Proteomic Analysis of Chronic Lymphocytic Leukemia Subtypes with Mutated or Unmutated Ig VH Genes*
Duncan A. E. Cochran
,
,
Caroline A. Evans
,
,
David Blinco
,
John Burthem
,
Freda K. Stevenson¶,
Simon J. Gaskell
,|| and
Anthony D. Whetton
,**
From the
Leukaemia Research Fund Proteomics Facility, Department of Biomolecular Sciences, and the || Michael Barber Centre for Mass Spectrometry, Department of Chemistry, University of Manchester Institute of Science and Technology, Manchester M60 1QD, and the ¶ Molecular Immunology Laboratory, Tenovus Research Laboratory, Southampton General Hospital, Southampton SO16 6YD, United Kingdom
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ABSTRACT
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Chronic lymphocytic leukemia (CLL) is a common hematopoietic malignant disease with variable outcome. CLL has been divided into distinct groups based on whether somatic hypermutation has occurred in the variable region of the immunoglobulin heavy-chain locus or alternatively if the cells express higher levels of the CD38 protein. We have analyzed the proteome of 12 cases of CLL (six mutated (M-CLL) and six unmutated (UM-CLL) immunoglobulin heavy-chain loci; seven CD38-negative and five CD38-positive) using two-dimensional electrophoresis and mass spectrometry. Statistical evaluation using principal component analysis indicated significant differences in patterns of protein expression between the cases with and without somatic mutation. Specific proteins indicated by principal component analysis as varying between the prognostic groups were characterized using mass spectrometry. The levels of F-actin-capping protein ß subunit, 14-3-3 ß protein, and laminin-binding protein precursor were significantly increased in M-CLL relative to UM-CLL. In addition, primary sequence data from tandem mass spectrometry showed that nucleophosmin was present as several protein spots in M-CLL but was not detected in UM-CLL samples, suggesting that several post-translationally modified forms of nucleophosmin vary between these two sample groups. No specific differences were found between CD38-positive and -negative patient samples using the same approach. The results presented show that proteomic analysis can complement other approaches in identifying proteins that may have potential value in the biological and diagnostic distinction between important clinical subtypes of CLL.
Chronic lymphocytic leukemia (CLL)1 is the most common adult B-cell malignancy in the Western world (1). CLL cells share particular immunophenotypic features (expression of mature B-cell markers, CD23, CD5, and weak expression of surface immunoglobulin) and demonstrate a pattern of expression of a range of genes that suggests the neoplastic cells are related to memory B-lymphocytes (2, 3). However, in different cases of the disease, there may be considerable variation of clinical behavior and of prognosis. The clinical course of CLL is heterogeneous; some patients progress rapidly to early death, whereas others exhibit a more stable disease lasting many years. A considerable research effort has therefore been directed toward the identification of markers to guide effective consideration of treatment. In this regard, recently it has been shown that CLL may be divided into clinically distinct groups depending on whether the variable region of the immunoglobulin heavy-chain locus (Ig VH) has undergone somatic mutation. Cases of CLL with somatic mutation of Ig VH have a more favorable prognosis than those where Ig VH has not undergone somatic mutation (46). Another potential prognostic marker in CLL is expression of CD38 on the cell surface where increased expression was indicated to be a marker for poor prognosis (4, 710). CD38 level has been suggested to correlate with unmutated Ig VH gene status, but more recent data suggests CD38 expression to be independently variable from VH gene status (1114). Thus, VH gene status and CD38 expression appear to be independent prognostic markers for CLL. Nonetheless, there is still a requirement for effective prognostic indicators in CLL that can rapidly enable treatment strategies to be mapped out.
