Carpten, John D.

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.

Abstract Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10−6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10−12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

Multiple myeloma (MM) is a neoplasm thought to arise from a damaged germinal center B-cell that progresses to a plasma cell clone arising in bone marrow. MM comprises 20% of all hematologic cancer deaths. Persons of African ancestry (AA) have a 1.5 to 2-fold higher risk compared to individuals of European ancestry (EA). Genetically driven differences in hematopoiesis may lead to variation in the levels white blood cell (WBC) subsets which could, in turn, be associated with MM etiology. There are differences in genetic determinants of WBC traits between EA and AA populations, with possible implications for the racial disparity in risk. We tested the above hypothesis using Mendelian randomization (MR), an approach that leverages genetic determinants of specific traits (i.e. WBC counts) to estimate their effects on the risk of an outcome; and a transcriptome-wide association study (TWAS), which utilizes genetic predictors of gene expression to identify susceptibility genes. These analytic approaches were applied to data from the African American Multiple Myeloma Study (AAMMS) consisting of 1813 cases and 8871 AA cancer-free controls to examine how differences in heritable WBC gene expression profiles influence MM risk. Genetic determinants of variation in WBC subsets in AA were obtained from the literature and supplemented with new genome-wide association findings in AA subjects from the UK Biobank cohort (n=6108). Odds ratios (OR) for MM per 1 standard deviation (SD) increase in each WBC phenotype were estimated using independent (linkage disequilibrium (LD) r2<0.10) variants with P<10-6 as genetic instruments. Analyses based on variants associated with WBC traits in AA populations did not identify any statistically-significant associations between MM risk and WBC overall (p=0.81, using 15 SNPs) or subsets (lymphocytes, monocytes, eosinophils, neutrophils and basophils, p>0.05 for each). However, when we applied genetic determinants of WBC identified in 330,000 cancer-free EA UK Biobank participants (P<10-8, replication P<0.05, LD r2<0.05), a statistically significant inverse relationship emerged between increasing lymphocyte counts and MM risk (OR=0.80, 95% CI: 0.66-0.97, p=0.02, using 385 SNPs), as well as increasing basophil counts (OR=0.63, 95% CI: 0.41-0.96, p=0.03, using 140 SNPs). Next, we examined the association between WBC gene expression profiles and MM risk in AAMMS data. We applied published and validated ancestry-specific models developed using the PrediXcan approach, which leverage germline genetic and transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) (Mogil et al. PMID: 30096133). The primary TWAS used gene expression models trained in AA subjects (n=233), with sensitivity analyses using models developed in AA and Hispanic subjects (n=585). The TWAS significance threshold was based on the number of genes with significant germline prediction models (p<0.05 and R2 ≥0.05) in AA, corresponding to P < 0.05/2700 = 1.85×10-5. The expression of two genes was significantly associated with MM risk: KANK1at 9p24.3 (P = 1.01×10-5) and DNAJC27at 2p23.3 (P = 1.56×10-5). KANK1is a candidate tumor suppressor gene for renal cell carcinoma and has recently been associated with MM risk in AA [Du, Blood 2017 130:3058]. Here we provide additional evidence for its role in MM etiology via gene expression-mediated mechanisms. DNAJC27 (previously known as RBJ) is a novel MM risk gene linked to constitutive activation of ERK in solid tumors. We also identified two suggestively associated genes: PRR14 (P = 1.34×10-4; combined AA-Hispanic sample: P = 1.56×10-6), which has been linked to MM risk in EA populations, and PARP16 (P = 9.46×10-5). To our knowledge this is the first study to comprehensively examine variation in WBC traits and gene expression profiles with respect to MM risk in AA. Our TWAS analysis leveraged data from the largest collection of genetic and gene expression data in AA, enabling ancestry-matched inference and identification of two novel risk genes. Although the limited availability of genetic instruments for WBC limited the power of MR analysis, findings using variants identified in European populations may offer some insight into trans-ethnic etiologic pathways and contribute to risk stratification strategies using genetic and blood cell count biomarkers. Future studies, particularly with MGUS-free controls, are needed to validate these results. Disclosures Song: Millennium Pharmaceuticals Inc: Employment. Rand:Ancestry.com: Employment. Ailawadhi:Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Nooka:Takeda: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; GSK: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation. Singhal:Bureau of Millennium/Takeda, Celgene, Janssen, Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Mehta:Millennium/Takeda, Celgene; stock in Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. Wolf:Takeda: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Fiala:Incyte: Research Funding. Terebelo:Jannsen: Speakers Bureau; Celgene: Honoraria; Newland Medical Asociates: Employment. Anderson:Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. Bernal-Mizrachi:TAKEDA: Research Funding; Kodikas Therapeutic Solutions, Inc: Equity Ownership; Winship Cancer Institute: Employment, Patents & Royalties. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Zonder:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees. Huff:Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees; Karyopharm, Sanofi, MiDiagnostics: Consultancy. Lonial:Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding; Genentech: Consultancy. Orlowski:Poseida Therapeutics, Inc.: Research Funding.

