• inhibitors of apoptosis;
  • Bcl-2 family;
  • lymphoma;
  • leukaemia;
  • apoptosis


  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

(De-)regulation of apoptosis plays an important role in normal and malignant lymphopoiesis. Apoptosis-regulating genes of the BCL-2 family and the recently identified inhibitors of apoptosis (IAP) family have been implicated in different types of non-Hodgkin lymphoma (NHL). To investigate whether expression of specific apoptosis-regulating genes correlated with different types of lymphoid malignancies, we measured the expression of five BCL-2 family genes, four IAP family genes and SMAC by real-time quantitative polymerase chain reaction in patient samples. In total, 137 samples from B- and T-cell acute lymphoblastic leukaemia (ALL), B-cell chronic lymphocytic leukaemia (CLL), six different NHL types and three control tissue types were analysed. The data were further analysed using cluster and discriminant analysis. Three specific expression patterns were identified for CLL, ALL and NHL respectively. CLL samples, as well as B-ALL and follicular lymphoma samples showed high similarity in the expression of these apoptosis-regulating genes and could be distinguished from each other and other diseases and controls. Discriminant analysis identified three members of the IAP family, C-IAP1, C-IAP2 and SURVIVIN, as the most informative genes to discriminate between these lymphoid malignancies.

During their lifespan, the fate of lymphocytes depends on apoptosis at a number of critical points. Survival of haematopoietic progenitors, development of the immune repertoire and activation and expansion of lymphocytes after stimulation are all tightly regulated by pro- and anti-apoptotic mechanisms (Rathmell & Thompson, 2002; Opferman & Korsmeyer, 2003). Defects in pathways that regulate apoptosis can promote the development of lymphomas. One example is the t(14;18) chromosomal translocation that results in aberrant expression of the anti-apoptotic Bcl-2 protein in follicular lymphoma (FL) and some cases of diffuse large B-cell lymphoma (DLBCL) (Tsujimoto & Croce, 1986; Jacobson et al, 1993). In addition, the t(11;18) translocation in mucosa-associated lymphoid tissue (MALT) lymphoma targets the inhibitor of apoptosis 2 gene (C-IAP2) (Dierlamm et al, 1999).

The balance between pro- and anti-apoptotic proteins determines whether the cell is induced to die in response to a cell death signal. The Bcl-2 family consists of homologous proteins that control apoptosis at the level of mitochondria (Cory & Adams, 2002). Bcl-2 family members with homology only in the BH3 (Bcl-2 homology 3) domain, such as Bid, are sensors for cellular stress signals. BH3-domain only Bcl-2-like proteins promote the activation of other pro-apoptotic Bcl-2 homologous proteins, such as Bax and Bak. These proteins stimulate the release of cytochrome c and second mitochondria-derived activator of caspases (SMAC) from the mitochondria (Wang, 2001). Anti-apoptotic Bcl-2 family proteins, like Bcl-2 and Bcl-xL, inhibit the release of these factors. In the cytosol, cytochrome c can induce the activation of caspases and subsequent apoptotic degradation of the cell. SMAC promotes caspase-activation by blocking members of the inhibitor of apoptosis (IAP) family (Du et al, 2000; Verhagen et al, 2000). The IAP family is a relatively new group of apoptosis-regulating proteins, which consists of a number of proteins that can bind to and inhibit caspases (Liston et al, 2003). Furthermore, there is growing evidence that members of the IAP family, including c-IAP1, c-IAP2, XIAP and Survivin, are involved in a variety of cancers (de Graaf et al, 2004).

As disturbances of the balance between pro- and anti-apoptotic proteins may be involved in the development of lymphomas, we investigated the expression of several of apoptosis-regulating genes in different subtypes of non-Hodgkin lymphoma (NHL) and lymphoid leukaemias. Real-time quantitative polymerase chain reaction (PCR) was performed to determine the expression of different BCL-2 family members, IAP family members and SMAC.

