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Microarray Gene Expression Profiling of B-Cell Chronic Lymphocytic Leukemia Subgroups Defined by Genomic Aberrations and VH Mutation Status

From the Department of Lead Discovery, Boehringer Ingelheim Austria, Vienna, Austria; Department of Internal Medicine III, University of Ulm, Ulm; Division of Molecular Genetics, German Cancer Research Center, Heidelberg; Institute of Computer Science, Ludwig-Maximilians-Universität, Munich, Germany.

Address reprint requests to Christian Stratowa, PhD, Department of Lead Discovery, Boehringer Ingelheim Austria, Dr Boehringer-Gasse 5-11, A-1121 Vienna, Austria; e-mail: christian.stratowa{at}vie.boehringer-ingelheim.com

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... REFERENCES

 
PURPOSE: Genomic aberrations and mutational status of the immunoglobulin variable heavy chain (VH) gene have been shown to be among the most important predictors for outcome in patients with B-cell chronic lymphocytic leukemia (B-CLL). In this study, we report on differential gene expression patterns that are characteristic for genetically defined B-CLL subtypes.

MATERIALS AND METHODS: One hundred genetically well-characterized B-CLL samples, together with 11 healthy control samples, were analyzed using oligonucleotide arrays, which test for the expression of some 12,000 human genes.

RESULTS: Aiming at microarray-based subclassification, class predictors were constructed using sets of differentially expressed genes, which yielded in zero or low misclassification rates. Furthermore, a significant number of the differentially expressed genes clustered in chromosomal regions affected by the respective genomic losses/gains. Deletions affecting chromosome bands 11q22-q23 and 17p13 led to a reduced expression of the corresponding genes, such as ATM and p53, while trisomy 12 resulted in the upregulation of genes mapping to chromosome arm 12q. Using an unsupervised analysis algorithm, expression profiling allowed partitioning into predominantly VH-mutated versus unmutated patient groups; however, association of the expression profile with the VH mutational status could only be detected in male patients.

CONCLUSION: The finding that the most significantly differentially expressed genes are located in the corresponding aberrant chromosomal regions indicates that a gene dosage effect may exert a pathogenic role in B-CLL. The significant difference in the partitioning of male and female B-CLL samples suggests that the genomic signature for the VH mutational status might be sex-related.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... REFERENCES

 
B-cell chronic lymphocytic leukemia (B-CLL) is the most common leukemia in the Western world. It is characterized by a highly heterogeneous clinical course, with some patients dying from their disease within months, while others have a normal life expectancy.¹ In recent years, this clinical heterogeneity had been shown to correlate with the pattern of genetic changes.²

Using modern molecular cytogenetic techniques, genomic aberrations are detected in more than 80% of chronic lymphocytic leukemia (CLL) cases.³ The most frequent abnormalities are losses of genomic material, namely deletions affecting chromosome bands 13q14, 11q22-q23, 17p13, and 6q21. The most common gains of genomic material affect 12q13, 8q24, and 3q26.³ Several of these genomic aberrations are among the most important prognostic factors in B-CLL: 17p and 11q deletions are associated with rapid disease progression and short survival times, whereas deletion of 13q14 as single abnormality predicts for longer survival times.³ The tumor suppressor gene p53 is affected by 17p deletions; ATM (ataxia telangiectasia mutated) is altered in a proportion of cases with 11q deletions, and candidate genes have been found in the critical region of 13q14 deletions.^4-19

Another important genetic marker, related to the stage of B-cell differentiation, is the recombination status of variable, diversity, and joining heavy chain immunoglobulin gene segments and the process of somatic hypermutation. Roughly one half of B-CLL cases exhibit somatically-mutated variable heavy chain (VH) genes.^20,21 As the presence of unmutated VH correlates with a more aggressive clinical course, the question arose whether the diagnosis B-CLL comprises two distinct disease entities.^20-28 However, the data presented in two recent expression profiling studies^19,29 reject this proposal based on the large overlap of the transcriptomes of VH-mutated and unmutated cases. While both transcriptomes appeared related to memory B-cells, in both studies a small set of genes was found differentially expressed in VH-mutated versus unmutated cases.

