Chng, Wee Joo

Abstract Background: Multiple myeloma (MM) is a plasma cell malignancy that is incurable with current approaches. The median survival for patients with MM is around four years and a significant proportion of patients experience a course characterized by multiple relapses treated with different therapies. The median survival for patients relapsing after the initial therapy is nearly 18 months and successive treatment strategies result in decreasing response durations, likely reflecting acquired drug resistance. In order to better understand the biological changes associated with advanced, relapsed, refractory MM, we compared gene expression profiles (GEP) of malignant plasma cells isolated from patients with relapsed refractory MM and compared them to plasma cells from patients with newly diagnosed MM. Methods: In order to obtain two relatively homogenous group of patients, we compared samples from 44 patient with newly diagnosed MM enrolled in the ECOG E1A00 clinical trial (comparing thalidomide and dexamethasone to dexamethasone alone) to 44 patients with relapsed refractory MM enrolled in a phase II trial of Velcade (SUMMIT), where most patients had four or more previous relapses. Plasma cells from bone marrow aspirates were separated by magnetic bead selection of CD138 positive cells and studied using Affymetrix HG-U133A chips using standard methodology. The arrays were analyzed using Genespring 7.2 software following GCRMA normalization and genes with differential expression between the two datasets were examined. Differentially expressed genes were further analyzed using Ingenuity Pathways Analysis program. Results: A total of 864 genes were identified which were at least two fold and significantly different between the newly diagnosed and relapsed patients. Using Ingenuity software, 437 of these genes were mapped to different biological networks. Examination of the canonical pathways demonstrated several important cellular pathways differentially regulated between the two groups. Several important mediators of the cytokines, receptors and respective signaling pathways appear to be down regulated in the relapsed group and included IGF-1, HGF, SDF-1 alpha, gp130 and importantly the MEK/ERK pathway. Additionally expression of adhesion molecules such as VCAM1 and PECAM was decreased in the relapsed group compared to newly diagnosed pts. There appear to increased tissue hypoxia in the relapsed marrow as indicated by up regulation of HIF-1 alpha as well increased levels of Placental growth factor. Myeloma cells from relapsed disease were characterized by decreased expression of mcl1, FLIP1, and bcl-xL and increased caspase 8 relative to newly diagnosed group. Also seen was decreased expression of the glucocorticoid and interferon receptors in the relapsed setting. Conclusion: Comparison of the GEP between MM cells from newly diagnosed and relapsed pts demonstrates important differences that have potential biological relevance. The plasma cell in the relapsed setting appears to be more independent of the tumor microenvironment. Additionally, differential expression of some of the genes provides clues to mechanisms of drug resistance commonly observed in the relapsed pts. We are in the process of validating some of the key findings from these analyses.

Abstract Chromosome 13 deletion (Δ13) is one the most common genetic abnormalities observed in multiple myeloma (MM) and confers a poor prognosis. Studies to identify critical tumor suppressor genes in MM till date have lack adequate resolution, and the molecular phenotype associated with Δ13 has not been established. In this study we seek to establish a molecular profile for Δ13 and to finely map the minimal common region of deletion (CDR) on chromosome 13 by using gene expression profiling (GEP) and array comparative genomic hybridization (aCGH). GEP was performed on RNA from purified plasma cells of 72 newly diagnosed MM and 50 human myeloma cell lines (HMCLs) using the Affymetrix U133A chip and U133plus chip (Affymetrix, Santa Clara, CA) respectively. Patients were assigned TC classes which correlates with underlying genetic subtypes. In addition, aCGH was performed on 79 MM samples (36 with GEP data) and 50 myeloma cell lines (HMCLs; 48 with GEP data) using a platform utilizing 60-mer oligonucleotides (Human Genome CGH 44B Oligo Microarrays, Agilent Technologies), which have a resolution of about 70Kb. Raw data was extracted using the Feature Extraction 8.1 and visualized using CGH Analytics 3.2 (Agilent Technologies). To define a signature that reflects the biological consequences of Δ13 and not its close association with some genetic subtypes of MM (example, t(4;14) and t(14;16)), we selected a training cohort consisting of cases belonging to D1, D2, 11q13 and none TC class such that cases with and without Δ13 are balanced for ploidy and TC classes (n=28). A 152-gene Δ13 signature was identified. Its specificity was confirmed by leave-one-out cross validation using the K-nearest neighbor (KNN) class prediction algorithm (predictive accuracy of 100% in the training cohort). It was subsequently validated in a validation cohort that includes t(4;14) and t(14;16) that were not use to derive this gene signature, maintaining a high predictive accuracy (88%). This gene-set may therefore reflect core transcriptional consequences of Δ13 and is enriched for genes involved in the cell cycle and apoptosis. Using aCGH, 20 patients have Δ13 (mostly whole chromosome) all involving the 13q14–q21 region. Of the 32 HMCLs with Δ13 (whole chromosome or interstitial), 22 have mono-allelic loss of RB1 whereas 4 had bi-allelic loss. Besides RB1, bi-allelic loss of several other genes in the 13q14–q21 region (CYSLTR2, CDADC1, ITM2B, PCDH9) was also observed. As the CDR is large and contains a number of genes, we utilized a set of criteria (bi-allelic loss in HMCLs, within 13q14–q21 CDR, gene expression significantly correlated with copy number, and loss could explain molecular phenotype) to narrow down the potential candidates. Only RB1 and ITM2B fulfilled these criteria. Furthermore, RB protein levels correlated well with mRNA levels and DNA copy suggesting that even in absence of mutations resulting in bi-allelic loss, mono-allelic loss of RB1 could be tumorigenic through a haploinsufficiency mechanism.

