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Structure VariabilityBinding-SiteLeukemia B Cells Show Limited

Igs Expressed by Chronic Lymphocytic

FaisFerrarini, Nicholas Chiorazzi, Anna Tramontano and FrancoCutrona, Fortunato Morabito, Silvia Bruno, Manlio Colombo, Emilia Albesiano, Davide Bagnara, GiovannaChailyan, Andrea N. Mazzarello, Xiao-Jie Yan, Monica Paolo Marcatili, Fabio Ghiotto, Claudya Tenca, Anna

http://www.jimmunol.org/content/190/11/5771doi: 10.4049/jimmunol.13003212013;

2013; 190:5771-5778; Prepublished online 1 MayJ Immunol 

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The Journal of Immunology

Igs Expressed by Chronic Lymphocytic Leukemia B CellsShow Limited Binding-Site Structure Variability

Paolo Marcatili,*,1 Fabio Ghiotto,†,1 Claudya Tenca,† Anna Chailyan,*

Andrea N. Mazzarello,† Xiao-Jie Yan,‡ Monica Colombo,x Emilia Albesiano,‡

Davide Bagnara,‡ Giovanna Cutrona,x Fortunato Morabito,{ Silvia Bruno,†

Manlio Ferrarini,x Nicholas Chiorazzi,‡,k,#,** Anna Tramontano,*,†† and Franco Fais†

Ag selection has been suggested to play a role in chronic lymphocytic leukemia (CLL) pathogenesis, but no large-scale analysis has

been performed so far on the structure of the Ag-binding sites (ABSs) of leukemic cell Igs. We sequenced both H and L chain V(D)J

rearrangements from 366 CLL patients and modeled their three-dimensional structures. The resulting ABS structures were clustered

into a small number of discrete sets, each containing ABSs with similar shapes and physicochemical properties. This structural clas-

sification correlates well with other known prognostic factors such as Ig mutation status and recurrent (stereotyped) receptors, but it

shows a better prognostic value, at least in the case of one structural cluster for which clinical data were available. These findings suggest,

for the first time, to our knowledge, on the basis of a structural analysis of the Ab-binding sites, that selection by a finite quota of

antigenic structures operates on most CLL cases, whether mutated or unmutated. The Journal of Immunology, 2013, 190: 5771–5778.

Chronic lymphocytic leukemia (CLL) is the most frequentform of leukemia in the western world and is charac-terized by a clonal expansion of neoplastic mature B

lymphocytes. CLL pathogenesis is still unclear, but it appears thatmany factors contribute to the evolution and expansion of theneoplastic clones (1).Analyses of the sequences of H and L chain variable regions

of Igs expressed on the surface of leukemic cells showed that theIGHV and IGL/KV regions undergo somatic hypermutation in∼50% of leukemic clones (2–4), and that patients with mutated

IGHV genes generally have a more indolent clinical course thanpatients with unmutated IGHV genes (5, 6).In addition, clones from different CLL patients express Igs that

contain remarkably similar IGHV amino acid sequences (7–11).The extent of these recurrent rearrangements, termed ‘‘stereotypedIgs,’’ in the CLL repertoire has been recently appreciated throughthe analysis of thousands of CLL H chain IGVs: ∼30% of CLLcases fall within 1 of .300 subgroups of stereotyped Igs (ste-reotyped subsets) (12–14).Altogether, although CLL Igs might be able to also mediate cell-

autonomous signaling dependent on intrinsic motifs, as it has beenrecently reported (15), the earlier findings suggest that Ag–Iginteraction might play a crucial role in CLL pathogenesis as well.It is still unclear, however, whether the role of Ags is crucial in allCLL cases or is restricted to only CLLs with stereotyped Igs,which are mostly unmutated (10, 16, 17).The definition of stereotyped Igs is mainly based on the HCDR3

amino acid composition and length. These play a crucial role inthe Ag–Ig interaction, but the shape of the whole Ag-binding site(ABS) obviously also depends on other Ig regions, and it is im-portant to analyze the whole binding site at the structural level.In their seminal work, Wu and Kabat (18) identified three se-

quence portions on each Ig VH, and VK or VL domain, the so-called hypervariable regions, with an extremely variable aminoacid composition in comparison with the other less variable parts.They correctly predicted such regions to assume a loop confor-mation and to be responsible for the selective binding of the Ag,and named them ‘‘complementary determining regions’’ (CDRs)in contrast with the surrounding ‘‘framework’’ regions. The workof Chothia and Lesk (19) and of some of us (20, 21) extended theanalysis and pointed out that five of six CDRs (LCDR1–3 andHCDR1–2) and a portion of the sixth loop (HCDR3), althoughpresenting a very variable sequence repertoire, usually adopt alimited set of backbone conformations referred to as canonicalstructures determined by the nature of relatively few residues thatare primarily responsible for their main-chain conformations.These residues are found both within the hypervariable regionsand in the conserved b-sheet framework (21).

*Department of Physics, Sapienza University of Rome, 00185 Rome, Italy;†Department of Experimental Medicine, University of Genoa, 16132 Genoa, Italy;‡The Feinstein Institute for Medical Research, North Shore–Long Island JewishHealth System, Manhasset, NY 11030; xDivision of Medical Oncology C, NationalInstitute for Cancer Research, 16132 Genoa, Italy; {Unita Operativa di Ematologia,Dipartimento di Medicina Interna, Azienda Ospedaliera di Cosenza, 87100 Cosenza,Italy; kDepartment of Medicine, North Shore–Long Island Jewish Health System,Manhasset, NY 11030; #Department of Medicine, Hofstra North Shore–Long IslandJewish School of Medicine, Hempstead NY 11549; **Department of MolecularMedicine, Hofstra North Shore–Long Island Jewish School of Medicine, HempsteadNY 11549; and ††Istituto Pasteur–Fondazione Cenci Bolognetti, 00185 Rome, Italy

1P.M. and F.G. contributed equally to this work.

Received for publication January 31, 2013. Accepted for publication March 28, 2013.

This work was supported by Associazione Italiana Ricerca sul Cancro (IG-10698 toF.F.; IG-10492 to M.F.); Compagnia di San Paolo (4824 SD/CV, 2007.2880 to F.F.);Fondazione Maria Piaggio Casarsa, Genova, Italy (to F.G.); the National Institutes ofHealth (Grant RO1 CA81554 to N.C.); and King Abdullah University of Science andTechnology (Grant KUK-I1-012-43 to A.T.). M.C. has a fellowship from the Asso-ciazione Italiana Ricerca sul Cancro 5 per Mille.