In the present study, a large number of molecularly well-defined B-CLL samples (n = 100) were subjected to genomic-scale expression profiling with respect to the most common genomic aberrations, as well as to VH mutational status. Highly significant B-CLL subtype predictors could be constructed that could be used both for diagnostic and prognostic purposes. Interestingly, the genomic signature for the VH mutational status seems to be sex-related. In addition, our data demonstrate a clear correlation between altered transcript levels and loss or gain of genomic material indicating that a gene dosage effect may exert a pathogenic role in B-CLL.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... REFERENCES

 
Samples
One hundred blood samples were obtained from B-CLL patients; these patients represent a subset of those previously reported.³ In the present study, 62 patients were male and 38 were female; the age at the time of study ranged from 30 to 87 years (median, 62.5 years). Sixty-two patients had Binet stage A, 33 had stage B, and five patients had stage C disease.

Mononuclear cells from all samples were isolated using Ficoll gradient, snap frozen, and stored at –80°C or –196°C. As control samples, CD19+ fractions from 11 healthy individuals were purified using MACS CD19 MicroBeads (Miltenyi Biotech, Bergisch Gladbach, Germany) from buffy coat preparations obtained randomly from our blood bank after informed consent. With regard to routine diagnostic use and in order to avoid further manipulation, CLL samples used throughout the study were not further manipulated after Ficoll purification, as the median CD19+ cell content was 88% (mean, 86% ± 14%).

Genomic Aberrations and VH Mutation Status
All samples were previously characterized for the presence of genomic aberrations and the VH mutation status as described.^3,26 Briefly, samples were representative for the dominant subgroups defined by genomic aberrations.³ The numbers of cases from the individual subgroups were: 17p- (n = 10), 11q- (n = 12), +12q (n = 20), 13q- (n = 51), 6q- (n = 9), 13q- as sole aberration (n = 34), and normal karyotype (n = 22). Forty-seven samples were VH-mutated and 53 were VH-unmutated (Table 1).

Expression Data Preprocessing
Saturation. The dynamic range of Affymetrix raw data is known to exhibit a ceiling varying slightly from experiment to experiment, typically around 45,000 units. Raw intensity values close to the ceiling are likely to be affected by a hardware saturation effect. Therefore, 149 genes exhibiting raw intensities above 43,000 units in at least one sample were excluded from the analysis.

Normalization. Raw expression data were normalized using a nonlinear method as described.³⁰ Briefly, the method considers pairwise comparisons of all experiments with one reference experiment. Here a control sample was chosen as reference. First, a subset of not differentially expressed genes was determined by selecting all genes with a rank difference between the two experiments below a threshold. A scatter plot ‘smoother’ (Supsmu, as provided by S-plus, version 3.3; Insightful, Seattle, WA) was applied to this subset for determining the normalizing transformation, which in turn was applied to all gene expression values for the experiment considered. Before normalization, data were converted to logarithmic values, which were further used in all subsequent analyses.

Variation filter. A variation filter was applied for selecting informative (ie, significantly varying) genes before unsupervised analysis. The ratio (variance/arithmetic mean) of the logarithmic expression values was used as a criterion with a threshold of 0.35.

Supervised Analysis
Statistical group comparisons. Genomic aberration and VH mutation status each define a partitioning of the samples into two subsets. Significantly differentially expressed genes were determined using the t statistic assuming equal variance in the two groups. For a more precise control of false discoveries, a data-specific null model was constructed by generating 1,500 modified data sets with permuted class labels. We followed the procedure described previously.³¹ Briefly, the t statistic was calculated for each gene according to the known separation into two groups. Genes were sorted by the t statistic. Subsequently, random class label permutations were performed. After each permutation, the t statistic was recalculated and genes were ranked by their score. All scores with a given rank i in any of the permuted data sets define the probability density function of the null model for rank i. As a result, one obtains rank-specific thresholds for t for a given level of significance.

