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Keywords:

  • mantle cell lymphoma;
  • blastoid variant;
  • array analysis;
  • differential expression

Abstract

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Summary. Mantle cell lymphoma (MCL) is a non-Hodgkin's lymphoma of B-cell lineage. The blastoid variant of MCL, characterized by high mitotic rate, is clinically more aggressive than common MCL. We used the cDNA array technology to examine the gene expression profiles of both blastoid variant and common MCL. The data was analysed by regression analysis, principal component analysis and the naive Bayes' classifier. Eight genes were identified as differentially deregulated between the two groups. Oncogenes CMYC, BCL2 and PIM1 were upregulated more frequently in the blastoid variant than in common MCL. This implied that the gp130-mediated signal transducer and activator of transcription 3 (STAT3) signalling pathway was involved in the blastoid variant transformation of MCL. Other differentially deregulated genes were TOP1, CD23, CD45, CD70 and NFATC. By using the eight differentially deregulated genes, we created a classifier to distinguish the blastoid variant from common MCL with high accuracy. We also identified 18 genes that were deregulated in both groups. Among them, BCL1, CALLA/CD10 and GRN were suggested to be oncogenes. The products of RGS1, RGS2, ANX2 and CD44H were suggested to promote tumour metastasis. CD66D was suggested to be a tumour suppressor gene.

Mantle cell lymphoma (MCL) is a non-Hodgkin's lymphoma of B-cell lineage. The malignant cells are derived from immature CD5+ virgin B cells in the mantle zone of lymphoid follicles. MCL is characterized by small to medium-sized lymphocytes with scant cytoplasm and nuclei of slightly irregular shape. In addition to common MCL, a large cell or blastoid variant of the disease has been described (Jaffe et al, 1987; Lardelli et al, 1990; Fisher et al, 1995). The blastoid variant is characterized by high mitotic rate of the tumour cells and poor prognosis in patients irrespective of cytomorphological subclassifications (Jaffe et al, 1987; Zukerberg et al, 1993; Ott et al, 1994; Fisher et al, 1995).

MCL is closely associated with the t(11;14)(q13;q32) chromosome translocation. At the molecular level, this aberration leads to a rearrangement of the BCL1 locus, resulting in the over-expression of the cyclin D1 gene. A recent study by Yatabe et al (2000) showed that 85% of MCL cases were cyclin D1 positive. The deletion of chromosome bands 11q22-q23 and the inactivation of the ATM gene are also characteristics of MCL (Monni et al, 1999; Schaffner et al, 2000; Zhu et al, 2000). P53 mutations were found in some MCL cases with blastoid variant morphology and poor prognosis (Greiner et al, 1996). The blastoid variant has also been reported to contain an increased number of chromosomal imbalances, high-level DNA amplifications and the tendency of being tetraploid (Ott et al, 1997; Beàet al, 1999). Abnormalities of the cyclin-dependent kinase inhibitors p16INK4a and p21Waf1 have also been found in blastoid variant cases (Pinyol et al, 1997, 1998). However, the mechanism and genetic background of the transformation from common MCL to blastoid variant are largely unknown.

In this study, we used the cDNA array technology to examine the gene expression profiles of both the common MCL and its blastoid variant.

Samples

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Eighteen patients, diagnosed with and treated for mantle cell lymphoma in the Helsinki University Central Hospital between May 1983 and February 1997, were included in the study. The age of the patients at diagnosis ranged from 51 to 87 years (median, 65 years). There were 11 men and seven women. Fourteen out of the 18 patients (78%) were at Ann Arbor stage III–IV at diagnosis. All the samples used in this study were taken at diagnosis before any treatment. The lymphomas were histologically examined and classified by one pathologist, according to the Revised European–American Classification of Lymphoid Neoplasms (REAL). Immunohistochemistry was performed on paraffin and/or fresh sections, using either the biotin–avidin method or the streptavidin–biotin method. Nine lymphomas were classified as common MCL and nine were blastoid variants. Patient details are summarized in Table I. All but one sample were tumour tissues from the lymph node and sample number 14 was from the spleen. Because the pathologist confirmed that the tumour cell population was more than 90% in all samples, no tumour cell enrichment was performed.

