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Abstract Introduction. Growing evidence suggests that immune cells that reside within the tumor microenvironment are dysregulated and functionally impaired, leading to defective anti-tumor immunity of the host. One of the major immunosuppressive mechanisms during tumor progression is expansion of regulatory immune cells. Here, we analyzed the immune cells within the bone marrow (BM) and the peripheral blood (PB) of 2 immunocompetent multiple myeloma (MM) mouse models. We next studied the role of regulatory T cells (Tregs) in MM pathogenesis. Materials and methods. To study the immune cell populations of the BM and PB, we used two immuncompetent mouse models and transplanted VK*MYC cells or 5TGM1 cells into C57BL/6 and C57BL/Kalwrij mice respectively. The immune cell populations and checkpoint receptor expressions were analyzed by CyTOF mass cytometer or flow-cytometry. Treg induction assay was performed in vitro to study the mechanism of Treg increase in the BM of myeloma injected mice. CD4+ CD25- cells were obtained from C57BL/Kalwrij mice and were co-cultured with 5TGM1 cells or B cells from C57BL/Kalwrij mice in vitro. Treg induction was compared by flow-cytometry. Transplantable VK*MYC cells were injected into "depletion of regulatory T cell" (DEREG) mice, which expresses a simian diphtheria toxin (DT) receptor-enhanced GFP fusion protein under the control of the FOXP3 gene locus, or their wild type littermates. DT injection into these mice leads to depletion of Tregs as previously described (J Exp Med. 2007; 204: 57-63). DT was given once every week for a total 3 times i.p to the DEREG mice or the littermate controls to specifically deplete Tregs and to study the role of Tregs during MM progression. Tregs (CD4+ FOXP3-GFP+ cells) were sorted from VK*MYC injected mice or non-injected DEREG mice BM using FACSAria cell sorter. Cells isolated were subjected to RNA sequencing. Gene Set Enrichment Analysis (GSEA) was performed to define differences in molecular signatures between MM-associated and normal Tregs. Results. The Treg proportion was significantly increased within the CD4+ T cells in the BM of myeloma cell injected mice from the early stage of disease compared to control mice, while in the PB, the increase was observed only at the late stages of disease progression. The effector T cell (Teff)/Treg ratio was significantly decreased in the BM at the end-stage myeloma bearing mice (P<0.01). Checkpoint related molecules (PD-1, LAG-3 and Tim-3) on Tregs and Teffs were up-regulated at the protein level in the BM of myeloma injected mice compared to control (P<0.01). These data indicate the activation of Tregs and decrease of Teff activity, leading to suppression of anti-myeloma T cell activity in the MM BM. Under in vitro co-culture conditions, 5TGM1 cells induced a significant increase in the number of Tregs from non-Treg CD4+ T cells compared to controls (P<0.01). 5TGM1 cell-induced Tregs presented with enhanced ki-67 expression, thus suggesting the ability of MM cells to induce Treg proliferation. Additionally, the trans-well co-culture experiment showed that the major mechanism for Treg induction by 5TGM1 was by direct contact with T cells rather than secreting factors. Significant increase in survival was observed in the VK*MYC injected DEREG mice under Treg depletion compared to the DEREG mice without Treg depletion and wild type littermates under DT treatment (P<0.001). The Treg depleted DEREG mice were accompanied with an increase of Teffs (P<0.01), indicating recovery of anti-myeloma T cell activity. RNA sequencing of BM Tregs from VK*MYC injected mice showed increased expression of immune checkpoint related molecules and increased Treg effector molecules (IL-10, Granzyme B) compared to BM Tregs from control mice, indicating a more functionally active phenotype of VK*MYC associated Tregs. GSEA showed an enrichment of genes involved in type-1 interferon signaling in VK*MYC associated Tregs. Conclusions. We used 2 immunocompetent MM mouse models to study the characterization and significance of Tregs in MM progression. CyTOF analysis and RNA sequencing data indicated Treg activation in the MM BM microenvironment. The Treg in vivo depletion experiment showed that Tregs have a significant role in MM progression. These data indicate that immunotherapy targeting Tregs may represent a novel therapeutic strategy for MM. Studies are ongoing to understand the roles of Tregs in human MM patients. Disclosures No relevant conflicts of interest to declare.

