Molecular Karyotypes of Hodgkin and Reed–Sternberg Cells at Disease Onset Reveal Distinct Copy Number Alterations in Chemosensitive versus Refractory Hodgkin Lymphoma

The annual incidence of Hodgkin lymphoma (HL) is estimated at 3 cases per 100,000 persons, making this malignancy one of the most common lymphomas in the Western world (1). The characteristic pathologic feature of classical HL is the presence of Hodgkin and Reed-Sternberg (HRS) cells, which usually comprise less than 3% of the affected mixed cellular lesion. There is compelling evidence suggesting that the pathognomonic HRS cells are an outgrowth of a malignant clone derived from a “reprogrammed” germinal center (GC) B cell that no longer expresses B-lineage–specific genes, such as POU2F2, POU2AF1, and PU.1, and may express genetic markers characteristic of other hematopoietic lineages like IL21 (interleukin), CCL17, CSF1, ID2, CD3, and CD4 (2–4). Recurrent genetic lesions in critical hematopoietic transcription factors have led to the discovery that constitutive activation of the NF-κB signaling pathway is essential for HRS cell survival and proliferation (4). In particular, gains of REL and deletions or inactivating mutations of TNFAIP3 (TNF-α–induced protein 3), gene involved in the NF-κB signaling pathway, have been detected in about 40% of classical HL (5–7). To gain further insight into the pathogenesis of HL and its lineage infidelity, comprehensive genomic characterization of HRS cells from primary HL tumor samples is necessary; however, the scarcity of HRS cells in the HL lesions remains a key limiting factor in unraveling the molecular consequences of this malignancy.

Treatment with doxorubicin-based combination chemotherapy regimens leads to complete remission in the majority (70%–90%) of HL patients (1). Unfortunately, HL patients who fail front-line or second-line therapies, including stem cell transplantation, are quite often young and have a poor median survival (<3 years; ref. 8). In primary refractory HL (PR HL), the poor response to initial therapy may be attributed to nonrandom genetic alterations that underlie disease aggressiveness at onset. Comparison of the genetic aberrations of HRS cells from patients who respond favorably to standard HL treatment protocols with those who fail treatment has the potential to provide critical insight into the clinical behavior of HL and guide future treatment (9). Moreover, to develop novel HL therapies directed against rational targets, we need focused investigations that lead to more thorough understanding of the genetic alterations of HRS, its lineage infidelity consequences, and drug resistance.

Until recently, the majority of genetic and molecular biology HL data was gathered from studies using HL continuous cell lines established from patients with relapsed HL (10–13). Distinguishing primary genetic alterations from secondary events in HL pathogenesis remains a challenge. Recently, high-resolution cytogenetic techniques that combine laser capture microdissection with microarray-based comparative genomic hybridization (aCGH) technology have provided new opportunities to investigate genome-wide DNA alterations in lesions of limited size (13–15). Genomic DNA–based aCGH experiments show less variability than mRNA expression arrays, and suspected “cryptic” DNA aberrations can be confirmed easily by FISH. Importantly, robust prognostic features for many hematologic malignancies have been revealed using a combination of clinical parameters and genetic data gathered from conventional cytogenetics and molecular techniques. Given the paucity of genomic analyses of HRS cells, we sought to characterize their DNA copy number alterations (CNA) with bacterial artificial chromosome (BAC)-based aCGH using DNA extracted from laser capture microdissected (LCM) CD30⁺ HRS cells. The objectives of this study were to determine the recurring CNAs in HL, compare the findings of the malignant HL lesions to those detected in “benign” GC B cells–the normal counterpart of HRS cells, and define the most common CNA differences between the chemotherapy responsive (CR) and PR HL samples.

