukemic subgroup of disease and predict patient outcome. Blood 2005; 105: 4445–4454.

  12. 12

    Schraders M, Pfundt R, Straatman HM, Janssen IM, van Kessel AG, Schoenmakers EF et al. Novel chromosomal imbalances in mantle cell lymphoma detected by genome-wide array-based comparative genomic hybridization. Blood 2005; 105: 1686–1693.

  13. 13

    Schraders M, Jares P, Bea S, Schoenmakers EF, van Krieken JH, Campo E et al. Integrated genomic and expression profiling in mantle cell lymphoma: identification of gene-dosage regulated candidate genes. Br J Haematol 2008; 143: 210–221.

  14. 14

    Sander S, Bullinger L, Leupolt E, Benner A, Kienle D, Katzenberger T et al. Genomic aberrations in mantle cell lymphoma detected by interphase fluorescence in situ hybridization. Incidence and clinicopathological correlations. Haematologica 2008; 93: 680–687.

  15. 15

    Hofmann WK, de Vos S, Tsukasaki K, Wachsman W, Pinkus GS, Said JW et al. Altered apoptosis pathways in mantle cell lymphoma detected by oligonucleotide microarray. Blood 2001; 98: 787–794.

  16. 16

    Zhu Y, Hollmen J, Raty R, Aalto Y, Nagy B, Elonen E et al. Investigatory and analytical approaches to differential gene expression profiling in mantle cell lymphoma. Br J Haematol 2002; 119: 905–915.

  17. 17

    Martinez N, Camacho FI, Algara P, Rodriguez A, Dopazo A, Ruiz-Ballesteros E et al. The molecular signature of mantle cell lymphoma reveals multiple signals favoring cell survival. Cancer Res 2003; 63: 8226–8232.

  18. 18

    Rosenwald A, Wright G, Wiestner A, Chan WC, Connors JM, Campo E et al. The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 2003; 3: 185–197.

  19. 19

    de Vos S, Krug U, Hofmann WK, Pinkus GS, Swerdlow SH, Wachsman W et al. Cell cycle alterations in the blastoid variant of mantle cell lymphoma (MCL-BV) as detected by gene expression profiling of mantle cell lymphoma (MCL) and MCL-BV. Diagn Mol Pathol 2003; 12: 35–43.

  20. 20

    Mestre-Escorihuela C, Rubio-Moscardo F, Richter JA, Siebert R, Climent J, Fresquet V et al. Homozygous deletions localize novel tumor suppressor genes in B-cell lymphomas. Blood 2007; 109: 271–280.

  21. 21

    Rizzatti EG, Falcao RP, Panepucci RA, Proto-Siqueira R, Anselmo-Lima WT, Okamoto OK et al. Gene expression profiling of mantle cell lymphoma cells reveals aberrant expression of genes from the PI3K-AKT, WNT and TGFbeta signaling pathways. Br J Haematol 2005; 130: 516–526.

  22. 22

    Salaverria I, Zettl A, Bea S, Moreno V, Valls J, Hartmann E et al. Specific secondary genetic alterations in mantle cell lymphoma provide prognostic information independent of the gene expression-based proliferation signature. J Clin Oncol 2007; 25: 1216–1222.

  23. 23

    Haslinger C, Schweifer N, Stilgenbauer S, Dohner H, Lichter P, Kraut N et al. Microarray gene expression profiling of B-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and VH mutation status. J Clin Oncol 2004; 22: 3937–3949.

  24. 24

    Zenz T, Mertens D, Dohner H, Stilgenbauer S . Molecular diagnostics in chronic lymphocytic leukemia—pathogenetic and clinical implications. Leuk Lymphoma 2008; 49: 864–873.

  25. 25

    Jares P, Campo E . Advances in the understanding of mantle cell lymphoma. Br J Haematol 2008; 142: 149–165.

  26. 26

    Bea S, Ribas M, Hernandez JM, Bosch F, Pinyol M, Hernandez L et al. Increased number of chromosomal imbalances and high-level DNA amplifications in mantle cell lymphoma are associated with blastoid variants. Blood 1999; 93: 4365–4374.

  27. 27

    Dohner H, Stilgenbauer S, Benner A, Leupolt E, Krober A, Bullinger L et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000; 343: 1910–1916.

  28. 28

    Chiorazzi N, Rai KR, Ferrarini M . Chronic lymphocytic leukemia. N Engl J Med 2005; 352: 804–815.

