Citations

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 The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative is a three-year program to analyze tumor tissue from hundreds of multiple myeloma (MM) patients via gene expression profiling (GEP), comparative genomic hybridization (aCGH), and exon re-sequencing. In addition, RNAi knockdown of selected genes in MM tumor cell lines is being evaluated to identify potential new targets. All genomic data generated is scheduled for placement in an open-access Multiple Myeloma Genomics Portal pre-publication and in near real-time (www.broad.mit.edu/mmgp). Additionally, samples are also destined for drug validation and correlative science on clinical protocols as this study moves forward. This comprehensive project is spearheaded by the MMRC and conducted via collaboration with the Eli and Edythe L. Broad Institute of MIT and Harvard, the Translational Genomics Research Institute (TGen), Mayo Clinic Arizona, and The Dana-Farber Cancer Center. The study is supported by the collection from member institutions of the MMRC of bone marrow aspirates and matched peripheral blood samples from over 1000 patients. Specific genomic technologies that are currently being employed across this sample set include GEP using Affymetrix Human Genome U133A 2.0 Plus Arrays, and, in parallel, efforts to identify regions of genomic gain and loss are using Agilent Human Genome CGH arrays. In contrast to other large-scale genomic projects based on exon-sequencing of targeted gene sets, this project will be the first to perform genome-scale single molecule sequencing (SMS) of DNA from patient specimens. Results will be targeted against candidate classes of genes (e.g. kinases, phosphatases, known oncogenes and tumor suppressors), and genes from GEP or within candidate regions of copy gain or loss identified by the aCGH experiments. Mutations will be further validated in an independent set of patient specimens. Finally we will attempt to identify points of vulnerability of MM through systematic loss-of-function screens in myeloma cell lines using high-throughput RNA interference (using both shRNA and siRNA platforms). Importantly, data generated from this genomics initiative will ultimately be made public pre-publication through the established MMRC Multiple Myeloma Genomics Portal. Data from all aspects of this project (sample collection and analyte isolation, GEP, aCGH, SMS, RNAi and bioinformatics) will be described in this presentation. The power of this study is the comprehensive collection of gene expression, CGH, and genome sequencing on a single reference set of clinically annotated samples. The addition of RNAi screens makes this a very important and unique data resource, which we hope will help expedite the discovery of novel targeted agents for MM scientific community.

TPS8057 Background: Multiple myeloma (MM) is the second most prevalent blood cancer, representing approximately 1% of all cancers. Although overall survival has improved in recent years due to new approved agents, the vast majority of MM patients (pts) ultimately stop responding to treatment. Moreover, about a quarter of MM pts characterized as “high risk” experience limited benefit from existing treatments. Seminal genomic sequencing research efforts, such as the MMRF CoMMpass study, have highlighted that a large number of MM cases harbor potentially actionable oncogenic molecular alterations and published reports on small numbers of cases suggest that Precision Medicine (PM) interventions clinically targeting such actionable drivers may benefit MM pts. These results suggest that PM approaches in MM are possible and should be further studied clinically. To that end, we have launched MyDRUG, a master protocol aimed at developing new myeloma regimens based on individual pt’s genomics. Methods: This study is a Phase I-II, multicenter master protocol in functional high risk MM patients (i.e patients having progressed from baseline treatment within 18 months without maintenance or 3 years on maintenance) with 1-3 prior lines of therapy. Patients are screened for actionable genomic alterations on the CLIA-grade MI-ONCOSEQ platform. Patients with actionable alterations are assigned to the appropriate targeted agent used in combination with a backbone regimen ixazomib, pomalidomide and dexamethasone (IPd), whereas patients without such alterations go on an immune arm (see Table). Inclusion criteria include measurable disease (as measured by M-protein and FLC) and acceptable hematologic and metabolic functions. A maximum of 12 evaluable pts will be accrued onto the phase I portion of each arm and an additional 21 evaluable phase II pts will be accrued onto each arm for a total of 27 evaluable pts at the MTD combination dose. The primary objective of the study is to evaluate ORR per IMWG consensus criteria. Secondary objectives are to assess adverse events (AEs), progression-free survival, and overall survival (OS). Experimental correlative aims include assessing molecular or clonal response, molecular and immune signatures of resistance/response, MRD. Clinical trial information: NCT03732703. [Table: see text]

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.