On approval from the Institutional Review Board of the City of Hope, primary formalin-fixed paraffin-embedded (FFPE) diagnostic samples were obtained from 27 patients including 15 patients with CR HL and 12 patients with PR HL. Clinicopathologic characteristics of the patients are summarized in Table 1. The international prognostic scores (IPS) of our study population were calculated as described (16). In this study, patients with an IPS of 2 or less were given a favorable designation and patients with an IPS of 3 or above were assigned to the unfavorable group. Control samples included 9 FFPE benign lymph node samples (4 males and 5 females) and GC cells from 10 FFPE reactive follicular hyperplasia (RFH) samples (2 males and 8 females). Each sample was submitted for conventional histopathologic processing to confirm HL involvement in the test samples or no evidence of malignancy in the control samples.

Five-micrometer serial sections from the FFPE tissue blocks were fixed onto PALM membrane slides (PEN-membrane; Zeiss) and processed as previously described (13, 14). A series of experiments designed to assess the impact of DNA source (e.g., archival material including frozen and FFPE), quantity, and amplification on array CGH was carried out to establish the FFPE aCGH protocol (13). Briefly, the slides were pretreated as follows for immunostaining: 1 hour at 65°C in a dry oven, 1 minute in xylene at room temperature, 5 minutes in 100% ethanol (2×), 5 minutes in 3% H₂O₂, and rinsed in dH₂O. Antigen retrieval was carried out at 98°C for 30 minutes using the decloaking chamber and decloaker universal heat retrieval buffer (both from Biocare Medical). Test sample slides were stained with monoclonal mouse anti-human CD30 (Dako Inc.) using the DakoCytomation Autostainer (Dako Inc.) per manufacturer's instructions. Control samples were stained with CD20. A total of 150 CD30⁺ HRS cells per test sample and 150 CD20⁺ GC cells per control sample were LCM into microfuge caps containing water.

Isolated LCM cells were randomly fragmented and amplified in the LCM collection tubes using the GenomePlex Single-Cell Whole-Genome Amplification Kit (WGA4; Sigma-Aldrich) according to product instructions. The quality and quantity of the isolated amplified DNA were assessed electrophoretically and spectrophotometrically. Samples were processed prior to aCGH using the GenElute PCR Clean-Up Kit (Sigma-Aldrich). A single sample was run in triplicate to evaluate assay reproducibility.

One microgram of genomic DNA was labeled using Bioarray Kit (Enzo Life Sciences) prior to subsequent hybridization to a RPCI 19K BAC array (Roswell Park) using a Gentac hybridization station (Genomic Solutions). Sex-mismatched pooled DNA from 20 healthy donors was used as reference DNA. The minimal tiling RPCI BAC array containing about 19,000 BAC clones was used (backbone consisted of ∼4,600 BAC clones with a BAC center-to-center distance of 165 kb; human genome build 36.1; ref. 13). Slides were scanned on the Genepix 4200A Scanner (Molecular Devices).

Two control studies were conducted to establish experimental reproducibility and quality control associated with multiple features of our study. These control studies had the objective of establishing standards for processing FFPE tissue, WGA, or eliminate problematic microarray calls associated with cross-hybridizing BACs or poor labeling/hybridization efficiency. First, 9 FFPE benign lymph node control samples were processed in an identical fashion to the test samples. Second, 1.05 ng of peripheral blood DNA (DNA equivalent of 150 cells) obtained from 6 normal, healthy individuals (3 males and 3 females) was amplified and analyzed by aCGH. Deviant calls from either of these control studies found in 2 or more microarrays were masked before the HL test samples were processed. Selection criteria for the reportable CNA calls in the HL samples were more than 5 BACs (∼700 kb in size) observed in 35% or more in either the sensitive or resistant samples with a value of P < 0.15 and not present in less than 20% (no more than 1 patient) of the benign control samples.

