Baker, Angela

Abstract Chromosome 13 (Δ13) abnormalities are found in greater than 50% of patients with Multiple Myeloma (MM). MM is most commonly defined by chromosome 13 monosomy or 13q loss (85%). Interstitial deletions comprise the remaining 15%. Many studies have revealed that Δ13 in MM are associated with poor survival and reduced response to therapy. Genes mapping to chromosome 13 may be involved in pathogenesis and/or progression of the disease due to loss of function from gene mutation or from epigenetic effects such as haploinsufficiency. In this study, array-based comparative genomic hybridization coupled with microarray technology (aCGH) is used to detect gene copy number loss on chromosome 13 from nine MM patient samples. Whole genome long-oligo microarrays constructed by Agilent Technologies were used which contain 40,000 genes that span the human genome with an average spatial resolution of ~75 kb. Using genomic DNA isolated from MM patients with interstitial deletions on chromosome 13, DNA was amplified, labeled and hybridized with a differentially labeled normal DNA reference to determine gene/genomic copy number changes. Arrays were analyzed to search for the minimum region of loss based upon single copy loss for a series of nearby mapping transcripts. A common region of loss of 2.2 Mb, at 13q14.2 was detected. Additionally, we investigated the correlation between genomic copy number change and the expression level for MM patients in the13q region. From an independent gene expression data set whose expression measurements were conducted with Affymetrix HG-U133A v2 microarrays, data was selected that corresponded to the samples used for current aCGH. Expression values from MM samples were divided by the mean expression values from 12 normal bone marrow samples for each gene and the resulting values were treated as surrogate ratios between MM and normal samples. Probes from both microarrays were then aligned according to their chromosomal positions and merged if their chromosomal positions overlapped. Composite chromosomal maps were generated that displayed the expression levels and copy number changes. The maps were used to differentiate chromosomal regions in 13q where copy number changes and expression levels show high correlation and regions where such correlation was not observed. Although the number of probes sampled in the expression microarray was much smaller than those in aCGH microarray, a chromosomal region of great interest, that encompasses 13q14.2, arose naturally from this analysis. Although the mechanism by which loss of 13q effects tumorigenesis in MM could be a haploinsufficiency model, we are not ruling out the presence of a tumor suppressor gene in this region. We are evaluating candidate tumor suppressor genes in the region for loss of function by mutational analysis and hypermethylation studies.

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 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 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.

Abstract Table 1. Samples to p53 deletions p 53 deletion FISH aCGH FISH and aCGH MGUS 142 - - SMM 108 - - Newly diagnosed MM - 182 - Relapsed/refractory MM 62 156 19 HMCLs - 48 - Total 312 386 19 Background: Inactivation of p53 by mutation or allelic loss is a rare event in multiple myeloma (MM) at the time of disease diagnosis and believed to be more common in the late stages of the disease. Here we defined the prevalence of p53 deletions in monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), newly diagnosed MM, relapsed MM and human myeloma cell lines (HMCLs). Indirect mechanisms of p53 inactivation such as amplification of MDM2, or deletion of CDKN2A (p14ARF) were also investigated. Patients and Methods: A total of 631 MM samples and 48 HMCLs were analyzed for p53 abnormalities (Table 1) and compared with a cohort of plasma cell leukemia (PCL) previously published by us. Interphase fluorescence in situ hybridization (FISH) and highresolution array-based comparative genomic hybridization (aCGH) were used to detect p53 deletions. MDM2 amplification and p14ARF loss were evaluated by aCGH. Overall survival (OS) was estimated through Kaplan Meier method. Results: The prevalence of p53 deletions was 1.4%, 3.7%, 8.9%, 20% and 87.5% in MGUS, SMM, newly diagnosed MM, relapsed MM and HMCLs respectively (Figure 1). Of interest patients in first relapse showed a p53 deletion prevalence of 18% in comparison with 33% for patients in second relapse or more. We obtained p53 deletion status in 8 patients noted to have deletion at the time of relapse and in whom we had previous samples stored. In 7 of 8 cases deletions had been acquired (absent in the previous samples). The median OS for relapsed MM patients with or without p53 deletion were 4.2 months and 37.8 months respectively (p <0.001). MDM 2 amplification and p14ARF loss was detected in 0.4% and 2% of new diagnosed MM, 0.6% and 5.1% of relapsed MM and 2% and 29% of HMCLs respectively. Discussion: Emergence of p53 deletion/mutations denotes progression genetic events in MM. Importantly we show for the first time the overriding importance of p53 deletions as prognostic markers in relapsed MM. A molecular staging system that is based on the presence of p53 inactivation may be more powerful than classifying patients based on the loss of p53. Other abnormalities involving p53 pathway regulators like MDM2 gain and p14ARF loss are infrequent events in MM, even in advanced disease. Figure 1. Prevalence of p53 deletions Figure 1. Prevalence of p53 deletions

