Comparative genome profiling across subtypes of low-grade B-cell lymphoma identifies type-specific and common aberrations that target genes with a role in B-cell neoplasia

Abstract

Background Low-grade B-cell lymphomas are a very heterogeneous group of tumors, whose differential diagnosis is frequently compromised by the lack of specific cytogenetic or molecular features. Our objective was to search for genomic features that allow a better molecular identification of the different types of lymphoma studied.Design and Methods We selected a panel of 87 low-grade B-cell lymphoma tumor samples that were unambiguously diagnosed (clinically and cytogenetically) as: follicular, splenic marginal zone, nodal marginal zone, lymphoplasmacytic, mantle cell, extranodal marginal zone MALT-type lymphoma or B-cell chronic lymphocytic leukemia. All samples were subjected to the same high-resolution genomic DNA analysis (array-based comparative genomic hybridization): a whole genome platform that contained 44000 probes distributed across the genome. Genomic imbalances were recorded, compiled and analyzed.Results Eighty percent of analyzed cases showed genomic imbalances (deletions and gain/amplifications) but the frequency of these imbalances ranged from 100% in mantle cell lymphomas to 33% in MALT lymphomas. A total of 95 new genomic imbalances affecting all lymphoma subtypes, were defined. We evaluated the extension of the genomic instability, detecting distinct patterns of genomic instability within subtypes. Specific pathways, such as nuclear factor kB (gains of REL and BCL11A, and losses of COMMD3, BIRC1, IKK1 and NFKB2), Polycomb group proteins (gain of BMI1 and deletion of PCGF6), DNA repair checkpoint pathways (deletion of 16q24 involving CDT1), or miRNA with a role in B-cell lymphoma pathogenesis (MIRN15A, MIRN16-1), were targeted by this genomic instability.Conclusions Although all subtypes of lymphomas showed gains and losses of DNA, the analysis of their genomic profiles indicated that there are specific aberrations in almost every subtype as well as frequent aberrations that are common to a large number of lymphoma types. These common aberrations target genes that are important in B-cell lymphomagenesis.

Introduction

Low-grade B-cell lymphomas account for approximately 50% of all lymphomas. They are distinguished by a relatively low proliferative index, small cell size, relatively large tumor masses and a paradoxical combination of advanced clinical stages associated with low clinical aggressiveness.1 The World Health Organization (WHO) classification includes at least seven major subtypes of low-grade B-cell lymphomas: follicular, splenic marginal zone, nodal marginal zone, lymphoplasmacytic, mantle cell, extranodal marginal zone MALT type and B-cell chronic lymphocytic leukemia.2,3 Each of these subtypes is considered a distinct disease, with specific molecular and morphological features, susceptible in many cases to lymphoma-type adjusted therapeutic protocols.4 Although some of the most common conditions are relatively well defined, this is not the case for the most infrequent, such as lymphoplasmacytic and nodal marginal zone lymphomas. Equally, the differential diagnosis between these low-grade B-cell types of lymphoma is frequently a hard task, compromised by the lack of specific cytogenetic or molecular features. As a consequence of the difficulties in the differential diagnosis, therapies for many lymphoma types are still frequently based on common protocols with variable success. The knowledge so far accumulated on molecular diagnosis has not been translated into the development of new, targeted therapies. Within this context, the development of new high-throughput techniques provides an opportunity for genome-wide analysis at different levels. In this study we made use of array-based comparative genomic hybridization (array-CGH) to scan genomic imbalances across a panel of gold-standard cases of different subtypes of low-grade lymphoma. Although conventional CGH (based on chromosome analysis) had been extensively used for the search of chromosome changes in lymphomas,5,6 only certain subtypes of low-grade lymphomas such as mantle cell,7–11 nodal marginal zone12–15 and chronic lymphocytic leukemia16 have been studied with array-CGH technology. These analyses have been done with different genomic platforms at very variable levels of resolution, preventing an exhaustive comparative study. We systematically applied the same high resolution assay to the seven subtypes of lymphoma to avoid misinterpretation of the data, to define specific genomic imbalances (deletions and gain/amplifications), to compare their distribution among subtypes and to produce, for the first time, a genomic instability profile of these tumors.