Microarray analysis of CLL patients is an obvious and potentially beneficial means of defining specific prognostic indicators in CLL. Genes whose expression levels significantly correlate with patient survival and/or with clinical staging have been found and categorized as encoding or potentiating activity of cell adhesion molecules (15). It remains to be seen if these proteins or their gene transcripts have value as disease markers. The transcriptomes of UM-CLL and M-CLL have been compared, and analysis has revealed differential expression of a relatively small number of mRNA species between the two CLL subtypes (23 transcribed genes of 12,000 genes analyzed). The restricted number of genes was able to distinguish mutated versus unmutated cases. However, a transcriptomics approach may not alone be sufficient to distinguish important differences between UM-CLL and M-CLL. For example, although CD38 expression has been shown to correlate with poor prognosis CLL, there is no change in CD38 at the level of the transcriptome (16). This may be because the level of transcription of a specific gene does not directly correlate with the level of expression of its protein product within the cell. Differences between gene transcription and protein expression are the result of post-transcriptional regulation, including altered mRNA translation, and protein stability (17). In neoplastic cells additional mechanisms include proteasome-mediated degradation of specific proteins induced by leukemogenic oncogenes (18). For these reasons, analysis of protein expression is both complementary and additive to the data obtained by transcriptomic analysis.
Here we have asked whether proteomic analysis reveals specific and potentially valuable diagnostic indicators for UM-CLL compared with M-CLL and/or for CD38-positive compared with CD38-negative CLL patient samples. Using principal component analysis (PCA) of 2D gels we have shown differential expression of proteins between UM-CLL and M-CLL samples, identifying potential marker proteins associated with these different CLL subtypes and thereby revealing potential value in this approach to the characterization of leukemia subtypes.
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EXPERIMENTAL PROCEDURES
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Clinical and Cytogenetic Features of Samples Studied
Following Southampton and Southwest Hampshire local research ethics committee approval, peripheral blood was obtained from 12 patients with classical B-cell CLL. Peripheral blood mononuclear cells were isolated by Ficoll-Paque gradient centrifugation (Amersham Biosciences), washed, and cryopreserved. CLL cells were not further purified to minimize in vitro manipulations. Patient samples were characterized for Ig VH gene mutation status and CD38 expression (5). Patients were selected to provide a representative selection of VH gene and CD38 status (Table I). Of these 12 patients, seven patients were CD38-negative, and five were CD38-positive; there were six each of UM-CLL and M-CLL in respect to Ig VH gene status. All cases scored 4 or 5 using the Royal Marsden scoring system for CLL diagnosis and were of stage A or B using Binet classification (19). Patient details are shown in Table I. Proteomic analysis was performed on six UM-CLL and six M-CLL samples (designated UM16 and M16, respectively).
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TABLE I Clinical details of CLL patients
The Ig VH gene expressed and its mutation status are shown for each patient. A greater than 2% deviation from germline VH sequence was considered as mutated. Disease stage is according to the Binet scale. WBC indicates white blood cells; for CD38, + and - indicate positive and negative expression as assessed by fluorescence-activated cell sorting analysis. N/A denotes data not available. As is usual in these cases, karyotypic abnormalities can be registered, and these are shown for cases where available.
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Two-dimensional Gel Electrophoresis
Protein lysates were prepared from frozen patient mononuclear cell material, which was resuspended in 350 µl of isoelectric focusing buffer (9 M urea, 2 M thiourea, 4% (w/v) CHAPS, 65 mM dithiothreitol, 0.5% (v/v) IPG buffer (Amersham Biosciences)) per 1 x 106 cells. The samples were then centrifuged at 12,000 x g for 15 min at 20 °C. Isoelectric focusing was performed using 18-cm immobilized pH gradient strips (pH 47) and the Multiphor II instrument (Amersham Biosciences), focusing for a total of 49 kV h at 20 °C. The second dimension was a standard SDS-PAGE protocol using the ISODALT system (Amersham Biosciences). Strips were equilibrated for 10 min in equilibration buffer (50 mM Tris-HCl, pH 6.8, 6 M urea, 30% (v/v) glycerol, 2% (v/v) SDS) containing 65 mM dithiothreitol and then for 10 min in the same buffer containing 240 mM iodoacetamide. Second-dimension gels were 10% SDS-PAGE gels of 160 x 180 x 0.75 mm. Gels were stained with silver using the protocol of Shevchenko et al. (20). For mass spectrometric identification, preparative gels of lysates from 5 x 106 cells were run, and the proteins were stained with colloidal Coomassie Blue. Gels were fixed in 50% (v/v) ethanol, 2% (v/v) orthophosphoric acid overnight, washed in three changes of double-distilled water over a 90-min period, and incubated in 34% (v/v) methanol, 17% (v/v) ammonium sulfate, 2% (v/v) orthophosphoric acid for 60 min prior to addition of Coomassie Brilliant Blue G at 0.066% (w/v). Gels were left to stain for 4 days. The gels were scanned, and the images were exported as tagged image format (.tif) files for analysis using Progenesis software (Nonlinear Dynamics, Newcastle, UK).