Introduction We have shown that in myeloma (MM) the immunomodulatory drugs (IMiDs) lenalidomide and pomalidomide, in a CRBN dependent fashion, inhibit thioredoxin reductase and thus increase intracellular oxidative stress as a consequence of peroxide accumulation (Sebastian et al. 2017). Accordingly, we have also shown that CRBN expression alone does not fully correlate with lenalidomide sensitivity, as the cells ability to decompose H2O2 is important - MM cells with lower H2O2 decomposition capacity are more sensitive to IMiDs. We hypothezised that the specific metabolic pathways used by cells for biomass production would influence IMiD sensitivity since they also influence this cellular oxidative capacity. Cellular proteins are key elements of this biomass, and required for the proliferation of MM cells and production of immunoglobulin proteins. Methods We cultured MM cells under various nutrients conditions to understand its effect on cell proliferation and drug responses. Cell viability was assessed by MTT assay, the Luminescent Assay, and direct cell counting. MM cells glycolytic rate was measured using the Seahorse XF analyzer. Western blots were performed to quantify various protein expression levels in MM cells cultured under different nutrients conditions. CRBN CRISPR/Cas9 KO plasmid was used to generate CRBN knockout cells and a lentivirus system was used to over-express CRBN in MM cells. Results MM cells resistant to lenalidomide, but still expressing functional CRBN, show a higher glycolytic pathway use than sensitive cells. We found that the lenalidomide sensitive cell line MM1.S is highly dependent on extracellular glutamine for cell proliferation. In contrast, JJN3, a lenalidomide resistant cells line with wild type CRBN, preferentially consumes glucose. We found that glutamine depletion from culture media completely abolished lenalidomide sensitivity in MM1.S cell line. Western blot analysis revealed that antibody production by MM1.S was primarily dependent on glutamine availability, and that JJN3 antibody production was dependent upon glucose availability. While glutamine depletion from culture medium completely abolished lenalidomide sensitivity in MM1.S cell line, IKZF1, and IKZF3 degradation was unchanged. We further studied the role of CRBN in glutamine dependent cellular biomass protein production using isogenic cell lines (+/- wtCRBN). We found that wtCRBN expressing cells proliferate more and show higher antibody production in the presence of glutamine, over CRBN negative isogenic cell lines. Lenalidomide treatment further increased glutamine dependent antibody production in wtCRBN expressing cells. One possibile explanation is that activation of glutamine catabolism can facilitate protein demethylation via the supplementation α-ketoglutarate (α-KG). Therefore, we tested whether lenalidomide treatment could induce protein demethylation in sensitive cell lines. Western blot probed with mono-methyl lysine antibody showed lenalidomide induced protein demethylation. We further confirmed that protein demethylation, and lenalidomide sensitivity is a consequence of elevated α-KG by treating cells with cell-permeable 5-octyl- α-ketoglutarate which act as a substrate of the lysine demethylases. 5-Octyl-α-ketoglutarate treatment inhibited cell proliferation preferentially in wtCRBN expressing cells, and also enhanced lenalidomide induced sensitivity. Protein demethylation is associated with protein ubiquitination and proteasomal degradation. We thus hypothesize that lenalidomide induced protein demethylation also likely increases proteasome inhibitors sensitivity in MM. Conclusion MM cells with preferential glutamine consumption are likely to be more sensitive to lenalidomide, and extracellular glutamine depletion can induce lenalidomide resistance. MM cells expressing CRBN, but not dependent on glutamine for protein biomass production are more likely to be resistant to lenalidomide. Our study postulates that CRBN protein requirement in lenalidomide sensitivity cells is restricted to high glutamine dependency, and that it is quite likely CRBN has a role in glutamine metabolism. Moreover, MM cells that consume more glutamine are under higher oxidative stress and exhibit less H2O2 decomposition capacity and therefore increasing sensitivity to IMiDs. References Sebastian , S., et al., Blood 2017 129:991-1007 Disclosures Stewart: Ono: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy. Bergsagel:Janssen Pharmaceuticals: Consultancy; Ionis Pharmaceuticals: Consultancy; Celgene: Consultancy. Fonseca:AbbVie, Amgen, Bayer, Celgene, Kite, Janssen, Juno, Merck, Pharmacylics, Sanofi, Takeda: Other: Consultant/Advisor; Prognosticatin of MM based on Genetic Categorization by FISH: Patents & Royalties; Adaptive Biotechnologies: Other: Scientific Advisory Board.

