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Introduction Extensive genomic characterization of multiple myeloma has identified subtypes with prognostic and therapeutic implications. In contrast, less is known about the myeloma epigenome. One challenge that has hindered epigenetic studies are assays amenable to biobanked specimens. Here, we sought to determine whether ATAC-seq and RNA-seq of myeloma cells from cryopreserved bone marrow aspirates recapitulated those from fresh samples and used this approach to investigate enhancers of myeloma oncogenes. Methods Consent and collection of specimens followed approved Institutional Review Board protocols. Mononuclear cells were enrichment by Ficoll gradient centrifugation and were either cryopreserved in 10% DMSO and RPMI media with 10% FBS or used to isolate viable CD138+CD38+ myeloma cells. RNA-seq used the mRNA HyperPrep kit (Kapa Biosystems) with RNA from 50,000 cells. ATAC-seq used the Tn5 transposase (Illumina) on 20,000 cells. Sequencing was performed on an HiSeq 4000 (Illumina). Sequencing data were quality and adapter trimmed using Trim Galore! And mapped to the GRCh37 genome using STAR (RNA-seq) or bowtie2 (ATAC-seq). MACS2 was used to determine chromatin accessible regions and R was used for downstream analyses. H3K27ac ChIP-seq from Jin et al. (Blood, 2018) were downloaded from the European nucleotide archive (PRJEB25605). RNA-seq from CoMMpass (NCT01454297) were downloaded from dbGaP phs000748.v7.p4. Enhancer RNAs were interrogated in intergenic regions excluding 500 bp upstream of TSSs and 5 kb downstream of transcription termination sites to avoid contamination from exonic mRNAs or intronic pre-mRNAs. Results We compared RNA-seq and ATAC-seq data from myeloma cells isolated from fresh bone marrow aspirates to those cryopreserved for up to 6 months from the same aspirate. RNA-seq and ATAC-seq data from fresh and frozen samples from the same aspirate were highly correlated with each other but distinct from other samples as depicted by principal component analysis (Fig. A,B). Inspection of CCND1 showed high levels of RNA in two patients and this was consistent in both fresh and frozen specimens as well as with FISH results indicating a t(11;14) translocation in these samples (Fig. C). Similarly, fresh and frozen specimens from the same patient showed consistent expression for CCND2 and MYC and these data corresponded with chromatin accessibility found near these genes (Fig. D, see regions shaded in gray). Based on these results we expanded our analysis to include 8 biobanked specimens, which identified 91,632 regions of chromatin accessibility that were enriched around plasma cell lineage genes such as IRF4, CD38, SLAMF7, and IGH. Chromatin accessibility often predicted proximal gene expression and this was especially pronounced for regions enriched for histone 3 lysine 27 acetylation (H3K27ac) - a mark of enhancers. Active enhancers are sometimes demarcated by enhancer RNAs (eRNAs) observable in RNA-seq data, thus we queried intergenic regions marked by chromatin accessibility and H3K27ac for eRNAs using RNA-seq data on 768 myeloma specimens from the CoMMpass study. This identified transcription at 4,729 of 13,452 potential regions. eRNA expression was highly correlated with proximal gene expression. To illustrate this point, we performed t-SNE clustering based on mRNA and eRNA expression and color-coded each sample by myeloma gene expression subtype (Fig. E). Interestingly, this identified several regions highly correlated with oncogene expression between myeloma subtypes. For example, an enhancer ~154 kb upstream of CCND2 was uniquely transcribed in the MAF subtype (Fig. F) and this was highly correlated with CCND2 expression (Fig. G). Conclusions Cryopreservation of myeloma bone marrow aspirates allows isolation and analysis of biobanked samples that produce RNA-seq and ATAC-seq data that are highly congruent with those obtained from fresh samples and this represents a strategy for retrospective genomic and epigenomic studies. Chromatin accessibility analysis identified distinct enhancer elements regulating oncogenes in myeloma subtypes providing mechanistic insight into myeloma pathology. Figure 1 Disclosures Lin: Amgen: Current Employment, Current equity holder in publicly-traded company. Hofmeister:Bristol Myers Squibb: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Nektar: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Oncopeptides: Honoraria; Oncolytics Biotech: Research Funding; Imbrium: Honoraria; Karyopharm: Honoraria, Research Funding. Nooka:Celgene: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Adaptive Technologies: Consultancy, Honoraria; Spectrum Pharmaceuticals: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: Personal Fees: Travel/accomodations/expenses, Research Funding; Karyopharm Therapeutics, Adaptive technologies: Consultancy, Honoraria, Research Funding; Oncopeptides: Consultancy, Honoraria. Lonial:GSK: Consultancy, Honoraria, Other: Personal fees; BMS: Consultancy, Honoraria, Other: Personal fees, Research Funding; Takeda: Consultancy, Other: Personal fees, Research Funding; Novartis: Consultancy, Honoraria, Other: Personal fees; Janssen: Consultancy, Honoraria, Other: Personal fees, Research Funding; Merck: Consultancy, Honoraria, Other: Personal fees; JUNO Therapeutics: Consultancy; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Honoraria; Onyx: Honoraria; Genentech: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy, Honoraria, Other: Personal fees; Sanofi: Consultancy; Abbvie: Consultancy. Boise:AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genetech: Membership on an entity's Board of Directors or advisory committees.