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.

Abstract Genetic abnormalities are important in the pathobiology of myeloma and established prognostic factors. High-density aCGH allow global unbiased detection of genomic gains and losses. In this study, we attempt to identify survival critical genomic aberrations by correlating aCGH-defined abnormalities with survival in 2 different cohorts of patients. aCGH was performed in 64 MM patients treated with high dose therapy from the Mayo Clinic using Agilent’s 44k DNA microarray genechip. In addition, a publicly available aCGH dataset performed in 67 MM patients treated with total therapy from the University of Arkansas Medical School using the lower density Agilent 22k chip (Human 1A v2) was also analyzed. Base 2 logarithms of the ratios between red and green intensities (log-ratios) were treated as a primary measure of copy number ratio between tumor and normal samples. To detect the regions of genomic aberration, values of a sliding window of 15 consecutive log-ratios were compared with those of an artificial ‘reference’ chromosome using the Wilcoxon rank sum test. The reference chromosome, constructed for each sample, was assembled by collecting log-ratio values close to zero. For this, 15 randomly sampled log-ratios from autosomal regions whose p-value of Wilcoxon signed rank test against 0 was larger than 0.05 were recursively accepted until the number of log-ratios in the assembled reference chromosome reached 1000. The p-values and their corresponding false discovery rates (q-values) for the copy number gain or loss were calculated independently for each sliding window and assigned to the center position of corresponding sliding window. The survival difference was examined between samples that had gained or lost a chromosomal region (sliding window) and samples that had not using the method of Kaplan-Meier and the statistical significance was assessed by the log-rank test. The sliding window was identified as gained or lost if P-value < 10 −4 and Q-value < 10[suo]-3. Sliding windows whose gains or losses led to poor survival with log-rank test p-value < 0.5, corresponding q-value < 0.5, and happened to ≥ 10% of all samples were retained. Several regions of genomic gains and losses were significantly associated with poorer survival with 5 deletion hotspots common between the 2 datasets (Table). We validated the prognostic significance of these survival critical loci using FISH in an independent dataset of 169 patients treated with high dose therapy. Clustering of these survival critical loci revealed clusters of MM characterized by low, intermediate or high number of these abnormalities, which are independent of ploidy and translocations subtypes. These subgroups have significantly different survival. In addition, the clustering analysis suggests the chromosome 13 loss is an early event whereas chromosome 1 abnormalities are late events. Our strategy therefore identified novel regions of genomic aberration that are important prognostically. However, it is likely that these regions are manifestation of genomically unstable tumors with complex abnormalities that have poorer prognosis. Chr Start End Length Cytoband 1 74,137,824 75,972,544 1,834,720 p31.1 1 62,433,123 84,792,809 22,359,686 p31.3 – p22.3 20 4,100,452 6,189,410 2,088,958 p13 – p12.3 20 6,717,170 13,142,205 6,425,035 p12.3 – p12.1 20 4,708,668 10,566,333 5,857,665 p13 – p12.2

