Ahmann, Gregory

Abstract Background: Clonal cytogenetic aberrations define multiple myeloma (MM) into unique subsets of the disease each one with unique clinical and biologic features, associations with laboratory and pathologic markers and presumed differential influence on disease progression. While it is widely accepted that translocations represent primordial events in the pathogenesis of MM, it is controversial whether all translocations can be associated with disease stability and non-malignant behavior (i.e. in MGUS/SMM). We have found the translocations in cases of MGUS using dual fusion, cIg enhanced, interphase FISH, while others have found this translocation at lower prevalence using single fusion interphase FISH or the RT-PCR for IgH/MMSET. We thus sought to clarify by expanding the study of the t(4;14) among patients with MGUS and SMM. Materials and methods: We studied 210 patients who had the diagnosis of MGUS and SMM and entered into the Mayo Clinic Dysproteinemia database. Patients were excluded if they had active MM, extramedullary disease or plasmacytoma. Patients with isolated lytic bone lesions could be included if they had no overt evidence of progression. All studied patients had provided written informed consent for the collection of a research bone marrow aliquot that was collected at the time of routine bone marrow procurement. All patients had cytospin slides that were studied for a t(4;14) using cIg-FISH as previously described by us and using a double fusion FISH strategy. The importance of this is that the strategy will detect unbalanced t(4;14) with loss of der(14). Results: Twenty patients (9.5%) had evidence of the chromosome translocation. Detailed demographic data was available in 19; 14 were women (74%), 13 of 19 (68%) had SMM and there was no bias for heavy chain IgA (26%). Patients with t(4;14) were slightly younger than those without the abnormality (52 vs. 61 years), had similar hemoglobin concentrations, had slightly higher concentration of the serum M spike (2.0 vs 1.75 g/dl) and had overall slightly higher bone marrow plasmacytosis (median 23% vs 10%). Patients were followed since diagnosis for a median of 33 months (range 2–162 months). Three patients progressed to active MM at 12, 12 and 26 months after original diagnosis (all three originally having indolent/SMM). Conclusion: The t(4;14) can occur in patients with MGUS and SMM and without evidence of progression to MM. The prevalence is similar to that observed in MM, albeit slightly lower (9.5% versus 14%). In most “benign” plasma cell neoplasms the translocation is associated with greater plasmacytosis and thus enriched for in the smoldering MM category. It is also likely that the t(4;14) confers a higher risk of progression to MM given; a) its enrichment in SMM, b) the slightly higher prevalence of the translocation in MM as opposed to MGUS/SMM, and c) the well documented aggressive clinical course of MM with t(4;14).

Abstract H-MM affects about 40–50% of patients, and a similar proportion of MGUS/SMM. The genetic evolution of the disease and the clinical implications of the co-existence of 13q- or t(14q32) are unknown. We conducted a global survey of genetic abnormalities in H-MM in order to describe genetic evolution pathways. H-MM cases from the Mitelman cancer genetic database were combined with cases from the Mayo Clinic MM genetic database. All abnormalities (regional/whole chromosome gains/losses, breakpoints and translocations) were plotted against a 344-chromosome band profile created in a matrix format, and the number of genetic abnormalities per tumor (NAPT) logged. The abnormalities present in more than 5% of tumors were considered recurrent and each karyotype was scored for the presence or absence of each of the recurrent abnormalities. To investigate the temporal pattern of acquisition of genetic abnormalities, all tumors with a given abnormality were selected and the distributions of NAPT plotted. The modes of these distributions were used as a value of ‘lateness’, referred to as time of occurrence (TO). The impact of co-existence of 13q- and t(14q32) on progression free survival (PFS) and overall survival (OS) in H-MM was determined for MM patients entered into an Eastern Cooperative Oncology Group chemotherapy trial. Four hundred and sixty-nine H-MM patients were included in the karyotypic analysis. The median chromosome number was 53 (range 48–74) and the median NAPT was 10 (range 1–25). The distribution of NAPT revealed a normal distribution, suggesting an obligatory acquisition of a given number of genetic abnormalities. The most common abnormalities were trisomies of chromosomes 3,5,7,9,11,15,19 and 21 affecting a third or more of the patients. Deletions and abnormalities of even-numbered chromosomes occur in 20% or less of patients. None of the more than 300 specific translocations detected were recurrent. However, 5 translocation breakpoints were recurrent including 14q32, 8q24 and 11q13. TO analysis suggest that for H-MM, the trisomies are early genetic events, translocations occur midway and deletions tend to be later events. Amongst the trisomies, correlation analysis does not suggest a particular order of acquisition. Clustering analysis using principal component analysis suggests that in H-MM, IgH translocations are secondary events associated with disease evolution rather than defining unique biological entities. Consistent with this, survival of H-MM patients is not affected by 13q- status but is negatively impacted by the presence of t(14q32) especially those with unknown partners (p<0.05). The data presented suggest that H-MM fit a proposed aneuploid model of cancer due to auto-catalytic chromosome reassortment (Duesberg et al Cell Cycle2003:2:202–210). The presence of tumor specific trisomies and the apparent similar phenotype induced by various combinations of trisomies are consistent with this model.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.