Table I.  Patient information.
Patient numberAge (years)Sex Type*cyclin D1Other immunophenotypic markers Stage IPI TTF§ OS
  • *

    BV refers to blastoid variant and C refers to common MCL.

  • Ann Arbor stage.

  • IPI: international prognostic index.

  • §

    TTF: time to treatment failure (in months).

  • OS: overall survival (in months).

161FBV+CD5+, IgD, IgM+, λ+, κ+, CD19+, CD20+213·173·17
266MBV+CD3, CD5+, IgD+, IgM+, CD20+, CD23, CD30, KI-67 > 80%4NA10·6410·64
365MBV+CD5+, CD19+, CD20+, CD10, CD79b+, FMC7+443·374·86
474FC+CD3, CD5+, IgD+, IgM+, λ+, κ, CD19+, CD20+, CD23, CD45+4413·2916·43
579FC+CD5+, IgD, IgM+, λ, κ+, CD19+, CD20+, LCA+447·077·07
687MC+CD5+, IgD weak, IgM+, CD231254·5554·55
775FBV+IgD weak, IgM+, λ+, CD19 weak, CD20+, LCA+444·035·49
854MC+CD3, CD5+, IgD+, IgM+, λ+, κ, CD19+, CD20+, CD23, CD43+, CD45+1157·3965·88
963MC+CD5+, IgD+, IgM+, λ+, κ, CD19+, CD20+4334·7453·42
1065MC+CD5+, IgD+, IgM+, λ+, CD19+, CD20+, LCA+447·7721·92
1164FC+CD5+, IgD+, IgM+, λ+, κ, CD19+, CD20+443·975·22
1261MBV+CD5+, IgD, IgM+, λ+, CD19+, CD20+4222·2149·36
1351MBV+CD5+, IgD+, IgM+, λ, κ+, CD10, CD19+, CD20+, CD23, CD43, CD45+, LCA+423·8019·87
1479FBV+CD5+, IgD+, IgM+, κ+, CD19+, CD20+, LCA+, DBA444414·8118·64
1568FBVCD5, IgD+, IgM+, λ, κ+, CD19+, CD20+, CD45+, LCA +, KOT11219·0247·60
1674MBV+CD5+, CD10, IgD+, IgM+, λ, κ+, CD19+, CD20+, CD23, CD45+, KI-67 > 30%4313·0613·06
1769MC+CD5+, IgD+, CD23, λ+, κ4NANANA
1860MC+CD3, CD5+, IgD+, IgM+, λ+, κ, CD19+, CD20+, CD23, CD45+4218·1525·65

Atlas human haematology/immunology cDNA expression array

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

The Atlas human haematology/immunology cDNA expression array (7737–1) was purchased from Clontech Laboratories. The array has 406 human cDNA clones, nine housekeeping cDNA clones and negative controls immobilized in duplicate dots on a nylon membrane. A list of the genes spotted on the array, including array co-ordinates and GenBank plus SwissProt accession codes, is available (http://www.clontech.com/atlas/genelists/index.shtml, Human Hematology 7737–1).

cDNA synthesis and hybridization

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Total RNA (3–4 µg) was reverse transcribed into cDNA, using the primers provided with the array by Clontech Laboratories, and labelled with 33P-dATP. Probe purification and hybridization to the array were performed following the manufacturer's instructions. The array was exposed to an imaging plate (BAS-MP 2040S; Fuji, Tokyo, Japan) at room temperature for 3–5 d and scanned by a phosphorimager (Bio-Imaging Analyser, BAS-2500; Fuji). The results were quantified and analysed using the atlas image 1·5 software (Clontech). The background was set at the median intensity of the blank space between the different panels of the array. Saturation of the hybridization signal was not observed. All filters used were from the same batch to minimize printing variations between the batches. To estimate the interexperimental variability, we performed duplicate experiments on three samples.