Design and Methods

Tumor samples and clinical data

Genetic and clinical diagnostic data were obtained for a series of 87 indolent non-Hodgkin’s lymphomas (NHL) provided by participating Institutions through the CNIO Tumour Bank. The series included 15 cases of follicular lymphoma, 15 of B-cell chronic lymphocytic leukemia, 14 of mantle cell lymphoma, 9 of nodal marginal zone lymphoma, 10 of lymphoplasmacytic lymphoma, 15 of splenic marginal zone lymphoma, and 9 cases of extranodal marginal MALT-type lymphoma. The histological diagnosis of each case was centrally reviewed, following the established WHO criteria.2 All cases were CD20-positive and Ki67-negative. Clinical and genetic data are summarized in Online Supplementary Table S1. All cases presented the standard morphology, clinical picture, analytical features, and peripheral blood morphology and phenotype described for each entity.

Array-CGH assays, fluorescence in situ hybridization (FISH) validation and data analysis

All samples were hybridized against Human Genome CGH 44K microarrays (Agilent Technologies, Palo Alto, CA, USA), spanning the entire human genome at a median resolution of ~75Kb. Genomic DNA was obtained from peripheral blood of ten healthy female donors. DNA was extracted with DNeasy tissue kits (Qiagen, Germantown, MD, USA). The same amount of each donor DNA was mixed in a female DNA pool to be used as reference DNA in all the hybridizations. Hybridizations were done according to the manufacturer’s protocols and all data analyzed as previously described.17,18 Copy number karyotypes of each tumor are recorded in Online Supplementary Table S1.

Recurrent regions affected by genomic imbalances were defined as a sequence of at least five consecutive altered probes common to a set of array-CGH profiles and smallest overlapping regions of imbalance (SORI) as a recurrent region that contains no smaller recurrent region.19 FISH assays were performed with commercial probes (Vysis Inc., Downers Grove, IL, USA) for the aberrations listed in Online Supplementary Table S1. The UCSC genome browser (http://genome.ucsc.edu) was used to select the bacterial artificial chromosome (BAC) clones covering the 7q31 region in splenic lymphoma: RP11-154N21, RP11-597L6, RP11-166D01, RP11-706J21, RP11-140I14, RP11-10I12 and RP11-138A9. The BAC were obtained from BACPAC Resource Centre (BPRC) at the Children’s Hospital Oakland Research Institute (Oakland, CA, USA). A commercial centromeric probe for chromosome 7 (Vysis Inc., Downers Grove, IL, USA) was used as a control. FISH assays were carried out according to the manufacturer’s instructions. FISH scoring of the 7q31 region fluorescence signal was carried out in each sample by counting the number of single copy gene and control probe signals in an average of 100 nuclei. A sample was considered deletion-positive when the ratio between the test region and control signal was near 0.5 in more than 50% of the tumor cells. To avoid overestimation of the number of genomic imbalances, due to such polymorphisms being detected by the array-CGH platform, we eliminated all DNA copy changes (genomic imbalances) observed in our samples that had at least 80% of their sequence overlapping with known polymorphisms included in the Database of Genomic Variants (version: 20 December 2006). GEPAS Suite software (http://gepas.bioinfo.cipf.es/) was used for unsupervised clustering, supervised analysis and gene annotation. Additional unsupervised clustering was performed with CLUSTER.20

Table 1.Description of the recurrent genomic aberrations (SORI) according to the lymphoma subtype.

Results

Genomic imbalances in the different subtypes of lymphoma

We analyzed 87 samples of unambiguously classified into the more frequent subtypes of low-grade B cell lymphoma. All samples were assayed by high resolution array-CGH in the same way and those regions affected by genomic imbalances were annotated for each case (Online Supplementary Table S1). The complete data set has been deposited in the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/projects/geo/) and is accessible through GEO series accession number GSE8918. Overall, 80% of the lymphomas showed an altered genome with this frequency ranging from 100% in mantle cell lymphoma to 33% in MALT lymphomas. Online Supplementary Figure S1 represents the frequency and localization of the imbalances found in each subtype of lymphoma. All genomic imbalances were compiled to define the SORI, both in the complete set of samples and separately according to subtype. The complete list of the 95 SORI (corresponding to 65 deleted and 30 gained regions) found is provided in Table 1, together with their size, frequency and other features. Novel findings were observed in all subtypes of lymphoma.