The genetic and transcriptional program of multiple myeloma has identified novel markers of high-risk disease and mechanisms of pathogenesis. However, there remains a significant gap between prospective identification of high-risk patients and our ability to determine all patients that experience poor outcomes. Epigenetic alterations in myeloma have been less studied, but have significant potential as a translatable biomarker. To better understand how epigenetic programming may contribute to myeloma pathogenesis, we characterized the myeloma DNA methylome. DNA from CD138+ enriched myeloma specimens from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were obtained after receiving permission from the CoMMpass Tissue Use Committee and Emory IRB. Whole genome bisulfite sequencing (WGBS) was performed by adapter ligation (Kapa HyperPrep) using fully methylated sequencing adapters followed by bisulfite conversion (Qiagen Epitect), library amplification (Kapa Uracil+ HiFi polymerase), and sequencing (NovaSeq 6000 S4 150bp paired-end reads). Sequencing data were mapped with Bismark and CpG methylation calls were extracted using R. WGBS DNA methylation data was generated for 120 specimens, including 87 baseline, 24 paired relapse, and 9 paired peripheral blood specimens. This yielded ≥5x coverage at 21,393,650 CpGs in 90% of samples with an average coverage of 20x. Myeloma exhibited extensive genomic hypomethylation such that the median level was 42% (range 21-67%) as compared to 64% in plasma cells from healthy individuals. Principle component analysis indicated most variation corresponded with hypomethylation, which occurred in megabase domains devoid of gene expression. In contrast, DNA methylation was retained in the bodies of expressed genes. Principle components 2 and 3 separated samples with t(4;14) translocations from others. This may be due to overexpression of WHSC1 driving excessive histone 3 lysine 36 di-methylation (H3K36me2), which in turn impacts the function of PWWP-domain containing DNA methyltransferases (DNMT3A, DNMT3B). Given the 3-fold variability observed in average methylation, we sought to understand if this was indicative of outcome. Analysis of 87 baseline specimens identified 23,386 loci where DNA methylation (or lack thereof) was prognostic of outcome (FDR ≤0.01) (Figure 1a). These prognostic loci were clustered into contiguous regions often found in gene bodies and could be used to stratify patients by progression-free and overall survival (P-value 8) (Figure 1b). Importantly, the prognostic value of these CpG were independent of t(4;14) status and ISS stage, indicating these high-risk patients would not have otherwise been identified. Furthermore, analysis of 24 relapse specimens from 22 patients indicated epigenetic remodeling occurred at these prognostic loci. Specifically, loci where the presence of DNA methylation was indicative of poor outcome gained DNA methylation in relapsed samples, and loci where lack of DNA methylation was indicative of poor outcome lost DNA methylation in relapse samples (Figure 1c). These relapse/refractory DNA methylation changes occurred in contiguous regions proximal to genes including PRKCE, MGMT, FHIT, WWOX, and HDAC9 (Figure 1d and data not shown). Cumulatively, these data identify myeloma epigenetic markers of outcome that undergo reprogramming in relapsed samples suggesting they may be indicative of therapeutic resistance. Disclosures Lonial: Genentech: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Karyopharm: Consultancy; Celgene Corporation: Consultancy, Research Funding. Boise:AstraZeneca: Honoraria, Research Funding; Genentech Inc.: Membership on an entity's Board of Directors or advisory committees.