To determine which genes are presumably critical in the HL malignant process, GC B cells from 10 lymph node samples showing RFH were processed identically to the test samples and compared with the HL CNAs. Genes associated with RFH had to meet the following selection criteria: large (>700 kb) CNAs observed 15% or more commonly in the RFH group (2 or more samples) with a value of P < 0.15, and observed in less than 20% of the benign control group. The CNAs associated with RFH were filtered prior to the comparative analysis of the chemosensitive and primary refractory disease CNAs. This final analysis generated a list of genes that could be used as a potential outcome predictor to discriminate between CR HL and PR HL at presentation. To test the outcome predictor in a preliminary manner, 4 additional primary HL lesions were analyzed in a blinded yet similar manner to the original 27 HL test samples and categorized using the proposed predictor model.

Data images were quantified by ImaGene (BioDiscovery, Inc.), and the chromosome regions of abnormal copy number across the genome were detected by Nexus (BioDiscovery, Inc). To explore gene function and interactions, the CNAs were uploaded into Ingenuity Pathway Analysis tools 3.0 (IPA; http://www.ingenuity.com) for alternative and canonical pathway mapping. IPA calculates a per network significance score by applying the hypergeometric distribution calculated via the Fisher's exact test for 2 × 2 contingency tables. The P value is calculated by comparing the number of user-specified genes of interest that participate in a given pathway, relative to the total number of occurrences of these genes in all pathway annotations stored in the Ingenuity Pathways Knowledge Base (IPKB).

Locus-specific FISH analyses were conducted to confirm the recurring CNAs detected by aCGH. The probes used for FISH analysis were carefully chosen and mapped within the genomic coordinates of the specific chromosome region showing gain or loss. In each case, a control DNA FISH probe from the opposite chromosome arm was included to confirm the CNAs in relation to the ploidy level of the HRS cell. To validate CNAs by FISH, CD30-stained IHC slides with AEC detection were scanned, localized, and recorded on the slide using the Bioview Duet Image Analyzer (BioView, Ltd.) prior to FISH analysis as described previously (17).

We background corrected and normalized the quantified images with print-tip loess after removing bad spots. We evaluated all pairwise correlations between normalized replicates by scatter plots and Pearson's correlations on the log₂ ratios.

From each patient and control FFPE sample, 150 cells (CD30⁺ HRS cells from test samples and CD20⁺ cells from control samples) were isolated from 5-μm thick tissue sections by LCM, amplified using WGA, and hybridized to a 19K whole-genome BAC array. Two control studies were conducted to reduce deviant calls associated with poor hybridization efficiency or amplification bias associated with WGA or FFPE tissues. In the first experiment, 1.05 ng of peripheral blood DNA (DNA equivalent of 150 cells) obtained from 6 normal individuals (3 males and 3 females) were WGA amplified and analyzed by aCGH. In the second study, 9 formalin-fixed benign lymph node control samples (4 males and 5 females) were processed similar to the test samples. A total of 235 of the 16,952 (1.4%) autosomal BACs showed a false-positive (gain or loss) result in the normal control samples; these BACs were masked from the test data analyses. In addition, no chromosomal site was called positive if its respective BACs showed gain or loss in more than 2 arrays from normal control samples. On the basis of these control data, and to minimize the risk of reporting false-positive results, thresholds for calling gains and losses were set at more than 35% of test samples. Reproducibility of the FFPE was carried out by analyzing 150 HRS cells from a single HL patient that were isolated independently from the block and processed by the LCM-WGA-aCGH method in triplicate. Supplementary Figure S1 shows the high correlation between the normalized log₂ ratio values of each possible pair of replicates (Pearson r = 0.90, P values uniformly <0.0001), confirming reproducibility of the assay and the array.

