Citations

AbstractThromboses are prevalent in POEMS syndrome, but few risk factors for POEMS‐associated thrombosis have been identified. The objective of this study is to identify novel risk factors for POEMS‐associated thrombosis. In this retrospective cohort of 230 POEMS patients, 27% developed thrombosis. Arterial events were slightly more common than venous. Stroke accounted for 26% of all thromboses and 53% of arterial events. There were differences in baseline features between the thrombosis group and the no thrombosis group, and these were driven by patients with arterial thrombosis. Risk factors for arterial thrombosis included thrombocytosis, elevated hemoglobin/hematocrit, extravascular volume overload, and splenomegaly. Hyperprolactinemia appeared to be a risk factor for venous thrombosis. The risk of thrombosis was most striking among men with elevated hemoglobin (32% vs. 5%, p < .001) and hematocrit (42% vs. 5%, p < .001) compared to men without. Most thromboses occurred prior to POEMS directed therapy, and most that occurred during therapy happened within 3 months of diagnosis. Twenty‐one percent of patients with thrombosis had recurrence. In recognition of high overall rates of thrombosis in this population, all patients with POEMS syndrome should receive prophylactic antiplatelet therapy, and clinicians should consider anticoagulation in patients with risk factors for POEMS‐associated thrombosis.

Abstract Introduction POEMS syndrome is a rare disease associated with a plasma cell dyscrasia with limited information regarding the role of ASCT. Small single institution series have demonstrated deep and durable responses after ASCT along with neurological improvement. Despite these benefits, ASCT is thought to have higher treatment related morbidity and mortality, limiting its use. We describe the outcomes from an international multicenter database of patients with POEMS syndrome undergoing ASCT. Methods We performed a retrospective analysis using the Center for International Blood and Marrow Transplant Research (CIBMTR) database to evaluate the outcomes of patients undergoing ASCT for POEMS syndrome. Standard descriptive methods were used to report patient characteristics. Univariate and multivariate analysis were performed to identify predicators for non-relapse mortality (NRM), relapse, progression-free and overall survival (PFS, OS). NRM was compared between POEMS and multiple myeloma (MM) patients who underwent ASCT during the same time period. Results Between 2008-2018, 331 pts with POEMS syndrome who underwent ASCT were identified. The median age was 51 years, with 66% males. Racial distribution was 65% Caucasians, 19% African American, 5% other, and 10% missing. Pre-transplant characteristics included 70% patients had Karnofsky score &lt;90, and 50% had HCT-CI ≥ 3, reflecting underlying disease severity and symptoms. The most common comorbidity was pulmonary (52%). Only 14% of patients were in very good partial response or better at the time of ASCT and 72 (22%) patients underwent ASCT without prior treatment. The median time from diagnosis to ASCT was 7 months and 74% underwent ASCT within 12 months of diagnosis. The most common mobilization strategy was GCSF +/- plerixafor in 50% of pts and 87% of pts received conditioning with 200mg/m2 of Melphalan. The median follow up was 48 (range 3-137) months. At day 100, NRM was 0.9 % (95% CI: 0.2-2.2%). At 4 years, NRM was 4.9% (95% CI: 2.6-7.9%), relapse 15.4% (95% CI 11.3-20.1%), PFS 79.7% (95% CI 74.5-84.3%) and OS 92% (95% CI 89.2-95.6%). Subsequent neoplasms were seen in 16 (5%) with 4 myeloid malignancy and 12 solid tumors. On multivariate analysis, age ≥ 60 years was associated with greater hazards of mortality, HR 2.6 (95% CI 1.2-5.6), p 0.01. The figure shows the comparable NRM between POEMS and MM (p 0.31). Conclusions: We report outcomes of the largest ASCT series of POEMS patients. Despite a high HCT-CI and low functional status among patients with POEMS syndrome, no difference in NRM was seen when compared to MM. Post-transplant outcomes were excellent and support single center data on the role of ASCT in this rare disease. Figure 1 Figure 1. Disclosures Kansagra: Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Alynylam: Membership on an entity's Board of Directors or advisory committees; Cota Health: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees. Dispenzieri: Alnylam: Research Funding; Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Takeda: Research Funding; Oncopeptides: Consultancy; Janssen: Consultancy, Research Funding. Kumar: Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; BMS: Consultancy, Research Funding; Oncopeptides: Consultancy; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Novartis: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Qazilbash: Bristol-Myers Squibb: Other: Advisory Board; NexImmune: Research Funding; Janssen: Research Funding; Biolline: Research Funding; Oncopeptides: Other: Advisory Board; Angiocrine: Research Funding; Amgen: Research Funding. Shah: GSK: Consultancy; Karyopharm: Consultancy; Oncopeptides: Consultancy; Indapta Therapeutics: Consultancy; Janssen: Research Funding; Nektar: Research Funding; Bluebird Bio: Research Funding; Teneobio: Research Funding; CareDx: Consultancy; BMS/Celgene: Research Funding; Sanofi: Consultancy; Sutro Biopharma: Research Funding; CSL Behring: Consultancy; Poseida: Research Funding; Precision Biosciences: Research Funding; Amgen: Consultancy; Kite: Consultancy. D'Souza: Sanofi, Takeda, Teneobio, CAELUM, Prothena: Research Funding; Imbrium, Pfizer, BMS: Membership on an entity's Board of Directors or advisory committees; Janssen, Prothena: Consultancy.