Address correspondence and reprint requests to Dr. Paolo Marcatili or Dr. FrancoFais, Department of Physics, Sapienza University of Rome, Piazzale le Aldo Moro 5,00185 Rome, Italy (P.M.) or Department of Experimental Medicine, University ofGenoa, Via De Toni 14, 16132 Genoa, Italy (F.F.). E-mail addresses: paolo.marcatili@gmail.com (P.M.) or franco.fais@unige.it (F.F.)

The online version of this article contains supplemental material.

Abbreviations used in this article: ABS, Ag-binding site; CDR, complementary de-termining region; CLL, chronic lymphocytic leukemia; HM, heavily mutated; HMM,hidden Markov model; M-CLL, mutated chronic lymphocytic leukemia case; SM,scarcely mutated; TM, template modeling; U-CLL, unmutated chronic lymphocyticleukemia case; w/o, without.

Copyright� 2013 by The American Association of Immunologists, Inc. 0022-1767/13/$16.00

www.jimmunol.org/cgi/doi/10.4049/jimmunol.1300321

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These studies made it possible to develop ad hoc modelingtechniques (22) to build Ab models accurate enough for theoret-ical and practical studies, such as docking (23), engineering (24),and comparison (25).Taking advantage of the above, we analyze in this article for

the first time, to our knowledge, the structures of Igs from CLLpatients, with the aim of evaluating whether information on theABS structure can provide novel insights into the Ag role in CLLpathogenesis. We modeled the structure of Igs derived from a co-hort of 366 CLL patients starting from the amino acid sequencesof their paired H and L chains, and studied the structural featuresof their ABS to highlight possible common patterns potentiallycorrelated with the pathological phenotype.

Materials and MethodsPatients, leukemic cells, IGV sequences, and analyses

After informed consent according to the Declaration of Helsinki, PBMCswere isolated from heparinized venous blood of patients with CLL. CLLdiagnosis was based on accepted clinical and immunophenotypic features(26). Rearranged IGHV-D-J and IGKV-J or IGLV-J paired segments weresequenced from the cDNA of 218 CLL patients as described previously(2, 3); in addition, 148 Ig sequences (IGHV + IGK/LV) were retrievedfrom GenBank, bringing the total number of analyzed cases to 366. All ofthe latter were submitted to the database by the research group of Dr. K.Stamatopoulos (Hematology Department and HCT Unit, G. PapanicolaouHospital, Thessaloniki, Greece), who verified that allelic exclusion wastaken into account (personal communication). Only samples with allelicexclusion of both IGHV and IGK/LV were included in the study. Se-quences were analyzed using the ImMunoGeneTics Information System(http://www.imgt.org/) (27). The mutational status of Ig clones was definedbased on both IGHVand IGK/LV. Patients with leukemic clones exhibiting,2% mutations in both V segments were labeled as ‘‘unmutated CLLcases’’ (U-CLL), whereas patients with $2% IGHV and/or IGK/LV so-matic mutations were defined as ‘‘mutated CLL cases’’ (M-CLL).

We also used a finer mutational classification by dividing the Igs intothree classes: heavily mutated (HM) Igs (IGHVand/or IGK/LV percentageof mutation $ 3%); scarcely mutated (SM) Igs (IGHV and/or IGK/LV $1% and ,3% mutations), and unmutated Igs (IGHV and IGK/LV per-centage of mutation , 1%). The cutoffs adopted for our three-class par-titioning are slightly different from those defined in a previous study onthe IGVH gene repertoire in splenic marginal zone lymphoma (28). Theclassification did not change when the sequences were inspected by run-ning IgBLAST on the National Center for Biotechnology Informationhuman gene database.

Ig data sets

We built a ‘‘test’’ data set by querying the DIGIT database (29) for allavailable human Igs for which the paired sequences of the L and H chainswere available. After inspecting the Ig description contained in the DIGITdatabase and the related PubMed entry, we discarded all Igs for which noreference to any published article could be found or not corresponding toan entry in Entrez Nucleotide (http://www.ncbi.nlm.nih.gov/nuccore/), aswell as all Igs already contained in our initial CLL data set. We ended upwith 2441 Igs for which complete information on canonical structures,loop length, and mutation rates could be retrieved using the tools providedby DIGIT (29).

Among the 2441 Igs of the ‘‘test’’ data set, 212 were from CLLs, and welabeled them as ‘‘test CLL’’ data set. Among the remaining 2229 Igs (‘‘testwithout [w/o] CLL’’), we also defined a ‘‘test AI’’ data set including the294 sequences for which the associated PubMed entry contained any ofthe MeSH terms ‘‘autoimmunity’’ (MeSH tree no. G12.450.192), ‘‘autoim-mune disease’’ (MeSH tree no. C20.111), or ‘‘autoantibody’’ (MeSH treeno. D12.776.124.486.485.114.323). All the remaining 1935 Igs were de-fined as the ‘‘test w/o (CLL-AI)’’ data set. All considered sequences in-cluded the full-length variable domains.

Ig three-dimensional models

We used the PIGS server, based on the canonical structure method, to derivethe sequence alignments of the Ig frameworks and to build the three-dimensional models of all Igs in our data set (22). We could build 342complete and correctly assembled models. The remaining 24 models werediscarded because the modeling procedure returned an incomplete or im-properly assembled model: in 14 models, the IGHV-IGL/KV packing was

incorrect; in 8 cases, no template was found to model the LCDR2, and in1 case, the HCDR3 was too long to be properly modeled.

Structural analysis and clustering

Structural superpositions were performed using the LGA package (30). Theloop coordinates as defined by Al-Lazikani et al. (31) plus the two residuesflanking the N and C termini of each loop were used for the superpositionof the ABSs. Two residues were considered as corresponding to each otherif the distance of their Cas after superposition was ,8 A.

The next step consisted in clustering the structures of the loops. To selectthe most appropriate metrics for clustering, we used the silhouette analysis(32), an effective and unbiased method for selecting the parameters leadingto the best cluster separations.

The tested distance metrics were root-mean-square deviation, globaldistance test, and template modeling (TM) score distance matrices for thesuperimposed structures (33). For each distance matrix, we performedagglomerative hierarchical clustering using the agnes function (Maechler& Rousseeuw, http://cran.r-project.org/web/packages/cluster/) and divisivehierarchical clustering (diana method, Maechler & Rousseeuw, http://cran.r-project.org/web/packages/cluster/) of the R package with a number ofclusters ranging from 10 to 50. The linkage functions used in our analysiswere complete, single, average, and Ward’s method (34).