Regression analysis. Expression data from two array experiments were logarithmically (10log) transformed and used to generate an xy-scatterplot, using the microsoft excel 2000 software. The x-co-ordinate of a dot represents the 10log value of the intensity of one gene in the reference array experiment, and the y-co-ordinate represents the 10log value of the intensity of the same gene in the sample array experiment. The regression line

  • image

of the xy-scatterplot and the Pearson correlation coefficient were generated using excel.

Standard deviation of vertical distances between the dots and the regression line was calculated. Two lines, which were equivalent to vertically moving the regression line upwards and downwards by 1·96 times the standard deviation units, were generated. The area defined by these two lines was the 95% confidence interval (95% CI). Spots beyond this area were identified as abnormally expressed genes (Fig 1) (Smid-Koopman et al, 2000). We compiled the results from the 18 samples versus reference analyses, and genes that were abnormally expressed in more than three samples were identified as deregulated.

image

Figure 1. The xy-scatterplot of the array results from one MCL patient and the B-cell reference. Each dot represents one gene whose co-ordinates were its expression levels in the two experiments. The middle line is the trendline y = ax + b, where a and b were calculated to be 0·899 and 0·4829 respectively. The Pearson coefficient was 0·6106. The upper and lower lines are the 95% confidence boundaries. Dots beyond the boundaries represent genes with abnormal expression levels.

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Principal component analysis (PCA). We applied the PCA method to score the genes according to their expression levels. PCA is a linear signal decomposition technique that summarizes the data as a linear combination of an orthonormal set of vectors. The idea is that a representation given by k first principal components explains the maximum amount of variance possible with k linearly transformed components.

Principal components can be estimated by first choosing the centre of gravity in the data as the origin. This may be achieved by first subtracting the background intensity from the intensity of each gene, followed by the subtraction of the intensities of the reference genes, then the mean value of the data set was subtracted from each data point. To give equal weighting to all cases, the data in each case must be standardized to unit variance by dividing all the values by the standard deviation. The covariance matrix is estimated from the data and the principal components are given by eigenvectors and associated eigenvalues of the covariance matrix. The first principal component is the eigenvector that has the largest eigenvalue of the covariance matrix. The first principal component was interpreted as the average expression levels of the genes (Hilsenbeck et al, 1999). Each gene was scored based on its first principal component projection.

Naive Bayes' classifier. Naive Bayes' classifier has been widely used in diagnostic applications (Duda et al, 2001). The inherent assumption in the naive Bayes' classifier is that the input variables are conditionally independent of the outcome. In other words, we assume that the gene expression measurements of each gene in each class are distributed according to normal distribution (Fig 2). With the expression data, we can estimate the parameters of the normal distributions. The parameters for the class distribution are the mean vector and the diagonal covariance matrix. The parameters were estimated by the method of maximum likelihood, which assigns the average value and the diagonal covariance matrix of the samples to the mean vector and the covariance matrix of the class densities respectively. Once the parameters of the classifier have been estimated, the model can be used for classification. To minimize the probability of misclassification, the classifier should assign each case to a class that maximizes the posterior class probability of that case. The probabilities can be calculated with the Bayes' rule, the most fundamental law of pattern recognition. If we were only interested in the assignment of patients to classes, we could apply a corresponding discrimination function that performs the assignment.

image

Figure 2. The Naive Bayes' classifier is demonstrated for NFATC. Blastoid variant cases are marked with crosses and common MCL cases with diamonds. The bell-shaped curves are the corresponding probability density functions for the classes. The vertical line marks the decision border to minimize the probability of misclassification. In this example, some of the samples were misclassified. Use of multiple genes markedly improved the classification accuracy.

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In order to assess how well the classifier would operate in a clinical setting where cases with unknown diagnosis are encountered, we applied a leave-one-out cross-validation method (Golub et al, 1999). We assume that there are n data points available for training. In leave-one-out cross-validation, we estimate a classifier based on n-1 data points and assess its accuracy with the nth data point, and repeat this effort n times with all possible divisions, to assess the accuracy of the classifier. The cross-validated test error is the average of all the errors made during the testing phase. To check whether the classification results were significant, we performed the following randomization experiment. The class labels of the observations were randomly permuted and a classifier was derived using the same method as for the original data. The accuracy of the classifiers with or without class label permutation was then compared. The procedure was repeated 10 000 times.