Abstract Hyperdiploid MM (H-MM), characterized by recurrent trisomies constitute about 50% of MM, yet very little is known about its pathogenesis and oncogenic mechanisms. Studies in leukemia and solid tumors have shown gene dosage effect of aneuploidy on gene expression. To determine the possible gene dosage effect and deregulated cellular program in H-MM we undertook a gene expression study of CD138-enriched plasma-cell RNA from 53 hyperdiploid and 37 non-hyperdiploid MM (NH-MM) patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Gene expression data was analyzed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Genes differentially expressed between H-MM and NH-MM were obtained by t-test (p<0.01). The majority of the differentially expressed genes (57%) were under-expressed in H-MM. Genes located on the commonly trisomic chromosomes were mostly (but not always) over-expressed in H-MM and constitute 76% of over-expressed genes. Chromosome 1 contained the most differentially expressed genes (17%) followed by chromosome 12 (9%), and 19 (8%). To examine the relationship of gene copy number to gene expression, we examined the expression of genes on chromosomes 9 and 15 in subjects with 2 copies (15 normal control and 20 NH-MM) and 3 copies (12 H-MM) of each chromosome as detected by interphase FISH. We then derived a ratio of the mean expression of each gene on these chromosomes between patients with 3 copies and 2 copies of the chromosome. If a simple relationship exists between gene expression and gene copy number, one would expect the ratio of expression of most genes on these two chromosomes to be about 1.5 in H-MM compared to NH-MM. However, many genes have ratios either higher than 2 or lower than 0.5. Furthermore, when the heterogeneity of cells with underlying trisomies is taken into consideration by correcting the ratio for the number of cells with trisomies, the actual ratio is always lower than the expected ratio. When the expression of genes on a chromosome was compressed to a median value, this value was always higher in the trisomic chromosomes for H-MM compared to NH-MM. The data suggests that although gene dosage influence gene expression, the relationship is complex and some genes are more gene dosage dependent than others. Amongst the differentially expressed genes with known function, 33% are involved in mRNA translation/protein synthesis. Of note, 37 of the top 100 differentially expressed genes are involved in these processes. In particular, 60 ribosomal protein (RP) genes are significantly (p<0.05) upregulated in H-MM. This signature in H-MM is not associated with increase proliferation as measured by PCLI. This predominant signature suggests that deregulated protein synthesis may be important for the biology of H-MM. Many of these RPs are involved in the synthesis of product of oncogenic pathways (e.g. MYC, NF-KB pathways) and may mediate the growth and survival of tumor cells. It is therefore possible that these tumor cells may be sensitive to the disruption of mRNA translation/protein synthesis. Targeting the mTOR pathway with rapamycin may therefore be useful for treatment of H-MM.