The best average silhouette value (0.146) was obtained using TM scoreas metric and a divisive clustering scheme with 21 clusters. Ig images weregenerated using the Pymol software (W. L. DeLano, The PyMOLMolecularGraphics System, 2002 HYPERLINK, http://www.pymol.org). Solvent-accessible surface electrostatic potentials were calculated using AdaptivePoisson-Boltzmann Solver (35).

Clustering analysis

Clustering results were compared with the IGHVand IGLVmutation status.As described earlier, we used two different classifications to represent themutation level of Igs, namely, the two-class partition with a 2% cutoff fordefining mutated and unmutated groups, and the three-class partition thatdivides CLLs in HM, SM, and unmutated samples. For each cluster and forthe two classifications, we computed the probability that an equal or highernumber of Igs belonging to the same class could be found by chance ina randomly extracted subset of the same size (hypergeometric distribution).The Bonferroni–Holm method (36) was used to correct for multiple test-ing. We assigned the smaller value between the two/three probabilityvalues to each cluster. The graphical representation of the cluster resultswas generated using the R package tool A2R.

CLL specificity in structural clusters

To test whether the structural clusters were describing specific featuresof CLL Igs rather than general Ig characteristics, we built, for each clustercontaining more than five Igs, a sequence-based hidden Markov model(HMM) (36) including the H and L chains of all Igs in the cluster. To thisend, we used the HMMER package with default parameters.

Each HMMwas used to score each Ig in the test data set and, for each Ig,the largest score was recorded. The same procedure was applied to the Igsin each of the clusters used to build the HMMs. For the sequence-basedclustering, IGVH–IGVL/K paired sequences of all Igs of our CLL dataset were clustered using the cd-hit software (37) with a sequence identitythreshold of 80%. The statistical difference between the Igs in the test dataset marked as CLL with all others was computed using the R imple-mentation of the Wilcoxon Mann–Whitney U test.

ResultsSample description and stereotyped receptor frequency

We sequenced the VH and VK/VL regions of a cohort of 366IgM+ CLL patients, 61.7% (226/366) of which expressed IgK and38.3% (140/366) IgL isotype. According to the two-class classi-fication described inMaterials and Methods, the cohort comprised47.3% (173/366) U-CLL samples, 63.6% (110/173) of whichexpressed k-isotype L chains and 36.4% (63/173) the l-isotypeL chains. Of the remaining 52.7% (193/366) M-CLL samples,60.1% (116/193) expressed the k-isotype L chains and 39.9%(77/193) the l-isotype L chains. Of the 366 CLL patients, 13.7%(50/366) expressed a stereotyped BCR: 48 of these belonged to 24different previously described CLL subsets (12, 14), whereas 2 didnot, thus defining a novel stereotyped subset (Supplemental TableI). The most represented CLL subsets were subsets 1, 2, and 6,

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respectively, representing 19.2% (n = 10), 19.2% (n = 10), and11.9% (n = 7) of all stereotyped receptors in the cohort.

Analysis of the Ig three-dimensional models

The definition of stereotyped BCRs is based on sequence infor-mation from the H chain only (12, 14). We conjectured that addinginformation on the L chain and focusing on structural features ofthe ABS would be more informative.We used the atomic coordinates of the ABS obtained by

structural modeling and quantified their structural similarity. Asdescribed in Materials and Methods, we could build reliablemodels for 342 of the 366 Igs in our CLL data set. Their ABSstructures were clustered as described, leading to the definition of21 well-separated clusters. The most populated cluster (cluster 2)

contained 28% of all Igs, followed by cluster 5 (13%), cluster 9(8.5%), and cluster 1 (7.6%). Altogether, 323 of the 342 modeledIgs (94.5%) fell in clusters containing at least 5 Ig clones. Only19 Igs (5.5%) were distributed in smaller clusters (SupplementalTable I). Fig. 1 illustrates all 15 clusters containing $5 Igs.Among these, seven (clusters 1, 2, 5, 6, 16, and 21) contained Igswith only k L chains and eight (clusters 3, 4, 7, 8, 9, 10, 19, and20) only l L chains. The genetic features of the samples belongingto the 21 clusters are listed in Supplemental Table I. All of thefollowing analyses were performed on the 15 clusters formed by.5 Igs.We analyzed the correlation between the structural clusters and

the mutational status of the Igs. Interestingly, we found that the 172M-CLL samples and 151 U-CLL Igs segregated with a significant

FIGURE 1. Structural clustering and mutational status of CLL Igs. (A) Hierarchical divisive clustering of the 342 modeled CLL Igs grouped according to

the structural similarity (TM score) of their ABSs. Clusters with five or more samples are shown. On the left, the silhouette value corresponding to different

number of clusters is shown. The optimal cut (corresponding to 21 clusters) is reported as a blue dot. (B) Two-class (upper bars, unmutated [U]; mutated

[M]) and three-class (lower bars, U, SM, HM) description of the mutational status of all samples. (C) Statistical analysis of the structural clusters. The

number of samples, according to their two-class (upper table) and three-class (lower table) description, is reported together with the probability that the

enrichment of Igs with the same mutational status observed in a cluster is due to chance alone. The p values ,0.05 are reported in bold.

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overrepresentation of either mutated or unmutated Igs in 5 of theclusters (clusters 1, 2, 3, 6, and 9), accounting for 53% of the cases(180/342; Fig. 1). If three different intervals of mutation are usedinstead of two (HM, SM, unmutated), 9 of the 15 clusters weresignificantly enriched in samples belonging to 1 of the 3 groups(clusters 1, 2, 3, 4, 6, 9, 18, 19, and 20), accounting for 224 of 342(65%) modeled Igs (Fig. 1).We also mapped the hydrophobicity and electrostatic potential

of the ABS surface of our models, and these properties also turnedout to be very similar in Igs within a cluster (Figs. 2–4). As an ex-ample, the structure of all Igs belonging to one cluster (cluster 19)is shown in Fig. 2. As can be appreciated from Fig. 2, remarkablesimilarities can be observed in terms of conformation, hydro-phobicity, and electrostatic potential of the ABS surface. Fig. 3shows, for each of the clusters, one representative Ig structure.Conserved hydrophobic patches can be identified in some clusters,located either in the center of the ABS (clusters 7 and 9), near H3(clusters 2, 6, 12, 18), or near the H2 loop (clusters 1 and 3; Fig.