Materials and Methods

  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Patient and donor material

In total, 117 patient samples were used for real-time quantitative PCR analysis: T-cell acute lymphoblastic leukaemia (T-ALL; n = 8), B-cell acute lymphoblastic leukaemia (B-ALL; n = 15 of which two pro-B-ALL, seven common B-ALL, four pre-B-ALL and two unspecified B-ALL), B-cell chronic lymphocytic leukaemia (CLL; n = 9), mantle cell lymphoma (MCL; n = 19), FL (n = 14), Burkitt lymphoma (BL; n = 11), marginal zone lymphoma (MZL; n = 16) (including 12 MALT lymphomas), splenic marginal zone lymphoma (SMZL; n = 7) and DLBCL (n = 18). The T-ALL, B-ALL and CLL samples contained at least 90% tumour cells as determined by flowcytometric analysis. The MCL and BL samples also had at least 90% tumour cells. FL samples contained at least 80% tumour cells. The MZL, SMZL and DLBCL samples contained 50–60% tumour cells. The diagnosis and percentage of tumour cells of each NHL sample was confirmed by histological examination with supplementary immunohistochemical staining (data not shown). As controls we used RNA from hyperplastic tonsils (Ton; n = 9), sorted T cells (n = 6, more than 90% purity) and B cells (n = 5, more than 90% purity) from peripheral blood of healthy donors. T- and B-cell fractions were sorted using immunomagnetic beads (anti-CD3 and anti-CD19, respectively; Miltenyi Biotec, Bergisch Gladbach, Germany). RNA from T-ALL, B-ALL and CLL patients was isolated from bone marrow or peripheral blood samples that had been characterised by immunophenotyping (data not shown). Frozen cells from CLL patients were kindly provided by Dr J.W. Gratama (Department of Internal Oncology, Erasmus Medical Centre, Rotterdam, the Netherlands). All patient and donor material was obtained after informed consent.

RNA isolation and cDNA reaction

Frozen NHL and tonsil tissue samples were cut on a microtome at −20°C. Twenty to forty slices of 10 μm were dissolved in Trizol (Life Technologies, Gaithersburg, MD, USA) or RNAzol B (Campro Scientific, Veenendaal, The Netherlands). Reverse transcription was performed with 1–2 μg of RNA solution in a total mixture of 20 μl first strand reverse transcriptase buffer with 10 mmol/l dithiothreitol (DTT), 625 μmol/l dNTPs, 2·5 ng/μl oligo dT (all from Life Technologies), 0·1 μg/μl random hexamers (Amersham Pharmacia, Piscataway, NJ, USA), 20 U RNAsin (Promega, Madison, WI, USA) and 200 U Moloney Murine leukaemia virus reverse transcriptase (Life Technologies). This reaction mixture was incubated at 20°C for 10 min, at 42°C for 45 min and at 95°C for 10 min. Subsequently, 80 μl of water was added and samples were stored at −80°C.

Quantitative PCR

Quantitative PCR was performed with the ABI/PRISM 7700 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Primer and probe sequences are given in Table I. PCR conditions were as follows: 10 min at 95°C followed by 45 cycles of 15 s at 95°C and 1 min at 60°C, with data collection in the last 30 s. All PCRs except the for reference gene PBGD, were performed in SYBR Green PCR buffer with 125 μmol/l dNTP mix, 1·25 U AmpliTaq Gold and 5 mmol/l MgCl2 (Applied Biosystems). The PCR for PBGD was performed in TaqMan Universal PCR MasterMix with 200 nmol/l Vic-labelled (6-carboxy-rhodamine) PBGD probe (Applied Biosystems). Primer concentrations for the PCRs were 300 nmol/l. All PCRs were performed in a total volume of 50 μl. PCR products were checked by gel-electrophoresis.

Table I.  Sequences for primers and PBGD probe used for real-time quantitative PCR detection of apoptosis-regulating genes.
PBGD forward primer5′-ggcaatgcggctgcaa-3′
PBGD reverse primer5′-gggtacccacgcgaatcac-3′
PBGD probe (VIC-labelled)5′-ctcatctttgggctgttttcttccgcc-3′
BCL-2 forward primer5′-tcgccctgtggatgactga-3′
BCL-2 reverse primer5′-cagagacagccaggagaaatca-3′
BAX forward primer5′-gagcggcggtgatgga-3′
BAX reverse primer5′-tggatgaaaccctgaagcaaa-3′
BCL-XL forward primer5′-ccacttacctgaatgaccacctaga-3′
BCL-XL reverse primer5′-cagcggttgaagcgttcc-3′
BAK forward primer5′-tgagtacttcaccaagattgcca-3′
BAK reverse primer5′-agtcaggccatgctggtagac-3′
BID forward primer5′-ccttgctccgtgatgtctttc-3′
BID reverse primer5′-tccgttcagtccatcccattt-3′
C-IAP1/BIRC2 forward primer5′-cagctgttgtcaacttcagatacca-3′
C-IAP1/BIRC2 reverse primer5′-agcccatttccaaggcagat-3′
C-IAP2/BIRC3 forward primer5′-gggaagaggagagagaaagagcaa-3′
C-IAP2/BIRC3 reverse primer5′-gaattacacaagtcaaatgttgaaaaagt-3′
XIAP/BIRC4 forward primer5′-caatatggagactcagcagttgga-3′
XIAP/BIRC4 reverse primer5′-gcactattttcaagataaaagccgtt-3′
SURVIVIN/BIRC5 forward primer5′-agccagatgacgaccccata-3′
SURVIVIN/BIRC5 reverse primer5′-caagggttaattcttcaaactgctt-3′
SMAC forward primer5′-cacaatggcggctctgaag-3′
SMAC reverse primer5′-ccacaacaggaacacacaaaca-3′