Abstract The objective of this study was to investigate the underlying molecular alteration in multiple myeloma at the protein level in order to identify regulators of pathogenesis, discover novel targets of therapy, and compare genetic and proteomic alterations. We employed antibody protein microarrays (BD Clonetech, CA) to measure changes in the patterns of protein expression between MM and normal plasma cells. The antibody array is a new technique enabling protein differences to be assayed directly by hybridizing fluorescently labeled protein mixtures from cell extracts onto glass slides spotted with 512 different monoclonal antibodies. CD138+purified plasma cells were obtained from cryopreserved bone marrow samples of 12 newly diagnosed patients with MM. The labeling index was high (1% cutoff) in 7 samples and low in the other 5. Control plasma cells were obtained from 9 pooled CD138+ purified normal donor bone marrow plasma cells. Interphase FISH analysis for 17p deletion, 13q deletion, and t(11:14) were performed. To assess differential expression, the mean of the ratios of Cy5/Cy3 for each sample were analyzed using the Clontech software to calculate an internally normalized ratio. The normalized data were analyzed by the Genespring software. Unsupervised clustering identified 4 groups of MM. Changes of protein expression ≥2 fold in 70% of the samples as compared to control were identified. There were 6 proteins differentially expressed between all MM samples and control cells including proteins in the ras signaling pathway (KSR-1), the ubiquitin pathway (Ubc-H6), cyclin-dependent kinases (CDK4), cytokines (IL-6), DNA toposisomerase II, and the rho-interacting serine-threonine kinase CRIK. Proteins differentially expressed in MM groups 1 and 2 compared to normal control included cell cycle regulators (cul-2, MCM6, PCNA, TGFb1), kinases (p70S6K, PKC), and chromatin regulators (Ran, AKAP450, Rad50). Protiens differentially expressed in MM group 3 included cell cycle regulators (CDK2, CLK1, MENA), apoptosis regulators (XIAP, caspase 4, perforin) kinases (IKKa and RAC1 in the Wnt signaling pathway) and P53 regulators, while proteins identified in MM group 4 included NFkB/ubiquitin proteins (IKKa and Ubch6), cell cycle regulators (c-myc, CDK4), p53 pathway proteins (53bp2), ras-signaling proteins (KSR1), and the kinase CRIK. There were no differences in protein expression between the high and low labeling index groups. 13q was identified in 5 (42%), 17 p in 1(8%) and t(11:14) in 1(8%) patients. 80% of the 13 q deletion cases clustered in MM group 1 and 2 patients. Cyclin D-1 was upregulated in 5 (42%) patients including the patient with (11:14) translocation. This is the first proteomic study of patients with MM. The results are consistent with previously identified genetic alterations in MM indicating that this novel technique could be used in identifying molecular changes in MM. It identifies novel proteins dysregulated in MM that differ between the 4 MM groups. These results may be used in the future to individualize therapy based on the proteins dysregulated in each group. For example, IKK inhibitors may be useful in group 3 MM patients, while mTOR inhibitors (upstream of p70S6K) could be used in groups 1 and 2 patients. Future correlations with gene expression arrays and prognosis in a larger cohort of patients is warranted.