Abstract The biological heterogeneity of myeloma can be largely explained by genetic subtypes characterized by primary translocations (t(4;14), t(11;14), and t(14;16)) or trisomies of odd-numbered chromosomes (hyperdiploid myeloma). In newly diagnosed patients, it is well established that these genetic subtypes have different survival. However, the prevalence and survival of the main genetic subtypes in relapse patients is unknown. In this study, we examined the prevalence and survival according to TC classes (a gene expression-based classification system which identifies the main genetic subtypes of myeloma) in patients with relapsed myeloma entered into bortezomib trials who had gene expression profiling (GEP) performed as part of a pharmacogenomics study (Bortezomib cohort). In comparison, we analyzed a large publicly available GEP dataset of newly diagnosed patients treated with total therapy II from the University of Arkansas for Medical Science (UAMS cohort, GEP data available on Gene Expression Omnibus, Zhan et al Blood 2006 epub). GEP was performed using the U133A and B chip for the bortezomib cohort and U133plus chip for the UAMS cohorts (Affymetrix, Santa Clara, CA). For both dataset, raw gene expression were log-transformed, median centered and analyzed using Genespring 7.0 (Agilent Technologies, Palo Alto, CA). A proliferation index (PI) based on the expression of 7 genes (TYMS, TOP2A, CCNB1, KIAA0101, CKS1B, UBE2C, TRIP13) was also calculated. As shown previously (Mulligan et al ASH 2005 abstract) the prevalence of the different TC classes in relapse patients was very similar to newly diagnosed patients (Chi-square p=0.25). In the bortezomib cohort, patients with t(11;14) had the worst survival when compared to patients with t(4;14), maf translocation and hyperdiploidy who had similar survival (survival of t(11;14) versus others, log-rank p=0.036). In contrast, among newly diagnosed patients (UAMS cohort) those with t(4;14) and maf translocations had the worst prognosis, whereas t(11;14) and hyperdiploid patients had the best prognosis (log-rank p=0.02). This interesting contrast in the prognosis within these main genetic subtypes of myeloma at diagnosis and relapse suggest that at relapse, the survival of these patients may be modulated by secondary events or possibly the treatment received. Indeed, a greater proportion of relapsed t(11;14) have a high PI (Chi-square p = 0.04) than newly diagnosed t(11;14). In contrast, there is no difference between the PI of newly diagnosed and relapsed hyperdiploid MM (TC D1, p=0.19). Our observations suggest that although the composition of the various genetic subtypes at diagnosis and relapse appear similar, the prognosis of some subtypes (especially t(11;14)) can be markedly different at relapse when modulated by secondary events (e.g. increase proliferation) whereas others (such as hyperdiploid MM) appear to behave similarly at diagnosis and relapse. Our observation also has important therapeutic implications as it suggests relapsed t(11;14) should be treated aggressively and drug combinations or addition of new agents may be more appropriate. Conversely, bortezomib seemed to be an effective therapy in relapse t(4;14) patients, improving the prognosis of these patients to that of genetic subgroups with good prognosis. Our study highlights the fact that different treatment strategies may be needed for newly diagnosed and relapsed patients even within same genetic subtypes.

Abstract Introduction: Molecular cytogenetic studies have revealed that, to a great extent, the heterogeneity of myeloma is largely dictated by the underlying genetic and cytogenetic aberrations present in the clonal plasma cells. Most recently the group from the University of Arkansas identified strong prognostic associations with an increased level of gene expression of a cell cycle associated gene, CKS1B. CKS1B favors cell cycle progression by promoting degradation of p27 with release of the cyclin dependent kinases and entry into mitosis. Patients and methods: To further test this hypothesis we studied: a) via FISH for CKS1B amplification in a cohort of patients treated at the Mayo Clinic with high dose chemotherapy and stem cell support, as well as a group of patients with cytogenetically defined hypodiploidy, and b) a cohort of myeloma patients that were studied by gene expression profiling. Gene expression analysis was performed on CD138-enriched plasma-cell RNA using Affymetrix U133A chips (Affymetrix, Santa Clara, CA). Results: Of 159 patients studied 46 exhibited FISH abnormalities consistent with increase number of signals for CKS1B (30%): amplification was marginal or low in 44 cases, and in only two cases the ratio between CKS1B and control probe was greater than 2.0. Therefore, the predominant pattern is one of gene duplication rather than amplification where gene copy usually exceeds 10 and ratio with control probe exceeds 5 (e.g. HER-2/neu amplification). Patients with CKS1B duplication had a higher prevalence of chromosome 13 deletion (72%), and t(4;14) (29%). The presence of CKS1B duplication was more frequent (53%) among 19 patients with hypodiploidy. Using gene expression data for CKS1B, we found a positive association with t(14;16)(q32;q23). The level of CKS1B gene expression positively correlated with the PCLI (p<0.0001) but there was heterogeneity in this relationship (r2 = 0.34). We found no correlation between the expression level of CKS1B and p27 (r2 = 0.008, p = 0.37). While CKS1B gene duplication had a negative effect on survival this effect was weak (29.9 versus 38 months, p = 0.124. Median follow-up is 55 months) and disappeared when the variable was entered into the multivariate model including PCLI, B2-microglobulin and all the major genetic abnormalities by FISH. Likewise the level of expression of CKS1B determined by gene expression only carried prognostic significance when extreme levels of expression were utilized as a prognostic variable. Discussion: In this study we show that increase copy number of CKS1B is present in one third of patients with MM (majority of these being gene duplication) and seem to be associated with a shorter overall survival, but the net effect of this is rather weak in our series. Further study is needed to understand its potential role as a progression factor in the plasma cell neoplasms.

