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NEOPLASIA
From Abteilung Molekulare Genetik and Zentrale Einheit
Biostatistik, Deutsches Krebsforschungszentrum, Heidelberg, and
Abteilung Innere Medizin III, Universität Ulm, Germany.
The B-cell lymphoproliferative malignancies B-cell chronic
lymphocytic leukemia (B-CLL) and mantle cell lymphoma (MCL) share characteristics, including overlapping chromosomal aberrations with
deletions on chromosome bands 13q14, 11q23, 17p13, and 6q21 and gains
on chromosome bands 3q26, 12q13, and 8q24. To elucidate the biochemical
processes involved in the pathogenesis of B-CLL and MCL, we analyzed
the expression level of a set of genes that play central roles in
apoptotic or cell proliferation pathways and of candidate genes from
frequently altered genomic regions, namely ATM, BAX,
BCL2, CCND1, CCND3, CDK2,
CDK4, CDKN1A, CDKN1B, E2F1, ETV5, MYC, RB1,
SELL, TFDP2, TNFSF10, and
TP53. Performing real-time quantitative reverse transcription
polymerase chain reaction in a panel of patients with MCL and B-CLL and
control samples, significant overexpression and underexpression was
observed for most of these genes. Statistical analysis of the
expression data revealed the combination of CCND1 and CDK4 as the best
classifier concerning separation of both lymphoma types. Overexpression
in these malignancies suggests ETV5 as a new candidate for a pathogenic factor in B-cell lymphomas. Characteristic deregulation of multiple genes analyzed in this study could be combined in a comprehensive picture of 2 distinctive pathomechanisms in B-CLL and MCL. In B-CLL,
the expression parameters are in strong favor of protection of the
malignant cells from apoptosis but did not provide evidence for
promoting cell cycle. In contrast, in MCL the impairment of apoptosis
induction seems to play a minor role, whereas most expression data
indicate an enhancement of cell proliferation.
(Blood. 2002;99:4554-4561) B-cell chronic lymphocytic leukemia (B-CLL) and
mantle cell lymphoma (MCL) are B-cell non-Hodgkin lymphomas (NHL) of
low and intermediate grades, respectively. Both diseases are types of follicle mantle-derived lymphoproliferative malignancies. B-CLL is the
most common leukemia in adults of the Western world and is associated
with the accumulation of immuno-incompetent B-lymphocytes with low
proliferative activity. These noncycling lymphocytes escape from the
induction of programmed cell death.1-5 A highly variable
clinical course with a median survival time of 7 to 10 years is
characteristic for B-CLL.6 In contrast, patients with MCL
have a median survival of 3 years,7 and MCL is
characterized by the (11;14)(q31;q32) translocation resulting in the
up-regulation of cyclin D1 (CCND1) by the immunoglobulin
heavy-chain enhancer elements.8,9
Both malignancies are characterized by common chromosomal aberrations.
Although MCL is associated with higher complexity of the karyotype,
there are striking similarities between common genetic aberrations in
MCL and B-CLL: deletions on chromosome bands 13q14, 11q23, 17p13, and
6q21 and gains on chromosome bands 3q26, 12q13, and
8q24.10-15 For some chromosomal loci, the affected genes
are identified, such as TP53 on 17p1316,17 and
ATM (ataxia telangiectasia mutated) on
11q23.18-21 However, the molecular mechanisms causing
B-CLL and MCL are still unknown. To determine to which degree cell
cycle progression or impairment of apoptosis induction plays a role in
the pathomechanisms of these neoplasias, we performed a quantitative
expression study of genes whose products have a central function in
both processes. Little is known with regard to quantitative expression
of the respective genes. Quantitative studies in MCL revealed
overexpression of cyclin D122,23 and MYC.24 In B-CLL faint expression of CD95
(TNFSF6) and CD95-R (TNFRSF6),5,25
overexpression of BCL2, steady-state expression of
TP53, and decreased expression of BAX in
progressive disease were reported.26
To obtain a comprehensive view of the activated or deregulated factors
and pathways with oncogenic potential, we analyzed a series of MCL and
B-CLL samples for expression of the following genes, which were
selected based on their localization in altered genomic regions or on
the function of their products in cell cycle and apoptosis control:
ATM, BAX, BCL2, CCND1, cyclin D3
(CCND3), CDK2, CDK4, p21
(CDKN1A), p27 (CDKN1B), E2F1,
ETV5, MYC, RB1, L-selectin
(SELL), DP2 (TFDP2), TRAIL
(TNFSF10), and TP53. Using the real-time
quantitative reverse transcription polymerase chain reaction (RQ-PCR)
technique,27 we identified genes that are overexpressed or
underexpressed in B-CLL and MCL, some of which were previously not
known to be altered in these diseases. The potential impact of our
results on the elucidation of the pathways that contribute to the
pathomechanisms and clinical behavior of B-CLL and MCL is discussed in detail.