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.

Background: A variety of risk factors have been described in multiple myeloma and current risk assessment incorporates ISS stage with specific FISH results and serum LDH (R-ISS). However, this model does not include all the current abnormalities described as prognostic for survival in multiple myeloma. Importantly, the impact of many of these high-risk abnormalities are not uniform. We examined if we can better integrate FISH results into a risk assessment tool to better predict the outcomes of newly diagnosed MM. Patients and methods: We studied a cohort of 1316 patients with FISH done within 6 months of diagnosis of MM, in whom results for commonly observed abnormalities were available. We specifically examined the individual impact of common translocations involving chromosome 14, MYC rearrangements, chromosome 1q gain (single or multiple duplication) and del13q/monosomy 13. A risk assessment system was developed, weighting each abnormality according to their Risk Ratio and integrating ISS stage and serum LDH into the final model construction. Overall survival was calculated from diagnosis, with those alive at last follow up being censored. Results: We first examined the impact of each of the above FISH abnormalities: 1) high risk translocations [t(4;14), t(14;16), or t(14,20)], 2) trisomies, 3) t(11;14), 4) MYCrearrangements, 5) del13q/monosomy 13, and 6) 1q gain . Each of the abnormalities, except for t(11;14), was prognostic for survival (Table 1 with the risk ratios). For 1q gain, the median OS was NR, 105 mos and 79 mos respectively for no abnormality, duplication of 1 copy and duplication of multiple copies, (p<0.001). On multivariate analysis, t(11;14) and trisomies were no longer prognostic for overall survival (Table 1). The cumulative impact of abnormalities demonstrated worsening survival in the presence of increasing numbers of abnormalities (Figure 1). Including ISS stage 3 and LDH > ULN as additional variables for prognostication indicated both were individually prognostic for OS. In a multivariate analysis, including these two and FISH abnormalities, 1q gain and LDH were not independently prognostic. The final model consisted of HR translocations, MYCrearrangements, del17p/monosomy 17, del13q/monosomy 13, and ISS stage 3. Each of these variables was weighted using their risk ratio and a composite score was developed using 998 patients for whom all variables were available (range: 0-7.9; median 1.8). Three patient groups were characterized: group 1 (0; 32%), group 2 (1-4; 58%) and group 3 (>4; 10%) with a median OS of 53 mos, 106 mos, and NR, respectively, p <0.001 (Figure 2). Conclusion: Using the most relevant FISH and laboratory factors, in a large cohort of patients, we refined the current system to develop a risk stratification system that predicts survival in patients with newly diagnosed MM treated with contemporary treatment regimens. This needs validation in future studies. Disclosures Kumar: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Gertz:International Waldenstrom Foundation: Research Funding; Annexon: Consultancy; Medscape: Consultancy, Speakers Bureau; Amyloidosis Foundation: Research Funding; Abbvie: Other: personal fees for Data Safety Monitoring board; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents & Royalties; Physicians Education Resource: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Appellis: Consultancy; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Amgen: Consultancy. Dispenzieri:Akcea: Consultancy; Janssen: Consultancy; Intellia: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Lacy:Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Cellectar: Consultancy. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy.