Abstract The use of high-resolution aCGH allows for whole genome screening for copy number changes even in tumors that rarely produce metaphase spreads. The aim of this study was to characterize the aCGH findings in B-CLL, lung MALT lymphoma, Waldenström’s Macroglobulinemia (WM), Multiple Myeloma (MM) and human myeloma cell lines (HMCLs). This study includes 301 B-cell malignancy samples (19 B-CLL, 19 MALT, 20 WM, 48 HMCLs and 195 MM) and an additional 76 melanoma samples. We found MM to have the highest number of abnormalities per karyotype, with 15.8±7.5 (mean ± SD) and 20.6±17.0 in hyperdiploid (H) and non-hyperdiploid MM (NH), respectively. The remaining diseases have a significantly lower number of abnormalities: 5.5±4.5 in B-CLL, 3.5±1.96 in MALT and 4±3.5 in WM. Aneuploidy and biallelic deletions were rare events in these three diseases. B-CLL has two highly recurrent abnormalities involving 11q22-q23 (29%) and 13q14.3 (33%). A minimal deleted region (MDR) of ∼108Kb was identified at 11q comprising ACAT1, NPAT and ATM. In 13q, a 280Kb MDR was recognized, including TRIM13, KCNRG, mir-15a and mir-16. The subgroup of MALT patients with t(11;18) had very few abnormalities and the majority are a consequence of unbalanced translocations (MALT, BIRC2 and BIRC3 deletions). In the remaining MALT samples, trisomy 3 and 18q gain were the most common abnormalities (31% each). The 6q16.3-q26 deletion was the most common abnormality in WM (45%). In 4 of 9 patients with 6q deletion there was an associated 6p gain. The same observation was found in 2 of 3 patients with 8p loss and concomitant 8q gain. Interestingly, 6p and 8q gain were not identified in patients without 6q and 8p deletions. Deletion of 17p was the most common recurrent abnormality observed in our panel of B-cell cancers, being identified in MM, WM and B-CLL. In B-CLL and WM, small mono and biallelic deletions of the NF-kB negative regulators, TRAF3 and NFKBIA, were identified, suggesting a potential involvement of this pathway in the pathogenesis of these diseases. Hierarchical clustering of the aCGH data in MM identified 3 distinct groups, NH-MM, H-MM and MM with deletions of chromosome 13 and 14. Interestingly, MGUS and SMM were more commonly clustered with H-MM. Several sub-clusters were observed in the H-MM group, the most prominent being made up exclusively of patients from TC class D1. This cluster was characterized by the presence of trisomy 11 and 21. The 11q13 TC group clustered and was highly associated with 11q amplifications due to unbalanced t(11;14). The 6q deletion defined a cluster with WM, MM and Melanoma patient with that abnormality. In the case of B-CLL and MALT, the only identifiable clusters were observed in cases with 13q loss and trisomy 3, respectively. These results highlight both, the common and unique aCGH findings in several B-cell cancers. By hierarchical clustering, MM was significantly divergent from the rest of the B-cell diseases. Due to the limited number of abnormalities in B-CLL/MALT/WM, the clustering of these groups was exclusively based on the common abnormalities. The use of aCGH has refined the MDRs of 11q and 13q in B-CLL. Additionally, the identification of deletions affecting the NF-kB pathway in B-CLL and WM has prompted us to hypothesize on the importance of constitutive NF-κB activation in these diseases.