Predictors. Linear and quadratic discriminant analysis (LDA; QDA) as implemented in PartekPro (Partek Inc, St Charles, Missouri) were used for the construction of classifiers. The preselection of genes followed the permutation test described above. In order to further reduce the number of genes, the following iterative method was applied. Two criteria were used to assess the quality of the predictor at each iteration step: (1) performance of the classifier on the complete data set; and (2) performance in a cross-validation test. The algorithm iteratively adds one gene to the classifier until the desired performance is achieved. Usually, for the first two or three genes, an exhaustive search for the optimal combination is feasible. For the construction of predictors using more genes, a genetic algorithm was used. QDA correctly predicts class membership with fewer genes in the majority of cases; however, it performed worse than LDA in the cross-validation test (overprediction). Therefore, only LDA classifiers are reported here.

Unsupervised Analysis
Dimensionality reduction. Principal component analysis (PCA) in gene space was used for calculating three-dimensional projections of samples. PCA maximizes the variance described by a given number of dimensions (principal components), thereby reducing dimensionality with minimal distortion. Here, PCA was done in the subspace of genes either differentially regulated between known groups or varying across all samples considered.

Hierarchical clustering. Objects (genes or samples, respectively) were clustered with Ward's method, using half square Euclidean distance as similarity measure. Results were visualized with the help of heatmaps and dendrograms. The heatmaps show color-coded expression levels (red = high expression, black = medium expression, and green = low expression). Sample trees are drawn horizontally, and gene trees vertically. PCA and clustering were performed with Spotfire DecisionSite (Spotfire Inc, Somerville, MA).

	RESULTS

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Association of Expression Profiles and B-CLL Genetic Subtypes
Microarray-based expression data of 100 B-CLL samples were analyzed considering the following genetic parameters: VH mutation status, 17p13, 11q22-q23, 13q14, and 6q21 deletion, as well as trisomy 12q13. Presence and absence of each of these features divided B-CLL subtypes into two classes (for numbers of samples in each class, see Table 1). Table 2 lists the number of differentially expressed genes that were determined for each classification using the rank-specific null model with a significance level of 0.05. For trisomy 12 and the VH status, the classical Student's t test was sufficient, since the permutation approach failed to deliver a t statistic better than calculated using the correct class labels. This indicates a particularly good class separation in these two cases.

View larger version (122K): [in this window] [in a new window]   Fig 1. Chromosomal localization of 20 of the top 25 differentially expressed genes (listed in Table 3) resulting from 17p13 deletion (orange), 11q22-23 deletion (green), and trisomy 12 (blue). Filled symbols indicate downregulation, open symbols upregulation. Deregulated genes cluster at the minimal deletion/gain segments and show the expected mode of deregulation (up-/downregulation).

View larger version (60K): [in this window] [in a new window]   Fig 2. Principal component analysis plots of the first three principal components defined by the respective predictor variables. Red symbols represent samples carrying a genomic aberration or VH-mutated samples, respectively; green symbols represent samples without that aberration or VH-unmutated samples; and magenta symbols represent incorrectly allocated samples. (A) –11q; (B) +12; (C) –13q; (D) –17p; (E) –6q; (F) VH.

View larger version (60K): [in this window] [in a new window]   Fig 3. (A) Hierarchical clustering and (B) principal component analysis (PCA) of B-cell chronic lymphocytic leukemia and control samples. The first six rows mark genomic aberrations and VH status, while the last row marks control (gray), female (red), and male (blue) samples. The PCA plot shows male (blue), female (red), and control (gray) samples.

