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Abstract Combination of carfilzomib with dexamethasone (Kd) is approved for use in relapsed and/or refractory multiple myeloma (RRMM), with carfilzomib administered twice weekly at 56 mg/m 2 (Kd56 BIW) or once weekly at 70 mg/m 2 (Kd70 QW). Post hoc cross‐trial comparisons were performed to compare efficacy and safety profiles of Kd70 QW vs Kd56 BIW dosing schedules using data from three trials of patients with RRMM: A.R.R.O.W., CHAMPION‐1, and ENDEAVOR. To select for comparable patient populations, side‐by‐side efficacy and safety comparisons were performed in subgroups of patients with 2‐3 prior lines of therapy who were not refractory to bortezomib. The overall response rate (ORR) was 69.9% (95% confidence interval [CI], 61.7‐77.2) for Kd70 QW and 72.4% (95% CI, 65.9‐78.2) for Kd56 BIW. Median progression‐free survival (PFS) was 12.1 months (95% CI, 8.4‐14.3) for Kd70 QW and 14.5 months (95% CI, 10.2—not evaluable) for Kd56 BIW. Frequency of grade ≥ 3 adverse events (AEs) was 67.6% for Kd70 QW and 85.3% for Kd56 BIW. Regression analyses (adjusting for prognostic factors) of all patients in the trials who received Kd70 QW vs Kd56 BIW estimated a PFS hazard ratio of 0.91 (95% CI, 0.69‐1.19; P = .47) and an ORR odds ratio of 1.12 (95% CI, 0.74‐1.69; P = .61). These results suggest that Kd70 QW has a comparable efficacy profile compared with Kd56 BIW and represents a convenient and well‐tolerated treatment for patients with RRMM.

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.

Abstract Background: Race/ethnicity is associated with disparate incidence and outcomes in MM, as seen in population-based studies. Their effect on outcomes has not been reported in prospective studies. Obesity is the most consistently associated modifiable risk factor for MM and is influenced by race/ethnicity but its association with outcomes has not been explored. We analyzed patient-level data from published ECOG-ACRIN/SWOG clinical trials in newly diagnosed MM (NDMM) patients (pts) to explore the interplay of race/ethnicity, obesity and outcomes. Methods: Pooled data from 9 ECOG-ACRIN/SWOG clinical trials (E1A06, E4A03, E1A00, E2A02, E5A93, E9486, S0204, S0232, S9321) in NDMM (1988-2011) were analyzed. Pts were divided by race: White (W), African-American (AA), other (O); race/ethnicity: Hispanic (H), non-Hispanic White (NHW), non-Hispanic AA (NHAA), non-Hispanic other (NHO); and body mass index (BMI): <25, 25-29.9, ≥30. Associations between these and baseline pt/disease characteristics were assessed using chi-square (categorical) and Wilcoxon rank sum (continuous) tests. Survival distributions were estimated with Kaplan-Meier method and compared between groups using log-rank test. Cox proportional hazards regression was used to estimate hazard ratios in univariate and multivariate models. Time to event data was censored at 6 yrs. Results: Pt distribution by race, race/ethnicity and BMI is shown in Table 1. Race: AA were younger than W (median 59 vs 62 yr), had significantly more females (48% vs 40%; p=0.001) and had a smaller proportion of pts ≥70 yr (18% vs 26%; p<0.001). Median BMI was highest in AA (27.2) and lowest in O (24) (p=0.001) but not significantly different categorically between W and AA only (p=0.908). AA had significantly more pts with hemoglobin (Hb) ≤10 g/dL than W (55% vs 36%; p<0.001). Median creatinine (Cr) was 1.3 mg/dL in O and 1.1 mg/dL in W (p=0.03). There were no differences by race for ECOG performance status (PS), MM stage, lytic lesions, paraprotein subtype, serum calcium (Ca) or β2-microglobulin (β2M). Enrollment to clinical trials over years was significantly different by race (p=0.009). The only study design showing different enrollment by race was with/out stem cell transplant (SCT) (p=0.03; 37% of AA vs 31% of W got SCT trials). Race/Ethnicity: NHO were the youngest (median 58 yr) and NHW the oldest (median 62 yr) subgroup (p<0.001). NHAA had the highest proportion of pts with BMI ≥30 (16%) and NHO the lowest (7%) (p=0.018). H had the smallest proportion of females (33%) while NHO had the largest (54%) (p=0.002). H had the highest number of pts ≥70 yr (28%) and NHAA the smallest (18%) (p=0.003). Anemia rates were significantly different (p<0.001) with Hb ≤10 g/dL in 36% NHW vs 56% in NHAA. No significant differences were seen for PS, MM stage, lytic lesions, paraprotein subtype or other lab parameters. H and NHAA were enrolled in larger numbers in 1992-2011 rather than prior or later years (p<0.001). Among the subgroups, H had largest proportion of patients (75%) on trials without novel agent vs 61.5% for NHW (p=0.047). NHAA had the highest proportion of patients on trials with SCT (38%) while NHO had the smallest (27.3%) (p=0.034). BMI: By gender, 30% of males and 39% of females having BMI <25 (p<0.001). BMI <25 had the highest proportion of pts with ISS stage III (31%) vs 21.5% for ≥30 (p=0.001). A similar trend was seen for DS stage (p=0.005). 64% of ≥30 pts had Hb >10 while 51% of <25 did (p=0.003). There was significant difference in continuous Cr distribution by BMI (p=0.004). Efficacy analysis did not show any significant difference in response rate by race (p=0.132), race/ethnicity (p=0.286) or BMI (p=0.896). In univariate/multivariate analysis, significantly worse overall survival (OS) was seen in males, age ≥70 yr, ECOG PS >0, ISS and DS stage III, presence of lytic lesions, Cr ≥2 mg/dL, Ca >12 mg/dL, Hb ≤10 g/dL. No significant difference was seen in OS or progression free survival by race, race/ethnicity or BMI (Figure 1). Conclusions: Significant differences exist in demographic and clinical characteristics of NDMM pts by race/ethnicity. Median OS was significantly associated with these characteristics but not with race/ethnicity itself. Observed racial/ethnic disparity in outcomes for MM pts may be overcome by access to comparable therapeutic options as seen in clinical trials. Obesity, while associated with MM incidence, does not seem to influence outcomes. Figure 1 Figure 1. Disclosures Ailawadhi: Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Stewart:Janssen Pharmaceuticals: Consultancy; Takeda Oncology: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy. Hussein:Celgene: Employment. Zonder:Takeda: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Pharmacyclics: Other: DSMC membership; Prothena: Consultancy, Honoraria. Barlogie:Mount Sinai Hospital: Employment.