Patients
Sample and RNA preparation
Real-time quantitative reverse-transcription PCR Each cDNA sample was analyzed in triplicate (aliquot of 1 µL each) using the ABI PRISM 7700 Sequence Detector (PE Applied Biosystems, Weiterstadt, Germany). Quantitative assessment of DNA amplification was detected either through the TaqMan probes, which are specific for the targeted gene, or through the dye SYBR Green. The TaqMan probe containing 2 dyes, a reporter and a quencher dye, fluoresces on removal of the quencher by the 5'-3' exonuclease activity of the Taq polymerase during PCR, and SYBR Green fluoresces on binding to dsDNA. The RQ-PCR reactions were carried out in a total volume of 50 µL according to the manufacturer's manuals for SYBR Green PCR Core Reagents and TaqMan Universal Master Mix (PE Applied Biosystems). The primer and TaqMan probe concentration were 300 nM and 200 nM, respectively.For thermal cycling, the following conditions were applied: 2 initial incubations of 2 minutes at 50°C and 10 minutes at 95°C, then 40 cycles of 15 seconds at 95°C and 1 minute at 60°C. For SYBR Green PCR reactions, these settings were extended with an initial step of 30 minutes at 37°C and 3 final steps of 15 seconds at 95°C, 15 seconds at 60°C, and 15 seconds at 95°C. The heating rate (ramping) between the last 2 steps was increased to 20 minutes to obtain a melting curve of the final RQ-PCR products (ABI Prism Dissociation Curve Software, PE Applied Biosystems, Foster City, CA). This is necessary because SYBR Green fluorescence may also be derived from side products such as primer-dimers. Oligonucleotides used for RQ-PCR are listed in Table
1. TaqMan probes were labeled by a 5' FAM
reporter and a 3' TAMRA quencher, except for the 5' VIC-labeled
reporter in the TaqMan PreDevelopedAssayReagents (PDAR, PE Applied
Biosystems), which were used as control amplicons to normalize the
template. Primers and TaqMan probes were all designed using Primer
Express software (PE Applied Biosystems).
