ALBERT

Description: 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.

Description: Multiple myeloma is the second most common hematological malignancy in the U.S. with an estimated 30,700 new diagnoses in 2018. It is a clonal disease of plasma cells that, despite recent therapeutic advances, remains incurable. Myeloma cells retain numerous characteristics of normal plasma cells including reliance on survival signals in the bone marrow for long term viability. However, malignant transformation of plasma cells imparts the ability to proliferate, causing harmful bone lesions in patients, and in advanced stages independence of the bone-marrow microenvironment. Therefore, we are investigating the molecular mechanisms of myeloma cell survival that allow them to become extramedullary. We identified syntenin-1 (SDCBP) as a protein involved in myeloma cell survival and a potential therapeutic target. Syntenin-1 is an adapter protein that has been shown to regulate surface expression of several transmembrane proteins by binding with membrane phospholipids and mediating vesicular trafficking of proteins throughout the cell. Syntenin-1 regulates the surface expression of CD138, a plasma/myeloma cell marker. Syntenin-1 has been shown to regulate apoptosis in numerous cancer cell lines including breast cancer, glioma, and pancreatic cancer but its role in multiple myeloma survival has not been studied. To determine if syntenin-1 expression has an effect on myeloma cell survival, we utilized the CoMMpass dataset (IA12), a longitudinal study of myeloma patients that includes transcriptomic analysis throughout treatment. We found that patients with the highest expression of syntenin-1 mRNA (top quartile) had significantly worse overall survival, progression-free survival, and a shorter response duration than those in the bottom quartile of expression. To determine if syntenin-1 has a role in myeloma cell survival, we used short hairpin RNA to knock down syntenin-1 (shsyn) in RPMI 8226 and MM1.s myeloma cell lines. We then determined the amount of cell death using Annexin-V staining flow cytometry four days following lentiviral infection. We found increased cell death in syntenin-1-silenced cells compared to our empty vector control in both RPMI 8226 (control=42.17%, shsyn=71.53%, p=0.04) and MM1.s cell lines (control=8.57%, shsyn=29.9%, p=0.04) suggesting that syntenin-1 is important for myeloma cell survival. Syntenin-1 contains two PDZ domains that allow it to bind to receptor proteins via their corresponding PDZ-binding motifs. We therefore wanted to look at correlation of syntenin-1 expression with CD138 and CD86, two PDZ-binding domain containing proteins expressed on the surface of myeloma cells. Using the CoMMpass dataset, we found patients with high expression of syntenin-1 had a median expression of CD86 that was twice as high as the total population (P

Description: 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

Description: 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.

Description: Aim and Background: Two PI3K inhibitors frequently used in laboratory evaluations are LY294002 and Wortmannin. Both inhibit all class I PI3K enzymes but are poorly soluble and have off-target activity. This led to the development of SF1126, a soluble conjugate of LY294002 that localizes to the tumor vasculature in vivo, inhibits dex resistant xenografts (Garlich, Cancer Res. 2008) and SF-1126 is currently in Phase I trials. Combination therapy with PI3K inhibitors and conventional anti-myeloma therapies are intriguing since preclinical studies suggest that PI3K inhibitors exhibit synergistic effect with low concentrations of vincristine, a microtubule destabilizing agent by dephosphorylating GSK-3β in tumor cells (Fujiwara, Mol. Cancer Ther. 2007) in the induction of apoptosis. Thus, it is imperative to perform pre-clinical studies in order to optimize the clinical use of agents with broad effects such as SF1126. Methods: Human myeloma cell lines and primary patient tumor cells were used in evaluating downstream effects of SF1126 as well as to evaluate the effect of combination therapy. There cell lines were used to evaluate the effect of SF1126 on global gene expression profiles. We have validated a set of RNA microarray analysis data obtained by SF1126 treatment alone or with bortezomib in myeloma cells and compared the inhibition of the upregulated or downregulated targets by timeline measurements of additions of SF1126 with bortezomib. RNA from MM cells was run on Illumina Human WG-6 v3 BeadChips at Emory microarray facility. MTT assays were used for the cell viability. AnnexinV staining was used to assess the level of apoptosis. Western blotting was performed using antibodies on various targets. Results: Treatment of MM.1R, OPM1, MM.1S myeloma cells with clinically achievable concentrations of SF1126 (2–14 uM) showed a response rate of cell killing in the range of 20% by 72hrs. Bortezomib alone at 4nM showed a response rate of cell killing in less than 20%. When they were combined the cell killing increased to 50% or more demonstrating additive benefit. To maximize the additive benefit, this led us to combine them in a sequential manner. In sequence assays, drug adminstrations was staggered by SF1126 and bortezomib. Both concurrent and addition of bortezomib prior to SF1126 were superior to SF1126 given to bortezomib in the MM.1R cell line. Western blotting data suggests that adminstration of SF1126 first increased the expression of p21Waf1/Cip1 protein with a modest reduction of phos-Akt in all the MM cells tested. Sequence data were consistent among several cell lines and consistent with apoptosis assays and also in primary CD38+ myeloma cells. Interestingly, there was little overlap in the number of genes upregulated or downregulated by SF1126 and bortezomib suggesting complementary molecular profiles. Microarray data based on MM.1R, OPM1, H929 cell lines revealed that p21Waf/Cip1 was among the upregulated genes upon the SF1126 treatment. Additionally, other off-target genes such as BCl2, genes in Wnt-pathway as well as in Akt pathway responsible for the antiapoptotic response during sequence administration are under investigation. Combination of SF1126 with conventional agents such as melphalan. dexamethasone, and the Imid lenalidoamide also demonstrate encouraging combination effects in myeloma cells. Conclusions: Sequence and combination administration could shed light in the preclinical setting to help guide the clinical evaluation of SF1126 as a single agent and in combination with bortezomib, dexamethasone, melphalan and lenalidoamide in myeloma.