Gel Analysis
Progenesis software (Nonlinear Dynamics) was initially used for spot detection and background subtraction for the gel set. Progenesis automatically created a global reference gel for the experiment based on patient M1. This was selected on the basis of containing the most spots and effectively provides a spot index for the analysis. Averaged gels were generated for each of the UM-CLL, M-CLL, CD38-positive, and CD38-negative gel sets. The averaged gels are a statistical combination of several gels to produce a gel that has mean spot values and associated error terms providing information about spot variation within the gel set. These gel groups were created for spot pattern comparison. The parameters for inclusion in the averaged gel were that any spot must be present on five of six gels. Following the automatic analysis, the experiment was manually edited to remove non-protein features such as speckles and the dye front that had been detected.
The averaged and reference gels were then recreated to reflect the amended spot detection. Background detection and normalization were performed again to account for any changes in overall volume caused by the manual editing. Spots were then rematched to their respective averaged gel and to the global reference gel using the spot-matching tool. Once all editing and rematching had been completed the gels were analyzed for protein spot differences.
Mass Spectrometry
Spots of interest were excised from the gel, followed by destaining, reduction, alkylation, and digestion with modified porcine trypsin (Promega, Southampton, UK) as described previously (2022). Samples for matrix-assisted laser desorption ionization time-of-flight (MALDI-ToF) analysis were prepared by mixing a small aliquot of the digestion supernatant with an equal volume of a solution of
-cyano-4-hydroxycinnamic acid (10 mg/ml in 1:1 acetonitrile, 0.1% v/v trifluoroacetic acid). Peptide mass fingerprinting was performed on a reflectron MALDI-ToF mass spectrometer (M@LDI, Micromass, Manchester, UK). All mass spectra were internally calibrated with trypsin autolysis peaks (m/z 842.51 or 2211.10). Mascot software (Matrix Science, London, UK) was employed for protein database searching using monoisotopic mass values for each spectrum. Protein identity was based on at least five matching peptides, an appropriate molecular weight and pI value as determined from the calibrated CLL 2D gel, and a p value of <0.05 within the Mascot software, indicating that the peptide mass fingerprint was derived from the protein identified in the search.
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RESULTS
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The clinical details of the samples obtained from patients diagnosed with CLL are shown in Table I. These 12 samples were chosen because they offered six samples with unmutated Ig VH and six samples with mutated Ig VH. Furthermore, within this set of samples, five samples that expressed the prognostic marker CD38 were available for comparison with seven samples with lower levels of CD38 expression. CD38 and Ig VH are considered to be independent prognostic markers. Our strategy was to compare samples as follows within the limited set of samples studied: UM-CLL versus M-CLL and CD38-positive versus CD38-negative. The aim was to identify potential prognostic protein markers co-expressed with either of these markers.
Clinical samples from six cases of M-CLL and six of UM-CLL were analyzed using two-dimensional gel electrophoresis. Gels were run between pH 4 and 7 to give a high level of spot resolution and were silver-stained. At least two separate experiments were performed for all samples, and similar protein spot patterns were obtained. Approximately 800 protein spots were detected on the silver-stained gels from the patient material. Due to the complexity of analyzing multiple gels from different patients, a comprehensive assessment of differences between images was not feasible based on visual inspection alone. Scanned images were therefore employed for analysis within the Progenesis software.