Background: Combinations of alkylating agents with proteasome inhibitors have demonstrated efficacy in newly diagnosed and relapsed multiple myeloma (MM), with melphalan or cyclophosphamide combinations being some of the commonly used regimens for initial treatment of MM. Ixazomib (Ixa) is an oral proteasome inhibitor that is approved for use in combination with lenalidomide for patients with relapsed MM. We examined if Ixa can be effectively combined with cyclophosphamide (Ctx) in order to develop a less expensive, all oral regimen for patients with relapsed MM. Patients and Methods: Patients with relapsed MM, who were proteasome inhibitor naïve OR have received less than 6 cycles of therapy with bortezomib and had a better than PR with no progression at the time of discontinuation, were enrolled. The primary objective was to determine overall response rate (ORR). Treatment consisted of Ixa 4mg PO days 1, 8, 15; Ctx 300 mg/m2 PO days 1, 8, 15, 22 and dexamethasone (Dex) 40 mg PO weekly in a 28-day cycle. Overall, 37 patients were accrued; data on 33 eligible patients were available for analysis as of July 18, 2019. Results: The median age was 71 (48-89), 61% were male and the median duration from diagnosis was 46 months (mos). Median number of prior lines of therapy was 4 (range 1-5), 76%, 42% and 67% respectively had a prior IMiD, proteasome inhibitor or stem cell transplant, respectively. At data cutoff, 22 (67%) had progressed, 4 (12%) had died and the median follow up of those alive was 21.3 mos. Fourteen patients are still receiving treatment, with median of 8.5 cycles. Most common reason for treatment discontinuation was disease progression (10 pts; 53%). The ORR was 60% including 6% CR and 24% VGPR. The median event free survival was 11.3 mos (95%CI: 9.0 - 26.8). Overall, 401 cycles have been administered across the study, with dose modifications/ hold required for Ixa, Ctx, and Dex in 9 (27%), 14 (42%), and 22 (67%) patients respectively, the most common reason being hematologic toxicity. A grade 3 or higher adverse event at least possibly attributed to the study drugs was seen in 77% of patients, hematologic in 67% and non-hematologic in 30%. (Table 1) The most commonly observed hematologic toxicities included thrombocytopenia, neutropenia, lymphopenia and anemia; for non-hematologic was nausea, diarrhea, peripheral neuropathy toxicity and fatigue. Conclusions: The combination of Ixa, Ctx and Dex (ICd) offers a convenient, all oral regimen for treatment of relapsed disease not refractory to proteasome inhibitors. The regimen has good efficacy in this group f heavily pretreated patients, with an acceptable toxicity profile. Disclosures Lacy: Celgene: Research Funding. Gertz:Ionis: Honoraria; Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria. Ailawadhi:Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy. Bergsagel:Janssen Pharmaceuticals: Consultancy; Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy. Fonseca:AbbVie, Amgen, Bayer, Celgene, Kite, Janssen, Juno, Merck, Pharmacylics, Sanofi, Takeda: Other: Consultant/Advisor; Prognosticatin of MM based on Genetic Categorization by FISH: Patents & Royalties; Adaptive Biotechnologies: Other: Scientific Advisory Board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding. Chanan-Khan:AbbVie: Research Funding; Pharmacyclics: Research Funding; Xencor: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding. Larsen:Janssen Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Russell:Imanis: Equity Ownership. Stewart:Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Conventional therapies to multiple myeloma (MM) are not aimed at specific molecular targets leading ultimately to treatment resistance. Recent reports have shown that iron is instrumental in cancer development and progression and that high intracellular iron levels are associated with poor prognosis. We have demonstrated that MM cells exhibit dysregulated iron homeostasis and that limitation of cytosolic iron inhibits MM cell growth both in vitro and in vivo. The potential therapeutic role of iron should be further investigated to better understand how targeting high-iron MM cells could prevent or delay MM development and recurrence. Our study will provide crucial insights into the iron biology of MM pathogenesis and may lead to novel MM therapy. In this study, two mouse models, young Vk*MYC and old KaLwRij mice, were injected with iron dextran (1.25 mg/kg, IP, once a week). Tumor burden was monitored by serial Serum Protein Electrophoresis (SPEP) tests, flow cytometry, and immunohistochemistry. In vitro co-culturing of ARP1 MM cells with macrophages was employed to determine iron transfer. To determine iron's roles in MM evolution, we injected iron dextran into Vk*MYC mice at 8-week age. Vk*MYC mice develop MGUS around 40-50 weeks with plasma cell (PC) bone marrow infiltration and kidney damage etc. Iron-dextran was used because it is primarily taken up by macrophages. After 14-16 weeks of iron injection, M spike was detected in the injected Vk*MYC mice. The percentage of bone marrow plasma cells (CD138+) were significantly increased to 9% in the Vk*MYC mice injected with iron compared to control mice injected with vehicle by flow cytometry and immunohistochemistry. The acceleration of disease progression via iron injection was also tested in KaLwRij mice, which also spontaneously develops MGUS in old age. M protein was detected in 12 of 15 mice (80%) injected with iron dextran for 10 weeks and 1 of 5 KaLwRij (20%) control mice at 18-months of age. CD138+ B220- plasma cells were determined by flow cytometry. A significant increase of CD138+B220- plasma cells in iron treated mice (4% versus 2%) was observed compared to vehicle control mice. Deparaffined sections of bone marrow from the above mice were stained with Prussian blue and confirmed positive staining of macrophages from iron administrated mice. These results indicate that iron accelerates MGUS development in vivo. We next evaluated whether MM cells accumulate iron from the microenvironment. ARP1 MM cells were co-cultured with primary macrophages derived from mouse bone marrow to mimic disease environment in vitro. Under these conditions, MM cells induced macrophage polarization from M0 to M1 and M2. Furthermore, increased macrophage polarization was confirmed in vivo from the KaLwRij mice injected with 5TGM1 MM cells. To confirm that MM cells uptake iron from macrophages, increased intracellular ferritin levels were observed by western blot in ARP1 MM cells following co-culture with iron-loaded macrophages. We observed that this increase in intracellular ferritin was mediated via the transferrin receptor. This iron mobilization was prevented by iron chelation. Additionally, we confirmed that ferritin levels were higher in CD138+ primary human MM cells compared to CD138- non-MM cells by western blot. Our data indicate that MM cells promote macrophage polarization resulting in the transferring of iron into MM cells. The blockade of iron trafficking between MM cells and macrophages might hold a promise for the prevention and therapy in MM. Disclosures Bergsagel: Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Zhan:BIPHARM LLC: Consultancy, Other: % Allocation of Profit.

