Bergsagel, P. Leif

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.

Abstract Introduction: Multiple Myeloma (MM) is consistently preceded by pre-malignant asymptomatic monoclonal gammopathies (AMG). To date, our understanding of the pathogenesis of progression to MM remains incomplete. Genetic analyses of AMG cells compared to MM-derived plasma cells (PCs) have found few differences, suggesting that progression may be mediated in part by tumour-extrinsic mechanisms. To comprehensively examine the cellular and molecular complexities of MM pathogenesis, we performed an unbiased single cell RNA-sequencing (scRNA-seq) analysis of tumour cells as well as immune cells from the tumour microenvironment (TME) derived from transgenic mice transitioning from AMG to MM. Methods: We employed the Vk*MYC immune-competent mouse model of MM (C57BL/6/KaLwRij), which is a clinically and biologically faithful model of untreated disease that similarly progresses from AMG to MM with age. We established an age-based cohort of Vk*MYC mice to recapitulate a range of MM disease stages, generated single-cell suspensions from flushed bone marrow and subjected these cells to scRNA-seq profiling (10x Genomics). Results: Across 12 samples profiled to date, our scRNA-seq dataset contains 82,853 high-quality cells, expressing 17,922 genes. We employed dimensionality reduction and unsupervised graph-based clustering to visualize and group transcriptionally-similar cell populations, which revealed 42 clusters. Expression of known marker genes and computed correlation scores with bulk gene expression reference datasets enabled annotation of cell types, revealing both malignant cells and non-malignant immune cell populations. We first focused on single cell T/NK profiles in our data given the emerging utility of immune checkpoint inhibitors that target these populations. Although we did not observe numerical differences in the proportion of CD8+ T cells across disease stages, analysis of immune checkpoint receptor genes revealed increased expression of Pdcd1 (PD-1) and Lag3 in CD8+ T cells from mice with disease. Co-expression of LAG3 and PD-1 proteins was also confirmed using a Vk*MYC transplantable model, with a positive correlation between disease burden (%CD138+/B220- cells) and %PD1+LAG3+ CD8+ T cells by flow cytometry. Consistent with reports of PD-1 and LAG3 co-expression on non-functional exhausted T cells, CD8+ T cells from diseased mice demonstrated elevated T cell exhaustion scores in our scRNA-seq dataset. These observations suggest that T cell exhaustion may be mediated by multiple immune checkpoint receptors during disease evolution. Although combinatorial treatment with PD-1 and LAG3 antibodies failed to induce tumour regression in mice with established disease, the addition of cyclophosphamide (Cy) to these antibodies resulted in marked improvement in survival of the mice compared to Cy alone, presumably by promoting immunogenic cell death. Studies exploring the combination of LAG3 and PD-1 antibodies as a strategy to inhibit transition from AMG to MM in the Vk*MYC mice are ongoing and will be reported. We also performed subclustering analysis of 5,228 Sdc1+ (CD138) PCs in our scRNA-seq dataset revealing 11 distinct clusters, with evidence of inter- and intra-tumoural heterogeneity across all Vk*MYC mice. Differential gene expression analysis revealed a non-malignant PC (nPC) cluster as supported by lower Myc transgene and Ccnd2 expression. Moreover, this cluster was predominantly comprised of cells from age-matched control mice or mice with earlier disease. Single-cell chromosomal copy number analysis revealed loss of Chr5 in the majority of tumour cells from MM mice, but not in the nPC cluster. Loss of Chr5 was observed in tumor subclones from all AMG mice suggesting that it is an early and potentially unifying event in Vk*MYC mice during disease progression. Further, the data support the establishment of intratumoural heterogeneity early in disease evolution. Conclusions: Our approach of using scRNA-seq to characterize the pathogenesis of disease evolution in MM has enabled simultaneous measurement of intratumoural heterogeneity and immune cell phenotypes in the TME. In turn, this has provided insights into mechanisms that may contribute to transition from AMG to MM, including induction of T cell exhaustion and loss of mouse Chr5. Ongoing and future work aims to evaluate whether these mechanisms can be exploited therapeutically in pre-malignant AMG. Disclosures Sebag: Amgen Canada: Membership on an entity's Board of Directors or advisory committees; Janssen Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene Canada: Membership on an entity's Board of Directors or advisory committees; Takeda Canada: Membership on an entity's Board of Directors or advisory committees. Pugh:Prosigna: Honoraria; N/A: Patents & Royalties: Hybrid-capture sequencing for determining immune cell clonality; N/A: Patents & Royalties: Combined hybrid-capture DNA sequencing for disease detection; Boehringer Ingelheim: Research Funding; Chrysalis Biomedical Advisors: Honoraria; Merck: Honoraria; DynaCare: Consultancy.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; 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; AbbVie: 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; Janssen: 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; Merck: 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; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.