Confirmation by real-time polymerase chain reaction (PCR) analysis

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Purified RNA (820 ng) was reverse transcribed by using the first-strand cDNA synthesis kit for reverse transcription (RT)-PCR (AMV) (Roche Diagnostics, Indianapolis, IN, USA). Real-time PCR was performed using the LightCycler rapid thermal cycler system (Roche Molecular Biochemicals, Mannheim, Germany), according to the manufacturer's instructions. Four genes were tested, namely AF17, ADA, RGS2 and CMYC. The primers were designed by TIB Molbiol (Berlin, Germany) (Table II).

Table II.  Sequences of primers applied in the real-time PCR analysis and their corresponding Tm values.
Gene Primer sequenceTm (°C)
AF17ForwardTgCTggAggTggACAACg58·5
ReverseTCTCCTgCTgCgTggATg57·8
RGS2ForwardgACCTgCCATAAAgACTgACCTT57·5
ReverseTTTAgggAAAgATgTACAACCAgC57·2
ADAForwardCCAgAAgATgAAAAgAgggAgC57·4
ReverseggACACATAgggTTCAggAgC57·1
CMYCForwardggCAAAAggTCAgAgTCTgg56·6
ReversegTgCATTTTCggTTgTTgC56·5

Reactions were performed in 10 µl volume with primer concentrations of 0·5 µmol/l each, Mg2+ concentrations optimized between 2 and 5 mmol/l, and 1 µl of cDNA. Nucleotides, FastStart Taq DNA polymerase and buffer were included in the LightCycler-FastStart DNA Master SYBR Green I kit (Roche Molecular Biochemicals). To confirm amplification specificity, the PCR products were subjected to melting curve analysis and subsequent agarose gel electrophoresis.

Regression analysis

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Forty-nine genes were identified as deregulated using the regression analysis. Thirty-seven genes were found to be deregulated in both common and blastoid variant MCL with frequencies ranging from 22% to 100%(Table III). Twelve genes were found differentially deregulated between common and blastoid variant MCL (Table IV).

Table III.  Genes found to be deregulated in both common and blastoid variant MCL by either regression analysis or PCA.
Method Gene
Regression analysisUpregulatedSCYA21, GPR13, IL6R/CD126, IL7R/CDW127, IL4R/CD124, SPN/CD43, BCL1, MYH11, PBX1, AF17, AML1, ENL, MVP, IRAP, CX3C, MIG, TNFC, CD5, ANX2, CD44H, TP12/CD6, TCF7,ILF2, PAX5, AREB6, DAPK1, SKY, SLP76, P1, GRN, GZMK
DownregulatedEBI2, ADA, CALLA/CD10, RGS1, RGS2, CD66D
PCAUpregulatedSCYA21, HLAC, CD5, GRN, BCL1, TCF7, TUBA1, AF17, CD44H, ANX2, TNFC, YWHAZ, GZMK, SLP76, PIM1, MIG, GPR13, KIAA0053, EVI2B, TP12/CD6
DownregulatedRGS1, RGS2, IRF1, ADA, CALLA/CD10, EBI2, GAPDH, CD66D, RBTN2, IL12R, VEGFR3, ARHH, LCP1, CD83, CD23, CIITA, PTPN7, C3DR, CD125, THPO
Table IV.  Genes found to be differentially deregulated in common and blastoid variant MCL.
GeneFrequencies in common MCLFrequencies in blastoid variant MCLBiological functionSuggested role in MCL
  • *

    Identified by regression analysis as differentially deregulated, but not by the naive Bayes' classifier.