We found that 87% of the low-grade follicular lymphomas showed genomic imbalances (represented by 21 SORI; 12 deletions and 9 gains). The most frequent gains were those of the short arms of chromosomes 7 and 8 (Table 1). 6q deletions (the most frequent imbalance) appeared as four independent SORI scattered through 6q13 to 6q24.1. A new aberrant small deletion on chromosome 2q31.1, which only affected the HOXD1 gene, was observed.

Eighty percent of B-cell chronic lymphocytic leukemias showed genomic imbalances that were used to identify six SORI (4 deletions and 2 gains). We were able to define the extent of some previously known imbalances more precisely. For example, deletion of 11q was narrowed down to a region of 18.8 Mb (which included the ATM gene). 13q deletions were observed as two recurrent deletions of 100 Kb and 300 Kb (Table 1), including the miRNA genes MIRN15A/MIRN16-1. Finally, we identify a discrete gain on 2p16 (100 Kb) that included the REL and BCL11A genes.

Mantle cell lymphoma was the most complex entity, with 48 SORI (12 gains and 36 deletions) being detected. As in follicular lymphoma, the deletion of 6q appeared as three independent SORI. We identified a deletion of 7q31.34 in three out of 14 samples – a region similar to the one that appears in splenic marginal zone lymphomas. This deletion was confirmed by FISH. As new findings we noted the small deletions on 10q24.3 (involving PCGF6, PDCD11 and TAF5 genes, which overlaps with the deletion observed in 13% of the follicular lymphoma cases that included the IKK1 and NFKB2 genes), 12q13.12 (WNT1), 19q13.32 (PUMA), 17q11.2 (MIRN365-2/MIRN193A) and 5q13 (also seen in lymphoplasmacytic lymphomas).

Lymphoplasmacytic lymphomas have never been studied by array-CGH. We observed aberrations in nine out of ten cases and defined six SORI, all of them corresponding to deletions. The most frequent event (44%) was monosomy of chromosome 19 and 6q deletions. A deletion on 5q13, similar to the one identified in mantle cell lymphoma, was observed. Splenic marginal zone lymphomas revealed genomic imbalances in 11 out of 15 samples. The specific 7q deletion was precisely identified from 7q31.32 to 7q34, spanning 22.5 Mb (Table 1) and was recurrently seen in six samples (40%). New regions of imbalance are deletions on 13q14.2 (overlapping with those seen in chronic lymphocytic leukemia and mantle cell lymphoma) and on 16p12. The gain of chromosome 3 was narrowed down to 3q26.33q29. Finally, seven out of nine nodal marginal zone lymphomas showed genomic imbalances. Trisomy of chromosome 3 was recurrently seen and it could be re-defined as a region located at 3q11q29. Newly defined genomic imbalances in nodal marginal zone lymphomas were gain of 6p and deletions on 1p36 and 19q13.2. Three out of nine MALT type lymphomas showed genomic imbalances. However, no recurrent SORI were found. Therefore, the MALT subgroup of samples was not considered for subsequent analyses.