Abstract Background and Aims. Despite the fact that the myeloma mutational landscape was elucidated several years ago, there are yet no approved precision therapies targeting specific driver mutations. The role of common, but somewhat enigmatic, mutational events remains elusive, for example, the loss-of-function mutation of the RNA-processing exosome ribonuclease, DIS3, the third most-common mutation after KRAS and NRAS mutations. Furthermore, complex clonal architectural patterns (such as subclonal BRAF mutations nested within RAS-mutant clones) complicate the design of targeted therapy trials. To generate precision in vivo models that recapitulate the complex clonal and mutational architecture of myeloma, we integrated two powerful technological platforms: the first RAS-driven myeloma in vivo model (VQ model) created through a collaboration between the Zhang and Asimakopoulos labs coupled with flexible avian leukosis virus (ALV)-mediated gene transfer that permits myeloma genome editing in a temporally- and spatially- controlled fashion (Prdm1: TVb-mRFP model)(Asimakopoulos and Varmus, J Virol, 2009; 83(10):4835-43). VQ is based on the state-of-art Vk*MYC model (Chesi et al., Cancer Cell 2008;13(2):167-80). VQ was generated by conditionally activating an NrasQ61R allele in Vk*MYC mice. The resultant VQ model recapitulates an advanced, proliferative and drug-resistant phase of disease characterized by RAS mutations. The transducibility of VQ restored the functionality of the ALV-gene transfer technology. This technology exploits the fact that avian retroviruses cannot transduce mammalian cells unless they are engineered to ectopically express an avian retroviral receptor (in this case, the receptor TVb, driven by a plasma-cell-specific regulatory elements). Recently, the ALV gene transfer technology was adapted to achieve CRISPR-Cas9 mediated gene editing in the tumor niche in vivo (Oldrini et al., Nat Comm 2018;9(1):1466). Approach. We generated TVb-expressing VQ lines (CD138+ B220neg) that home to the bone marrow upon transplantation into Prdm1:TVb-mRFP fully immunocompetent syngeneic recipients in the C57BL6/J background. These VQ cells were engineered to express the nuclease Cas9. To determine the efficiency of gene transfer through ALV vectors (as a prelude to sgRNA delivery), pilot experiments were conducted using two types of ALV vector carrying a fluorescent reporter, GFP (green fluorescent protein): a) an RCAS-type vector that drives reporter expression from the native ALV LTR and b) an RCAN-type vector that drives reporter expression from an internal CMV promoter. Results. ALV titers achieved were routinely in excess of 107-108 infectious units/mL. Parental Vk*MYC cells expressing the avian receptor TVb were poorly transducible with ALV vectors. By contrast, VQ cells expressing TVb were readily transducible. At multiplicity of infection (MOI)=2, 68% of VQ myeloma cells expressed reporter fluorescence after a single round of transduction. At MOI=5 the transduction rate rose to 80%. The mean fluorescence intensity (MFI) positively correlated with MOI, consistent with higher-copy proviral integration at higher MOI. At equivalent MOI, GFP fluorescence from RCAS vectors was more intense than GFP fluorescence driven by an internal CMV promoter in RCAN vectors, suggesting that the ALV LTR is a stronger promoter than CMV in VQ myeloma cells. 100% transduced VQ cells were CD138+B220neg at all conditions tested. Conclusions and Current Focus. We are using ALV-CRISPR technology to generate precision myeloma models to support the next wave of personalized therapy and immunotherapy for this recalcitrant and incurable cancer. Progress on three in vivo models under construction will be presented at the meeting:Subclonal Braf mutation nested within clonal Nras mutation. These models will be treated with next-generation RAF inhibitors (Karoulia Z et al.Nat Rev Cancer 2017;17(11):676-91).Dis3 loss-of function mutation in constellation with co-clonal Ras mutation. The pathogenic consequences of Dis3 disruption remain poorly understood as no parallels exist in other cancers.The murine homolog of the t(4;14) translocation. We are targeting breakpoint cluster regions within perfectly syntenic sequences on mouse chr. 5 (containing Fgfr3 and Mmset loci) and mouse chr.12 (containing the murine Igh locus). Disclosures No relevant conflicts of interest to declare.