Abstract One potential approach to improve the results of autologous stem cell transplantation (ASCT) in multiple myeloma (MM) involves the use of more potent induction regimens; the achievement of deeper remission pre-ASCT should translate into longer progression-free (PFS) and overall survival (OS). To evaluate this hypothesis, we assessed the influence of the percent reduction in serum monoclonal protein (M protein) pre-ASCT in patients (pts) treated with dexamethasone-based regimens (most often VAD) on the best response, PFS and OS achieved after ASCT. Between 2000–2006, 376 pts with IgG (267) or IgA (109) non-progressive MM underwent ASCT. Median age was 59 (21–73) yrs; 60% were male. The median hemoglobin at diagnosis was 103 g/L, creatinine 98 μmol/L, β2-microglobulin 241 nmol/L and albumin 35 g/L. Maintenance therapy was given to 84 (22%) after ASCT and consisted of corticosteroids in 29, thalidomide in 10, both in 41 and other regimens in 4 pts. Patients were divided into 4 groups based on the per cent reduction in M protein after induction therapy: group A, ≥ 99%; group B, 90–98%; group C, 50–89%; group D, < 50%. Post-ASCT responses included complete remission (CR), very good partial remission (VGPR) (≥ 90% reduction in M protein), PR (≥ 50% reduction) and stable disease (SD). Median follow-up from diagnosis is 37.4 mos and from ASCT 24.1 mos. For all pts, the median OS from diagnosis is 90.8 mos (95% CI 73.9–129.1 mos) and from ASCT 63.9 mos (95% CI 50.8–69.4 mos), while the median PFS from ASCT is 21.3 mos (95% CI 19.1–23.3 mos). Maintenance therapy had no significant effect on PFS (p=0.49) or OS (p=0.59). The post-ASCT results in evaluable patients according to the percent reduction in M protein after induction therapy are summarized below. We conclude: High-grade remissions after dexamethasone-based induction therapy are uncommon, with only 2% achieving ≥ 99% and 13% achieving 90–98% reduction in serum M protein; post-ASCT CR and VGPR rates were higher in these 2 groups; there was no significant difference in PFS or OS-based on protein response prior to transplant; whether newer induction regimens, which incorporate novel agents and which produce more CRs and VGPRs before ASCT, will confer better PFS and OS post-ASCT will be of great interest. Table 1 Group N CR (%)1 VGPR (%)1 PR (%)1 SD (%)1 Median PFS (mo)2 Median OS (mo)3 1p<0.001;2p=0.77;3p=0.79 A 7 50 50 Not reached Not reached B 47 19 64 17 23.3 Not reached C 232 14 17 68 1 20.7 59.1 D 87 11 7 54 28 21.1 69.4 Figure Figure Figure Figure