Primary tissue blocks were obtained from 15 CR and 12 PR patients. The clinical and pathologic characteristics of the patients are listed in Table 1. Recurring chromosome imbalances were observed among all 27 HL samples. The most frequent gains (>65%) mapped to 2q37.3, 7p21.1, 8q24.3, 16q23.3-q24.3, 9q34.13-q34.3,14q32.33, 19q13.33, and 20q13.33 (Supplementary Data; Table 1) and included genes associated with growth and proliferation (AHR, BAI1, BOP1, COMMD5, LY6E, PTP4A3, SLURP1, CBFA2T3, SLC7A5, NOTCH1, FOXF1, TRAF2, IRF8, S100B, and MYH14), cell cycle (AKT1, CDK10, and SUMO3), drug metabolism (CYP11B1, CYP11B2, and SLC19A1), angiogenesis and cell adhesion (COL18A1, CDH4, ITGB2, and FOXC2), apoptosis regulation (FOXF1 and GPR132), immune and lymphatic development (CBFA2T3, IL17C, IRF8, CLEC11A, RXRA, SPIB, and ICOSLG) and invasion, metastasis or cancer relatedness (VAV2, PSCA, PTP4A3, GINS2, FUT7, TUBB2C, KLK, POLD1, and TFF2). Losses observed in more than 40% of HL samples encompassed SPRY1, NELL1, SLC1A3, GDNF, IL7R, SKP2 (S-phase kinase-associated protein 2), ID4 (inhibitor of DNA binding 4), PPARGC1A, and TXNIP (thioredoxin interacting protein). Because ploidy differences are inherent to HRS cells, to accurately validate the CNAs observed by aCGH, HRS-specific FISH confirmation studies using locus-specific probes that mapped within the genetic coordinates of the recurring CNAs were paired with a DNA control probe that localized to the opposite chromosome arm. These chromosome region–specific FISH analyses confirmed the presence or absence of aCGH-defined CNAs in CD30⁺ HRS for 15 regions in 3 to 5 patient samples for each paired FISH probe set (Fig. 1).

The genomic differences observed at more than 35% in the CR HL patients clustered within 2 chromosomal bands on chromosome 16 (16q13 and 16p11.2) and showed gains of genes known to regulate T-cell trafficking or NF-κB activation (CCL22, CX3CL1, CCL17, DOK4, and IL10; Table 2). In comparison to the CR samples, the 12 PR patients showed frequent loss of genes localized to 1q21.2, 4q27, 5p15, 11p14.3, and 17p12 with recurrent gains of chromosome bands 2q37.2, 13q31.2, 19q13.3, and 22q13.33. These data were also compared with the IPS of our study population (Table 1). Of interest, the 39 unique genes we identified in regions with CNAs comparing patients with favorable versus unfavorable IPS scores showed remarkable overlap with the 63 unique genes identified in chemosensitive and primary resistant genomic profiles (Table 2), with 36 unique genes appearing in both lists. The probability of this occurring by chance follows a hypergeometric distribution (18, 19), and the associated P value was less than 0.0001.

To delineate which genes are more likely to play a critical role in HL oncogenesis, normal GC cells from 10 RFH samples were analyzed and compared with the HL results. As described in Materials and Methods, strict criteria were established to call recurring gains and losses associated with CR HL, PR HL, and benign RFH. Figure 2A shows the number of genes concordant and discordant among these 3 groups observed in more than 35% of the samples tested in each group. The first assessment of these data identified 151 genes frequently altered in both CR HL and PR HL samples but not detected in the benign RFH samples, 81 genes commonly detected in the CR HL and 312 genes more frequently observed in the PR HL tumors and not in the other 2 groups. The 10 most frequent genetic alterations detected among the 151 HL CNAs included losses of genes in 5p13 (AMACR, GDNF, and SKP2), gains of 7q36 (SHH, sonic hedgehog homolog), and gains of 9q34 (ABL1, CDK9, ENG, LCN2, PTGES, and TSC1). A second assessment of the CR HL and PR HL gene subsets was conducted requiring a frequency difference of more than 20% and a value of P = 0.15 between the sensitive and resistant samples (Fig. 2B). In this analysis, 19 genes were clearly more common in chemosensitive samples, whereas 44 genes were more prevalent in the primary refractory tumors (Table 2).