Abstract Abstract 606 Multiple myeloma is a complex malignancy with multiple underlying genetic events. Our group has spent considerable effort over the last 15 years to elucidate the genetic underpinnings of myeloma. Most recently, we used array-based comparative genomic hybridization (aCGH) as a discovery tool in 62 myeloma patients and 46 myeloma cell lines. In that preliminary screen using the Agilent 44B aCGH platform (∼70kb resolution) we identified a diverse array of abnormalities, which resulted in constitutive activation of the NF-kB pathways. That initial analysis concentrated on the 43 genes we identified as potential targets of the 13 homozygous deletion events detected in the patient samples. A pathway analysis of these genes revealed a single pathway involving TRAF3, TRAF2, BIRC2, BIRC3, and CYLD. This first analysis focused exclusively on abnormalities present in the patient samples as we worried some abnormalities identified exclusively in the cell lines might not be relevant to the pathogenesis of myeloma in patients. However, several abnormalities were equally or more frequent overall but occurred exclusively in cell lines including CDKN2C (14 samples), CDKN1B (4 samples), KDM6A/UTX (4 samples), RB1 (3 samples), TP53 (3 samples). Given the fact that KDM6A/UTX deletions were as frequent as many of the best-described tumor suppressors it seemed like a good candidate but in the absence of patient events or a known function at the time it was not prioritized for further study. Recently, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, we have completed the analysis of a cohort of 250 myeloma patient samples by aCGH using the Agilent 244A aCGH platform (∼15kb resolution) and gene expression profiling using the Affymetrix U133Plus2.0 genechip. In this cohort with a significantly improved aCGH platform we identified 17 genes that are recurrently inactivated by homozygous deletions including DIAPH2 (15 samples), CDKN2C (14 samples), TRAF3 (11 samples), CYLD (8 samples), BIRC2/3 (7 samples), KDM6A/UTX (6 samples), and RB1 (5 samples). Based on the significant improvement in resolution and data quality achieved with the Agilent 244A aCGH platform we rescreen all of the cell lines on this improved platform. This significantly changed the frequency of several homozygous deletions in this population with the most frequently targeted genes now being CDKN2C (20 samples), KDM6A/UTX (13 samples), DIAPH2 (7 samples), RB1 (4 samples), TP53 (4 samples), CDKN1B (4 samples), and TRAF3 (4 samples). Moreover, as part of the genomic characterization of a spontaneous myeloma mouse model that we have developed, Vk*-Myc, we have identified recurrent (∼50%) homozygous deletions of Kdm6a/Utx. Therefore, one of the genes most commonly targeted by a homozygous deletion in human and mouse myeloma is KDM6A/UTX. In late 2007 after we had identified the first patients with KDM6A/UTX deletions it was shown that UTX is a functional histone demethylase that removes methyl groups from histone H3 lysine 27 (H3K27me). Given the high incidence of deletions and the fact that MMSET, the overexpressed target gene of t(4;14) in myeloma, is predicted to methylate H3K27, H3K36, and/or H4K20 by evolutionary conservation we developed the hypothesis that myeloma is characterized by abnormalities that result in excessive H3K27me (typically a repressive chromatin mark). Given the extensive whole genome sequencing occurring in the MMRC genomics initiative we elected to focus our resequencing efforts on the cell lines exclusively. These studies identified an additional 4 samples with LOH and an inactivating mutation bringing the total percentage of inactivated cell lines to 33%. Clearly, in the expanded patient and cell line cohorts the inactivation of KDM6A/UTX is not independent of MMSET overexpression suggesting they may act independently or synergistically. We are currently attempting to identify the genes controlled by KDM6A/UTX inactivation to better understand the functional consequences of this highly recurrent event. However, in the mouse model unlike the patient or cell lines, the gene expression profiling has identified a gene expression signature that differentiates UTX inactivated and functional samples suggesting an oncogenic function of inactivation. Disclosures: No relevant conflicts of interest to declare.