Quantification of the PCR signals was performed by comparing the cycle threshold value (Ct) of each sample in duplicate, with the Cts of a dilution series of cDNA of the cell lines NB4 (for BCL-2 family genes) or HeLa (for IAP genes and SMAC) in water. The results were normalised using the PBGD expression of each sample. Samples that yielded a Ct for PBGD higher than 30 (approximately <5 ng RNA) were excluded. In view of the presence of a pseudogene for XIAP on chromosome 2, the XIAP PCRs were checked for contribution of a genomic DNA signal to prevent interference with the detection of XIAP expression.

Data analysis

One-way anova was performed on the log-transformed gene expression data with the disease or tissue types as independent variables using the Statistical Package for the Social Sciences (spss) software, version 6 (SPSS Inc., Chicago, IL, USA). In case of significance, posthoc Bonferroni multiple comparisons were run to determine whether the genes were differentially expressed between the different disease or tissue types. Unsupervised hierarchical cluster analysis was performed on the log-transformed PCR data, using cluster and treeview software ( (Eisen et al, 1998), to generate a dendrogram depicting the similarity of expression between samples with supplementary correlation coefficients for samples within a cluster. sas/stat software (SAS Institute, Cary, NC, USA) was used to perform a stepwise discriminant analysis, which identified the genes that discriminated best between the different lymphoma types and control groups (STEPDISC procedure). Subsequently, cluster analysis was repeated with the four most discriminatory genes between the different groups.


  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Several lymphoid malignancies show specific expression profiles of apoptosis-regulating genes

To determine differences in expression of apoptosis-regulating proteins, cDNA-specific real-time quantitative PCRs were designed for BCL-2 family members BCL-2, BAX, BCL-XL, BAK and BID. Real-time quantitative PCRs were also designed for IAP family members C-IAP1, C-IAP2, XIAP and SURVIVIN and the IAP-inhibiting factor SMAC. The expression of these genes was analysed in 117 diagnostic patient samples of different NHL and lymphoid leukaemias and 20 control samples (tonsils and normal T and B cells).

We tested whether disease-specific expression patterns could be defined. The expression data of both BCL-2 family and IAP family genes were therefore depicted per disease type (Fig. 1A). Roughly three types of expression profiles could be distinguished. A distinct profile was seen for the CLL samples, characterised by strikingly high C-IAP2 (P < 0·001) and extremely low SURVIVIN (P < 0·001) expression with relatively high expression of BCL-2 (P < 0·001) and XIAP (P < 0·001) compared with the other disease types. The ALL samples had relatively low C-IAP2 (P < 0·001) expression levels compared with NHL and CLL samples and high SURVIVIN expression compared with CLL (P < 0·001). The samples from the different NHL types showed more variation in expression patterns, but C-IAP2 expression was higher than in ALL and lower than in CLL (P < 0·001), whereas SURVIVIN expression levels were similar to those of ALL. The control T and B cells had relatively high expression levels for all genes tested except SURVIVIN (Fig. 1B). In contrast, hyperplastic tonsils had generally low expression levels and the variation in expression between the samples was very high (Fig. 1B).


Figure 1. ALL, CLL and NHLs exhibit different expression profiles of apoptosis-regulating genes. (A) Real-time quantitative PCR expression was performed on cDNA from patient samples for apoptosis-regulating genes of the BCL-2 family (BCL-2, BAX, BCL-XL, BAK and BID), IAP family (c-IAP1, C-IAP2, XIAP, SURVIVIN) and SMAC. The expression levels of all genes were depicted for each individual sample per disease type on a logarithmic scale. The CLL samples exhibited a specific expression profile with remarkably high C-IAP2 and low SURVIVIN expression relative to the ALL and NHL samples. The profile of ALL samples showed relatively low C-IAP2 expression, whereas SURVIVIN expression was high compared with CLL. (B) Real-time quantitative PCR expression of apoptosis-regulating genes was performed on cDNA from control tissue samples; tonsils (Ton), peripheral T and B cells. In B- and T-cell samples all genes except SURVIVIN were highly expressed, whereas tonsil samples showed low expression levels of all genes.