Abstract Chromosome 13 abnormalities (Δ13), monosomy or deletion, are seen in greater than 50% in patients with Multiple Myeloma (MM) and are associated with poor survival and reduced response to therapy. Δ13 is suggestive of a tumor suppressor model. In this study, we use a comprehensive, high-throughput approach involving array-based technologies to identify genes on chromosome 13 that are important in the pathogenesis and/or progression of MM. Array-based Comparative Genomic Hybridization (aCGH) is being conducted to identify a minimum region of 13q loss in a series of MM cell lines and patient samples. DNA is isolated, labeled and hybridized with a differentially labeled normal DNA reference to determine gene/genomic copy number changes. Arrays are analyzed to search for the minimum region of loss based upon single copy loss for a series of nearby mapping transcripts in each cell line. Data will be cross-referenced to determine the true minimum region of 13q loss in MM. Also, to discover genes harboring potential inactivating mutations, we are using inhibition of nonsense-mediated RNA decay (NMD) coupled with cDNA microarray. This modification of the Gene Identification by NMD Inhibition (GINI) method, first introduced by Noensie and Dietz (2001), is used to identify genes on chromosome 13 that may be inactivated due to premature truncating codons (PTCs). This method is based upon a cell’s natural ability to survey for and degrade RNA species that contain PTCs by activation of the nonsense mediated RNA decay (NMD) pathway. Treating cells with the drug Emetine can inhibit NMD and increase the expression of genes likely harboring truncating mutations. Preliminary data have revealed several candidate genes, with increased ratios after Emetine treatment, which map to chromosome 13. Upon defining a minimum region of loss based upon aCGH data, we will prioritize genes from our NMD study for mutational analysis. It our hope to also perform an array-based CpG island screen to simultaneously analyze all CpG islands on chromosome 13 to detect genes possibly inactivated by hypermethylation. This comprehensive, organized approach incorporating high-throughput techniques for the detection of genomic loss, mutation, and methylation is the first of its kind to our knowledge and may maximize the potential for the identification of a 13q tumor suppressor in MM.

Introduction Despite continuous improvement of clinical outcome in multiple myeloma (MM), disease relapse remains a major challenge, leading to progressively shorter remissions and fewer treatment options. Strategies attempting to counteract this challenge include recent efforts resulting in an increase in the availability of novel promising anti-MM agents and targeting specific genetic profiles of the disease. In this context, we aim to develop predictive models of sensitivity and resistance to novel compounds by connecting an ex vivo high-throughput drug screen with genetic, transcriptomics, FISH, and clinical features. Methods Twenty compounds (afatinib, afuresertib, belinostat, buparlisib, cobimetinib, CPI-0610, crenolanib, dinaciclib, dovitinib, JQ1, LGH447, osimertinib, OTX015, panobinostat, romidepsin, selinexor, sunitinib, trametinib, venetoclax, and vorinostat) were selected based on overall promising anti-MM activity from an ex vivo high throughput drug screen with a panel of 79 single agents incubated for 24 hours. The area under the curve (AUC) was used to rank order the ex vivo responses for each compound and the lowest and highest quartile samples were identified for further analysis. Clinical data and FISH data, including t(11;14), t(4;14), t(14;16), del(17p), +1q, monosomy 13, and MYC rearrangement, were collected. Targeted DNA sequencing was performed using a 2.3 Mb custom capture panel covering 139 MM-relevant genes. mRNA-sequencing was performed and differential gene expression analysis in the highest and lowest quartile identified subsets of markers positively and negatively associated with the AUC response for a given compound. An additional unbiased selection of markers using lasso techniques was performed, resulting in predictive generalized linear models (GLM) for each agent. Responses from the remaining intermediate samples were estimated with the predictive models, with overall predictive ability assessed by correlating predicted AUCs with their actual counterparts. Results Our integrative analysis was performed on 50 primary patient samples (36% untreated and 64% relapsed MM). Venetoclax, dinaciclib, romidepsin, panobinostat, osimertinib, belinostat and selinexor were the most active compounds in the cohort. Interestingly, LGH447, dovitinib, selinexor, JQ1, OTX-015, cobimetinib, and trametinib showed increased activity in relapsed MM when compared to untreated samples (Wilcoxon Test; p<0.05). We generated GLMs using an average of 92 markers (range 64-107) per compound, combining mRNA-sequencing expression with FISH and mutation data. The analysis proposed in the present study was validated through the unbiased selection of BCL2 among the subset of markers included in the GLM predicting sensitivity to venetoclax, a first-in-class orally bioavailable selective BCL2 inhibitor. Expression level of critical NF-kB and cell cycle genes, such as BIRC3, CKS1B, PAX5, NFKB2, and CCND2, were included in 60% of our predictive models. Mutations of DNA repair genes (ATM,TP53) were included in the GLMs of three epigenetic therapies, one histone deacetylase inhibitor and two BET inhibitors, associated to ex vivo resistance to the drugs. The presence of monosomy 13 was also a marker for ex vivo resistance for five epigenetic therapies, four HDAC inhibitors and one BET inhibitor. The three BET inhibitors, JQ1, CPI-0610, and OTX015, were among the compounds most accurately predicted by our integrative approach, with Spearman correlation values between 0.773-0.858. Overall, our models accurately predicted the ex vivo response for 16 (80%) of the compounds (r>0.7). Five (25%) of these compounds displayed a remarkably accurate prediction model in both training (highest and lowest quartiles) and validation (intermediate quartiles) samples (r>0.8). Conclusions The GLM data integration approach enabled the establishment of effective predictive models, identifying FISH, transcriptomics, and mutations of putative driver genes important in anti-MM agent responsiveness. In addition, the resulting dataset is promising for future research focusing on the discovery of novel mechanisms of action and establishing markers of sensitivity and resistance to novel compounds. We are currently increasing our dataset and seek to create an omnibus approach that predicts responses to multiple anti-MM agents simultaneously. Disclosures Bergsagel: Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Stewart:Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy.