Data normalization To obtain a calibration graph, cDNA from the B-CLL cell line EHEB was serially diluted 8 times in H2O at a ratio of 2:1 and was measured in every single RQ-PCR run. A single RQ-PCR run consisted of the analysis of one amplicon measured at the different dilutions, the patient and the control samples. The fluorescence threshold (Ct) value is equal to the cycle number when the fluorescence reaches a set threshold. The resultant calibration graph (Ct versus log unit of the standard template [UST], which is a virtual value for the amount of cDNA) correlates Ct values with the amount of template in the PCR reaction. This was performed for every amplicon separately. To standardize the amount of sample cDNA, 4 endogenous control amplicons were used: the housekeeping genes coding for phosphoglycerate kinase 1 (PGK1) and lamin b1 (LMNB1) (SYBR Green), cyclophilin A (PPIA), and hypoxanthine phosphoribosyl-transferase (HPRT1) (TaqMan; PDAR). The average value of all 4 amplicons served as calibrator for a first normalization. Thereafter, the patient data were normalized using the mean value of all control sample data yielding the relative expression of each gene. In the following equation, which summarizes the calculation process, logarithmic values are used; otherwise values between 1 and 0 would be mathematically underrepresented.Equation for data normalization
Statistical data analysis Two approaches were used to analyze the discrimination ability of the gene expression data: (1) using a measure of between-sample separation and (2) using a measure of within-sample homogeneity.We applied cutoff analyses for each gene individually to discriminate
controls from lymphoma samples using maximally selected
In addition, we used the concept of Classification and Regression Trees (CART)31 to classify into the control and the 2 patient groups. The tree is built by recursive partitioning using entropy as a measure of within-node homogeneity. The tree was pruned using 10-fold cross-validation where the pruning criterion is the cross-validated error rate. CART was applied to the expression data of each gene individually and to the full set of genes simultaneously. The 2 genes identified as the most important ones for the 3-group classification in the joint analysis are displayed in a scatterplot describing the partition for the data induced by these genes. All statistical analyses were performed using the software package S-Plus (Insightful, Seattle, WA).
Quantitative expression of the following cell cycle and
apoptosis-associated genes were analyzed by RQ-PCR in series of 19 to
34 B-CLL, 6 to 15 MCL, and 5 to 10 CD19+ control samples
obtained from healthy persons: ATM, BCL2, BAX, cyclin D1
(CCND1), cyclin D3 (CCND3),
CDK2, CDK4, TP53, RB1, p27 (CDKN1B), p21
(CDKN1A), MYC, E2F1, DP2
(TFDP2), ETV5, and TRAIL (TNFSF10) as
well as L-selectin (SELL). Furthermore, because of the known
interrelationships, the ratios for BCL2/BAX and
CCND1/CCND3 were calculated. The data are displayed in
Figure 1, and the results of the
statistical analyses are presented in Table 2.
For the genes ATM, BAX, RB1,
CDKN1A, and TFDP2, there was no statistically
significant discrimination between controls and patients (maximally
selected Analysis of the RQ-PCR data by CART revealed that the expression of CCND1, CCND3, CDK2, CDK4, CDKN1B, E2F1, and TNFSF10 allowed a classification concerning the control, B-CLL, and MCL groups. The respective decision regions and overall error rates are listed in Table 2. A 34.4-fold up-regulation of CCND1 discriminates well between the MCL and B-CLL patients (compare data in Figure 1). For CCND3, down-regulation by a factor of 4.3 separates 47% of the MCL patients in one group. Normalization of CCND1 data using CCND3 as calibrator showed no difference to the CCND1 expression profile. The CDK2 data revealed a group of the MCL patients with more than 3.13-fold up-regulation (53.8%) distinguished from a heterogeneous group of controls and patients below. The CART method indicated 3 groups according to the CDK4 expression level. Controls were separated from the patients by the 2.03-fold increase in expression, and a second cut-off discriminates the MCL patients with a 12.0- to 51.3-fold overexpression (76.9%) from a B-CLL group with 2.03- to 12.0-fold overexpression (84%). Similarly, CDKN1B expression data permitted distinction of these 3 groups, clustering 79% of B-CLL patients and one MCL patient (10%) in one group and 50% of the MCL patients in the second group. For E2F1, the cut-off value of 3.83 allowed separation of 67% of the MCLs. According to the expression level of TNFSF10 one group with 45% MCL patients was indicated. The expression levels of the genes tested, their pathways, and possible
interactions are illustrated in graphic format in Figure
2. The best separation of the 3 sample
groups (B-CLL vs MCL vs control) was achieved when combining the
expression parameters of the genes coding for cyclin D1 and CDK4
(Figure 3).