Possible genetic interactions for those genes listed in Table 2 were examined in the context of the curated list of published molecular interactions in IPKB. In addition to an association with the NF-κB signaling pathway, IPA revealed connections between genes in the calcium signaling and IL2/IL21 signaling pathway (Fig. 3). Key genes from these studies were used to construct a putative outcome model for predicting chemosensitive or primary refractory disease (Fig. 4). Of interest, all 7 patients showing loss of the SLC17A6 (solute carrier family 17, sodium-dependent inorganic phosphate cotransporter member 6) gene had PR disease; however, the 4 patients showing gains of the SLC12A3 (solute carrier family 12 sodium/chloride transporters member 3) gene were CR. Second, patients showing no gain of 16q13 and loss of 4q27 or 17p12 were commonly associated with a refractory phenotype (9 of 11 patients). Third, patients showing 16q13 gain with either few CNAs (10 of 12), or no loss of 4q27 or 17p12 (3 of 3) were usually CR.

On the basis of the relatively high frequency of IL21/IL2 loss at 4q27 and BCL3 overrepresentation in our PR HL cohort, we hypothesized samples without gains in 16q13 and losses of IL21/IL2 at 4q27,11p14.3/SLC17A6, or 17p12 with or without concurrent gains of BCL3 portend a highly likely drug-resistant HL phenotype. To test this CR versus PR outcome model in a preliminary manner, we first recategorized all 27 patients according to the proposed model and predicted the outcome correctly in 23 (85%) of 27 patients (Fig. 4). Four newly acquired HL samples were then tested in a blind fashion. One sample showed loss of 4q27 without BCL3 gains and was correctly assigned primary refractory; the remaining 3 cases were correctly designated “chemosensitive;” however, 1 “chemosensitive” patient (BK21) who initially responded to treatment suffered relapse of disease 10 years later.

The identification of recurring cytogenetic and molecular alterations in HL has been thwarted by the infrequent occurrence of HRS cells in affected lesions (4). To gain further insight into the molecular genetic alterations in HL, we used a combined LCM-WGA-aCGH approach to analyze the genomic composition of HRS cells from 27 diagnostic samples from patients with classical HL. As expected, the molecular karyotypes of the HRS cells revealed numerous CNAs with recurring gains more common than losses due to the ploidy variation associated with mononucleated Hodgkin and multinucleated Reed–Sternberg cells. To refine our analysis, we limited this study to include the most common CNAs, defined as gains and losses in more than 35% of the HL samples, and confirmed by chromosome region–specific FISH analyses in CD30⁺ HRS.

Previous studies have shown that multiple signaling pathways and transcription factors are deregulated in HRS cells including NF-κB, JAK (Janus-activated kinase)-STAT, and PI3K (phosphoinositide-3-kinase)-AKT (4, 7, 20). Interestingly, regions with the most frequent gains in this study housed NOTCH1 and TRAF2, transcription factors, which mediate NF-κB activation (21, 22). Gains at the AKT1 locus support a role for constitutive activation of PI3K in HL (20, 23) and further support data linking activated AKT1 to NF-κB signaling, p53 inhibition, and enhanced resistance to apoptosis (24). Overrepresentation of the FOXC2 gene, a known transcriptional regulator of CXCR4 and delta-like 4 (DLL4; ref. 25) implies the downregulation of the Notch inhibitor Deltex1 to promote constitutive activation of NOTCH1 activity in HRS cells (21). In this study, gains of the forkhead (FOX) transcription factor FOXF1 gene were consistently observed with gains of SHH, supporting the supposition that NF-κB regulates SHH expression to further advance NF-κB–mediated proliferation and apoptosis resistance (26, 27). Other critical gains detected included the CDK10, a cyclin-dependent kinase gene, which plays a role in cellular proliferation; GPR132, a member of the G-protein–coupled receptor (GPCR) family that slows cell proliferation and repairs damaged DNA; and SUMO3, which encodes a small ubiquitin-related modifier involved in nuclear transport, DNA replication, breakage and repair, mitosis, signal transduction, and the cellular SUMOylation system (28).