Progenesis software was employed to detect spots and subtract background on a spot-specific basis as described above. A representative gel is shown in Fig. 1 and is annotated to show the location of spots excised and identified by peptide mass fingerprinting for purposes of in-gel calibration for pI and molecular weight. In-gel calibration was performed using the Progenesis program (see Fig. 1). The proteins identified are listed in Table II.

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FIG. 1. Two-dimensional gel analysis of proteins from patients expressing somatic mutated or unmutated Ig VH genes. A representative gel is shown from the B-cell CLL patient set. 60 spots identified by peptide mass fingerprinting for purposes of in-gel calibration for pI and molecular weight are shown. The spot numbers represent those assigned by Progenesis.
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TABLE II Identities of proteins from B-cell CLL patient gels
Proteins identified by peptide mass fingerprinting are listed. Spot numbers were assigned by Progenesis and correspond to the spot locations shown in Fig. 1.
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For inter-gel comparisons the spot volume was normalized to give a fractional value of the total spot volume/gel. In addition to the global reference (composite) gel derived from all CLL samples, another level of composite gels (averaged gels) can be created from groups of gels to reflect the characteristics of distinct subgroups within the Progenesis experiment. Thus, averaged gels were created for the M-CLL and UM-CLL gels and for the CD38-positive and CD38-negative gels. Having generated these averaged gels, spot matching was performed between the gel sets, and the protein expression levels/spot were compared for each gel group. Consistent with microarray studies that show similarities at the mRNA level, we found overall similarity, although not precise identity, in protein expression patterns.
PCA of the individual gels of the dataset was employed to further investigate the level of similarity between the UM-CLL and M-CLL gels and also rapidly to identify protein spots that contribute to any potential differences between the two groups. PCA is a statistical analysis technique that mathematically transforms a number of potentially correlated variables into a smaller number of uncorrelated variables called principal components. PCA is thus a dimensional reduction technique. This method of analysis calculates the Eigen system of a given matrix of data and has been demonstrated to be of value in proteomic analysis (23). It can be used to determine quantitative alterations of protein spots and thus potentially to identify co-regulated proteins that may have prognostic importance. The results of this analysis are presented in Fig. 2A, which shows the spatial distribution of the protein spots in the patient groups using a 2D PCA projection based on spot volume. Thus, the information within the 2D gels analyzed using PCA can discriminate between the UM-CLL and M-CLL samples by indicating those spots that lie outside the main clusters.

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FIG. 2. Statistical analysis of UM-CLL and M-CLL samples using principal component analysis. The distribution of 2D gel datasets is shown in 2D PCA space based on volume. PC1 is spot volume, and the second dimension, PC2, is a characteristic of the spot volume. A, patients are labeled for Ig VH status (M or UM) and CD38 expression (- or +). B, patients are highlighted by a square, and protein spots are also shown for the B-cell CLL patient dataset. Spots lying outside the main clusters that are significantly different between the patient groups are circled, and their identities as determined using mass spectrometry are shown.
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We then performed additional statistical analysis of normalized volume for the potential discriminatory spots, comparing the relative abundance of those protein spots shown by PCA to discriminate between UM-CLL and M-CLL. Spots that showed statistical significance (p < 0.05 using Mann-Whitney U test) are shown in Fig. 2B, and histograms of their expression levels are presented in Fig. 3. The gel location of those "discriminatory proteins" is shown in Fig. 4. The others show some trend of differing expression, but the p values were >0.05 using either the Students t test or the Mann-Whitney U test. Proteins identified as significantly altered between M-CLL and UM-CLL are the following: F-actin-capping protein ß subunit, 14-3-3 ß protein, and laminin-binding precursor protein. On PCA plots, these proteins lie above the x axis and in the region where patients M1M6 cluster (Fig. 2B). The predicted molecular weight and pI of protein spots extrapolated from in-gel calibration spots were used to corroborate protein identifications. PCA of CD38-positive (UM2UM5 and M2) versus CD38-negative (UM6, M1, and M3M6) patient gels did not reveal any protein spots obviously outside the main cluster (Fig. 2B). Additionally, protein spots that discriminated between M-CLL and UM-CLL did not show any significant change in respect to the CD38 expression.