3). Based on the classifications adopted in the literature (38, 39),a summary of the ABS characteristics are reported in Table I. It isapparent that these characteristics, even if very difficult to quantifyin an objective way in protein models, can provide an overview ofthe main differences and similarities between the ABSs belongingto different clusters and to the same cluster, respectively. It is tobe expected that they are related to the nature of the respectiveAgs. For example, Abs with deep pockets, grooves, and flat sitesare often specific for small molecules, peptides, and proteins, re-spectively. Interestingly, Igs belonging to the same ABS cluster,thus sharing high structural similarity, do not necessarily show ahigh level of sequence similarity, as demonstrated by the examplesshown in Fig. 4.The length of some hypervariable loops belonging to some

clusters also shows a bias (Table I). For instance, cluster 2 includes

FIGURE 2. Hydrophobicity and electrostatic potentials of samples in

cluster 19. The solvent-accessible surface of all CLL samples of cluster 19

is colored according to the Eisenberg hydrophobicity scale (right, hydro-

phobic in green, hydrophilic in white) and to the electrostatic potentials

(left, red is negatively charged, blue is positively charged). Molecules are

shown from the Ag point of view (ABS is visible, L chain is on the left, H

chain on the right, H3 loop on top). The region of the hierarchical clustering

corresponding to cluster 19 and sample names are reported on the left.

FIGURE 3. Structures of representative samples for each of the clusters

in Fig. 1. For each structural cluster containing at least five Igs, a repre-

sentative sample has been selected to provide a view of the physico-

chemical properties of the Igs in the cluster. Solvent-accessible surfaces

are colored according to the Eisenberg hydrophobicity scale (right, hy-

drophobic in green, hydrophilic in white) and to the electrostatic potentials

(left, red is negatively charged, blue is positively charged).

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Igs with short L1, L3, and H3 loops; cluster 1 those with long andhydrophobic H2 and H3 loops; in cluster 4, a long L3 associateswith short L1 and H3 loops; whereas in cluster 5, the oppositepattern is observed. In cluster 6, a groove is formed by a short H2and long H1 and H3 loops. A long H3 is present in clusters 12 and18 as well. Notably, cluster 2 contains a remarkable fraction(∼25%) of our CLL Igs and, despite its numerosity, it is still ratherhomogeneous in terms of the included structures, most of whichcontain a cavity in the ABS surface and have short, hypervariableloops (Fig. 1, Table I).It has been reported that the IGHV gene mutational status

correlates with the overall survival of affected patients. This is alsothe case, on average, for Igs in our CLL patients for which clinicalinformation is available (Fig. 5A). Remarkably, a different patternis observed for Igs belonging to cluster 2, for which there aresufficient clinical data and a balanced distribution of mutated andnonmutated Igs to carry out a statistically significant analysis.Mutated and unmutated Igs belonging to this cluster have almostoverlapping overall survival curves and very similar median of thesurvival time (174 versus 172 mo; Fig. 5B). We compared thisresult with that obtained by selecting at random 10,000 times thesame number of Igs as in cluster 2 from our data set, whereaskeeping the same ratio of mutated and unmutated samples. In only6.6% of the cases was the difference in the median survival timebetween patients with mutated and nonmutated Igs in the randomsampling smaller than or equal to that observed in cluster 2. Thisimplies that the survival of CLL patients is not necessarily onlycorrelated to the mutational status of their Igs, but, as in the caseof the cluster 2 samples, might be related to the ABS propertiesand, therefore, most likely to the recognized Ag.The BCR stereotype of the obtained structural clusters is re-

markably biased, with cluster 4 containing all the 12 subset 2 BCRsamples, cluster 1 all subset 6, cluster 2 most of the subset 1 (7/10),and cluster 7 containing both of the 2 novel stereotyped BCRsidentified in this study for the first time, to our knowledge (Sup-plemental Table I).

CLL specificity of the structural clusters

To verify whether the structural clustering captures, in whole or inpart, features that are specific for CLL Igs, we built HMMs for each

of the clusters using the sequences of its members and used them toclassify the sequences of the independent Ig data sets described inMaterials and Methods that are the ‘‘test’’ data set composed byIgs from CLL patients not present in our original data set (n = 212)and the ‘‘test w/o CLL’’ data set including non-CLL Igs (n = 2229).The resulting similarity score distributions showed that members

of the ‘‘test CLL’’ had a significantly higher score than those fromthe ‘‘test w/o CLL’’ Igs (p = 0.0014). We also repeated the pro-cedure on our ‘‘test AI’’ and ‘‘test w/o (CLL-AI)’’ data sets, andobserved that the scores for the ‘‘test AI’’ samples were rathersimilar to those of CLL Igs. Accordingly, when the method wasapplied to the ‘‘test CLL’’ and ‘‘test w/o (CLL-AI)’’ Igs (theremaining non-CLL/nonautoreactive Igs, n = 1935), the differencebetween the scores of the former and latter data sets was evenmore pronounced (p = 7.1 3 1025; Fig. 6A). These results indi-cate that our structural clustering well reflects the properties ofCLL Abs, and that these are somewhat similar to those of auto-reactive Ig-binding sites, as it has been suggested before by studiesshowing that CLLs originate from self-reactive B cell precursors(40, 41). However, it was also observed that although U-CLLsexpressed autoreactive Abs, most M-CLLs did not (41). In linewith this latter observation, when scoring ‘‘test AI’’ Ig sequencesusing the HMMs generated from each of our structural clusters,the scores with the HMMs generated from clusters 2 and 9, whichcontain mostly mutated Igs, were significantly lower than with theothers (p = 3.9e-10; see also Supplemental Table II, showing thescores of AI Abs with HMM derived from clusters enriched inmutated and unmutated Igs).It is relevant to stress that the classification abilities of the HMMs

originate from the structural based protocol that we used forclustering. This is proven by the following experiment. We clus-tered the Igs using sequence similarity as a metric (see Materialsand Methods) and obtained 16 clusters with $5 elements ac-counting for 44% of the Igs and 141 very small clusters includingall the remaining ones. HMMs built on the sequence-based 16clusters were unable to distinguish between the elements of the‘‘test CLL’’ and ‘‘test w/o CLL’’ data sets and between those of the‘‘test CLL’’ and ‘‘test w/o (CLL-AI)’’ data sets (Fig. 6B).This finding also demonstrated that, although there is a corre-

lation between germline usage and structural similarity, the latter

Table I. Summary of the predominant structural and physicochemical properties of the ABS surface, and amino acid lengths of the hypervariable loops,for clusters with five or more samples