  29. 29

    Messmer BT, Messmer D, Allen SL, Kolitz JE, Kudalkar P, Cesar D et al. In vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells. J Clin Invest 2005; 115: 755–764.

  30. 30

    Dighiero G, Travade P, Chevret S, Fenaux P, Chastang C, Binet JL . B-cell chronic lymphocytic leukemia: present status and future directions. French Cooperative Group on CLL. Blood 1991; 78: 1901–1914.

  31. 31

    Hallek M, Cheson BD, Catovsky D, Caligaris-Cappio F, Dighiero G, Dohner H et al. Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia updating the National Cancer Institute-Working Group 1996 guidelines. Blood 2008; 111: 5446–5456.

  32. 32

    Seiffert M, Stilgenbauer S, Dohner H, Lichter P . Efficient nucleofection of primary human B cells and B-CLL cells induces apoptosis, which depends on the microenvironment and on the structure of transfected nucleic acids. Leukemia 2007; 21: 1977–1983.

  33. 33

    van de Wetering M, Oving I, Muncan V, Pon Fong MT, Brantjes H, van Leenen D et al. Specific inhibition of gene expression using a stably integrated, inducible small-interfering-RNA vector. EMBO Rep 2003; 4: 609–615.

  34. 34

    Pscherer A, Schliwka J, Wildenberger K, Mincheva A, Schwaenen C, Dohner H et al. Antagonizing inactivated tumor suppressor genes and activated oncogenes by a versatile transgenesis system: application in mantle cell lymphoma. FASEB J 2006; 20: 1188–1190.

  35. 35

    Korz C, Pscherer A, Benner A, Mertens D, Schaffner C, Leupolt E et al. Evidence for distinct pathomechanisms in B-cell chronic lymphocytic leukemia and mantle cell lymphoma by quantitative expression analysis of cell cycle and apoptosis-associated genes. Blood 2002; 99: 4554–4561.

  36. 36

    Kameda H, Watanabe M, Bohgaki M, Tsukiyama T, Hatakeyama S . Inhibition of NF-kappaB signaling via tyrosine phosphorylation of Ymer. Biochem Biophys Res Commun 2009; 378: 744–749.

  37. 37

    Bohgaki M, Tsukiyama T, Nakajima A, Maruyama S, Watanabe M, Koike T et al. Involvement of Ymer in suppression of NF-kappaB activation by regulated interaction with lysine-63-linked polyubiquitin chain. Biochim Biophys Acta 2008; 1783: 826–837.

  38. 38

    Schmechel SC, LeVasseur RJ, Yang KH, Koehler KM, Kussick SJ, Sabath DE . Identification of genes whose expression patterns differ in benign lymphoid tissue and follicular, mantle cell, and small lymphocytic lymphoma. Leukemia 2004; 18: 841–855.

  39. 39

    Bertoni F, Rinaldi A, Zucca E, Cavalli F . Update on the molecular biology of mantle cell lymphoma. Hematol Oncol 2006; 24: 22–27.

  40. 40

    Greiner TC, Dasgupta C, Ho VV, Weisenburger DD, Smith LM, Lynch JC et al. Mutation and genomic deletion status of ataxia telangiectasia mutated (ATM) and p53 confer specific gene expression profiles in mantle cell lymphoma. Proc Natl Acad Sci USA 2006; 103: 2352–2357.

  41. 41

    Falt S, Merup M, Gahrton G, Lambert B, Wennborg A . Identification of progression markers in B-CLL by gene expression profiling. Exp Hematol 2005; 33: 883–893.

  42. 42

    Jackson AL, Bartz SR, Schelter J, Kobayashi SV, Burchard J, Mao M et al. Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol 2003; 21: 635–637.

  43. 43

    Snove Jr O, Nedland M, Fjeldstad SH, Humberset H, Birkeland OR, Grunfeld T et al. Designing effective siRNAs with off-target control. Biochem Biophys Res Commun 2004; 325: 769–773.

  44. 44

    Tschuch C, Schulz A, Pscherer A, Werft W, Benner A, Hotz-Wagenblatt A et al. Off-target effects of siRNA specific for GFP. BMC Mol Biol 2008; 9: 60.

  45. 45

    Tashiro K, Konishi H, Sano E, Nabeshi H, Yamauchi E, Taniguchi H . Suppression of the ligand-mediated down-regulation of epidermal growth factor receptor by Ymer, a novel tyrosine-phosphorylated and ubiquitinated protein. J Biol Chem 2006; 281: 24612–24622.