View larger version (76K): [in this window] [in a new window]   Fig 4. Hierarchical clustering of (A) male and (B) female B-cell chronic lymphocytic leukemia (B-CLL) together with healthy control samples. The first six rows indicate genomic and VH status as in Figure 3A. (C) Significance of VH-mutated versus unmutated class separation determined for male and female B-CLL patients, separately.

View larger version (13K): [in this window] [in a new window]   Fig 4. Hierarchical clustering of (A) male and (B) female B-cell chronic lymphocytic leukemia (B-CLL) together with healthy control samples. The first six rows indicate genomic and VH status as in Figure 3A. (C) Significance of VH-mutated versus unmutated class separation determined for male and female B-CLL patients, separately. (continued)

	DISCUSSION

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Class Predictors
As demonstrated in Table 4, highly significant B-CLL subtype predictors could be constructed for all evaluated biologic parameters. The number of genes necessary to construct the respective predictors varied between five genes for 17p-, 6q-, +12, and 12 genes for VH status. Interestingly, the percentage of correctly allocated samples is inversely correlated to the number of genes necessary for the predictor. It is noticeable that the genes used for each predictor did not cluster in the regions affected by the genomic aberration, since sets of genes constituting a predictor may represent only one of several possible sets performing equally well, due to the usage of a genetic algorithm for the construction of the predictors.

Genomic Aberrations
A clear correlation between the chromosomal location of differentially expressed genes and the genomic region affected by the most frequent aberrations was observed. This effect could be detected with statistical significance despite the fact that there were unbalanced group sizes. Also, the type of the aberration (trisomy v deletion) correlated with the direction of the changes (ie, up- or downregulation) on the transcript level. Trisomy 12, which usually affects the whole chromosome 12, was related to an increase in transcript levels scattered along the whole chromosome, while 11q and 17p deletions, which affect critical regions in bands 11q22-q23 and 17p13, respectively, were associated with downregulation of transcripts in these circumscribed regions (Fig 1). Although the 13q deletion was associated with a specific gene expression signature as well, it did not exhibit a strong locus correlation. One possible explanation is that the size of the deletions is, in general, far smaller than that of the other chromosomal deletions. The observation of a clear correlation between altered transcript levels and loss or gain of genomic material is proof-of-principle that even without CD19+ selection, distinct gene expression signatures can be observed in genetic subgroups of B-CLL. An analogous effect was reported previously for trisomy 8 occurring in a subset of acute myeloid leukemia cases,³² but not in a study which investigated the effects of the 11q deletion only in B-CLL in a small number of cases.³³ The occurrence of an explicit correlation between genomic aberrations and the deregulation of genes located in the respective chromosomal region to such an extent in B-CLL patients presents a surprising finding and suggests a gene dosage effect as a likely pathogenic factor and a potential pathomechanism in B-CLL.

Since the critical genes of pathogenic relevance have not yet been defined for most of the recurrent aberrations in B-CLL, those genes, which reside in the imbalanced genomic region, and the expression of which deviates in the same direction, provide good candidates for being disease-relevant. Among these downregulated genes were ATM and p53, both of which are considered genes with pathogenic relevance in B-CLL. ATM was shown to be mutated in a subset of B-CLL cases exhibiting 11q deletion.^4,7,10,34 B-CLL cases with 17p deletion exhibit intragenic mutations of the second p53 allele in the majority of cases, providing strong evidence for its role as a disease gene in B-CLL.^8,9,17,18 Recently, the overlapping pathways of ATM and p53 were investigated by studying the expression profiles of TP53- and ATM-mutant cells after DNA damage.³⁵ Interestingly, two further genes were found which may be implicated in the pathogenicity of B-CLL, namely GPS2/AMF1 located on 17p13 and CASP1 at 11q23. Overexpression of GPS2, a modulator of p53 transactivation, resulted in increased apoptosis in cells on exposure to ultraviolet irradiation.³⁶ Caspase 1 is a transcriptional target of p53 and contributes in part to p53-mediated apoptosis.³⁷ Thus, downregulation of GPS2 and CASP1 genes is consistent with a proposed defect in apoptosis.