Abstract Background: Single autologous stem cell transplant (ASCT) is considered the standard of care for younger multiple myeloma (MM) patients (pts). However, it is not curative and virtually all patients will ultimately relapse. As more options, such as biological therapy, are available to treat relapsed disease, the role of a second ASCT as salvage therapy is unclear. Method: Retrospective review of all MM pts who received a 2nd ASCT as salvage therapy at Princess Margaret Hospital. Results: Between February 1997 and June 2007, 61 MM pts received a second ASCT for relapsed MM at our institution. Median age was 56 yrs (range 35–71) at second transplant. 37 pts (61%) were male. Immunoglobulin subtype included IgG (36), IgA (14), light chain (6), nonsecretory (3), IgM(1) and IgD (1). Transplant conditioning regimen for first transplant was high dose melphalan (MEL) 140–200 mg/m2 in 51, MEL/etoposide(E)/TBI in 4, and MELl/TBI in 2. 2nd ASCT conditioning consisted of MEL + TBI +/− E in 2, MEL alone in 58 and Busulfan/Cyclophosphomide in 1. The median time from diagnosis to first transplant was 9.7 months (2.0–74.2). The median time to relapse after the first transplant was 32.6 months (9.7–85.6), with a median interval between transplants of 45.1 months (19.7– 115). Two transplant-related deaths occurred (3%). Response to first transplant was 4 CR (8%), 34 PR (68%), 2 MR (4%) and 10 SD (20%). Nineteen pts went on to maintenance therapy between transplants. Response to second transplant was 4 CR (8%), 41 PR (80%), 5 SD (10%) and 1 PD (2%). Median progression-free survival (PFS) was 15.8 months (0–63.8) while median overall survival (OS) was 4.2 years (0–7.0) after 2nd ASCT. The relationship between progression-free interval after 1st ASCT and the outcome of the 2nd ASCT is summarized Table 1. Patients can be stratified into two groups, those with a disease free interval of less than or greater than 24 months. The use of maintenance therapy did not differ between the two groups, 6 (40%) in patients with PFS ≤24 months and 13 (28%) in patients with PFS >24 months. Conclusions: 2nd ASCT is a feasible and safe salvage therapy in patients with relapsed MM. 2nd ASCT is effective in providing a median progression free survival of 1.25 years and median overall survival of 4.2 years after 2nd ASCT - results that compare favourably with other salvage approaches. Patients with a longer disease free interval after 1st ASCT experience a better progression free survival and overall survival after 2nd ASCT. It is reasonable, therefore, to consider a 2nd ASCT if the time to progression is greater than 2 years after first ASCT. Outcomes Based on Time to Progression Post 1st ASCT PFS post 1st ASCT # of pts median PFS post 2nd ASCT PFS post 2nd ASCT1 Median OS post 2nd ASCT OS post 2nd ASCT2 1 p< 0.005, 2 p< 0.05 ≤24 months 15 12.7 months 1 year: 46% 3.5 years 1 year: 85% 2 year: 11% 2 years: 76% 3 years 0% 3 years: 63% 4 years: 31% > 24 months 46 19.8 months 1 year: 74% 5.9 years 1 year: 91% 2 year: 45% 2 years: 82% 3 year: 31% 3 years: 65% 4 years: 55%

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 Bortezomib (BTZ) is highly effective in the treatment of multiple myeloma; however, emergent drug resistance is common. The underlying mechanisms of such proteasome inhibitor resistance are still incompletely understood. Methods To further understand its resistant mechanism, we generated eight multiple myeloma (MM) cell lines resistant to bortezomib (BTZ) by exposure to increasing drug concentration: five of them acquired novel PSMB5 mutations. Given the rarity of similar mutations in over 1,500 analyzed MM patients, we explored in depth the role of the proteasome on MM cell viability and BTZ sensitivity by systematically deleting the major proteasome targets of BTZ by CRISPR. Results We demonstrated that MM cell lines without PSMB5 were surprisingly viable (mutation corresponding yeast gene pre2 is lethal). PSMB5 mutated, BTZ resistant, MM cell lines were re-sensitized to BTZ when PSMB5 was experimentally deleted, implying that this mutation is activating in its drug resistance function. In contrast PSMB6 knockout was lethal to MM cell lines, which were efficiently rescued by re-introduction of wild type PSMB6. Interestingly, reduction in PSMB6 levels also prevented the splicing of the major catalytic subunits PSMB5, PSMB7, PSMB8 and PSMB10. PSMB6 engineered with no splicing function or catalytic activity, also restored viability, inferring that the contribution of PSMB6 to proteasome structure is more important than functional activity. Supporting this observation, BTZ sensitivity was restored in resistant MM cells line by introducing low level expression of mutated PSMB6 lacking splicing function. As with PSMB6, PSMB7 knockout was lethal to MM cell lines. In contrast, loss of immunoproteasome subunits PSMB8 and PSMB9 was neither lethal nor restored sensitivity to BTZ. Our results demonstrate that expression of the three constitutive proteasome subunits PSMB5, PSMB6 and PSMB7 is highly co-dependent. This dependence is relying on the structure, but not the function, of PSMB5 and PSMB6. Conclusions In summary, PSMB5 and PSMB6, but not PSMB8 and PSMB9, are highly relevant for BTZ sensitivity in MM. Absence of PSMB6 or PSMB7, but not PSMB5, was lethal in MM cell lines. Expression of PSMB5, PSMB6 and PSMB7 was highly co-dependent. Together these findings suggest that the modulation of expression rather than function of PSMB5, PSMB6 or PSMB7 may be a new therapeutic strategy. Disclosures No relevant conflicts of interest to declare.

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.

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.