Abstract Activation of NFkB has been noted in late B-cell malignancies, but no genetic basis for this activation has been shown except for rare mutations of the NFkB pathway in lymphoma and none have been described in MM. Bortezomib, highly tumoricidal in a subset of MM patients, inhibits NFkB suggesting it’s role as a therapeutic target. Here we show for the first time the critical importance of mutations that dysregulate this potential survival pathway in human myeloma cell lines (HMCL) and new diagnosis MM patients. We performed high-density oligonucleotide array CGH (Agilent 44k) on CD138+ selected MM cells from 68 patients and 42 unique HMCL. Initially we focused on small loci, bi-allelically deleted, as markers for inactivation of potential tumor suppressor genes. We next developed FISH probes for the identified loci to confirm the bi-allelic deletions seen by aCGH. We next sequenced all samples with monoallelic deletions, and additional unselected patients, and identified additional inactivating mutations. Multiple novel tumor suppressor genes (TSG) were identified and validated, whose loss of function (both in patients and HMCL) leads to activation of the non-canonical NFkB pathway including: TRAF2 (9q34), TRAF3 (14q32), BIRC2/BIRC3 (11q22) and CYLD (16q12). Functional studies showed that HMCL with deletion or mutations in these genes have markedly increased NFkB2 p52/p100 ratios relative to other HMCL (and for BIRC2/BIRC3 relative to an isogenic line that retains one copy of BIRC2/BIRC3), indicating genetic activation of the non-canonical NFkB pathway. Reintroduction of deleted TSG into HMCL inhibited cell growth and resulted in cell death (see data on abstract by J. Keats et al). We next focused our analysis (of combined aCGH and GEP data) on positive regulators of this pathway. Rare IgH translocations of NIK have recently been identified in MM. By aCGH we identified amplification and rearrangements of NIK. We identified dysregulated expression of TNF family receptors that activate the non-canonical pathway: CD40 (translocation), TACI (amplification) and LTBR. All of the HMCL with these mutations also showed increased NFkB2 p52/p100 ratios, consistent with activation of the non-canonical NFkB. These mutations correlate with a gene expression profile of NFkB activation that is detected in one half of patients. In summary, we have determined for the first time the genetic basis for constitutive NFkB activation - present in 19/42 HMCL, and we estimate in at least one quarter of newly diagnosed patients (approximately one half of those with evidence of NFkB activation by GEP). We propose a mechanism in which positive (TACI, CD40, LTBR) and negative regulators (TRAF2, TRAF3, BIRC2/BIRC3, CYLD) of the pathway converge on NIK to mediate the processing of NFKB2 p100 to p52. The preponderance of genetic evidence suggests that drugs that target the non-canonical NFkB pathway, will be most effective in treating the 50% of MM patients with NFkB activation.