Upregulated genes
CMYC 044%Major cell cycle regulator (G1 to S transition); apoptosis activator.Oncogene
BCL233%56%Anti-apoptotic oncogeneOncogene
TOP167%11%Relaxes DNA supercoils by cleaving a single strand of duplex DNA and passing the complimentary DNA strand through the cleaved strand before religation.Not known
IL1B*11%44%Interleukin-1β precursorNot known
CD4556% 0A transmembrane protein tyrosine phosphatase; essential for the activation of T and B cells; involved in the integrin-mediated adhesion and migration of immune cells; negatively regulates interleukin 3-mediated cellular proliferation, erythropoietin- dependent haematopoiesis, and antiviral responses in vitro and in vivo.Tumour suppressor
CD58*44%67%CD58 antigenNot known
OX40L*11%44%OX40 ligandNot known
CD7022%56%Belong to the tumour necrosis factor superfamily. Together with its receptor CD27, they act as a checkpoint in lymphocyte survival early after antigenic stimulation.Oncogene
NFATC56%11%Transcription factor that plays a central role in inducing gene transcription during the immune response.Functions through downstream targets
PIM1 056%Cytoplasmic serine/threonine kinase; regulator of bcl2; promotes cell proliferation and prevents apoptosis.Oncogene
Downregulated genes
CD2333%67%A low-affinity receptor for IgE; cell surface marker that is negative in MCL but positive in small lymphocytic lymphoma/chronic lymphocytic leukaemia (SLL/CLL); also used to distinguish transformed SLL/CLL from MCL blastoid variant.Not known
C3DR*22%33%Complement C3D receptorNot known

Principal component analysis

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Based on scores obtained from the PCA projection of gene expression data, genes with the highest and lowest scores were considered to be deregulated (Fig 3). We chose 20 genes with the highest scores and 20 genes with the lowest scores (Table III), as Fig 3 indicated that the two sets of 20 genes from each end had scores that were very different from the rest of the genes.

image

Figure 3. PCA projection of gene expression scores sorted for the 415 genes. Downregulated genes are on the leftmost part of the figure and upregulated genes are on the rightmost part of the figure. Dashed vertical lines mark the limits for 20 genes.

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Genes deregulated in both common and blastoid variant MCL

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

After comparing the 37 genes obtained from the regression analysis and the 40 genes from PCA, we identified genes that both analyses identified as deregulated in common and blastoid variant MCL (Table V). The genes that may have pathogenic roles in MCL were BCL1, CALLA/CD10, GRN, RGS1, RGS2, ANX2, CD44H and CD66D.

Table V.  Genes deregulated in both common and blastoid variant MCL.
GeneBiological functionsSuggested role in MCL
Upregulated genes
SCYA21CC chemokine attracting T cells, mature dendritic cells and natural killer cells; plays an important role in controlling lymphocyte-endothelial cell recognition and lymphocyte recruitment in vivo.Normal body function
GPR13Probable G-protein-coupled receptorNot known
BCL1Major cell cycle regulator (G1 to S transition)Oncogene
AF17Fusion partner gene of MLL; conserved with septin family genesNot known
MIGCXC chemokine attracting tumour-filtering T cells; involved in the regulation of lymphocyte recruitment and the formation of the lymphoid infiltrates observed in autoimmune inflammatory lesions, delayed-type hypersensitivity, some viral infections, and certain tumours.Normal body function
CD5T-cell surface glycoprotein CD5 precursorNot known
ANX2Endothelial cell-surface Ca2+ -binding proteinTumour metastasis promoter
CD44HMajor cell surface receptor for hyaluronic acidTumour metastasis promoter
TP12/CD6T-cell differentiation antigen CD6 precursorNot known
TCF7T cell-specific transcription factor 1Not known
GRNSupports tumorigenesis in some cell models and is the only growth factor able to overcome the cell cycle block that occurs in murine fibroblasts after deletion of a functional IGF-1 receptor.Oncogene
GZMKGranzymes are major components of cytosolic granules that play important roles in the secretary pathway of T and natural killer cell-mediated cytotoxicity against virally infected host cells and tumour cells.Normal body function
Downregulated genes
EBI2Epstein–Barr vrius-induced G-protein-coupled receptorNot known
ADAADA deficiency is the cause of one form of severe combined immunodeficiency diseaseNot known
CALLA/CD10Mammalian zinc-endopeptidase; inhibition of neprilysin results in increased proliferation and maturation of B cells.Oncogene
RGS1Regulator of G-protein signallingTumour metastasis promoter
RGS2Regulator of G-protein signallingTumour metastasis promoter
CD66DCarcinoembryonic antigenTumour suppressor