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FIG. 3. Histograms of normalized spot volumes for spots that vary significantly across the dataset. Relative values were calculated by dividing the spot volume by the total volume of spots present in each gel. The data for the individual patients are shown together with the mean values for the pooled UM-CLL and M-CLL samples. Error bars represent the S.E.
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FIG. 4. Averaged gel for M-CLL (M1M6) showing the spot locations of individual spots. The locations of protein spots that differ in expression between M-CLL and UM-CLL are highlighted. These were excised and identified by peptide mass fingerprinting following in-gel tryptic digestion. Details of their accession numbers, molecular weights, and pIs are shown in Table II.
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In addition to those proteins whose level of expression differed between M-CLL and UM-CLL, the two clinical groups were also discriminated by the presence or absence of one cluster of spots. The protein was present in all the M-CLL samples but not in the UM-CLL samples (labeled spots N13, Fig. 5A). This region was identified to be a major difference between the two sample sets using PCA and histogram analysis (see Figs. 2 and 5B). It was also shown to be a presence/absence feature using the difference map analysis procedure of Progenesis (data not shown). Mass spectrometric analysis of the 3 spots within this region (see Fig. 5C) revealed a similar peptide mass spectrum for each, which suggested the spots contained forms of the same protein. Database searching identified the 3 spots as nucleophosmin/B23.2. These forms of the protein were not detectable in UM-CLL samples.

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FIG. 5. Nucleophosmin in UM-CLL and M-CLL samples. CLL samples for identified proteins that vary between these two sample sets included nucleophosmin. A, the gel area of interest for this protein is shown with three protein spots, which are labeled N1, N2, and N3. B, relative intensities for protein spot(s) shown in A were calculated by dividing the spot volume by the total volume of spots present in each gel. The data for the individual patients are shown together with the mean values for the pooled UM-CLL and M-CLL samples. Error bars represent the S.E. C, mass spectra of in-gel digests of spots H1H3 (Fig. 5A) observed during 2D gel analyses of lysates from cells derived from M-CLL patients only. The observed peptide MH+ ions characteristic of nucleophosmin are labeled on the spectrum for spot N2. T, tryptic peptide.
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DISCUSSION
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The objective of this study was to define potentially robust and valuable prognostic indicators that correlate with CD38 expression and/or Ig VH mutation status. Densitometric comparisons of gel images allowed identification of subtle changes in many protein levels between patients. Appropriate 2D gel analysis software enables the identification of differences between patient sample sets in several ways. First, compilation of reference gels representing all the patient samples of a particular type allows relatively rapid progress to be made in data analysis. Second, analysis of these data using techniques such as PCA allows identification of spots that vary considerably between sample sets. Third, the generation of histograms displaying normalized spot volume (or other features such as peak height) for all the samples means that it is relatively easy to determine and statistically validate the consistency of an observation between sample sets.
This set of experiments was performed using CLL patient subgroups, based on either Ig VH mutation status or CD38 expression level. PCA using the gels for the UM-CLL and M-CLL patients identified a subset of proteins for further examination (Fig. 2). These proteins were also assessed for relative expression level in each sample (Figs. 3 and 5B). This analysis of relative spot intensities between M-CLL and UM-CLL revealed the contribution of each sample to the differences within the composite gels. To check that this approach did not exclude any differences, all the histograms from matched spots were analyzed, and we found no other major differences. The basic similarity between the 2D gel patterns of UM-CLL and M-CLL seen in these experiments reflects the results from microarray analysis of the CLL samples that indicate that the two groups have very similar transcriptional activity and resemble normal memory B-cells (24). Reliable prognostic indicators are still required to predict and further characterize the clinically variable course of CLL. Proteins identified by the proteomic approaches employed in this study are those with relatively high abundance; we suggest therefore that the proteins differentially expressed may have value in the diagnostic distinction between the somatic mutated and unmutated subtypes of CLL.