Abstract H-MM affects about 40–50% of patients, and a similar proportion of MGUS/SMM. The genetic evolution of the disease and the clinical implications of the co-existence of 13q- or t(14q32) are unknown. We conducted a global survey of genetic abnormalities in H-MM in order to describe genetic evolution pathways. H-MM cases from the Mitelman cancer genetic database were combined with cases from the Mayo Clinic MM genetic database. All abnormalities (regional/whole chromosome gains/losses, breakpoints and translocations) were plotted against a 344-chromosome band profile created in a matrix format, and the number of genetic abnormalities per tumor (NAPT) logged. The abnormalities present in more than 5% of tumors were considered recurrent and each karyotype was scored for the presence or absence of each of the recurrent abnormalities. To investigate the temporal pattern of acquisition of genetic abnormalities, all tumors with a given abnormality were selected and the distributions of NAPT plotted. The modes of these distributions were used as a value of ‘lateness’, referred to as time of occurrence (TO). The impact of co-existence of 13q- and t(14q32) on progression free survival (PFS) and overall survival (OS) in H-MM was determined for MM patients entered into an Eastern Cooperative Oncology Group chemotherapy trial. Four hundred and sixty-nine H-MM patients were included in the karyotypic analysis. The median chromosome number was 53 (range 48–74) and the median NAPT was 10 (range 1–25). The distribution of NAPT revealed a normal distribution, suggesting an obligatory acquisition of a given number of genetic abnormalities. The most common abnormalities were trisomies of chromosomes 3,5,7,9,11,15,19 and 21 affecting a third or more of the patients. Deletions and abnormalities of even-numbered chromosomes occur in 20% or less of patients. None of the more than 300 specific translocations detected were recurrent. However, 5 translocation breakpoints were recurrent including 14q32, 8q24 and 11q13. TO analysis suggest that for H-MM, the trisomies are early genetic events, translocations occur midway and deletions tend to be later events. Amongst the trisomies, correlation analysis does not suggest a particular order of acquisition. Clustering analysis using principal component analysis suggests that in H-MM, IgH translocations are secondary events associated with disease evolution rather than defining unique biological entities. Consistent with this, survival of H-MM patients is not affected by 13q- status but is negatively impacted by the presence of t(14q32) especially those with unknown partners (p<0.05). The data presented suggest that H-MM fit a proposed aneuploid model of cancer due to auto-catalytic chromosome reassortment (Duesberg et al Cell Cycle2003:2:202–210). The presence of tumor specific trisomies and the apparent similar phenotype induced by various combinations of trisomies are consistent with this model.

Abstract Background: Thalidomide and dexamethasone combination has been shown to be an effective therapy for multiple myeloma (MM). In newly diagnosed patients with MM, the combination results in overall response rates of over 70% of patients. However, a considerable number of patients do not respond and will need to proceed to other therapies. The ability to identify patients who are unlikely to respond to particular therapies will allow tailoring of therapeutic approaches and in turn will reduce unnecessary toxicity and morbidity from lack of rapid disease control. With the availability of new technology such as gene expression profiling (GEP), this has become a possibility. We employed this approach to identify genes that can reliably predict lack of response to the thalidomide dexamethasone combination. Methods: Patient samples from the Eastern Co-operative Oncology Group clinical trial (E1A00), that compared thalidomide-dexamethasone combination with dexamethasone, and patient samples from the phase II Mayo Clinic study of thalidomide and dexamethasone, both for newly diagnosed MM, were used for this study. Thirty newly diagnosed patients who were evaluable for response were included in the analysis. GEP was performed using the Affymetrix U133A microarray platform as per manufacturer’s recommendation. The Affymetrix output (CEL files) was imported into Genespring 7.2 (Agilent Technologies) microarray analysis software, normalized across chips using GCRMA followed by per gene normalization to median. For the purposes of this study, responses were defined as reduction in the serum paraprotein (or the urinary M protein in the absence of a serum M protein) of >=50% (PR), 25 to 49% (MR), increase of >=50% (PD) and stable disease for the remaining. Results: Five of the 30 patients had no response to the thalidomide Dexamethasone therapy. Using the class prediction tool available in Genespring (Support Vector Machines), we identified 25 genes that reliable predicted non responders (PD, NC) from the responders (MR, PR, and CR). See table for a list of predictive