Classification by naive Bayes' classifier

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

By using the 12 genes found to be differentially deregulated between common and blastoid variant MCL by the regression analysis, we were able to correctly classify 14 of the 18 patients. Therefore, the cross-validated test error of the classification was 0·222. For permuted data sets, better results were found only in 280 trials out of 10 000 randomization experiments, which corresponded to a significance level of 0·028.

We decided to investigate whether we could improve the classification by using subsets of the 12 genes. This could not only increase the accuracy of the classifier, but also be a way to identify and confirm the genes that are indeed differentially deregulated between common and blastoid variant MCL. We tested gene sets consisting of 1–11 genes with different gene composites. The best classifier was a set of eight genes (Table IV). The mean classification error was 0·111. Randomization results showed that 18 out of 10 000 trials yielded better results, which corresponded to a significance level of 0·018. Therefore, we concluded that these eight genes were significant in separating the blastoid variant from the common MCL. The most interesting genes among them were CMYC, BCL2, PIM1, CD45, CD70 and NFATC.

Real-time PCR analysis

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

To confirm the cDNA array results, we performed real-time PCR analysis. Because the amount of RNA was limited, we were able to analyse only four genes, AF17, ADA, RGS2 and CMYC, on some of the samples. Eighty per cent of the results were in accordance with the array analysis results (Table VI).

Table VI.  Concentration ratios of selected genes in MCL samples and the reference in real-time PCR analysis.
GeneAF17RGS2ADACMYC
PCRArrayPCRArrayPCRArrayPCRArray
  • *

    Numbers 1–14 refer to MCL samples 1–14.

  • †Concentration ratios of the gene in the MCL sample versus the B-cell reference. The concentrations were calculated using the β-globulin standard by the lightcycler data analysis program.

  • norm., normal expression; up, upregulated; down, downregulated; ND, not determined.

1*4·16up0·116down0·128downNDnorm.
24·43up0·023down0·095down4·59norm.
32·03up0·006down0·288down43·64up
45·58up0·0006down0·114down2·22norm.
513·32up0·058down0·395downNDnorm.
61·40up0·076down0·157down1·04norm.
72·92up0·029down0·422downNDnorm.
82·00up0·058down0·378down1·52norm.
91·22up0·043down0·148norm.3·81norm.
101·44up0·018down0·198downNDnorm.
110·27up0·007down0·085downNDnorm.
123·51up0·003down0·344down3·44norm.
133·00up0·057down0·111down8·05up
140·86up0·011down0·134downNDup

Discussion

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

In this study, we used the cDNA array technology to study the gene expression profiles of common and blastoid variant MCL. By using various data analysis methods, we identified marker genes for MCL and marker genes that could distinguish blastoid variant cases from the common MCL cases. These findings will improve our knowledge of MCL and its blastoid variant transformation. The methods and data analysis strategies used here are applicable in other array studies.

References

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

CD19-positive B cells pooled from normal adenoid palatine tonsils were used as the reference. There were issues that needed to be considered when interpreting the results: (1) normal tonsil B cells are CD5-negative unless they are strongly activated; (2) CD19 is a pan-B-cell marker that is not only expressed in mantle zone B cells, but also in germinal centre and marginal zone B cells.

Two statistical methods were always used in choosing the marker genes: regression analysis plus PCA in choosing genes deregulated in both common and blastoid variant MCL, and regression analysis plus naive Bayes' classifier in choosing genes differentially deregulated between the two groups. The combined use of two different statistical analysis methods yielded only a handful of deregulated genes, which should be considered as the most significantly deregulated ones, and the influence from the choice of reference was minimized. However, we should be aware that some genes not chosen by this stringent rule might also have important pathogenic roles in MCL. Therefore, other genes in Table III deserve some attention.