  46. 46

    Beyaert R, Heyninck K, Van Huffel S . A20 and A20-binding proteins as cellular inhibitors of nuclear factor-kappa B-dependent gene expression and apoptosis. Biochem Pharmacol 2000; 60: 1143–1151.

  47. 47

    Cooper JT, Stroka DM, Brostjan C, Palmetshofer A, Bach FH, Ferran C . A20 blocks endothelial cell activation through a NF-kappaB-dependent mechanism. J Biol Chem 1996; 271: 18068–18073.

  48. 48

    Pham LV, Tamayo AT, Yoshimura LC, Lo P, Ford RJ . Inhibition of constitutive NF-kappa B activation in mantle cell lymphoma B cells leads to induction of cell cycle arrest and apoptosis. J Immunol 2003; 171: 88–95.

  49. 49

    Contente A, Dittmer A, Koch MC, Roth J, Dobbelstein M . A polymorphic microsatellite that mediates induction of PIG3 by p53. Nat Genet 2002; 30: 315–320.

  50. 50

    Smith ML, Chen IT, Zhan Q, Bae I, Chen CY, Gilmer TM et al. Interaction of the p53-regulated protein Gadd45 with proliferating cell nuclear antigen. Science 1994; 266: 1376–1380.

  51. 51

    Chipuk JE, Kuwana T, Bouchier-Hayes L, Droin NM, Newmeyer DD, Schuler M et al. Direct activation of Bax by p53 mediates mitochondrial membrane permeabilization and apoptosis. Science 2004; 303: 1010–1014.

  52. 52

    Kerley-Hamilton JS, Pike AM, Li N, DiRenzo J, Spinella MJ . A p53-dominant transcriptional response to cisplatin in testicular germ cell tumor-derived human embryonal carcinoma. Oncogene 2005; 24: 6090–6100.

  53. 53

    Adamsen BL, Kravik KL, Clausen OP, De Angelis PM . Apoptosis, cell cycle progression and gene expression in TP53-depleted HCT116 colon cancer cells in response to short-term 5-fluorouracil treatment. Int J Oncol 2007; 31: 1491–1500.

  54. 54

    Phang JM, Donald SP, Pandhare J, Liu Y . The metabolism of proline, a stress substrate, modulates carcinogenic pathways. Amino Acids 2008; 35: 681–690.

  55. 55

    Pulverer B, Sommer A, McArthur GA, Eisenman RN, Luscher B . Analysis of Myc/Max/Mad network members in adipogenesis: inhibition of the proliferative burst and differentiation by ectopically expressed Mad1. J Cell Physiol 2000; 183: 399–410.

  56. 56

    Whibley C, Pharoah PD, Hollstein M . p53 polymorphisms: cancer implications. Nat Rev Cancer 2009; 9: 95–107.

  57. 57

    Zenz T, Mohr J, Edelmann J, Sarno A, Hoth P, Heuberger M et al. Treatment resistance in chronic lymphocytic leukemia: the role of the p53 pathway. Leuk Lymphoma 2009; 50: 510–513.

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We thank Dr Michael Rogers for helpful discussions and a critical review of this paper, Professor Reiner Siebert and Dr Inga Vater from the University of Kiel for kindly providing additional MCL patient material, Verena Gschwend and Angela Schulz for brilliant laboratory support and Dr Ludger Altrogge (Lonza, Cologne, Germany) for the excellent collaboration and technical support. This study is supported by the German José-Carreras leukemia foundation (DJCLS R 06/13v) (DJCLS R 08/22v) and the Fritz Thyssen foundation ( MB is funded by the Helmholtz Alliance for Systems Biology. AR and EH are supported by the Interdisciplinary Center for Clinical Research (IZKF), University of Würzburg, Germany. GO is supported by the Robert-Bosch-Foundation.

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Correspondence to P Lichter.

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Farfsing, A., Engel, F., Seiffert, M. et al. Gene knockdown studies revealed CCDC50 as a candidate gene in mantle cell lymphoma and chronic lymphocytic leukemia. Leukemia 23, 2018–2026 (2009) doi:10.1038/leu.2009.144

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  • B-cell chronic lymphocytic leukemia
  • mantle cell lymphoma
  • siRNA screen
  • functional assays in primary CLL cells
  • CCDC50

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