genes with identified genes. Conclusion: Using a combination of two datasets we have identified a set of genes that can be used to reliably predict lack of response to thalidomide and dexamethasone in patients with newly diagnosed MM. We think that this represents a step towards creation of custom microarrays spotted with genes that are capable of predicting lack of response (or response) for the purpose of tailoring therapy to the patient. Gene Predictive strength hypothetical protein FLJ20719 1.655 Tubulin alpha 6 1.653 SEC3-like 1 (S. cerevisiae) 1.649 Ribosomal protein L18 1.546 Ribosomal protein L4 1.432 Tubulin alpha 6 1.409 cleavage stimulation factor 1.393 Tubulin, alpha, ubiquitous 1.393 CD99 antigen 1.38 hmel2 1.346 Leptin receptor 1.291 Integrin-linked kinaseSMC4 structural maintenance of chromosomes 4-like 1 1.269 Cornichon homolog 1.255 Williams-Beuren syndrome chromosome region 5 1.217 Ubiquitin specific protease 1 1.2 Sarcoma antigen NY-SAR-91 1.2

Abstract Marginal zone B-cell lymphomas (MZL) represent a sub-group of indolent B-cell lymphomas, normally localized in the “marginal zone” of the secondary lymphoid follicles. Depending on the site of involvement, three distinct sub-types have been identified: extra nodal MZL of MALT type, splenic MZL (SMZL), and nodal MZL (NMZL). MALT lymphoma is the most frequent of the MZL subtypes, representing 70% of MZL and 7% to 8% of non-Hodgkin lymphomas. SMZL and NMZL comprise the remaining 20% and 10% of MZL, respectively, and account for less than 1% of NHL. The relative rarity of these lymphomas and difficulty in distinguishing them from other low-grade B-cell lymphoma subtypes like lymphoplasmacytic lymphomas (LPL), making therapeutic decisions difficult. The aim of this study was to perform a comprehensive high-resolution study to identify distinctive molecular markers in MZL and LPL, which would help to better define these lymphomas at genomic level and distinguish them from other types of low-grade B-cell lymphoma. We analyzed 119 patients, including 22 pulmonary MALT (pMALT), 20 non-pulmonary MALT (npMALT), 34 SMZL, 21 NMZL and 22 LPL (8 Waldenström’s Macroglobulinemia and 14 non-WM) cases. Human Agilent 244A platform was used by array-based comparative genomic hybridization (aCGH) analysis. Overall, 93% of patients have an altered genome. LPL had the highest amount of copy number abnormalities (median of 7.5 per patient; average of 11.7; range: 0–44), followed by npMALT (6.5; 7.6; 0–33), NMZL (5; 8.3; 0–31), SMZL (4; 7.1; 0–56) and pMALT (3.5; 4.3; 1–11). When the overall size of the abnormalities was considered, LPL was also the most affected disease with an average size of 245.15Mb included in copy number changes per patient, followed by NMZL (188.6Mb), npMALT (187.2Mb), SMZL (126.4Mb) and pMALT (109Mb). Deletions were more commonly observed than amplifications but the affected area was significantly smaller (average of 55.2Mb and 118.6Mb included in deletions and amplifications per genome, respectively). Partial or total gains of chromosome 3 and 18 were recurrently identified in all the entities, being the most common abnormalities in npMALT (60% and 50%, respectively) and pMALT (27% each). Trisomy 12 was also ubiquitously observed, ranging from 24% of NMZL to 9% of pMALT. Deletions in 6q were commonly identified in LPL (55%), npMALT (35%) and, in a less extent, in SMZL (12%) and NMZL (5%), but not in pMALT. Biallelic and focal monoallelic deletions at 6q23 clearly delineate a minimal deleted region (MDR) that includes TNFAIP3 as being the target gene of the abnormality. Deletions on 7q were found in SMZL and npMALT, thus delineating two MDR. Interstitial 11q deletions were found in LPL, NMZL and SMZL, but not in MALT. Chromosome 13 abnormalities were monosomies in NMZL and interstitial 13q14 deletions in LPL and SMZL, comprising MIRN15A and MIRN16-1 in the MDR. Partial X chromosome gains were common to all the entities but involving different areas: Xq27.3–q28 gains were recurrent events in LPL and NMZL, but not in SMZL and MALT, which were characterized by p arm gains or trisomies. Finally, some recurrent abnormalities were exclusive of specific entities: 1p deletions and 1q gains were only common in NMZL; a focal 2p gain including BCL11A and REL was found in npMALT and partial 8q gains were only identified in LPL. Trisomy 4 was only identified in the subgroup of LPL with WM, but not in the remaining LPL cases or in the remaining diseases. In this study we made a compendium of chromosomal abnormalities present in a group of related diseases, in which the molecular basis remains poorly defined. Using this high-resolution approach we were able to establish recurrent and disease-specific patterns of abnormalities and minimal deleted/amplified regions were redefined. Furthermore, TNFAIP3, a negative regulator of the NF-kB pathway, has been identified as being a commonly affected gene in MALT, LPL and NMZL, highlighting its potential role in pathogenesis.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.