In addition, we should be cautious about the interpretation of three genes that were identified as deregulated in both common and blastoid variant MCL: T-cell surface glycoprotein CD5 precursor gene, T-cell differentiation antigen CD6 precursor gene and T cell-specific transcription factor 1 gene. We used purified B cells as the reference (B-cell population > 95%), whereas the samples are unpurified lymphoma specimens. Because the proportion of the reactive T-cell population in mantle cell lymphoma tumour tissues may range from 5% to 20%, there is a possibility that the overexpression of these genes may be due to T-cell contamination.

Genes differentially deregulated in common and blastoid variant MCL

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

CMYC, PIM1 and BCL2 were upregulated more frequently in blastoid variant cases than in common MCL cases. The product of CMYC is a major regulator of cell growth and CMYC expression is co-ordinately regulated through multiple signalling pathways (reviewed by Kiuchi et al, 1999). One of the pathways of activating the CMYC gene has been shown to be mediated by signal transducer and activator of transcription 3 (STAT3) upon the gp130 stimulation (Kiuchi et al, 1999). The deregulation of the CMYC gene plays a central role in the pathogenesis of lymphoid malignancies as well as in other types of cancer (reviewed by Dang et al, 1999). The cytoplasmic serine/threonine kinase gene PIM1 has been found frequently overexpressed in adult acute leukaemia patients (Amason et al, 1989), implying its role as an oncogene. PIM1 co-operates closely with CMYC in tumorigenesis (Breuer et al, 1989; van Lohuizen et al, 1989, 1991). It is also a target of the gp130-mediated STAT3 signalling, and co-operates with CMYC to promote cell proliferation and cell cycle progression. PIM1 also co-operates with CMYC to induce BCL2 expression and inhibit apoptosis (Shirogane et al, 1999). The simultaneous upregulation of CMYC and PIM1 further demonstrated the close co-operation between them, and suggested that the gp130-mediated STAT3 signalling pathway was involved in the blastoid variant transformation of MCL.

Abnormalities in CD45 and CD70 have been found in other types of lymphoma. Reduced levels of CD45 expression have been found in B-cell chronic lymphocytic leukaemia (B-CLL) patients with poor prognosis (Sembries et al, 1999). Expression of CD70 was found to be expressed in malignant cells of B-CLL, follicular lymphoma, large B-cell lymphoma and MCL (Lens et al, 1999), although CD70 was infrequently expressed in normal human B cells (Lens et al, 1996).

NFATC belongs to the NFAT (nuclear factor of activated T cells) family of transcription factors. In addition to T cells, they are widely expressed in other classes of immune-system cells. Many genes that encode transcription factors, cytokines and cell surface receptors are potential targets of NFAT (Rao et al, 1997).

The reason for the frequent upregulation of TOP1 in common MCL but not in the blastoid variant is not clear. However, point mutations in the TOP1 gene or TOP1 downregulation were found in human cancer cell lines that have acquired resistance to topoisomerase (topo) I inhibitors in vitro (Pommier et al, 1994). It would, therefore, be interesting to determine whether topo I inhibitors could be effective in the treatment of common MCL.

Genes deregulated in both common and blastoid variant MCL

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

Annexin II heterotetramer is known to enhance the conversion of plasminogen to plasmin by tissue-type plasminogen activator (tPA) (Cesarman et al, 1994). It has also been shown to interact with the cystein protease cathepsin B (Mai et al, 2000a) and with extracellular matrix proteins, e.g. collagen-I and tenascin-C (Chung et al, 1996). The interaction between them may enhance tumour cell detachment, invasion and mobility (Mai et al, 2000b). The cell surface molecule CD44 is involved in various important biological events, including tumour metastasis (reviewed by Ponta & Herrlich, 1998). It has also been shown that, in mammary epithelial cells, the vast majority of CD44 interacts with annexin II in lipid drafts (Oliferenko et al, 1999). The simultaneous upregulation of both ANX2 and CD44H in MCL demonstrated their close relationship and possible roles as tumour metastasis promoters.