ABSCluster

ABS Shapea

Charge HydrophobicityNo. of

Samples (%)L1 Average(6SD)

L3 Average(6SD)

H1 Average(6SD)

H2 Average(6SD)

H3 Average(6SD)Cavity Groove Planar

1 + + 26 (7.6) 12.1 6 1.5 9.2 6 1.0* 5.0 6 0.0* 17.0 6 0.0* 18.9 6 3.7*2 + 95 (27.8) 11.4 6 1.3*** 9.1 6 0.9*** 5.2 6 0.5 16.8 6 0.7 14.3 6 3.3***3 + — + 6 (1.8) 11.5 6 1.2 10.5 6 1.0 5.0 6 0.0 17.0 6 0.0 20.3 6 3.84 + — 23 (6.7) 11.0 6 0.0*** 11.4 6 1.3*** 5.1 6 0.5 16.7 6 0.7 12.2 6 4.6***5 Partial 48 (14.0) 16.0 6 1.6*** 9.0 6 0.9*** 5.2 6 0.6 16.9 6 0.7 18.5 6 3.9**6 + + 24 (7.0) 11.0 6 0.3* 9.4 6 0.9 6.1 6 1.0*** 16.2 6 0.4*** 19.4 6 2.4**7 + + + + 14 (4.0) 13.6 6 0.8* 10.6 6 0.5*** 5.3 6 0.7 16.9 6 0.8 18.2 6 4.08 Partial — 5 (1.4) 11.6 6 1.3 10.6 6 1.7 5.0 6 0.0 17.0 6 0.0 17.2 6 5.29 + + 29 (8.5) 13.6 6 0.7* 10.2 6 1.1* 5.3 6 0.6 16.5 6 0.5* 14.6 6 3.8*10 + 12 (3.5) 11.0 6 0.0** 10.3 6 1.0 5.5 6 0.9* 16.3 6 0.5 17.5 6 4.912 + + + 8 (2.3) 12.6 6 2.7 10.1 6 0.8 5.0 6 0.0 16.7 6 0.5 19.2 6 5.215 + Partial + 12 (3.5) 11.8 6 1.9 9.4 6 1.2 5.0 6 0.0 17.0 6 0.0 16.9 6 4.618 + Partial + 8 (2.3) 11.0 6 0.0* 10.9 6 0.3** 5.2 6 0.7 16.9 6 0.6 20.5 6 7.1*19 + — + 8 (2.3) 12.0 6 1.4 12.2 6 1.2** 5.0 6 0.0 17.0 6 0.0 16.2 6 3.320 + — 5 (1.4) 16.0 6 0.0** 10.0 6 1.0 5.0 6 0.0 16.6 6 0.5 19.0 6 7.6Whole CLL

data set342 (100) 12.5 6 2.1 9.8 6 1.27 5.25 6 0.6 16.6 6 0.7 16.5 6 4.7

aAccording to criteria described previously (38, 39). The average length of hypervariable loops L1, L3, H1, H2, and H3 for each cluster was compared with the averagelengths evaluated over all other Igs of the CLL data set (permutation test).

*p , 0.05, **p , 0.01, ***p , 0.001.

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captures additional relevant information that is not, or perhaps onlyvery weakly, correlated with germline usage, such as the HCDR3structure and the VL/VH packing.

DiscussionIt has been a matter of discussion for several years whether theB lymphocyte clones that accumulate in CLL patients displaywidely distributed Ag specificity among the billions of self and

nonself antigenic epitopes encountered by the immune system, oralternatively, if they express a restricted set of ABSs. Support tothe first hypothesis is provided by the observation that stereotypedIgV rearrangements exist (2, 7–9), even though they are mostlyfound in U-CLL cases (10, 16, 17). Furthermore, a classificationbased on stereotyped receptors accounts for only a fraction ofCLL patients, and these are distributed in quite a large number ofsubsets (.300). This is likely due to the fact that the definitionof stereotyped BCRs is mostly based on the HCDR3 amino acidsequence composition and length, which only partially contributesto the shape of the ABS of an Ig.In this study, we exploited the availability of our accurate

protocol for modeling the structure of Igs and of our collection ofpaired VL/VH sequences of Igs to investigate whether a betterunderstanding of the CLL Igs could be obtained by taking intoaccount the shape of their complete binding site.To this end, we built models of all the complete Igs from CLL

patients obtained by us and retrieved from public sources, and clus-tered them on the basis of the structural similarity of their binding sites.Interestingly, the samples, both U-CLL and M-CLL, could be

partitioned in a limited number of clusters. Notably, this is not thecase if the clustering is done on the basis of sequence similarity.Most members of the clusters shared interesting properties

other than their structural similarity, such as the type of L chain(k or l), BCR stereotypes, and mutational status. In some instan-ces, members of the same cluster display remarkable homogeneityalso in terms of the IGHV-IGK/LV usage and H/L chains pairing.For example, Igs belonging to cluster 1 all carry the IGHV1-69

gene combined with IGKV L chains, and cluster 3 contains all

FIGURE 4. Examples of high structural similarity of Igs with low se-

quence identity. Samples CLL038 and N1405 (top, cluster 2) have different

IGHV, IGHD, and IGKV genes, and their L and H chains share only 73 and

49% sequence identity, respectively. Samples CLL282 and CLLGN24

(cluster 4, middle) use different IGHV, IGHD, and IGHJ genes, and samples

CLL048 and CLL270 (cluster 15, bottom) use different IGHD, IGHJ, and

IGKV genes. In all cases, the pairs have a nearly identical binding site.