Genomic analysis of the whole series

The analysis of the complete series revealed that some SORI were present in several different subtypes of lymphoma. Overall, we found that 35 genomic regions were altered in more than four different subtypes of low-grade lymphoma (Table 2). The deletion in 6q that appears in almost all subtypes was delineated by ten independent SORI extending from 6q12 to 6q25. The gained region on the short arm of chromosome 2 was delineated by three SORI and included the REL gene. Gain of 3q26q29 and 12q13q14 affected five and six out of the six investigated subtypes of lymphoma, respectively. We also found deletions in 11q23, 13q14, 17p13 and 19q13.12 and gains of 8q24, 18q21.23 that were present in most subtypes. Taking advantage of the predefined SORI, we conducted a supervised clustering analysis to define the most representative genomic markers from each subtype (Figure 1). The 50 most significant SORI were then tested in an unsupervised clustering analysis (Figure 2). The different subtypes of low-grade lymphoma were segregated with different efficiencies, probably due to the sample size. While cases of mantle cell lymphoma and chronic lymphocytic leukemia clustered approximately all together, cases of follicular lymphoma and splenic marginal zone lymphoma segregated into at least two branches, possibly as a reflection of two distinct patterns of genomic instability as we describe below. The lymphoplasmacytic and nodal marginal zone lymphomas failed to cluster properly.

Apart from the global profiles based on regions of genomic imbalances that were provided by array-CGH for each tumor, we conducted a genomic instability quantitative study of all subtypes of low-grade lymphoma assessed by different parameters, which gives a unique view of the genomic instability that a tumor has undergone at diagnosis.21 This novel approach enables greater understanding of the behavior of each lymphoma subtype. We took into account the number of whole chromosome aberrations, number of DNA breaks (referred to as copy number transitions) within a chromosome, high-level amplifications or homozygous deletions, and the number of chromosomes containing such transitions21 (Online Supplementary Table S2). We could infer different patterns of genomic instability among the subtypes of lymphoma. Mantle cell lymphoma and chronic lymphocytic leukemia showed a homogeneous pattern of genomic instability characterized mainly by the occurrence of copy number transitions and structural aberrations (Figure 3). Mantle cell lymphoma was the most genomically unstable subtype of lymphoma with a median of 14 copy number transitions per case (range, 2 to 38) and four high level amplifications. Chronic lymphocytic leukemia presented the same type of homogenous instability profile as mantle cell lymphoma, although the median value of copy number transitions was lower (median 4; range, 0 to 17). In contrast, splenic marginal zone, follicular and lymphoplasmacytic lymphomas were heterogeneous in terms of genomic instability. Each subtype seemed to contain at least two distinct patterns. While some samples behaved similarly to those of mantle cell lymphoma and chronic lymphocytic leukemia, others either failed to show any type of aberration or only had numerical changes.

Discussion

We present here a detailed comparative analysis of the DNA genomic imbalances found in a representative set of the most relevant subtypes of low-grade B-cell lymphoma. Although several works have been published describing genomic imbalances detected by array-CGH in B-cell lymphomas, each one of them was focused on a single subtype such as mantle cell lymphoma,7–11 nodal marginal zone lymphoma12–15 or chronic lymphocytic leukemia.16 In addition, almost every one of the studies were done using different genomic platforms with different types of clones (BAC, oligos, etc), densities, resolution and data analysis approaches. This tremendous variability has hampered the comparison of the incidence and nature of genomic imbalances among subtypes of low-grade lymphomas. Our approach was intended to minimize these sources of variability and to facilitate comparisons. To this aim, all samples were assayed with the same high resolution genomic platform, including about 44,000 probes covering the whole genome at an average resolution of 75Kb, with a redundant coverage of known cancer genes. This platform has been used for other types of tumors17,18 but never for the analysis of low-grade lymphomas. The second major point was the careful choice of samples, which were gold-standard representative cases of each subtype of lymphoma. For this purpose, all pathological diagnoses were performed in a single institution. All samples were carefully chosen to fulfil the standard clinical and pathological criteria of each of the subtypes.

Table 2.Distribution of recurrent genomic aberrations that are found in more than four subtypes of low-grade B-cell lymphoma.

Figure 1.Supervised clustering of the genomic imbalances detected by array-CGH. This analysis was conducted considering the six predefined subtypes of low-grade lymphoma.

Figure 2.Hierarchical unsupervised clustering of the genomic imbalances detected by array-CGH. This analysis was done using SORI as variables. SORI are defined in detailed in the table at the right of the dendogram. A color (shown at the bottom of the figure) was attributed to each subtype for better visualization.