Abstract MYC expression is frequently dysregulated in multiple myeloma (MM). In one comprehensive study, MYC structural variations (SV) were found in nearly half of MM cases (Affer et al. Leukemia 2014). The prevalence was higher in hyperdiploid (HRD) tumors (65%) compared to non-hyperdiploid (NHRD) tumors (36%). The large amount of tumor DNA required for all of the genomic studies performed may have biased the samples analyzed (e.g., to those with higher tumor burdens). To validate the findings of recurrent MYC SV in another dataset, we analyzed the CoMMpass data. We analyzed long-insert whole genome sequencing (WGS) data from diagnostic samples in 420 patients from the IA7 release (dbGAP phs000748) for SV. The results of clinical data, processed WES (SNV), RNASeq (gene expression) and WGS (copy number) data from IA8 (http://research.themmrf.org) were used to calculate survival, RAS and NFKB pathway mutation (WES), TC class (RNA), NFKB index (RNA) and hyperdiploid index (WGS). MYC SVs were identified in 38% of tumors. They were present in 53% HRD and 28% NHRD, and by TC in 55% D1, 43% D2, 36% MMSET, 26% MAF, 13% CCND. Juxtaposition of an Ig enhancer (IgH, IgK, IgL) close to MYC was the most common MYC SV, representing ~40% of the MYC SV. Other enhancers identified have mostly been reported previously, with the most frequent being NSMCE2 (12% of SV) and TXNDC5 (5% of SV). Intrachromosomal SV (deletions, inversions, duplications) not associated with any known enhancer were also frequent (18% of SV). As expected, MYC expression was higher in tumors with MYC SV compared to those without (~2.4 fold, p-value<0.0001). We used Gene Set Enrichment Analysis (GSEA) to identify activated pathways that might substitute for MYC in HRD patients without MYC SV and observed significant activation of the NFkB pathway. Interestingly, examining patients with RAS/MAPK pathway (NRAS, KRAS, BRAF, FGFR3 - abbreviated RAS) mutations (identified in 50% of all patients) the same pattern was observed: in the absence of RAS mutation, there was a significantly higher NFkB index. In general, the HRD tumors seem to have either activation of RAS and/or MYC, or activation of NFkB (Figure). There was no difference in overall survival in patients with versus those without MYC SV. We developed a clinically applicable sequencing platform to identify MYC SV, which cannot be reliably identified by FISH. We sought to validate this targeted capture approach, where in addition to the coding exons of 81 interesting genes described previously (M3P), we also pulled down the region surrounding MYC (2 Mb), IgH (0.5 Mb), IgK (50 kb) and IgL (100 kb) allowing us to additionally identify SV in MYC and Ig loci. Using this approach we identified IgH translocations in 29/30 samples with translocations previously identified by FISH (97%). Moreover, we identified MYC SV in 19/22 patients with SV previously identified by mate-pair WGS (86%). Importantly, sequencing identified the precise translocation breakpoint, and identity of the enhancer dysregulating MYC, which may be important variables. In one informative patient two different MYC SV were present at diagnosis, only one of which was still present following a partial response to four cycles of lenalidomide and dexamethasone. This suggests that the two MYC SV are in different subclones, one of which was much more sensitive to the treatment. Interestingly, the enhancer dysregulating MYC in the sensitive subclone harbors 5 strong Ikaros binding sites identified by ChIPseq, suggesting one intriguing mechanism for sensitivity to lenalidomide. To summarize, we verified in a large dataset that MYC expression is frequently dysregulated by SV in MM (38%), and the RAS/MAPK (50%) and NFKB (23%) pathways are frequently activated by mutations. Surprisingly, given their generally good prognosis, nearly half of HRD tumors seem to be MYC driven, while this is true for only a quarter of NHRD tumors. HRD tumors not driven by MYC or RAS appear to be driven by NFKB. Remarkably, the pathways most commonly activated by mutation in MM: CCND, MYC, RAS, NFKB are common to many cancers and have been studied extensively individually. To understand their clinical impact in MM we have developed a comprehensive custom capture sequencing panel that identified 97% of IgH translocations, 86% of MYC SV, and as well as SNV and CNV of 81 recurrently mutated genes. It will be important to include such a comprehensive genetic analysis to complement clinical trials in the future. Figure. Figure. Disclosures Stewart: Bristol Myers Squibb: Consultancy; Celgene: Consultancy; Takeda Oncology: Consultancy; Janssen Pharmaceuticals: Consultancy.