Abstract Waldenström macroglobulinemia (WM) is a lymphoid neoplasm associated with accumulation of lymphoplasmacytic cells in the bone marrow and an elevated monoclonal IgM in the serum. Although the genetic and molecular mechanisms behind the pathogenesis of WM are not well understood, losses of the long arm of chromosome 6 have been found as the only recurrent genomic aberration. These deletions, mostly starting at 6q21, are observed in upwards of 50% of patients and involve the majority of the clonal cells. To better characterize pathologic gene expression signatures in different types of WM at the molecular level (6q deleted versus not), we examined gene expression level changes of more than 20000 human gene probes in bone marrow tissues of clinically proven WM patients. We used Affymetrix HG-U133A v2 chips (n = 24). Clonal cells were purified using a combination of anti-CD138 and anti-CD19 Miltenyi™ magnetic microbeads. Based on cIgM-FISH results, we classified samples into two groups - 6q normal (ND) group and 6q deletion (D) group. We then proceeded to identify genes that showed differential expression between the two groups. We selected 165 differentially expressed genes (DEG) using SAM, and underwent further investigation of biological annotations of them using Gene Ontology™ (GO) terms. Among165 DEG, only 32 showed high level of expression in ND group compared to D group while 133 showed high level of expression in D group. As expected, all genes that reside in 6q region showed under-expression in D group, constituting 53% (17 genes) of genes under-expressed in D group (32 genes). According to GO analysis, the 6q-deletion-induced under-expression affects many biological processes, and is associated with a transcriptional upregulation of downstream genetic cascades. This likely indicates release of regulatory control by a tumor suppressor gene. Genes involved in cell growth and maintenance, cell communication and morphogenesis are also over-expressed in D group. Interestingly, a gene that is involved in innate immune response and inflammatory response shows under-expression in D group but is not accounted for by other mechanisms. In similar vein, a gene that is involved in negative regulation of cytokine biosynthesis showed over-expression in D group. We think these transcriptional characteristics can provide basic framework for differentiating subtypes of WM and search for genes associated with WM pathogenesis

Abstract Abstract 604 We have conducted a pilot study to interrogate both CpG hyper- and hypomethylation in Multiple Myeloma (MM) using the Illumina GoldenGate (GG) BeadArrayTM high-throughput assay. Specifically, we utilized the Illumina Methylation Cancer Panel I, which spans 1,505 CpG loci selected from 807 genes including known tumor suppressor genes and oncogenes. To date we have profiled 50 CD138+ MM specimens, 4 MM cell lines and 6 normal CD138+ plasma cell samples (each representing a pool of 3-5 individuals) obtained from the bone marrow of patients who have undergone hip or knee replacement surgeries. We have obtained highly reproducible and reliable DNA methylation profiles with an average r2 of greater than 0.98 when the β values were compared between technical replicates. Further, we generated an internal control panel for the array by performing methylation specific PCR (MSP) to determine the methylation status of several genes found on the array (CDKN2A, E-Cadherin, MGMT, hMLH1) in 4 MM cell lines. A differential methylation analysis was conducted between normal plasma cells and MM samples using the BeadStudio Methylation Module v3.2. Differential scores for each CpG locus on the array were generated that correspond to significant differences between groups. Scores greater than (hypermethylation) or less than (hypomethylation) 13 represent ‘Diff scores' that are statistically significant (p values < 0.05) and this cut-off threshold is used to determine differentially methylated loci. Globally, when comparing MM to normal plasma cells, hypomethylation occurred at 42% of probes on the array, and was more common than hypermethylation, which occurred at only 21% of probes. Interestingly, 77% of probes situated in non-CpG islands were hypomethylated compared to 26% or probes in CpG islands. On the contrary hypermethylation occurred mainly in CpG islands (26% of probes) whereas only 7.4% of probes in non-CpG islands were considered hypermethylated. A total of 946 CpG loci were differentially methylated between normal plasma cells and MM. Among the most significantly differentially methylated CpG loci between normal plasma cells and MM were FZD9, ALOX12, MMP-3, MMP-8, and p16. Array comparative genomic hybridization (aCGH) data availabel for this same sample set was used to generate a hyperdiploid index for each sample by identifying those samples with whole chromosome gains at minimally three odd numbered chromosomes. There were 751 CpG