FIGURE 5. Overall survival (OS) analysis with respect to somatic

hypermutation. Kaplan–Maier curves for OS calculated separately for U-

CLL (,2% mutation) and M-CLL ($2% mutation) samples from the

whole CLL data set (A) or cluster 2 CLLs (B). Clinical data are available

for only a fraction of patients; therefore, the analysis was performed for

n = 101 CLLs distributed across all clusters (A) and n = 26 cluster 2 CLLs

(B). Median survival values were 120 and 196 for U-CLL and M-CLL

samples in the whole data set (p = 0.0011) (A) and 172 and 174 for cluster

2 U-CLL and M-CLL samples (B).

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unmutated Igs that use the IGHV1-69 gene combined with L chainsthat almost exclusively use genes of the IGLV3 family.Cluster 4 includes almost half of all SM cases of our CLL cohort

and all subset 2 stereotyped cases of the data set. These CLLs usethe IGHV3-21/IGLV3-21 genes and are known to have unfavorableclinical outcome regardless of their mutational status (42). Inter-estingly, cluster 4 includes IGHV3-21, but also IGHV3-48 andIGHV3-11 CLL cases. In a previous study (43), a very large dataset of HCDR3 amino acid sequences was used to cluster CLLcases based on their HCDR3 sequences, and the IGHV3-21 genepredominated in a cluster where also CLLs using the IGHV3-48and IGHV3-11 genes were present. This was interpreted as sug-gestive of the presence of some functional constraint that we cannow also relate to the structure of the binding site. Interestingly, thecomposition of our cluster 4 indicates that the IGHV3-21, IGHV3-48, and IGHV3-11 CLL genes generate a structurally similarbinding site (almost) only when paired with the IGLV3-21 L chaingene.We used our structural clustering results to generate statistical

models of their members in the form of HMMs. The generatedHMMs have discriminative power in that they are able to identifyCLL Igs in a large data set not including the Igs used for clusteringand, remarkably, are also able to separate AI Abs from non-AI ones.

Also in this case, no discriminative power could be achieved usinga sequence-based classification.The ability of the structure-based HMMs to identify common

features among AI Abs can, in principle, be due to a bias in the dataset because sequenced AI Abs react to a subset of specific Ags.However, it is very likely that this potential bias is not, or is notsolely, responsible for our finding given the fact that in several casesauto-Ags have been proved to be reactive with CLL clones (44).Our results strongly suggest that specific features of the CLL Igs

reside in the overall atomic structure of their binding site andtherefore provide support to the hypothesis that a finite number ofantigenic structures may be involved in CLL pathogenesis.Our data also show that the features of the ABS are partially

captured by the stereotype subset classification, even though thelatter relies only on the amino acid sequence of CDR3. For ex-ample, our cluster 1 contains all subset 6 stereotyped Igs, cluster 4all subset 2 stereotyped cases, cluster 2 mostly includes subset 1stereotyped BCRs (7/10), and cluster 7 contains the 2 novel ste-reotyped BCRs identified for the first time, to our knowledge, inthis study.The possibility of clustering CLL Igs on the basis of their

functional properties as determined by the structure of their bindingsite and potentially by their recognized Ag raises the interestingquestion of whether there is any correlation with the clinicaloutcome of the patients. This might be expected because some BCRstereotype subsets (14, 17, 45–48), as well as the mutational status(5, 6), are known to correlate with the patient prognosis.Clinical data are available for only a limited set of patients;

however, the analysis of our most populated cluster (cluster 2)shows very promising results.This cluster mostly contains M-CLL cases and a smaller fraction

of U-CLL cases. As mentioned earlier, M-CLLs have generallylonger life expectancy, less need for therapy, and better response totreatment when compared with U-CLLs. However, the U-CLLcases within cluster 2 display a clinical outcome much more fa-vorable than what is generally observed for U-CLL patients. Shouldthis result be confirmed on a larger cohort of patients, it wouldindicate that clinical outcome is correlated to the structure of thebinding site and only indirectly linked to the IGHVmutation status.In this view, cluster subdivision would be of help in better clas-sifying the numerous outliers observed using the more simplistic U-CLL and M-CLL classification.This is the first time, to our knowledge, that Igs from CLL

patients were analyzed from a structural point of view, and webelieve that our results point to the relevance of using this ap-proach on a larger scale, which can now be easily handled bycurrent methodologies for modeling and structural analysis.This is a perspective study and the clinical data are still rather

sparse. Clearly we will continue to monitor the clinical outcome ofthe patients enrolled in this study, as well as repeat the analysis onsamples that will become available in the future, becausewe stronglybelieve that our strategy has the potential to lead to advances inunderstanding the nature of CLL and in managing patients.The correlation between ABS structure and clinical outcome,

if confirmed, may provide novel tools for a more robust prognosticstratification of CLL, also thanks to the fact that sequencing of theIgVL can easily become a standard laboratory test, as it is alreadythe case for IgVH sequencing, and the modeling and clusteringprotocols are very well defined and available.Currently, it is essentially impossible to identify the Ag given the

structure of the cognate Ab-binding site, but this might change inthe future and hopefully we might also be able to gain insight in thenature of the Ags associated with CLL pathogenesis, which wouldobviously have important applications for therapy.

FIGURE 6. Box plots of scores obtained by scanning the test data sets

with sequence-based HMMs derived from the structural clusters. Score

distributions on Igs from a ‘‘test’’ data set obtained by assigning to each

‘‘test’’ Ig a score reflecting its similarity with the representative profiles

(HMM) of each cluster. The ‘‘test’’ data set is divided in three ‘‘test’’ sets,

namely, ‘‘Test CLL’’, ‘‘Test AI,’’ and ‘‘Test w/o (CLL+AI)’’ (all remaining

Igs of the ‘‘test’’ set after subtracting ‘‘Test CLL’’ and ‘‘Test AI’’). The

data for the three-dimensional clusters are shown in (A); those obtained

using the paired VH–VK/VL amino acid sequence are shown in (B). Box

edges indicate the first and third quartiles; the whiskers extend to the most

extreme data points, and the central bar indicates the median. Dashed gray

area represents the 5th and 95th percentile of scores obtained on the same

Igs used to build the HMMs. (A) Distributions of the scores for HMMs

built on the 15 most populated three-dimensional clusters of our CLL data

set. (B) Distributions of the scores for HMMs built on the 16 most popu-

lated clusters of paired VH–VK/VL amino acid sequences.

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DisclosuresThe authors have no financial conflicts of interest.

References1. Chiorazzi, N., K. R. Rai, and M. Ferrarini. 2005. Chronic lymphocytic leukemia.

N. Engl. J. Med. 352: 804–815.2. Fais, F., F. Ghiotto, S. Hashimoto, B. Sellars, A. Valetto, S. L. Allen,

P. Schulman, V. P. Vinciguerra, K. Rai, L. Z. Rassenti, et al. 1998. Chroniclymphocytic leukemia B cells express restricted sets of mutated and unmutatedantigen receptors. J. Clin. Invest. 102: 1515–1525.

3. Ghiotto, F., F. Fais, E. Albesiano, C. Sison, A. Valetto, G. Gaidano, J. Reinhardt,J. E. Kolitz, K. Rai, S. L. Allen, et al. 2006. Similarities and differences betweenthe light and heavy chain Ig variable region gene repertoires in chronic lym-phocytic leukemia. Mol. Med. 12: 300–308.