Regulators of G protein signalling (RGS) increase the intrinsic rate of the hydrolysation of the GTP coupled to the α subunit of the heterotrimeric G protein, leading to the inhibition of downstream signalling of the G protein (Kehrl, 1998). They have been shown to affect the migration of B cells (Moratz et al, 2000; Reif & Cyster, 2000). RGS1 has also been found to inhibit chemoattractant-induced migration and reduce rapid chemoattractant-triggered adhesion (Bowman et al, 1998). Two of the RGS family members RGS1 and RGS2 were found to be downregulated in our MCL samples, suggesting a role in preventing tumour cell invasion and metastasis.

The carcinoembryonic antigen CGM1 precursor (CD66d) belongs to the CD66 family, whose members appear to affect a variety of biological processes, such as the development of cancer (Thompson et al, 1991; Obrink, 1997). While little is known about CD66d, its sibling CD66a is suggested to have tumour suppressing functions in colon carcinoma cell lines (Kunath et al, 1995), as well as in prostate (Kleinerman et al, 1995), breast (Luo et al, 1997) and bladder cancers (Kleinerman et al, 1996). As CD66D was found to be downregulated in our study, the role of CD66D as a tumour suppressor gene in MCL deserves further investigation.

The upregulation of BCL1 is in accordance with the immunohistochemistry study (Table I). The role of cyclin D1 in MCL is well established and it is not discussed further here.

Data analysis

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References

In many array studies, deregulated genes are identified when the ratio of the gene expression level in the sample and the reference exceeds a certain threshold. Unlike the glass-based array, the sample and the reference were hybridized in different experiments to the filter-based array, leading to bigger interexperimental variations. An efficient normalization process was required in order to analyse the data reliably. The regression analysis treats the expression levels of all genes in array experiments as a population and compares it with another population. The coefficients a and b in the regression function

  • image

should, in principle, explain the major interexperimental variations, namely the overall intensity variation caused by the different amounts of RNA used and the background variation caused by different exposure time. In PCA, we subtracted the average value of all gene expression data on an array from the expression data of each gene, then divided this figure with the variance. This is also a simple and efficient way to normalize the data from different experiments.

The regression analysis is, however, rather laborious, especially if the number of samples is large. Another disadvantage is the use of frequencies, which are sensitive to the choice of thresholds. The PCA is more powerful in treating the sample set as a group and is less labour-intensive.

Many creative statistical analysis strategies have been applied in the tumour class prediction and classification field. In our study, we used the naive Bayes' classifier. This method and its variants have been applied to cancer classification (Golub et al, 1999). Although we could classify our samples with high accuracy using the classifier we developed and the classification result was statistically significant, the real value of the classifier can only be shown when new cases can be predicted accurately. However, our approach has shown that a successful classification may shed light on the genes that are differentially expressed between the cancer subtypes. It is a good confirmation tool to verify the genes identified by regression analysis.

In conclusion, we identified genes that might have pathogenic roles in MCL and its blastoid variant, and genes that might be involved in the MCL blastoid variant transformation. We demonstrated the successful use of various statistical analysis methods in array data management. These findings will benefit both the future research of the MCL disease and the development of array technology.

References

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Samples
  5. Reference
  6. RNA isolation
  7. Atlas human haematology/immunology cDNA expression array
  8. cDNA synthesis and hybridization
  9. Data analysis
  10. Confirmation by real-time polymerase chain reaction (PCR) analysis
  11. Results
  12. Regression analysis
  13. Principal component analysis
  14. Genes deregulated in both common and blastoid variant MCL
  15. Classification by naive Bayes' classifier
  16. Interexperimental variability
  17. Real-time PCR analysis
  18. Discussion
  19. References
  20. Genes differentially deregulated in common and blastoid variant MCL
  21. Genes deregulated in both common and blastoid variant MCL
  22. Data analysis
  23. Acknowledgments
  24. References
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