Prominent losses observed in HRS cells targeted genes with prominent roles in cell-cycle progression and tumor suppression. Both SKP2 (5p13.2) and ID4 (6p22.3) have been linked to the pathogenesis of lymphoma (29, 30). Inactivation by hypermethylation of ID4 in follicular and diffuse large cell lymphoma suggests a tumor suppressor (TSG) role in malignant lymphoma (30). Similar studies in other cancers also indicate that SPRY1 and NELL1, 2 receptor tyrosine kinase signaling genes (31, 32) also have tumor suppressor roles. SKP2 (F-box protein family) encodes a critical component of the ubiquitin ligase complex that regulates the G₁/S transition of the cell cycle by mediating target proteins such as CDKN1B cyclin-dependent kinase inhibitor and RELB (33). Consistent with the phenotype of HRS cells, SKP2-knockout experiments in mice show a hypoproliferative phenotype with serious cellular defects including nuclear enlargement, polyploidy, an increased number of chromosomes, and chromosomal instability (34). Two other notable losses observed in HL were the TXNIP, a gene required for natural killer cell maturation, and PPARGC1A, a gene reported to antagonize NF-κB activation and the inflammatory gene expression of PTGS2 (prostaglandin endoperoxide synthase-2; also known as cyclooxygenase-2 or COX2; ref. 35).

A comparison between the HRS and normal GC molecular karyotypes showed a shared relationship with gains in 4 transcription factors associated with hematopoietic stem cells and early lymphocyte development. IRF8 is a key transcription factor that modulates the myeloid versus lymphoid lineage choice by hematopoietic stem cells via interactions with the ETS family proteins encoded by SPI1 or SPIB. SPI1 encodes an ETS domain transcription factor (PU.1) that activates gene expression during myeloid and B-lymphoid cell development, whereas SPIB acts as a lymphoid-specific enhancer to promote development of plasmacytoid dendritic cells or natural IFN-producing cells (36, 37). Specifically within the GC, IRF8 is part of the transcriptional network governing B-cell lineage differentiation including the transcriptional regulation of activation-induced cytidine deaminase and BCL6 (37). Finally, ICOSLG is a ligand for the T-cell–specific cell surface receptor ICOS, and the product of this gene acts as a costimulatory signal for lymphoid proliferation and cytokine secretion to mediate local tissue responses to inflammatory conditions. Of interest, ICOS is highly expressed on tonsillar T cells, which are closely associated with B cells in the apical light zone of GC (38). Taken together, the commonality of CNAs observed between GC and HRS cells substantiates a hematopoietic stem cell–derived progenitor cell relationship (4, 39).

A further comparison of the GC and aCGH HL results defined a subset of 151 CNAs consistently associated with HL. The 10 most frequent gene alterations in HL localized to 3 chromosomal regions and involved genes associated with inflammation, cell-cycle progression, and NF-κB/PI3K signaling: losses of AMACR, GDNF, SKP2 at 5p13, gain of SHH/7q36, and gains of ABL1, CDK9, ENG, LCN2, PTGES, and TSC1 at 9q34. As noted earlier, loss of SKP2 appears to be critical for the HRS hypoproliferative phenotype. The short-lived protein Cdk9 (the product of the CDK9 gene) is also regulated by the SCF (SKP2) ubiquitin ligase complex, suggesting loss of SKP2 and gains of CDK9 allows for a stronger association with TRAF2 and continued NF-κB activation (40). LCN2 (lipocalin 2, previously known as NGAL) mediates inflammatory responses and has binding sites for NF-κB, the hematopoietic transcription factors GATA-1 and SPI1 (PU.1), as well as the matrix metalloproteinases (MMP), which protect them from degradation and promote tumor cell invasion and metastasis (41). A link between MMP and LCN2 in this study is intriguing, as Steidl and colleagues recently reported that overexpression of MMP11 is consistently associated with a tumor-associated macrophage expression profile and shortened survival in HL (9). Finally, gains of PTGS2 (formerly known as COX2) are thought to enhance tumor angiogenesis, suppress antitumor immunity, and possibly play an important role for cytokine-driven PTGS2 NF-κB activation (42).