Excluding MALT lymphomas, which in our series were not found to show recurrent changes, 85% of the samples displayed some degree of genomic instability. This percentage ranged between 100% for mantle cell lymphoma and 73% for the splenic marginal zone lymphomas. Novel findings were observed in all studied groups and are summarized in Table 1. Recurrent SORI were compared only with recurrent aberrations that have been reported based on array-CGH, whenever possible, or on conventional CGH approaches. With regards to follicular lymphomas,22–27 we confirmed some data and we redefined the gain on chromosome 7, which was reduced to 7p, and the nature of the 6q deletions,4 which appeared as four independent SORI scattered through 6q13 to 6q24.1. Additionally, a deletion of the HOXD1 gene, likely affecting of the NOTCH pathway via RBPJk,28 was observed. As reported previously,5,16,29,30 80% of chronic lymphocytic leukemia samples show genomic imbalances. Comparing our data with those of a recent study that used 10 K and 50 K single nucleotide polymorphism arrays to scan for genomic aberrations in peripheral blood or bone marrow from 70 patients with chronic lymphocytic leukemia,30 we better defined two small recurrent deletions on 13q to 100 Kb and 300 Kb. The latter deletion included the miRNA genes MIRN15A/MIRN16-1, which have been found to be deleted or down-regulated in the majority of patients with chronic lympocytic leukemia.31 Of interest, we redefined the gain of the 2p16 region as a frequent finding in chronic lympocytic leukemia. This common region includes the REL and BCL11A genes. All mantle cell lymphoma samples showed a highly aberrant genome. We were able to confirm the majority of published data,8–10 but also discovered some new altered regions. For the first time, we found, in 21% of the mantle cell lymphoma samples, the same 7q deletion that has been seen in splenic marginal zone lymphoma (discussed below). A new set of deletions, involving several genes of interest, was also identified. The deletion on 10q24 affects PCGF6 and PDCD11. PCGF6 codes for a protein that contains a RING finger motif (closely related to those of the polycomb group (PcG) proteins, RNF110/MEL-18 and BMI1) which has been shown to interact with some polycomb group proteins and act as a transcription repressor. PDCD11, which shares 82% identity with NFBP, codes for a protein that induces activation of NFκB and the FasL promoter, leading to apoptosis in Jurkat cells.32,33 On the other hand, the COMMD3 genomic sequence is disrupted by the aberration found on 10p12 that also resulted in the gain of BMI1. COMMD3 in a suppressor of NFκB activation34 and BMI1 is a well known oncogene from the polycomb group that represses the INK4A locus.35 A deletion on 17q11.2 included the loss of the miRNA MIRN365-2/MIRN193A genes, which have been described to be altered in human cervical cancer.36

Figure 3.Genomic instability profile of the six subtypes of low-grade lymphoma. Each tumor was analyzed by the same 44K array-CGH. All DNA changes were categorized in eight different parameters (Online Supplementary Table S2) but only three of them are shown in this figure: number of chromosome transitions (CNT) (black bars), number of chromosomes with structural changes (light gray bars) and the number of aneuploid chromosomes (dark gray bars). Different patterns of genomic instability may be observed: while all mantle cell lymphomas showed a regular profile of structural and numerical aberrations, splenic marginal zone lymphomas seemed to configure two classes (no changes or a few recurrent changes).

Analysis of lymphoplasmacytic lymphoma samples helped us to re-define the reported deletion of 6q4 as two SORI within 6q22.3–q25.1. A deletion on 5q13, similar to the one detected in mantle cell lymphomas, was recurrently observed. This deletion involved BIRC1, a gene that contains BIR domains that have been shown to be essential to prevent apoptosis in PC12 cells after stimulation of the tumor necrosis-α receptor (a key component of the NFκB pathway).37

Splenic marginal zone lymphomas have never been analyzed by a genomic approach. Comparing our results with those obtained using chromosome-based CGH,38–40 we defined several deletions in chromosomes 6p, 13q 16p, 16q, and 22q. The well-known deletion on 7q was precisely identified from 7q31.32 to 7q34, spanning 22.5 Mb, confirming recent data obtained from other molecular cytogenetic analyses.41 We found that this deletion occurs almost exclusively in 45% of splenic marginal zone lymphomas with a likely effect on the expression of microRNA genes that cluster within this region.42 Surprisingly, we detected the same lesion in three out of 14 cases of mantle cell lymphoma that were clearly diagnosed as such (CD20, CCND1 and FISH for the CCND1 rearrangement).