Despite the increasing knowledge of recurrent genomic alterations and the affected corresponding genes in B-CLL and MCL, the pathomechanism of the 2 diseases is not well understood. Although there is considerable overlap between the genomic regions altered in B-CLL and MCL, the clinical courses are very different and there is more aggressiveness in MCL, which suggests differences in the affected pathways. In general, 2 principal phenomena lead to the accumulation of tumor cells and are known to be crucial for malignant diseases: deficiency in programmed cell death and induction or enhancement of cell cycle progression. To elucidate the biochemical processes involved in the pathogenesis of B-CLL and MCL, we analyzed the expression level of a set of genes whose products are known to play central roles in one of these phenomena or even in the selection of apoptotic versus cell proliferation pathways. Furthermore, we analyzed the expression of new candidate genes, located in the frequently altered genomic region 3q26 (TFDP2, ETV5, TNFSF10) and playing a role in distinct apoptotic or cell cycle control pathways. Although cellular expression profiles can be analyzed on even larger sets of genes using arrays,32,33 the quantitative assessment of gene expression using DNA chips is inferior to RQ-PCR. We here report on the detailed analysis of the expression of 17 genes, revealing the yet unknown aberrant expression of a new candidate gene (ETV5), and we provide strong evidence for highly distinctive pathomechanisms in B-CLL and MCL. The potential impact of most of the observed gene up- and down-regulations on the mechanism of each malignancy is summarized in Figure 2. TP53 and ATM, the genes recurrently mutated in B-CLL and in MCL, function upstream of the immediate cell cycle control. Entry into S phase is tightly associated with the function of the p53 protein, a transcription factor that is stabilized and activated on DNA damage, and in turn it regulates the transcription of a large number of genes, including the p21 inhibitor, which is capable of silencing the cyclin-dependent kinases (CDKs) essential for S-phase entry.34,35 The inactivation of both alleles of the ATM gene by deletion and deleterious point mutations in most of the patients indicated that ATM plays a pathogenic role in MCL.21 P53 and ATM both recognize signals in response to DNA damage and induce repair mechanisms,36 affecting whether a cell continues to proliferate or enters a cell death program. Because there was no deviation of the expression of ATM on the RNA level and the expression of TP53 was heterogeneous, the transcript levels of both genes did not indicate a different use of pathways. In contrast, the RNA level of factors downstream of p53 allowed such an assessment. With respect to the regulation of apoptosis, B-CLL and MCL differ considerably (Figure 2). The antiapoptotic BCL2 oncogene plays an important role in B-cell malignancies. BCL2 is consistently expressed in B-CLL.37 Hanada et al38 found 2- to 25-fold higher levels than in normal lymphocytes. We could show an even higher range of expression for approximately two thirds of the B-CLL patients and modest up-regulation in more than half the MCL patients. The BAX data showed no such significant differential expression levels. Because BAX counters the antiapoptotic effect of BCL2 and promotes apoptosis,39 the high ratio of BCL2 to BAX in the patient groups protects the malignant cells from apoptosis, with stronger dominance in B-CLL. TRAIL (TNF-related apoptosis-inducing ligand) is a member of the TNF ligand superfamily and is capable of inducing apoptosis.40 Interestingly, TRAIL has the potential to kill tumor cells more efficiently than nonmalignant cells.41 Our data indicate an overexpression in 45% of MCL patients that would foster apoptosis but might be antagonized by increased BCL2 levels. Indeed, 3 of the 4 comparable patients showed coactivation of TNFSF10 and BCL2, which would argue against a significant role of apoptosis in MCL. The difference between B-CLL and MCL is even higher with regard to the expression level of factors responsible for cell cycle progression (Figure 2), such as CDKs and coregulators. Cyclin D1 belongs to the G1 cyclins and plays a key role in the cell cycle regulation during G1 phase by cooperating with CDKs.42,43 In MCL, overexpression of cyclin D1 results from the translocation t(11;14), by which CCND1 comes under the control of the immunoglobulin enhancer.44 The MCL samples showed a higher level of cyclin D1 expression than the subset of up-regulated B-CLL samples, indicating a lesser impact in the B-CLL