Objective To identify the association between aneuploidy and clinical outcome in patients with relapsed and/or refractory multiple myeloma (RRMM) who participated in MMRF (Multiple Myeloma Research Foundation) sequencing study at University of Michigan. Background: Aneuploidy, defined by abnormal copy number changes of chromosomes, is one of the hallmarks in cancer, reflecting and also contributing to genome instability (Ye, Regan et al. 2018). Approximately 90% of cancers have gained or lost one or both arms of at least one chromosome (Taylor, Shih et al. 2018). In recent years, large scale sequencing efforts have extended aneuploidy study, identifying DNA somatic copy number alterations (SCNAs). Published data suggest aneuploidy has a stronger impact on prognosis than gene mutations in multiple myeloma (Walker, Boyle et al. 2015; Bolli, Biancon et al. 2018; Jamal-Haniani, Wilson et al. 2017). Methods: Fifty one RRMM patients from our institute participated in Clinical-Grade Molecular Profiling of Patients with Multiple Myeloma and Related Plasma Cell Malignancies (MMRF-002) from Multiple Myeloma Research Foundation (MMRF) between March 2016 and November 2018. Genomic DNA was obtained from CD138+ sorted myeloma cells and a peripheral blood sample from each patient. Capture exome sequencing on a targeted panel of 1500 genes was performed by the Illumina HiSeq 2500 (2x115bp paired-end reads, average 520X coverage). Copy number changes, loss of heterozygosity (LOH) and tumor purity were jointly estimated using an in-house pipeline for matched tumor/normal libraries. We assessed aneuploidy using chromosomal and arm level SCNAs which are determined by the median of SCNAs and summation of gain and loss of SCNAs for a given chromosomal arm, which are then used for a time-to-event analysis of overall survival (OS) of the patients. The differences between Kaplan-Meier overall survival curves were tested using the log-rank test. Hazard ratios (HR) were estimated from Cox proportional hazard regression. A threshold of significance was taken as p<0.05. Results Patient demographic data is summarized in Table 1. All the patients in this study harbor DNA somatic copy-number abnormalities (SCNAs) on the arm level for at least one chromosome (Figure 1). Gain of a chromosome or a whole arm mostly occurs on 1q, 3, 5, 7, 9, 11, 15, 19, 21 while loss was noted in 1p, 8p, 13, 16q, and 17p. At a global-level, we find that whole genomic aneuploidies (genomic-level across all chromosomes) have a significant association with inferior OS. Specifically, patients with a high frequency of genomic aneuploidies (defined as number of SCNA >8, the first quartile) had a significantly worse prognosis than those with low number of SCNA events (<=8) (p=0.037, Figure 2). Arm-level aneuploidies with high incidences include 11q (54.9%), 13q (52.9%), 15q (51.0%), 1q (51.0%), 9q (51.0%), a finding consistent with previous studies (Walker, Leone et al. 2010). Aneuploidies in 10p (11.8%) and 10q (7.8%) are uncommon in agreement with previous studies (Tricot G, Sawyer et al. 2017) as myeloma structural or numerical abnormalities rarely occur on chromosome 10. Using univariate analysis, we identified five arm-level aneuploidies which are significantly associated with worse OS: 10p, 10q, 11p, 18q and 20q. It is noteworthy that aneuploidies in either arm of chromosome 10 (10p and 10q) had significant negative OS prognostic effect: 10p (median survival for diploid 28.9 vs. aneuploid 4.1 mo; p<0.001) and 10q (28.9 vs 3.8 mo; p=0.015). Similar results with a negative impact on OS were found from 11p (not reached vs 13.8 mo; p=0.036); 18q (28.9 vs 8.5 mo; p=0.025); and 20q (28.9 vs 3.1 mo; p=0.009). Given the small sample size we did not conduct a multivariable analyses and multiplicity adjustment in this preliminary analyses. Conclusion Aneuploidy measured by SCNAs is correlated with unfavorable survival in relapsed/refractory myeloma. The higher the occurrence of aneuploidies, the worse the overall survival, illustrating of the impact of cancer genome instability. Disclosures Ye: Janssen: Research Funding; Karyopharm: Research Funding; Portola: Research Funding; MingSight: Research Funding; Sanofi: Research Funding. Talpaz:Imago BioSciences: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; CTI BioPharma: Research Funding; Constellation: Research Funding; Incyte: Research Funding; Novartis: Research Funding; Samus Therapeutics: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy.