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Expression of IAP genes C-IAP1, C-IAP2 and SURVIVIN characterises different lymphoid malignancies

As our data indicated that there were distinct expression profiles, we performed a stepwise discriminant analysis to determine which of the genes were most important for distinguishing the samples from different disease types (and controls). This analysis revealed that three IAP genes, C-IAP2, SURVIVIN and C-IAP1, were the best discriminators for the lymphoma types. Less informative for discriminating lymphoma types were BCL-2, followed by the other BCL-2 family genes, XIAP and SMAC (Table II). To investigate whether lymphoma types could be distinguished with this limited set of apoptosis-regulating genes, we performed an unsupervised hierarchical cluster analysis with the four best discriminating genes (Fig. 2). Cluster analysis with C-IAP1, C-IAP2, SURVIVIN and BCL-2 yielded a tight clustering of all CLL samples (yellow box) and nearly all B-ALL samples (green box). The control samples also clustered into a tonsil (purple box) and T-and B-cell (blue box) group. The cluster analysis also revealed a cluster containing all FL samples (red box).

Table II.  Stepwise discriminant analysis indicates that three members of the IAP family are the most informative genes for distinguishing different lymphoid disease types and controls.
RankGene (Ln)Partial R2
  1. Stepwise discriminant analysis was performed on the expression data from apoptosis-regulating genes in lymphoid malignancies and control samples using sas version 8 statistical analysis software. The analysis was performed on 123 samples; 14 samples were excluded because the expression level of one or more genes was not available for this sample. Ten rounds of analysis were performed, in which each round selected the variable (gene) that was most informative for discriminating the samples in addition to the selected variables in the previous rounds. This resulted in a ranking order of variables that discriminated best between the different disease and control groups. The partial R2 is given as a measure for the amount of variance that is explained by each gene, taking into account previous genes in the ranking order. The stepwise discriminant analysis identified three IAP family members, C-IAP2, SURVIVIN and C-IAP1 as the genes with the most discriminative power


Figure 2. CLL, B-ALL and control samples can be distinguished from NHL samples by cluster analysis of apoptosis-regulating genes. Unsupervised hierarchical cluster analysis was performed on the log-transformed expression data of the apoptosis-regulating genes that showed the most discriminatory power in the (supervised) stepwise discriminant analysis (Table II). Each horizontal row represents an individual patient or control sample. Each vertical column represents the expression of the indicated gene, in which the intensity of the red or green colour is a measure for high or low expression respectively. Average expression is indicated in black and expression levels that were not determined are in grey. The dendrogram (left-hand side) was generated by joining the samples with the highest similarity in gene expression. The horizontal distance between samples in the dendrogram is a measure for the similarity in gene expression. A shorter distance between two samples correlates with a higher similarity in gene expression. Cluster analysis of BCL-2, C-IAP1, C-IAP2 and SURVIVIN expression resulted in tight clustering of the CLL (yellow box) and ALL samples (green box), which indicates a high similarity in expression between the samples in the respective groups. A cluster of FL (red box) was also revealed. Tonsil samples (purple box) and normal T- and B-cells (blue box) also had a highly similar expression profile. The individual samples from the other diseases were not very similar in expression of the apoptosis-regulating genes indicated by a dispersed distribution in the dendrogram.

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When the expression of only the three most informative IAP genes was taken into account, the groups that showed high similarity in expression in the cluster analysis (B-ALL, CLL, FL, tonsils, B and T cells; Fig. 2) could clearly be distinguished from each other (Fig. 3A). This indicated that an expression signature of three IAP genes could define several lymphoid malignancies. By determining the ratio between the two most discriminatory genes, we found significant differences between several disease types (Fig. 3B). The C-IAP2/SURVIVIN ratio was significantly higher in CLL than in any of the other diseases or control tissues (P < 0·001). Both T- and B-ALL had lower C-IAP2/SURVIVIN ratios compared with the other lymphoid malignancies (P < 0·05). The NHL types had intermediate ratios, with relatively higher ratios for SMZL and FL and lower ratios for BL (mutually significant with P < 0·001).