loci that were differentially methylated between hypderdiploid and non-hyperdiploid tumors with WEE1, FRZB, ELK1, CLDN4, and SHH being among the most significant. We also applied the pathway enrichment analysis tool GeneGo v5 (Agilent) to these data, which identified cell adhesion/ECM remodeling, developmental pathways and several immune response pathways as the most significantly enriched pathways between normal and plasma cells with ephrin signaling pathways in the context of cell adhesion as the most significantly enriched pathway between hyperdiploid and non-hyperdiploid tumors. We hope to integrate methylation data with orthogonal datasets including gene expression, aCGH, and DNA sequence data being developed as part of the MMRF Genomics Initiative. Further validation of hits will also be followed up by functional studies to assess the true biological relevance of differentially methylated genes. This study represents one of the first methylome interrogation studies in MM and points towards global hypomethylation as possibly an important mechanism driving myelomagenesis. Our data also suggest that differential methylation of genes may be a significant determinant of hyperdiploid biology. Determining the set of critical genes and pathways based on the myeloma methylome is likely to lead to an improved understanding of biological mechanisms involved in myelomagenesis, ultimately leading to new and improved methods of diagnosing and treating this disease. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 1923 Our understanding of the genetic abnormalities associated with the development of multiple myeloma has increased significantly in the last decade. However, very little is known about how, or if, myeloma tumor genomes change with time and if therapeutic interventions influence these events. To address these issues we studied a cohort of 29 patients for whom at least two serial samples (1-65 months, median 19 months) were available for analysis. Each serial pair was analyzed by both array-based comparative genomic hybridization (aCGH) and microarray gene expression profiling (GEP) to identify DNA copy number abnormalities (CNA) at a 25kb resolution and gene expression differences present in the bulk of the tumor mass. Though this does not address the intra-clonal heterogeneity that may exist at a given time point, it does answer if the bulk of the tumor mass is changing with time. This study has unearthed several surprising and clinically relevant findings. First, myeloma tumor genomes are not as unstable as previous cytogenetic analyses suggest. In 40% of patients we observed no detectable CNA changes (1-37 months, median 12 months). In 24% of patients we observed the exclusive acquisition of new CNA (1-12, median 3.5) (3-22 months, median 18 months). In 36% of patients we observed both the loss (1-20, median 3) and gain (1-33, median 21) of CNA (5-43 months, median 20 months). Because time was not a significant influence on the detection of stable or unstable genomes we compared CNA changes with TC class and found patients with the high-risk 4p16 and maf IgH translocations were over-represented in the latter subset of patients. These observations raise the question of what happens between multiple rounds of therapy and if different regimens influence these phenotypes differently. For two patients with no CNA changes between the first two time points there was an additional sample that extended the follow-up by 52 and 12 months. Again no CNA changes were seen between diagnosis and these final samples taken 63 and 50 months later. For one patient with CNA changes (5 shared, 29 lost, and 32 gained) we have a detailed time course of 5 samples from diagnosis through to end-stage plasma cell leukemia. This patient received continuous lenalidomide-dexamethasone (Rd) for 20 months and progessed with a clone containing a BIRC2/3 deletion, which activates the NFKB pathway. The patient received single agent PR-171 and a bortezomib containing regimen and unexpectedly, the tumor genome observed in the third sample was almost identical (32 shared, 2 lost, and 4 gained CNA) to the first time point, including two copies of BIRC2/3. Subsequently, the patient received melphalan-prednisone-bortezomib (MPV) and the tumor genome observed in the fourth and fifth samples, which were identical, were similar to that seen in the second sample (24 shared, 13 lost, and 39 gained CNA). To understand these observations better we performed FISH to ascertain if the observed clones were detectable earlier, albeit at a low frequency. These experiments proved that the two dominant subclones observed at time points 1 and 3 versus 2, 4, 5 were mutually exclusive at the single cell level. Moreover, both of these clones were detectable at diagnosis with 12% of the tumor mass being the second subclone that eventually evolved into plasma cell leukemia. Interestingly, we assayed 5 of the 39 unique