pathomechanism. This is further corroborated by the observed increase in CDKN1B. Interestingly, in B-CLL patients with up-regulation of CDKN1B, 4 of 6 also showed an increase in expression of CDK4. Because p27 antagonizes the proliferative effect of CDK-cyclin complexes, this observation argues further against an enhancement of cell cycle progression in B-CLL. Conversely, MCLs were shown to have increased p27 protein degradation,45 possibly contributing to the up-regulation of CDK-cyclin complexes in this disease. Cyclin D3 functions similarly to cyclin D1 by activating CDK4 and CDK6,46 and it acts together with cyclin D2 as a main regulator for the G1/S phase transition.47 Motokura et al48 found in synchronized HeLa cells that the mRNA levels of cyclin D1 and cyclin D3 were regulated reciprocally throughout the cell cycle. For cyclin D3 we were able to demonstrate lower expression levels for 47% of the MCL patients, which correlates with a cyclin D1-mediated induction of a consecutive down-regulation of cyclin D3.47 CDK4 and CDK6 form complexes with D-type cyclins, the most important substrate of which is RB1 and related pocket proteins. Increased expression in CDK4 is again higher in MCL, corresponding to the higher level of cyclin D1 in this disease. Because CDK2 is also highly expressed in MCL, cyclin-dependent activation of cell cycle progression is a key aspect of the pathomechanism of mantle cell lymphoma. In line with this observation is the inactivation of the CDK4 inhibitor p16 through genomic alterations.49-51 The up-regulation of CDK2, CDK4, and CCND1 is likely to result in the activation of RB1, upon which E2F is released and the transcription of S-phase enzymes is induced.52,53 In line with the activation of this central pathway is the finding of a higher expression of E2F1 transcripts in two thirds of the MCL patient group: E2F1 is a member of the E2F family, which is specifically active in late G1 and the subsequent S phase of the cell cycle.54 Because MYC plays an activating role in CDK-cyclin complexes, mitogen-induced or ectopically expressed MYC also shifts quiescent cells into the S phase.53,55 More than 28% of the MCL patients showed up-regulated expression, and approximately 55% of the B-CLL patients showed down-regulated expression. Thus, the RNA expression level of this gene is again in full agreement with the overall picture emerging from this study, which is that cell cycle progression is the dominant effect in MCL but is only of minor importance in B-CLL. Extensive statistical analysis of the expression data resulted in the identification of genes whose expression provides the best discrimination between the 2 B-cell lymphomas and the control samples. As presented in Figure 3, the best separation of the 3 sample groups was achieved when combining the expression parameters of the genes coding for cyclin D1 and CDK4. Thus, it can be predicted that diagnostic tools for the distinction of MCL and B-CLL, which are based on assessment of the transcript level as shown for cyclin D1 alone,23 would be highly specific if the test is a combination of these 2 markers. Because 3q26 is frequently altered in MCL (48%) and in B-CLL (5%), 3 genes localized within this chromosome band and associated with
apoptotic or cell cycle control pathways were selected for this
study
We are grateful to Gunnar Wrobel (Heidelberg) for helpful discussions and Margit Pförsich (Ulm) for assistance in cell sorting.
Submitted August 13, 2001; accepted February 15, 2002.
Supported in part by grants from the Bundesministerium für Bildung und Forschung (01KW 9935) and the European community (QLG1-CT2000-00687).
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked "advertisement" in accordance with 18 U.S.C. section 1734.
Reprints: Peter Lichter, Abteilung Organisation komplexer Genome (H0700), Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany; e-mail: m.macleod{at}dkfz.de.
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Top
Abstract
Introduction
) were required for
the diagnosis, and variations were observed only in a minority of
patients (in MCL, CD5
for example, CD5 positivity
2 statistics.
2.25 to 9.48, and
taking absolute values yields numbers between 0.02 and 9.48. The upper
limit of the resultant one-sided 99% confidence interval of the
cut-off value for these transformed data was 2.56. With
5.90 = 22.56 this number and its reciprocal value
0.17 ( = 1/5.90) were set as limits of a normal range of
CCDN1 expression to identify significant differential
expression (Table 
.01). No
further P value adjustment was performed to account for
multiple testing.