4. Hadzidimitriou, A., N. Darzentas, F. Murray, T. Smilevska, E. Arvaniti,C. Tresoldi, A. Tsaftaris, N. Laoutaris, A. Anagnostopoulos, F. Davi, et al. 2009.Evidence for the significant role of immunoglobulin light chains in antigenrecognition and selection in chronic lymphocytic leukemia. Blood 113: 403–411.

5. Damle, R. N., T. Wasil, F. Fais, F. Ghiotto, A. Valetto, S. L. Allen,A. Buchbinder, D. Budman, K. Dittmar, J. Kolitz, et al. 1999. Ig V gene mutationstatus and CD38 expression as novel prognostic indicators in chronic lympho-cytic leukemia. Blood 94: 1840–1847.

6. Hamblin, T. J., Z. Davis, A. Gardiner, D. G. Oscier, and F. K. Stevenson. 1999.Unmutated Ig V(H) genes are associated with a more aggressive form of chroniclymphocytic leukemia. Blood 94: 1848–1854.

7. Ghiotto, F., F. Fais, A. Valetto, E. Albesiano, S. Hashimoto, M. Dono,H. Ikematsu, S. L. Allen, J. Kolitz, K. R. Rai, et al. 2004. Remarkably similarantigen receptors among a subset of patients with chronic lymphocytic leukemia.J. Clin. Invest. 113: 1008–1016.

8. Messmer, B. T., E. Albesiano, D. G. Efremov, F. Ghiotto, S. L. Allen, J. Kolitz,R. Foa, R. N. Damle, F. Fais, D. Messmer, et al. 2004. Multiple distinct sets ofstereotyped antigen receptors indicate a role for antigen in promoting chroniclymphocytic leukemia. J. Exp. Med. 200: 519–525.

9. Tobin, G., U. Thunberg, A. Johnson, I. Eriksson, O. Soderberg, K. Karlsson,M. Merup, G. Juliusson, J. Vilpo, G. Enblad, et al. 2003. Chronic lymphocyticleukemias utilizing the VH3-21 gene display highly restricted Vlambda2-14gene use and homologous CDR3s: implicating recognition of a common anti-gen epitope. Blood 101: 4952–4957.

10. Tobin, G., U. Thunberg, K. Karlsson, F. Murray, A. Laurell, K. Willander,G. Enblad, M. Merup, J. Vilpo, G. Juliusson, et al. 2004. Subsets with restrictedimmunoglobulin gene rearrangement features indicate a role for antigen selec-tion in the development of chronic lymphocytic leukemia. Blood 104: 2879–2885.

11. Widhopf, G. F., II, L. Z. Rassenti, T. L. Toy, J. G. Gribben, W. G. Wierda, andT. J. Kipps. 2004. Chronic lymphocytic leukemia B cells of more than 1% ofpatients express virtually identical immunoglobulins. Blood 104: 2499–2504.

12. Agathangelidis, A., N. Darzentas, A. Hadzidimitriou, X. Brochet, F. Murray,X. J. Yan, Z. Davis, E. J. van Gastel-Mol, C. Tresoldi, C. C. Chu, et al. 2012.Stereotyped B-cell receptors in one-third of chronic lymphocytic leukemia:a molecular classification with implications for targeted therapies. Blood 119:4467–4475.

13. Murray, F., N. Darzentas, A. Hadzidimitriou, G. Tobin, M. Boudjogra,C. Scielzo, N. Laoutaris, K. Karlsson, F. Baran-Marzsak, A. Tsaftaris, et al.2008. Stereotyped patterns of somatic hypermutation in subsets of patients withchronic lymphocytic leukemia: implications for the role of antigen selection inleukemogenesis. Blood 111: 1524–1533.

14. Stamatopoulos, K., C. Belessi, C. Moreno, M. Boudjograh, G. Guida,T. Smilevska, L. Belhoul, S. Stella, N. Stavroyianni, M. Crespo, et al. 2007. Over20% of patients with chronic lymphocytic leukemia carry stereotyped receptors:pathogenetic implications and clinical correlations. Blood 109: 259–270.

15. Minden, M. D., R. Ubelhart, D. Schneider, T. Wossning, M. P. Bach,M. Buchner, D. Hofmann, E. Surova, M. Follo, F. Kohler, et al. 2012. Chroniclymphocytic leukaemia is driven by antigen-independent cell-autonomous sig-nalling. Nature. 489: 309–312.

16. Sutton, L. A., E. Kostareli, A. Hadzidimitriou, N. Darzentas, A. Tsaftaris,A. Anagnostopoulos, R. Rosenquist, and K. Stamatopoulos. 2009. Extensiveintraclonal diversification in a subgroup of chronic lymphocytic leukemiapatients with stereotyped IGHV4-34 receptors: implications for ongoing inter-actions with antigen. Blood 114: 4460–4468.

17. Thorselius, M., A. Krober, F. Murray, U. Thunberg, G. Tobin, A. Buhler,D. Kienle, E. Albesiano, R. Maffei, L. P. Dao-Ung, et al. 2006. Strikingly ho-mologous immunoglobulin gene rearrangements and poor outcome in VH3-21-using chronic lymphocytic leukemia patients independent of geographic originand mutational status. Blood 107: 2889–2894.

18. Wu, T. T., and E. A. Kabat. 1970. An analysis of the sequences of the variableregions of Bence Jones proteins and myeloma light chains and their implicationsfor antibody complementarity. J. Exp. Med. 132: 211–250.

19. Chothia, C., and A. M. Lesk. 1987. Canonical structures for the hypervariableregions of immunoglobulins. J. Mol. Biol. 196: 901–917.

20. Morea, V., A. Tramontano, M. Rustici, C. Chothia, and A. M. Lesk. 1998.Conformations of the third hypervariable region in the VH domain of immu-noglobulins. J. Mol. Biol. 275: 269–294.

21. Tramontano, A., C. Chothia, and A. M. Lesk. 1990. Framework residue 71 isa major determinant of the position and conformation of the second hyper-

variable region in the VH domains of immunoglobulins. J. Mol. Biol. 215: 175–182.

22. Marcatili, P., A. Rosi, and A. Tramontano. 2008. PIGS: automatic prediction ofantibody structures. Bioinformatics 24: 1953–1954.

23. Pedotti, M., L. Simonelli, E. Livoti, and L. Varani. 2011. Computational dockingof antibody-antigen complexes, opportunities and pitfalls illustrated by influenzahemagglutinin. Int. J. Mol. Sci. 12: 226–251.