Our genome-wide discrimination analyses on CNAs revealed distinct clusters that correlated closely with the favorable and unfavorable IPS in our HL patients suggesting the possibility of different chemoresponsive genomic profiles at disease presentation. To test this hypothesis, we compared the 2 distinct HL subgroups, selected 20 genes from 10 variable CNAs, and uploaded the data into IPA tools 3.0 to explore gene function and interactions. The data suggested that a key difference between the CR and PR molecular karyotypes may be opposing calcium signaling and IL21/IL2 pathway interactions within the tumor microenvironment. The CR patients showed frequent gains of 16q13, a chromosome region known to house genes that regulate T-cell trafficking or NF-κB activation (CCL22, CX3CL1, CCL17, DOK4, and IL10). HRS cells are known to secrete CCL17 and CCL22 which attracts TH2 and regulatory T cells; report higher numbers of CD4⁺CD25 (high) FOXP3⁺ regulatory T cells are reported to predict for improved survival and prognosis in non–Hodgkin lymphoma (43). Further characterization of the tumor microenvironment may provide insight on the differential regulation of regulatory T cell function and survival in HL.

In contrast, PR HL showed frequent loss of genes at 4q27, 5p15, 11p14.3, 17p12 and gains at 13q31.2 and 19q13.31. Losses of 2 cytokine genes, IL2 and IL21 within 4q27, were evident in about 50% of the PR HRS cells. IL21 exhibits potent antitumor responses, is homologous to IL2, and shares the common cytokine receptor γ chain of the IL2 receptor family (44). Although IL21 expression is usually restricted to subsets of CD4⁺ T cells and natural killer cells (44), two recent studies describe aberrant expression of IL21 activating STAT3 and STAT5 in HL continuous cell lines and a subset of primary HRS cells (3, 45). Moreover, Scheeren and colleagues (45) have shown that constitutive activation of STAT5 by IL21 results in activated NF-κB activity in immortalized B cells that have lost their B-cell phenotype and therefore mimic HRS cells, supporting a link between STAT5 and NF-κB in a subset of HL.

Conversely, the loss of IL21 in PR HL, as seen in our study, may prompt additional mutations in HRS cells to maintain cell survival in a cytokine-independent manner. One such complementary mutation may be gain of BCL3/RELB at 19q13.3. Previous to this report, only rare primary cases of HL (∼15%) showed BCL3 gains or translocations in HRS cells (15, 46); however, gains of BCL3 appear to be more common in HL cell lines (12). In this study, we show that BCL3 gains are common in PR HL but infrequent findings in CR HL (∼50% vs. 15%, respectively). Of interest, BCL3 not only regulates the NF-κB signaling pathway by modulating the DNA-binding activity of NF-κB transcription factors involved in apoptosis and cell growth but also inhibits p53-induced apoptosis (47). Because IL21 is a potent inducer of BCL3 (48), the absence of IL21 may trigger BCL3 gene amplification as a mechanism for HRS cell survival. Interestingly, gains or overexpression of BCL3 have been associated with poor prognosis in other lymphoid malignancies including HTLV-1–induced ATLL and multiple myeloma (48, 49). Finally, losses within chromosome band 17p12 could possibly result in genomic rearrangements leading to overexpression of CD68, a gene housed within the neighboring chromosome band 17p13.1. Increased numbers of CD68⁺ macrophages in HL have been correlated with short disease-free survival and a high likelihood of relapse post–autologous stem cell transplantation (9). Based on our aCGH HL findings, we propose HL samples without gains in 16q13 and losses of IL21/IL2 at 4q27, 11p14.3/SLC17A6 and/or 17p12 and possibly concurrent gains of BCL3 portend a chemorefractory HL phenotype. A preliminary evaluation of the model appeared to support our hypothesis and the finding of 16p13 gains in the CR samples suggests that genes housed in this region may predict for a chemosensitive HL phenotype. More detailed explorations of these loci are needed to identify precise and reproducible prognostic genetic markers in HL.