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.

Background The precise molecular mechanism and targets of lenalidomide specific anti-Multiple Myeloma (MM) activity remain largely unknown, although, cereblon (CRBN) has been identified as a central mediator of activity with IRF-4 acting as a downstream molecule. Lenalidomide resistance in MM cells which, despite CRBN depletion, are able to restore their IRF-4 levels suggests the existence of alternative pathways. Methods To measure apoptosis and gene expression analysis 1x 106cells were incubated with 10μm to 50μm of lenalidomide for 24 to 96 hours before they were examined by annexin-PI and FACS analysis. Gene and protein expression were measured by RT-PCR, western blot, and immunohistochemistry. For lenalidomide followed bortezomib serial studies, cells were incubated with 10μm of lenalidomide for 48 hours and later treated with bortezomib for 24 to 48 hrs. CRBN and immunoglobulin light chain knock-down were performed by lentiviral mediated shRNA. Results We found lenalidomide induces Endoplasmic reticulum stress (ERS) via accumulation of intracellular immunoglobulin proteins, but only in the presence of CRBN in a panel of MM cell lines. This accumulation of intracellular proteins resulted in ERS as documented by western blot and RT-PCR in OPM2 and MMIS cell lines. We next utilized OPM2 cells knocked down for CRBN and control, non-target (NT), and treated with different doses of lenalidomide. After three days of treatment an ERS response was occurring in OPM2-NT cells but not in CRBN knockdown cells. Using Western blot we found decreased XBP-1u and increased XBP-1s, ERS marker proteins, in CRBN positive (NT) cells after 72 hours of lenalidomide treatment, but not in CRBN knock-down cells. Another ERS marker GRP78/BiP was also clearly accumulated after lenalidomide treatment in CRBN positive cells. Inactivation of the tumor suppressor p53 by degradation is a mechanism utilized by cells to adapt to ERS has been reported. We also found that p53 protein down regulated after lenalidomide treatment in CRBN positive OPM2 and MMIS cells. We have analyzed other isogenic cell lines MM1.S (lenalidomide-sensitive) and isogenic MM1.Sres (lenalidomide-resistant) HMCLs and found that lenalidomide induces ERS in MM1.S but not MM1.Sres cells. CRBN is important for lenalidomide to induce immunoglobulin protein accumulation and ERS was further proved by over-expressing CRBN in OCI-My5 MM cell line and subsequent lenalidomide treatment. To test the effect of light chain accumulation as ER stressors, we knocked down immunoglobulin light chain lambda in human MM cell line OPM2. Knock-down of lambda light chain in OPM2 cells resulted in lenalidomide resistance and it induced endoplasmic reticulum stress mediated anti-myeloma activity. Our preliminary data reveals that lenalidomide mediated progressive ERS can positively enhance bortezomib induced apoptosis in an in-vitro MM model. We pre-treated myeloma cell lines with lenalidomide and subsequently treated with bortezomib. MM cells pre-treated with lenalidomide for two days and there after treated with bortezomib clearly shown increased sensitivity to bortezomib induced apoptosis than non-pre-lenalidomide treated cells. Conclusion Here we show that lenalidomide causes accumulation of immunoglobulin light chains, inducing progressive ER stress, resulting in anti-myeloma activity and that effect depends on the presence of CRBN. Proteasome inhibitors such as bortezomib also sensitize MM cells to endoplasmic reticulum stress-mediated apoptosis. Our data demonstrated the synergistic effect of lenalidomide and proteasome inhibitors, likely due to the augmented protein stress. This data support the use of serial therapy utilizing pre-lenalidomide treatment followed by bortezomib or carfilzomib treatment. Disclosures: Fonseca: Medtronic: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; Genzyme: Consultancy; BMS: Consultancy; Lilly: Consultancy; Onyx: Consultancy, Research Funding; Binding Site: Consultancy; Millennium: Consultancy; AMGEN: Consultancy; Cylene: Research Funding; Prognostication of MM based on genetic categorization of the disease: Prognostication of MM based on genetic categorization of the disease, Prognostication of MM based on genetic categorization of the disease Patents & Royalties.