Figure 3. Analysis of a limited set of apoptosis genes shows that CLL, ALL and FL samples can be defined primarily by IAP genes. (A) Expression of three IAP genes distinguishes CLL, B-ALL, FL and control samples. The graph depicts the natural logarithmic value (Ln) of expression of the three IAP genes that were the most informative genes for distinguishing different disease types and control groups. The expression levels for SURVIVIN (x-axis), C-IAP2 (y-axis) and C-IAP1 (z-axis) are shown in the samples from CLL, B-ALL and FL patients and control samples from tonsils, T and B cells. These groups exhibited distinct expression patterns in the cluster analysis (Fig. 2) and could even be distinguished from each other based on the expression of three genes from a single apoptosis-regulating gene family; the IAP family. (B) Differences in C-IAP2/SURVIVIN ratio between CLL, ALL and NHL samples. For each individual sample the ratio between C-IAP2 and SURVIVIN expression was determined and the logarithmic values are depicted per disease group. The dash represents the median per disease group. Distinctly high C-IAP2/SURVIVIN ratios were found for CLL, whereas ALL samples had low ratios. The C-IAP2/SURVIVIN ratios for NHL samples had intermediate values.

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  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

As members of the BCL-2 family and the recently identified IAP family are involved in chromosomal translocations that occur in NHLs, we investigated the expression of these gene families in several NHL types and other lymphoid malignancies. We chose a real-time quantitative PCR strategy because it is a very specific and sensitive method (with a large dynamic range of several logs) that may reveal differences in expression of genes that are expressed at low levels. Cluster analysis of microarray data has been used successfully to generate expression profiles that characterise several types of lymphoid disease and is also applied to generate expression profiles that predict clinical outcome in leukaemias and lymphomas (Staudt, 2003). In this study, we combined real-time quantitative PCR with cluster analysis to investigate whether expression profiles could be defined in lymphoid malignancies based on the expression of genes of the apoptosis pathway.

By simultaneously analysing a larger panel of apoptosis-regulating genes we found that many of the investigated lymphoid diseases showed a remarkable similarity in expression, which resulted in clustering of the individual samples in the cluster analysis. CLL, B-ALL and FL had specific expression patterns, on the basis of which they could be distinguished from all other disease types and controls.

B-ALL consists of different subtypes of aggressive leukaemia. Despite the heterogeneity in our B-ALL samples (consisting of pro-B-ALL, common B-ALL, pre-B-ALL and unspecified B-ALL samples), we found a characteristic expression profile of apoptosis-regulating genes in B-ALL, with low expression of C-IAP2. Cluster analysis confirmed high similarity in gene expression of the B-ALL samples. The B-ALL profile may point to similarity in apoptosis pathways in the progenitor haematopoietic cells from which ALL originates, which is distinct from the other lymphoid malignancies that originate from more mature B-lymphocytes.

Recently, profiling of FL biopsies revealed that prognostically important gene expression patterns could be discerned (Dave et al, 2004). In fact, these signatures reflected the presence or absence of tumour-infiltrating immune cells in the sample. The signatures described here may partially have been influenced by non-malignant cells in the sample. This may particularly be true for the MZL, SMZL and DLBCL, as these samples sometimes contained significant amounts of non-malignant cells. For the CLL, B-ALL and T-ALL samples, this is less likely, as these samples were all more than 90% pure when assessed by flowcytometric analysis. Furthermore, contamination with a few percent of non-malignant cells cannot explain the under-representation of genes, such as SURVIVIN expression, in CLL.

The CLL cells are characterised by a low rate of proliferation and prolonged life span, which suggests that defective apoptosis pathways, rather than aberrant cell cycle progression, are important for the pathogenesis of CLL (Reed, 1998; Caligaris-Cappio & Hamblin, 1999). Our data indicate that a small set of apoptosis-regulatory genes characterises CLL. The remarkably low expression of SURVIVIN and high expression of C-IAP2 in CLL resulted in a high ratio between C-IAP2 and SURVIVIN. This ratio absolutely discriminated CLL from all other lymphomas, leukaemias and control tissues. In this study CLL samples were singled out as a disease entity with a very specific expression signature of apoptosis-regulating genes that could be narrowed down to three genes belonging to a single family of inhibitors of apoptosis. High expression of IAP family members C-IAP1, C-IAP2 (and XIAP) has been reported before in CLL and was suggested to be part of an anti-apoptotic profile mediated by tumour necrosis factor receptor-associated factor 1 (TRAF1) and, to a lesser extent, TRAF2 (Munzert et al, 2002). Microarray approaches have also shown that CLL samples exhibited a common and characteristic gene expression profile (Klein et al, 2001; Rosenwald et al, 2001; Jelinek et al, 2003), but these were based on the expressions of thousands of genes. By focussing on apoptosis-regulatory genes we were able to identify the discriminatory power of IAP family genes for this type of disease. In our study BCL-2 family genes were shown to be less characteristic for the expression profiles of different lymphoid malignancies. This does not mean that the BCL-2 family is not important for CLL. Indeed, recent reports show high constitutive expression of several members of the BCL-2 family in CLL and indicate that induction of apoptosis correlates with changes in expression of some BCL-2 homologues (Sanz et al, 2004; Mackus et al, 2005).