Additionally, significant changes were observed in the expression of genes, which do not map to the affected loci. These might be downstream effectors of the genes directly affected by the loss or gain and may contribute to the disease phenotype as well. For example, deletion of 6q21 is accompanied by elevated expression levels of members of the HIST1 major histone gene locus on chromosome 6 (6p21-p22), which are highly transcribed in proliferating cells.³⁸ However, this may be the result of an unrecognized amplicon at locus 6p21-p22.

VH Mutation Status
The impact of the VH mutation status on the transcriptome in B-CLL has been investigated by Klein et al¹⁹ and Rosenwald et al.²⁹ In both studies, an unsupervised approach failed to yield a partitioning into preferentially VH-mutated and preferentially unmutated samples. In contrast, in the present study, hierarchical cluster analysis using a gene set selected by a variation filter revealed such a partitioning. Surprisingly, this partitioning was observed only for samples from male patients. Possible reasons for this observation are the following: (1) the larger number of samples used for this study (100 v 28 and 34 in the studies by Klein et al and Rosenwald et al, respectively); and (2) application and parameterization of a variation filter before clustering: here, a rather stringent variation filter was applied to eliminate as much "noise" as possible, allowing less than 100 genes varying markedly across all experiments to pass. With regard to the genes distinguishing mutated from unmutated samples, there was significant overlap with the data from the two previous studies. Lipoprotein lipase (P < 4 x 10^–9), dystrophin (P = 2 x 10^–6), ZAP70 (P < 4 x 10^–4), and BCL7A (P = 4 x 10^–4) show a P value for differential expression less than .001 in Rosenwald et al study and our analysis, as well as a signal-to-noise score greater than 2.0 in Klein et al. Therefore, a similar signature of the two VH subgroups was identified in the current analysis after the omission of CD19+ selection as compared to the previous studies.^19,29 The finding that only male patients could be partitioned into predominantly VH-mutated and unmutated by hierarchical clustering was unexpected and may hint at sex as a novel parameter. Interestingly, an incidence of male predominance among the VH-unmutated group was described in the initial description of the differences in clinical outcome based on IgV_H genotype.²³ Thus, sex may indirectly influence clinical outcome and even B-cell maturation and differentiation.

Increasingly, it is recommended to complement classical staging systems and clinical parameters by molecularly-defined prognostic factors. Analysis of genomic aberrations requires fluorescence in situ hybridization while determination of the VH status involves amplification of IgV_H genes followed by sequencing. Currently, both methods are not amenable to routine diagnostic use for most laboratories. Recently, the development of a robust DNA array based on comparative genomic hybridization (matrix-CGH) for the automatic analysis of recurrent genomic imbalances was described.³⁹ The present study demonstrated the successful construction of combined B-CLL subtype predictors for both genomic aberrations and VH mutation status, and suggest that clinical diagnosis and prognosis can be optimized by including gene expression–based predictors. Of course, it will be important to validate our gene expression predictor in a prospective cohort of patients before its routine implementation in clinical practice. Overall, it is anticipated that in the future, a combination of different microarray technologies will result in even more accurate diagnosis and prognosis for B-CLL.⁴⁰

	Appendix

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	Authors' Disclosures of Potential Conflicts of Interest

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Appendix Authors' Disclosures of... REFERENCES

	Acknowledgment

 
We thank Roland Varecka and Susanne Karner for excellent technical support, as well as K.K. Wilgenbus for valuable discussions.

	NOTES

 
Supported by grant 01KW9936 within the German Human Genome Project funded by the German Federal Ministry of Education and Research.

Presented at EuroBiochips 2003, London, England, May 20-23, 2003.

Authors' disclosures of potential conflicts of interest are found at the end of this article.

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Submitted December 16, 2003; accepted June 24, 2004.

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