24. Morea, V., A. M. Lesk, and A. Tramontano. 2000. Antibody modeling: impli-cations for engineering and design. Methods 20: 267–279.

25. Almagro, J. C., M. P. Beavers, F. Hernandez-Guzman, J. Maier, J. Shaulsky,K. Butenhof, P. Labute, N. Thorsteinson, K. Kelly, A. Teplyakov, et al. 2011.Antibody modeling assessment. Proteins 79: 3050–3066.

26. Cheson, B. D., J. M. Bennett, M. Grever, N. Kay, M. J. Keating, S. O’Brien, andK. R. Rai. 1996. National Cancer Institute-sponsored Working Group guidelinesfor chronic lymphocytic leukemia: revised guidelines for diagnosis and treat-ment. Blood 87: 4990–4997.

27. Pommie, C., S. Levadoux, R. Sabatier, G. Lefranc, and M. P. Lefranc. 2004.IMGT standardized criteria for statistical analysis of immunoglobulin V-REGION amino acid properties. J. Mol. Recognit. 17: 17–32.

28. Bikos, V., N. Darzentas, A. Hadzidimitriou, Z. Davis, S. Hockley, A. Traverse-Glehen, P. Algara, A. Santoro, D. Gonzalez, M. Mollejo, et al. 2012. Over 30%of patients with splenic marginal zone lymphoma express the same immuno-globulin heavy variable gene: ontogenetic implications. Leukemia 26: 1638–1646.

29. Chailyan, A., A. Tramontano, and P. Marcatili. 2012. A database of immuno-globulins with integrated tools: DIGIT. Nucleic Acids Res. 40(Database issue):D1230–D1234.

30. Zemla, A. 2003. LGA: A method for finding 3D similarities in protein structures.Nucleic Acids Res. 31: 3370–3374.

31. Al-Lazikani, B., A. M. Lesk, and C. Chothia. 1997. Standard conformations forthe canonical structures of immunoglobulins. J. Mol. Biol. 273: 927–948.

32. Rousseeuw, P. J. 1987. Silhouettes: a graphical aid to the interpretation andvalidation of cluster analysis. J. Comput. Appl. Math. 20: 53–65.

33. Zhang, Y., and J. Skolnick. 2004. Scoring function for automated assessment ofprotein structure template quality. Proteins 57: 702–710.

34. Anderberg, M. R. 1973. Cluster Analysis for Applications. Academic Press, NewYork.

35. Baker, N. A., D. Sept, S. Joseph, M. J. Holst, and J. A. McCammon. 2001.Electrostatics of nanosystems: application to microtubules and the ribosome.Proc. Natl. Acad. Sci. USA 98: 10037–10041.

36. Eddy, S. R. 1998. Profile hidden Markov models. Bioinformatics 14: 755–763.37. Li, W., and A. Godzik. 2006. Cd-hit: a fast program for clustering and comparing

large sets of protein or nucleotide sequences. Bioinformatics 22: 1658–1659.38. Lee, M., P. Lloyd, X. Zhang, J. M. Schallhorn, K. Sugimoto, A. G. Leach,

G. Sapiro, and K. N. Houk. 2006. Shapes of antibody binding sites: qualitativeand quantitative analyses based on a geomorphic classification scheme. J. Org.Chem. 71: 5082–5092.

39. MacCallum, R. M., A. C. Martin, and J. M. Thornton. 1996. Antibody-antigeninteractions: contact analysis and binding site topography. J. Mol. Biol. 262:732–745.

40. Sthoeger, Z. M., M. Wakai, D. B. Tse, V. P. Vinciguerra, S. L. Allen,D. R. Budman, S. M. Lichtman, P. Schulman, L. R. Weiselberg, andN. Chiorazzi. 1989. Production of autoantibodies by CD5-expressingB lymphocytes from patients with chronic lymphocytic leukemia. J. Exp. Med.169: 255–268.

41. Herve, M., K. Xu, Y. S. Ng, H. Wardemann, E. Albesiano, B. T. Messmer,N. Chiorazzi, and E. Meffre. 2005. Unmutated and mutated chronic lymphocyticleukemias derive from self-reactive B cell precursors despite expressing differentantibody reactivity. J. Clin. Invest. 115: 1636–1643.

42. Bomben, R., M. Dal Bo, D. Capello, D. Benedetti, D. Marconi, A. Zucchetto,F. Forconi, R. Maffei, E. M. Ghia, L. Laurenti, et al. 2007. Comprehensivecharacterization of IGHV3-21-expressing B-cell chronic lymphocytic leukemia:an Italian multicenter study. Blood 109: 2989–2998.

43. Collis, A. V., A. P. Brouwer, and A. C. Martin. 2003. Analysis of the antigencombining site: correlations between length and sequence composition ofthe hypervariable loops and the nature of the antigen. J. Mol. Biol. 325:337–354.

44. Catera, R., G. J. Silverman, K. Hatzi, T. Seiler, S. Didier, L. Zhang, M. Herve,E. Meffre, D. G. Oscier, H. Vlassara, et al. 2008. Chronic lymphocytic leukemiacells recognize conserved epitopes associated with apoptosis and oxidation. Mol.Med. 14: 665–674.

45. Bomben, R., M. Dal Bo, D. Capello, F. Forconi, R. Maffei, L. Laurenti, D. Rossi,M. I. Del Principe, A. Zucchetto, F. Bertoni, et al. 2009. Molecular and clinicalfeatures of chronic lymphocytic leukaemia with stereotyped B cell receptors:results from an Italian multicentre study. Br. J. Haematol. 144: 492–506.

46. Chu, C. C., R. Catera, K. Hatzi, X. J. Yan, L. Zhang, X. B. Wang, H. M. Fales,S. L. Allen, J. E. Kolitz, K. R. Rai, and N. Chiorazzi. 2008. Chronic lymphocyticleukemia antibodies with a common stereotypic rearrangement recognize non-muscle myosin heavy chain IIA. Blood 112: 5122–5129.

47. Rossi, D., and G. Gaidano. 2010. Biological and clinical significance of ste-reotyped B-cell receptors in chronic lymphocytic leukemia. Haematologica 95:1992–1995.

48. Tobin, G., U. Thunberg, A. Johnson, I. Thorn, O. Soderberg, M. Hultdin,J. Botling, G. Enblad, J. Sallstrom, C. Sundstrom, et al. 2002. Somaticallymutated Ig V(H)3-21 genes characterize a new subset of chronic lymphocyticleukemia. Blood 99: 2262–2264.

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