Abstract MM is characterized by widespread genomic instability. Two broad genetic subtypes of MM defined by ploidy exist. Hyperdiploid MM (H-MM) is characterized by trisomies of chromosome 3, 5, 7, 9, 11, 15, and 19, and generally lacked primary translocations involving the immunoglobulin heavy chain locus (IgH) whereas the non-hyperdiploid MM (NH-MM) is characterized by primary IgH translocations such as t(11;14), t(4;14) and t(14;16). Using high-density array comparative genomic hybridization (aCGH, Agilent 44K array), we catalogued the different aberrations on each chromosome arm and also the total number of aberrations per tumor (NAPT), as an indication of the degree of genomic instability, in a large cohort of 194 MM patients. Both H-MM and NH-MM have high number of genomic aberrations, 15.8±7.5 (mean ± standard deviation) versus 20.6±17.0 respective. NH-MM has significantly more genomic losses (11.8±11.0 versus 4.1±3.8), whereas H-MM has slightly more genomic gains (11.7±5.0 versus 8.8±9.0). Most of the abnormalities in NH-MM are structural abnormalities whereas they are mainly whole chromosome abnormalities in H-MM. Based on these abnormalities, we cluster the patients using hierarchical clustering. The 2 main branch of the resulting dendogram delineate hyperdiploid and non-hyperdiploid patients. Sub-clusters of each ploidy subtype are apparent. For H-MM, a cluster with mainly trisomies, including that of chromosome 21, and another cluster with additional deletions (13, 16q, and less trisomy 21 and trisomy 11) were apparent. For NH-MM, a group with 1q amplification and 13 deletion, which also tend to have loss of chromosome 4, 6q, 16q and 14, and another group with little abnormalities or just 6q loss, and chromosome 13 and 14 loss were identified. We identified some genetic difference between NH-MM and H-MM in additional to known difference such as the trisomies and chromosome 13 deletion. Loss of chromosome 4 (18% versus 0.9%, p<0.0001), loss of chromosome 14 (33% versus 16%, p=0.02) and loss of chromosome 22 (21% versus 4%, p=0.0008) are more common in NH-MM. Based on the distribution of NAPT, a cut-off of 20 abnormalities per tumor segregate the patients into 2 groups with significantly different survival. Those with greater NAPT have significantly shorter survival (median overall survival of 20 months versus 88 months, Log-rank p=0.006). There is no correlation between NAPT and ploidy or presence of chromosome 1 abnormalities suggesting that genomic complexity itself is associated with survival. Our study showed that both H-MM and NH-MM are genomically unstable although the pattern of abnormalities is distinct. In addition, based on clustering analysis, new pattern of genetic abnormalities are observed that allow further refinement of the ploidy subtypes. In addition, genomic complexity is also an important prognostic factor.

Abstract Chromosome 13 abnormalities (Δ13), monosomy or deletion, are seen in greater than 50% in patients with Multiple Myeloma (MM) and are associated with poor survival and reduced response to therapy. Δ13 is suggestive of a tumor suppressor model. In this study, we use a comprehensive, high-throughput approach involving array-based technologies to identify genes on chromosome 13 that are important in the pathogenesis and/or progression of MM. Array-based Comparative Genomic Hybridization (aCGH) is being conducted to identify a minimum region of 13q loss in a series of MM cell lines and patient samples. DNA is isolated, labeled and hybridized with a differentially labeled normal DNA reference to determine gene/genomic copy number changes. Arrays are analyzed to search for the minimum region of loss based upon single copy loss for a series of nearby mapping transcripts in each cell line. Data will be cross-referenced to determine the true minimum region of 13q loss in MM. Also, to discover genes harboring potential inactivating mutations, we are using inhibition of nonsense-mediated RNA decay (NMD) coupled with cDNA microarray. This modification of the Gene Identification by NMD Inhibition (GINI) method, first introduced by Noensie and Dietz (2001), is used to identify genes on chromosome 13 that may be inactivated due to premature truncating codons (PTCs). This method is based upon a cell’s natural ability to survey for and degrade RNA species that contain PTCs by activation of the nonsense mediated RNA decay (NMD) pathway. Treating cells with the drug Emetine can inhibit NMD and increase the expression of genes likely harboring truncating mutations. Preliminary data have revealed several candidate genes, with increased ratios after Emetine treatment, which map to chromosome 13. Upon defining a minimum region of loss based upon aCGH data, we will prioritize genes from our NMD study for mutational analysis. It our hope to also perform an array-based CpG island screen to simultaneously analyze all CpG islands on chromosome 13 to detect genes possibly inactivated by hypermethylation. This comprehensive, organized approach incorporating high-throughput techniques for the detection of genomic loss, mutation, and methylation is the first of its kind to our knowledge and may maximize the potential for the identification of a 13q tumor suppressor in MM.