With regards to nodal marginal zone lymphomas, data from a chromosome-based study43 and from a recent paper that included another seven cases12 showed that genetic aberrations resulting in the gain of several regions of chromosome 3 constitute a common marker for this subtype of lymphoma. We found this change in two of nine samples in our series and, more importantly, we have narrowed down the gain to a smaller region located at 3q11q29. Newly defined genomic imbalances in nodal marginal zone lymphomas were the gain of 6p and deletions on 1p36 and 19q13.2, also partially described in other marginal zone lymphomas.12

There were several interesting findings from the genomic analysis conducted on all the samples pooled together (Table 2). Apart from the complete description of the deletions on 6q, several markers may be considered as the general profile of low-grade lymphoma: gains of 2p16, 3q26q29, 12q13 8q24 and 18q21 as well as losses of 11q23, 13q14, 17p13 and 19q13.12 have emerged as significant genomic regions of interest. All of them harbor genes that play a role in B-cell proliferation. As an example, the gained region of the short arm of chromosome 2 is delineated by three SORI and includes the REL gene.44 Amplification of this gene has been previously described in diffuse large B-cell lymphoma45 and we also demonstrated its overexpression in splenic marginal zone lymphoma.42 Generalizing, our results identify a set of commonly deleted or gained regions, independently of the diagnosis, which point to specific pathways, such as NF-κB (gains that involved REL and BCL11A, and losses that involved COMMD3, BIRC1, IKK1 and NFKB2), polycomb group proteins (gain of BMI1 and deletion of PCGF6), DNA repair checkpoint pathways (deletion of 16q24 involving CDT1), or miRNA with a role in B-cell lymphoma pathogenesis (MIRN15A, MIRN16-1).

Reports of genomic instability (Figure 3) have been published for bladder, breast, and neuroblastoma tumors.21,46,47 An emerging feature of this type of analysis is that genomic variables such as the number of transitions and the fraction of the genome altered in a single tumor may correlate with clinical evolution. Further follow-up and recruitment of more samples for our series will facilitate, in due time, a similar analysis for low-grade lymphomas. It is notable that the differences in genomic instability reports from each subtype of lymphoma reflect the same heterogeneity that is observed in the clustering analysis (Figure 1). Additionally, the majority of the genomic imbalances found in lymphoplasmacytic and nodal marginal zone lymphomas were also observed in other subtypes (mostly mantle cell and follicular lymphomas) and practically no specific aberrations were identified. These findings raise doubts about the diagnostic criteria for lymphoplasmacytic and nodal marginal zone lymphomas, and suggest that these tumors may not correspond to lymphoproliferative diseases with a distinctive molecular pathogenesis.

Acknowledgments

the authors thank the excellent work of the staff of the Spanish National Tissue Bank (especially to Laura Cereceda) and the technical work done by our technicians: Gloria Soler and M. Carmen Martin.

Footnotes

• Funding: this study was supported by grants from the Ministerio de Sanidad y Consumo (PI040555, G03/179, PI051623, PI05/2742), and the Ministerio de Educación y Ciencia (SAF 2005-04340, SAF2005-00221, SAF2004-04286), Spain. BIF has a Marie Curie PhD Early Stage Research Training fellowship and JS has a PhD fellowship from the Ministerio de Educación y Ciencia.
• The online version of this article contains a supplementary appendix.
• Authorship and Disclosures BIF and JS performed the experiments and analyzed the data. JFG, MM, FC, and SM provided, diagnosed and selected the cases. AC helped with the management of the bioinformatics of data. MAP and JCC designed the experiments. BIF, MAP and JCC analyzed the data and wrote the paper. The authors reported no potential conflicts of interest.

• Received September 10, 2007.
• Revision received November 19, 2007.
• Accepted December 17, 2007.