Abstract History of prior cancer is a widespread exclusion criterion in cancer trials. Up to 80% of NCI-sponsored and 80% of industry-sponsored lung cancer trials exclude patients with a prior cancer. We suspect this exclusion is commonly applied in other cancer trials, although no definitive data exist. Prior cancer is especially common among older patients and those with certain cancer types. Among patients >65 years, 15.1% overall have prior cancer. In Multiple Myeloma, the most common plasma cell disorder (PCD), prevalence of prior cancer is as high as 17.4%. Examining the prior cancer exclusion criterion is important because the number of US cancer survivors is large and rapidly growing. Arbitrarily excluding cancer survivors from trials is not evidence-based and exclusion presumably arises from assumptions that higher mortality of patients with prior cancer could hinder study conduct and bias trial outcomes. However, few data exist to support this assumption of higher mortality. In fact, we demonstrated that lung cancer patients with a prior cancer have similar or lower mortality risk, compared to those without prior cancer. In this abstract, we reviewed prior cancer related eligibility criteria in three of the most common PCD (Multiple Myeloma, Amyloidosis and Waldenström Macroglobulinemia) clinical trials sponsored or endorsed by 5 major Co-Operative groups in North America (Table 1). We use descriptive statistics (n, %) and Fisher's exact tests to describe characteristics of trials with and without exclusion criteria. Of 33 trials, 26 (79%) excluded patients with prior cancer as follows - active cancer (12%), within 2-3 years of PCD diagnosis (9%), or within 5 years of PCD diagnosis (55%). Many trials had exceptions to prior cancer exclusion. Specifically, 67% of trials allowed non-melanoma skin cancer, 58% allowed in-situ cervical cancer, 12% allowed early stage prostate cancer. Table 2 shows the association between clinical trial characteristics and prior cancer exclusion. There was no association between prior cancer exclusion and phase of study, transplant studies, or survival end point. Exclusion criteria varied across year of activation (p=0.01); for example, 75% of studies activated 1990-1999 excluded prior cancer, compared to 100% of studies activated after 2010. Type of PCD was marginally associated with exclusion criteria (p=0.08); 74% of Multiple Myeloma trials excluded patients with prior cancer compared to 100% of the Amyloidosis and Waldenström Macroglobulinemia trials. Conclusion: A substantial proportion of potential participants may be excluded from PCD clinical trials because of a history of prior cancer. This practice impacts accrual, generalizability, and fair access to cutting-edge treatments and the highest level of clinical care. As treatment outcomes for other cancers continue to improve, it is likely that the prevalence of multiple primary cancers will increase. This exclusion criterion is applied widely across studies, including more than two-thirds of those with non-survival endpoints. Our study is the first to examine the prevalence and potential impact of prior cancer exclusion in PCD clinical trials. Using population-based cancer registry data, we plan to undertake further research to understand the appropriateness and ramifications of this standard exclusion policy in PCD cancer trials. Disclosures Collins: Agios: Research Funding; Bristol Myers Squibb: Research Funding; Celgene Corporation: Research Funding; Arog Pharmaceuticals: Research Funding. Devine:Kiadis Pharma: Consultancy. Usmani:Amgen, BMS, Celgene, Janssen, Merck, Pharmacyclics,Sanofi, Seattle Genetics, Takeda: Research Funding; Abbvie, Amgen, Celgene, Genmab, Merck, MundiPharma, Janssen, Seattle Genetics: Consultancy. Anderson:Amgen: Speakers Bureau; Takeda: Speakers Bureau; Celgene: Speakers Bureau. Kumar:Janssen: 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; Roche: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding.

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 259 Despite recent progress, our understanding of the genomic basis of diffuse large B cell lymphoma (DLBCL) is incomplete. We have performed massively parallel whole exome sequencing with deep coverage of 57 primary tumor samples from patients with DLBCL and compared them to matched normal tissue to gain insight into the genetic defects leading to DLBCL. We define genes that are significantly enriched for somatic mutations, and thus, may be drivers of the disease. In addition to identifying genes that are well established to be functionally relevant in DLBCL, such as MyD88, CARD11, EZH2, and BCL2, we also discovered new genes that are significantly mutated that have not been described to play a role in DLBCL previously, such as MLL2, 3 and4 genes and PCLO. One of the challenges for the understanding of DLBCL biology is to determine the relative contribution of different genes with somatic mutations to the pathogenesis of the disease in an individual patient. Identification of functionally relevant mutated genes that only occur in a small number of patients based on prevalence alone may require thousands of patient samples. We therefore devise several strategies to discover genes with a high likelihood of functionally relevant mutations independent of their prevalence. Genes that do not co-occur with other mutated genes in the same patients, i.e. are mutually exclusive, are more likely to represent strong drivers, and to occur early in the development of DLBCL. We determined that TP53, BCL2, PIM1, and CD79B fall into this group. We apply pathway-focused analysis to identify mutated genes with low prevalence that are highly likely to