Several proteins were identified that contribute to differences between M-CLL and UM-CLL. F-actin-capping protein ß subunit is expressed at higher levels in M-CLL compared with UM-CLL. This protein plays a role in the organization of the subcortical actin-cytoskeleton. It is also apparent that other molecules linked to adhesion/cytoskeletal activation were found to have discriminatory value between UM-CLL and M-CLL in PCA. First, the studies indicated a significant difference between the groups in relation to the expression of the laminin-binding protein precursor. The laminin-binding protein precursor is a precursor of the 67-kDa laminin receptor but also has a range of functions in its precursor form; these functions include the activation of Rho GTPase (25) and the binding and transport to the nucleus of molecules that promote cell migration and cell survival (26). It is interesting to note that PCA also identified two electrophoretically distinct forms of actin as having potential discriminatory value (labeled Actin 1 and Actin 2 in Fig. 2B). Further analysis showed that the ratio of these actin forms differed between the two clinical groups of CLL, a finding that is again consistent with altered organization of actin molecules within the clinical subtypes; however, this difference failed to reach statistical significance (27).
Therefore, our description of altered expression of the proteins in the mutated and unmutated subgroups of CLL may reflect altered cytoskeletal activity between those subgroups of patients. These findings are consistent with earlier functional studies of CLL adhesion and motility such as that performed by Vincent et al. (28), which demonstrated that despite a similar expression of adhesion molecules, cases of CLL with an "activated phenotype" displayed increases in both motility and adhesion. Such findings may have significant clinical relevance because in other microarray studies, the transcription of factors inducing cellular adhesion was linked to the advanced clinical stage of CLL, suggesting that increased adhesion may be associated with more aggressive CLL (15, 29). Furthermore, microarray studies have shown unmutated cases of CLL to have an activated phenotype (13).
In respect to the protein nucleophosmin the multivariate roles of this protein offer many lines of speculation as to why it may contribute to differences between the subtypes of CLL. Nucleophosmin is a ubiquitously expressed nucleolar phosphoprotein that shuttles between the nucleus and cytoplasm. One potentially relevant function concerns the interaction between nucleophosmin and p53. Nucleophosmin interacts directly with p53 to regulate its stability and transcriptional activation and is crucial for p53 function (30). Loss or mutation of p53 in CLL is associated with disease progression and poor prognosis (31, 32). Thus, the presence of a group of nucleophosmin spots only in M-CLL cells may act to enhance p53 stability and function in those cases and may relate to their improved clinical outcome.
The increased level of 14-3-3 ß protein in M-CLL compared with UM-CLL may also be interesting in this respect. 14-3-3 proteins are ubiquitously expressed and regulate key proteins involved in proliferation, apoptosis, and transcriptional regulation (including p53). They bind proteins via serine/threonine-phosphorylated residues in a context-specific manner, analogous to the Src homology 2 (SH2) and phosphotyrosine binding (PTB) domains. They are, however, distinct from SH2 and PTB proteins because 14-3-3 proteins act as direct regulators of their targets (33, 34).
In this study, proteomic analysis has identified putative candidates for further investigation in respect to the differences between UM- and M-CLL samples that either may contribute to differential prognosis or may be developed as markers for disease outcome. We have outlined a method to determine differences between cell sample groups that may be applied more widely. In this paradigm study we have demonstrated that proteomic analysis has a potentially valuable role in the investigation of clinical material. Furthermore, results obtained by proteomics are both complementary and additive to the results of microarray analysis (16).
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FOOTNOTES
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Received, June 12, 2003, and in revised form, October 10, 2003.
Published, MCP Papers in Press, October 13, 2003, DOI 10.1074/mcp.M300055-MCP200
1 The abbreviations used are: CLL, chronic lymphocytic leukemia; Ig VH, immunoglobulin heavy-chain locus; M-CLL, CLL with somatic mutation of Ig VH; UM-CLL, CLL without somatic mutation of Ig VH; MALDI-ToF, matrix-assisted laser desorption ionization time-of-flight; PCA, principal component analysis; 2D, two-dimensional; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonic acid. 
* The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. 
Both authors contributed equally to this work. 
** To whom correspondence should be addressed. Tel.: 44-161-200-4184; Fax: 44-161-236-0409; E-mail: tony.whetton{at}umist.ac.uk
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