Abstract Multiple myeloma (MM) is characterized by clonal expansion of malignant plasma cells (PCs) and aberrant production of monoclonal immunoglobulin detected as an M spike using serum protein electrophoresis. In the United States, MM represents ~15% of hematologic malignancies and is one of the few cancers increasing in incidence (e.g., 14,400 in 1996 to 30,770 in 2018, from SEER). Previously, a Vk*MYC mouse model was described, in which AID-dependent activation of MYC transgene in germinal center (GC) B cells catalyzes a highly penetrant, indolent MM after a prolonged latency, suggesting that additional genetic mutations are required for the malignant MM progression. Recent sequencing of paired tumor/normal samples from advanced or refractory MM patients identified that constitutive activation of Ras signaling pathway (KRAS: 23%; NRAS:20%; BRAF: 8%) associates with MM progression and therapy resistance. To determine whether oncogenic Nras promotes the progression of Myc-induced indolent MM to a malignant stage, we generated NrasLSL Q61R/+; Vk*MYC; IgG1-Cre (VQ) mice along with single mutant mice Vk*MYC; IgG1-Cre or NrasLSL Q61R/+; IgG1-Cre. To boost NrasQ61R expression in GC B cells, 6-7 weeks old mice were immunized with NP-CGG. A significant fraction of VQ mice developed M-spike after immunization and subsequently died of a highly aggressive MM, which was characterized by high proliferative index, hyperactivation of AKT and ERK pathways, and disease hallmarks (e.g. osteolytic lesions, anemia, and kidney injury). VQ myeloma was readily transplantable into serial syngeneic recipients. In our preliminary study, combined Bortezomib and AZD6244 treatment attenuated MM phenotypes and prolonged the survival of VQ recipients. To facilitate easy molecular or genetic manipulations of VQ MM cells, we established two cell lines from primary cells. These cells could be easily infected (see another abstract from our co-author, Dr. Asimakopoulos's group). The cultured MM cells also express high levels of CD155, a ligand of immune checkpoint TIGIT, which has been recently reported to mediate a significant immune checkpoint blockade in MM patients. We further investigated the transcriptional signature of VQ MM cells (from bone marrow [BM] and from lymph nodes [LN]) vs wild-type (WT) plasma cells using the Illumina Bio-Rad's Single-Cell Sequencing platform. Illumina's Sure Cell app was used to de-multiplex samples, process barcodes and perform single-cell 3' gene counting. The processed data were then analyzed using Seurat R package to yield cluster information and their associated enriched genes. Our results revealed distinct clusters of CD138+ MM and WT cells, and also showed high similarity & overlap between BM & LN MM samples. Consistent with the genetic changes introduced to VQ MM cells, both Myc and Nras expression levels were significantly elevated in MM clusters compared to WT clusters. We also found that transcriptional levels of several potential therapeutic targets were significantly elevated in MM clusters, including Integrin alpha 4 and metabolic enzymes. Currently we are validating the scRNA-Seq results. Taken together, we generated a novel mouse model in which activation of Vk*MYC and oncogenic Nras in GC B cells results in a highly malignant, transplantable MM. The VQ mouse model represents an important innovation that will serve as a platform to investigate pathogenesis of resistant and refractory multiple myeloma and allow for testing the efficacy of novel therapeutic agents. Disclosures No relevant conflicts of interest to declare.