Multiple myeloma is a malignancy of terminally differentiated, antibody secreting B cells known as plasma cells. Normal B cell differentiation and cell fate are coupled to epigenetic and transcriptional reprogramming, including a proliferation-dependent global loss of DNA methylation (Barwick et al., 2016, 2018). However, relatively little is known about the epigenetic changes that underlie myelomagenesis and how these may contribute to pathogenesis. To this end, we are analyzing the DNA methylome of myeloma specimens from the MMRF CoMMpass trial (NCT01454297), which has already characterized the mutational, structural, and transcriptional landscape of nearly 1,000 myelomas from newly diagnosed patients. CoMMpass specimens were obtained from a centralized biobank with approval from the CoMMpass Tissue Use Committee and Emory IRB. DNA isolated from CD138+ myeloma specimens was subjected to reduced representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS). In total, DNA methylation was derived for over 24 million CpGs with an average of 18x coverage. WGBS data from normal B cells and plasma cells was obtained with permission from the BluePrint project (Agirre et al., 2015) via the European Genome Archive. DNA methylation levels were associated with PFS and OS using a cox proportional regression. We have determined the DNA methylome for 36 primary myeloma specimens and an additional 84 specimens are currently being sequenced. Relative to normal B cells that had an average DNA methylation level of 89.1%, plasma cells and myelomas exhibited a progressive demethylation with mean levels of 71.3% and 43.7%, respectively. While this is consistent with previous observations (Agirre et al., 2015; Salhia et al., 2010), WGBS revealed that myeloma in particular was characterized by large hypomethylated domains. These large hypomethylated domains encompassed genes that were devoid of gene expression whereas DNA methylation remained unchanged in the bodies of genes that were highly expressed. Although the majority of these hypomethylated domains were common across myelomas, there existed many regions where methylation levels varied between myelomas and these differences commonly corresponded with local gene expression differences. To understand if these specific patterns of DNA methylation were indicative of disease pathogenesis, DNA methylation levels were compared to PFS and OS. This identified 2,594 regions where the level of DNA methylation was prognostic of outcome (P≤0.001). Reduced DNA methylation corresponded with poor outcome at 88.5% (N=2,298) of these regions, which included loci proximal to cell cycle genes such as MYC, E2F3, CCND1, and CCNE1. Only 11.5% (N=296) of regions associated with outcome had higher levels of DNA methylation associated with poor prognosis. These regions tended to be proximal to genes involved in B cell receptor signaling, such as PLCG2 and VAV2. Although the expression of several of these genes was also prognostic of survival, the majority were not, indicating that the epigenetic state contains a unique prognostic value. These data indicate that myeloma undergoes profound epigenetic remodeling that is co-ordinate with changes in gene expression. Perhaps the most striking feature were megabase domains of hypomethylation. That DNA methylation was preferentially retained in the bodies of expressed genes suggests that a molecular mechanism and/or cellular selection occurs to maintain methylation at genes whose expression is required for myeloma cell survival. Despite the small number (N=36) of myeloma specimens analyzed thus far, the large number of regions associated with survival indicates the potential prognostic value of DNA methylation in myeloma. Furthermore, DNA methylation indicative of outcome only partially overlapped with the prognostic value of gene expression, indicating DNA methylation has independent value as a biomarker of outcome in myeloma. This may be due, in part, to the fact that DNA methylation is a very stable modification that not only reflects the current gene expression program, but is also indicative of the cell history and potential. Integrative genetic, epigenetic, and transcriptional analysis from WGBS of 120 CoMMpass myeloma specimens will be presented, including matched baseline and relapsed specimens from 25 patients. Disclosures Lonial: Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.

Patients with the plasma cell malignancy multiple myeloma now benefit from treatments such as proteasome inhibitors, immunomodulatory imide drugs (IMiDs), autologous stem cell transplant, and monoclonal antibodies. However, 20% of patients still relapse or die within two years and are deemed 'high risk'. Current markers fail to identify all high-risk patients resulting in misdiagnoses, therefore additional markers for this deadly form of the disease are required. To better understand and identify high-risk myeloma, we analyzed the structural variant landscape of 826 myelomas from newly-diagnosed patients using whole genome sequencing as part of the CoMMpass trial (NCT01454297). High-confidence somatic structural variants were determined using DELLY and quality control metrics to exclude regions with sequencing anomalies. Myeloma from newly diagnosed patients had a median of 21 somatic structural variants including 7 duplications, 2 deletions, 7 inversions, and 3 translocations. The number of deletions, duplications, and translocations corresponded to poor progression-free (PFS) and overall survival (OS), with translocations being the most significant (P These data identify IgL translocation as an independent marker of poor prognosis regardless of translocation partner, and suggest this may be due to the failure of this myeloma subtype to benefit from IMiDs. One potential mechanistic explanation is that the IgL enhancer is one of the most robust enhancers of gene expression and is therefore uniquely resistant to therapeutic inhibition. Indeed, the IgL enhancer is bound by several transcription factors at some of the highest levels in the B cell / myeloma epigenome, including BRD4, MED1, and IKZF1. This last factor is particularly interesting as IKZF1 is the target of IMiDs, and thus high-levels of IKZF1 occupancy at the IgL enhancer may be more difficult to fully deplete therapeutically than other loci. This may explain why patients with IgL-translocated myeloma do not benefit from IMiDs whereas patients with IgH- or IgK-translocated myeloma do. Finally, the co-occurrence of myeloma with IgL-translocation and hyperdiploidy is particularly unfortunate, as hyperdiploidy is routinely tested for clinically, whereas IgL-translocations are rarely diagnosed, likely resulting in their misclassification as standard risk. Figure: IgL translocations portend poor prognosis. a Circos plot showing the repertoire of IgL translocations in newly diagnosed myeloma where line thickness denotes frequency (key bottom left). b Kaplan-Meier analysis of IgL translocated [t(IgL)] patients (N=81) as compared to non-t(IgL) (N=745) for progression-free (PFS; top) and overall survival (OS; bottom). P-values were calculated using a Cox proportional hazards Wald's test or permutation based P-value with 1,000 permutations based on the hazard ratio. Disclosures Neri: Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Bahlis:Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Nooka:Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kaufman:Janssen: Consultancy; Roche: Consultancy; Karyopharm: Other: data monitoring committee; Abbvie: Consultancy; BMS: Consultancy. Lonial:Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.