have a functional impact. For instance, we identified G13D mutations in KRAS in two patients, and mutations in BRAF, NOTCH1, SOCS1, and PTEN in other patients that were previously described in other malignancies. These mutated genes are identified as part of significantly enriched gene sets, including the B cell receptor signaling/NFkB pathway, JAK/STAT, and antigen processing gene sets. In addition to these pathways that are important for physiological B cell activation, we find that many histone methyltransferases are enriched as a geneset in addition to MLL2. We identified a number of hotspots that are subject to somatic mutation. Our data imply that many of these hotspots may be targets of activation-induced cytidine deaminase (AID), which is usually involved in somatic hypermutation, suggesting a possible mechanism of how somatic mutation in DLBCL is mediated. In summary, our data define novel genetic defects in DLBCL by whole exome sequencing of 57 patients. Our data suggest that driver mutations may not be randomly distributed across the genome, but may occur in a directed fashion. We identified new genes not previously implicated in DLBCL, and have developed strategies to prioritize mutations that are most likely to be drivers of the disease. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Multiple myeloma (MM) is a hematologic cancer characterized by a diversity of genetic lesions-translocations, copy number alterations (CNAs), and single nucleotide variants (SNVs). The prognostic value of translocations and of CNAs has been well established. Determining the clinical significance of SNVs, which are recurrently mutated at much lower frequencies, and how this significance is impacted by translocations and CNAs requires additional, large-scale correlative studies. Such studies can be facilitated by cost-effective targeted sequencing approaches. Hence, we designed a single-platform targeted sequencing approach capable of detecting all three variant types. Methods: We designed oligonucleotide probes complementary to the coding regions of 467 genes and to the IgH and MYC loci, allowing a probe to closely match at most 5 regions within the genome. Genes were selected if they were expressed in an independent RNA-seq MM data set and harbored germline SNP-filtered variants that: (1) occurred with frequency >3%, (2) were clustered in hotspots, (3) occurred in recurrently mutated "cancer genes" (as annotated in COSMIC or MutSig), or (4) occurred in genes involved in DNA repair and/or B-cell biology. IgH and MYC tiling was unbiased (with respect to annotated features within the loci) and spanned from 50 kilobasepairs (kbps) upstream of both regions to 50 kbps downstream of IgH and 100 kbps downstream of MYC. Results: We performed targeted sequencing of 96 CD138-enriched samples derived from MM patients, as well as matched peripheral blood leukocyte normal controls. Sequencing depth (mean 107X) was commensurate with that of available exome sequencing data from these samples (mean 71X). Samples harbored a mean of 25 non-silent variants, including those in known MM-associated genes: NRAS (24%), KRAS (22%), FAM46C (17%), TP53 (10%), DIS3 (8%), and BRAF (3%). Variants detected by both platforms showed a strong correlation (r^2 = 0.8). The capture array detected activating, oncogenic variants in NRAS Q61K (n=3 patients) and KRAS G12C/D/R/V (n=5) that were not detected in exome data. Additionally, we found non-silent, capture-specific variants in MTOR (3%) and in two transcription-related genes that have been previously implicated in cancer: ZFHX4 (5%) and CHD3 (5%). To assess the potential role of deep subclonal variants and our ability to detect them, we performed additional sequencing (mean 565X) on six of the tumor/normal pairs. This revealed 14 manually-reviewed, non-silent variants that were not detected by the initial targeted sequencing. These had a mean variant allele frequency of 2.8% and included mutations in DNMT3A and FAM46C. At least one of these 14 variants occurred in five of the six re-sequenced samples. This highlights the importance of this additional depth, which will be used in future studies. Our approach successfully detected CNAs near expected frequencies, including hyperdiploidy (52%), del(13) (43%), and gain of 1q (35%). Similarly, it inferred IgH translocations at expected frequencies: t(4;14) (14%), t(6;14) (3%), t(11;14) (15%), and t(14;20) (1%). As expected, translocations occur predominantly within the IgH constant region, but also frequently 5' (i.e., telomeric) of the IGHM switch region, and occasionally within the V and D regions. We detected MYC -associated translocations, whose frequencies have been the subject of debate, at 10% (n=9 patients), with five involving IgH, three having both partners in or near MYC, and one having both types. Finally, our platform detected novel IgH translocations with partners near DERL3 (n=2), MYCN (n=1), and FLT3 (n=1). Additional evidence suggests that DERL3 and MYCN may be targets of IgH-induced overexpression: of 84 RNA-seq patient samples, six exhibited outlying expression of DERL3, including one sample in which we detected the translocation in corresponding DNA, and one exhibited outlying expression of MYCN. Conclusion: Our MM-specific targeted sequencing strategy is capable of detecting deeply subclonal SNVs, in addition to CNAs and IgH and MYC translocations. Though additional validation is required, particularly with respect to translocation detection, we anticipate that such technology will soon enable clinical testing on a single sequencing platform. Disclosures Vij: Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy; Takeda, Onyx: Research Funding.