Even though BAC aCGH technology is robust and provides high signal-to-noise ratios when working with challenging samples, such as HRS cells, captured by LCM from FFPE tissue, there are some limitations to its use for development of new clinical prognostic assays in HL. Because aCGH does not detect inactivating mutations and our selection criteria precluded the detection of small deletions, loss of the small approximately 16-kb TNFAIP3 (or A20) at 6q23.3 was not detected in this study. We also did not observe amplification of the 42-kb REL gene within 2p16.1 as reported by others in about 30% to 40% of classical HL (5, 46); however, gains within chromosome band 19q13.3 which houses both BCL3 and RELB were observed. Most likely, our strict inclusion criteria of investigating only gains and losses observed in more than 35% of samples tested and within a 5-BAC region (>700 kb) precluded detection of the small gene gains and losses, suggesting the necessity for higher resolution microarray platforms in future studies.

Clarification of the genetic alterations and molecular mechanisms that underlie the oncogenic HRS cell are likely to improve our current understanding of HL. In this study, we identified DNA CNAs in HL by aCGH using DNA extracted from microdissected CD30⁺ HRS cells, showing the potential utility of this approach for defining prognostic parameters. Despite the limited sample size, this HL investigation is one of the largest to date analyzing the molecular karyotypes of well-characterized, primary diagnostic HL samples with long-term follow-up. The molecular data showed a clear relationship between CNAs of key genes associated with the NF-κB signaling pathway, the tumor microenvironment, cell-cycle regulation and apoptosis; however, the functional consequences of many of these genetic lesions and their complex interactions in HL tumorigenesis remain to be elucidated. Finally, the genetic differences observed between the chemosensitive and PR HL molecular karyotypes, in this study and others (5–7, 9), suggest that important pathogenetic and prognostic differences are present at disease onset. Given the paucity of PR HL tumor samples available for study and the rarity of HRS cells within these lesions, it is imperative that we unite our efforts for the collection of retrospective HL samples with long-term follow-up data for ongoing studies to confirm, refine, and define relevant biomarkers in PR HL. Further studies exploiting these genetic lesions and defining their role in the pathogenesis of HL will be essential in developing new targeted therapeutic approaches.

During the manuscript review process, an interesting and similar aCGH in classical HL study using laser microdissected HRS cells was published by Steidl and colleagues (50). Both studies agree that gains and losses of genes involved in the NF-κB signaling pathway are recurrent findings in classical HL. Unlike our study, Steidl and colleagues (50) report on the significance of gains and overexpression of the MDR gene ABCC1 in their treatment failure cohort, which consisted of lymph node samples collected from both treatment-naive and treatment-exposed HL patients. We did not find a correlation between gains of ABCC1 and drug resistance; however, our population consisted only of newly diagnosed pretreatment samples and we used a slightly different method (FFPE not frozen samples). Although our study size is smaller, we only utilized samples collected at HL disease presentation (no relapsed samples) to eliminate bias associated with treatment for our comparison of chemosensitive versus PR HL. This more homogenous patient population in combination with stringent gain and loss threshold requirements makes our data equally compelling.

M.L. Slovak is currently an employee of Quest Diagnostics. The other authors declared no potential conflicts of interest.

The authors thank Aaron Theisen for his critical reading of the manuscript and helpful suggestions.

This work was supported in part by a Lymphoma SPORE Developmental Research Award, Cancer Center grants at Roswell Park Cancer Institute (NCI CA016056) and the City of Hope (NCI CA33572 and CA-30206), The Tim Nesvig Lymphoma Research Fund, and a private donation in memory of Pearl Ruttenberg.

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.