Abstract 3974 Background and Aim. There have been groundbreaking efforts to sequence Multiple Myeloma (MM) genomes to better define the landscape of this disease. Similar efforts to integrate multiple different 'omic' data types of clinical relevance remains a challenge. We sought to develop a systematic approach to filter the spectrum of genomic alterations through the integration of multi-platform gene expression, methylation, SNP, and microRNA microarrays with transcriptome and whole genome data, combined with clinical outcome. This approach to define clinically relevant molecular signatures in myeloma was tested using three human myeloma cell lines (HMCLs), RPMI8226, MM.1s and KMS11and compared to publically available datasets. Materials and Methods. DNA and RNA were isolated from HMCLs and applied to array-based platforms: Illumina Omni1 Quad, Illumina Human HT12v4.0 Expression BeadChip, Illumina Infinium Human Methylation 27K and 450K, and Affymetrix MicroRNA GeneChip 2.0 following manufacturers protocols. Illumina TruSeq RNA and DNA protocols were used to generate RNA-Seq and DNA-Seq (mate-pair and paired-end) libraries that were analyzed using the Illumina HiSeq2000 instrument. A novel filter was developed to identify changes specific to each HMCL and applied to each platform, separately and combined. Genes were further filtered according to their clinical relevance based on publicly available data (GSE9782) from clinical trials using quantile survival analysis. Genomic changes predicted in the array-generated data were further examined and supported by RNA- and DNA-seq data, and validated by the use of external, publicly available MM cell line (MM genome portal; Keats 2007, Cancer Cell) and patient data (GSE26849; Chapman 2011, Nature). Statistical significance of results was set at 0.01. Results. We focused our analytical pipeline development on identifying genes specifically altered in KMS11, with MM1s and RPMI8226 specific and common signatures similarly defined. Based on our cell line specific filter statistic, 120 genes were identified as having KMS11-specific significant differences: 48 expression (18 over-expressed-; 23 under-expressed), 19 methylation (10 hypermethylated; 9 hypomethylated), and 53 genes featured in regions of gain (28) or loss (15). Among the 48 genes defined as having KMS11-specific expression, 12 were identified in combination with KMS11-specific methylation changes (3 over-expressed and hypomethylated; 9 under expressed and hypermethylated). Twenty of the 120 genes showed a significant association with overall survival, 5 of which showed a significant association with treatment response. Among these genes, Epidermal growth factor receptor pathway substrate 15 (EPS15) displayed a combination of increased CpG methylation, copy number loss, and low expression. We validated EPS15 expression is lowest in KMS11 using qRT-PCR and at the protein level by western blot analysis. Western blot analysis of 10 HCMLs demonstrates that EPS15 is absent in KMS11 as well as NCI-H929 and is low in OPM-2. Interestingly all three of these lines have the t(4;14). Additionally, based on publicly available aCGH and expression data, we were able to independently validate our results in both MM cell lines and patients, with a prevalence of deletion in this region in 27% of 46 MM cell lines and 16% of 234 MM patient samples. Finally, low EPS15 expression was associated with significantly shorter overall survival (OS) among MM patients treated with Bortezomib, in contrast with significantly longer OS among Dexamethasone treated patients. Conclusion. We developed an analytical pipeline for integrative analyses to obtain molecular signatures in MM using multiple genomic data types. This approach may be useful for guiding treatment decisions, in addition to the identification of genomic changes associated with MM. We initially focused on EPS15, as one of a few filtered genes that showed clinically relevant expression differences to treatment. While this gene is located on 1p23, a deletion 'hotspot' in MM, the combination of its deletion and hyper-methylation, along with its expression difference association in response to current treatment for MM provide support for studying this gene in myeloma. Taken together, these results provide a template for large-scale, integrative informatics analyses of studies in MM as well as across many tumor types. Disclosures: No relevant conflicts of interest to declare.