CNA observed in the final two samples and only one, the 17p13 deletion, was detectable earlier. This suggests the MPV regimen effectively eliminated a clone that was previously sensitive to Rd and selected for a dramatically evolved subclone that was previously sensitive to two different proteasome inhibitors. Although it is clear that the high-risk patients are enriched in the subset with the most changes, it is not clear if the specific drugs used (Melphalan vs IMID vs proteasome inhibitor) or intervention strategy (Cycled vs continuous/maintenance) and perhaps the response achieved (PR vs CR) influences these events. These observations do highlight two important clinical concepts that need to be considered in the future. First, the meaning of a partial response needs further investigation as this may reflect effective elimination of one subclone but not another. Second, because some patients are not changing or can revert back to a previous subclone we need to consider re-chanllenging patients with previously effective regimens when patients progress. Disclosures: Fonseca: Genzyme: Consultancy; Medtronic: Consultancy; BMS: Consultancy; AMGEN: Consultancy; Otsuka: Consultancy; Celgene: Consultancy, Research Funding; Intellikine: Consultancy; Cylene: Research Funding; Onyx: Research Funding; FISH probes prognostication in myeloma: Patents & Royalties. Stewart:Millennium: Consultancy; Celgene: Honoraria. Bergsagel:Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Millennium: Speakers Bureau; Novartis: Speakers Bureau.

Abstract Abstract 2810 Poster Board II-786 We wished to explore the genetic events associated with disease progression and development of drug resistance in multiple myeloma (MM). To do so 11 patients were studied in whom at least two (range 2-3) temporally distinct samples of tumor DNA and RNA were available. The baseline genetic initiating event was defined for all patients (3 were genetic high risk; one with t(14;16) two with t(4;14)) as well as the gene expression profile (GEP) defined risk score using the Little Rock 70 or 17 gene panel (only one, the t(14;16) was GEP defined high risk). High resolution array CGH and gene expression were then performed on each sample. Of the 8 patients with a “low risk” tumor initiating event and low risk GEP score, 6 patients had no, or only one, copy number abnormality (CNA) change between the two temporally distinct MM samples. In stark contrast the 3 genetic high risk at baseline had between 17 and 40 distinct CNA changes at the time of progression. For all 11 patients 89 CNA were acquired with progression whereas 19 previously abnormal regions disappeared suggesting clones with these abnormalities were extinguished by the therapy received. In total we detected 0-40 CNA changes between the various timepoints, median 1, mean 10.7. The acquisition of new CNA was much more common than the loss of CNA. We then focused more specifically on the t(4;14) patient with the highest number of CNA changes. This patient has a well documented clinical course of having a sustained two year VGPR to Len/dex and then progressing while still taking Len/dex. Comparison of the pre and post-Len/dex samples identified 40 CNA changes(the most of any pair studied to date). Only six CNA were shared between the two samples, which included deletions of chr4, 9, 12, 13, and X plus a t(4;14) translocation. These likely represent the initiating “driver” tumor events. The new CNA we identified originated from both remodeled genomic changes and the emergence of unique changes, indicating a new tumor clone had emerged while the previously dominant clone had regressed (e.g. a deletion of a large segment of chromosome 8 at diagnosis was no longer observed in the relapse sample). The newly acquired CNA encompassed 3968 genes (13.7% of the genes in the genome), however, only 1235 of these genes (4%) were expressed in this patient at diagnosis (1188 in the typical myeloma patient). Since 1235 genes is still a large number we hypothesized that whole genome sequencing (WGS) would help elucidate the mechanism of lenalidomide resistance. We isolated DNA from germline tissue and CD138 purified tumor cells including: diagnostic, first relapse and second relapse samples. Utilizing SOLiD (Applied Biosystems, Foster City, CA) sequencing technology, we have completed fragment library WGS on both the germline and the final tumor samples. Quality control measures report the average number of sequence reads per start point to be less than 1.2, indicating the library is primarily composed of unique molecules. In addition, approximately 40% of the sequence reads map uniquely to the genome. Together these quality measures indicate our sample libraries are complex and provide good representation of the genome. Data on the whole genome sequence of myeloma at diagnosis and at the time of progression will be presented. Disclosures: Bergsagel: Celgene: Consultancy.