In FL, dysregulation of the Bcl-2 apoptosis pathway is thought to be involved in malignant transformation. We found high similarity in expression between the FL samples in the cluster analysis of the four best discriminatory genes. These data indicated that FL is characterised by an apoptosis expression signature, but members of the IAP family, rather than BCL-2 family genes, define the FL-specific profile, suggesting that IAPs might be important regulatory proteins in this type of lymphoma.

In control B- and T-cell samples all genes except SURVIVIN were highly expressed, whereas tonsil samples showed low expression levels of all genes. These distinct expression levels in control samples were found for both anti- and pro-apoptotic genes and therefore the apoptotic balance may be set at different levels in the controls compared with the patient samples. The high variation in expression of the tonsil samples may reflect the presence of different cell types in the tissue.

The other lymphoid malignancies did not exhibit specific apoptotic expression profiles. These disease groups may contain several subtypes that differ in the expression of apoptosis-regulating genes, e.g. the three distinct disease entities of DLBCL revealed by microarray profiling (Alizadeh et al, 2000; Rosenwald et al, 2002; Shipp et al, 2002). Other lymphoid malignancies may be characterised by apoptosis genes other than those tested in this study or by genes from pathways other than apoptosis, e.g. the cell proliferation profile of MCL (Rosenwald et al, 2003).

Our data implicate that the expression of three genes belonging to the IAP family, C-IAP2, SURVIVIN, C-IAP1, discriminates CLL, B-ALL and FL from other T- and B-cell malignancies. These three IAP genes provide more information for the classification of lymphoid samples than the BCL-2 family genes. The IAP expression profile found in this study should be confirmed in a larger, independent group of CLL, B-ALL and FL. It would also be interesting to investigate whether IAP expression correlates with clinical outcome or therapeutic efficiency in lymphomas.

In recent years, many studies have revealed links between members of the IAP family and the development of haematological cancer (Liston et al, 2003; de Graaf et al, 2004). Both c-IAP1 and c-IAP2 can promote survival through multiple mechanisms, including caspase inhibition, proteosomal degradation and nuclear factor-κB activation (Liston et al, 2003). High levels of cIAP-2 and c-IAP1 could therefore enhance apoptosis resistance, which may contribute to the development of lymphoid malignancies. Survivin is involved in apoptosis inhibition as well as cell cycle regulation (Giodini et al, 2002; Altieri, 2003) and was shown to be a tumour marker with an expression limited to cancer cells (Ambrosini et al, 1997). Furthermore, SURVIVIN expression is an unfavourable prognostic factor in several cancers (Altieri, 2003). We indeed found high levels of SURVIVIN expression in many of the disease types compared with the control samples. However, MZL, SMZL and especially CLL exhibited low SURVIVIN expression, which underlined previous findings that SURVIVIN was not expressed in some low-grade lymphomas (Ambrosini et al, 1997; Granziero et al, 2001).

Combining real-time quantitative PCR analysis of a limited set of genes with cluster analysis enabled us to focus on the pathway of apoptosis and to pinpoint apoptosis-regulating genes that discriminate several types of lymphoid malignancies. This approach may also be useful for investigating the discriminatory power of genes in other pathways. In our study the expression of IAP family members characterised different lymphoid malignancies. If IAP genes play a role in the disrupted balance of apoptosis in lymphomas, they might be targets for new therapeutic strategies. Future studies investigating IAPs may enhance our understanding of the role of aberrant apoptosis pathways in malignant transformation of these diseases.


  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

We would like to thank Dr H. Straatman (Department of Epidemiology and Biostatistics, University Medical Centre Nijmegen, the Netherlands) for assistance with the statistical analysis and Dr J.W. Gratama (Department of Internal Oncology, Erasmus Medical Centre, Rotterdam, the Netherlands) for his kind gift of CLL patient material.


  1. Top of page
  2. Summary
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
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