Abstract Background: There has been a steady improvement in the survival of patients with multiple myeloma (MM) due to better understanding of the pathophysiology, use of novel agents, and risk-adapted therapy. These improvements were largely observed in clinical trials and tertiary center populations and questions remain about the generalizability of these findings to patients in community settings. Here, we compared the experience of patients seen at Mayo Clinic, Rochester (MAYO) and those followed in the Surveillance, Epidemiology, and End Results Program (SEER), to gain a better understanding of current trends in survival. Methods: We studied all patients with MM diagnosed between 01/2004 and 12/2018 seen at MAYO and those followed in SEER (18 registry research data 2000-2018, 11/2020 submission). For comparison with SEER, MAYO patients who were alive at the end of SEER follow-up (12/31/2018) were censored. For the estimation of relative survival, patients alive at the end of 2019 were censored to align with the published United States general population rate tables. Overall survival (OS) estimates were calculated using the method described by Kaplan and Meier. Multivariable-adjusted proportional hazards regression models were used to assess the association between the 5-year interval of diagnosis and overall survival. Expected OS estimates were calculated based on United States general population rate tables from the Human Mortality Database using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for trends in SMRs over time. Results: The median age at diagnosis was 3 years lower in patients seen at MAYO compared to patients followed in SEER. The proportion of patients 75 years and older at the time of diagnosis was higher in SEER (26.3%) compared to MAYO (15.3%). Median OS was 6.7 years (95% CI 6.3-7.1) for MAYO compared to 4.6 years (95% CI 4.5-4.7) in SEER (Figure 1). When stratifying OS by year of diagnosis (in 5-year-intervals: 2004-2008, 2009-2013, 2014-2018), improvements were seen in both patient populations over time, albeit to a greater extent in patients seen at MAYO (Figures 2 and 3). The results were consistent after adjusting for age and sex in both MAYO and SEER. There were improvements in OS across all three 5-year intervals and age groups in both patient populations. In MAYO, these improvements were most pronounced during the second 5-year interval for younger patients (&lt; 65 years) and more pronounced during the last 5-year interval for older patients (&gt; 65 years). In SEER, the improvements were more gradual across the three 5-year intervals, but it was noted that the relative improvements in younger patients were greater than in older patients during the last 5-year interval. Early mortality decreased over time in both patient populations: from 11% to 6% at MAYO and from 23% to 16% in SEER (Figure 4). To examine the observed improvements in the context of the survival trends of the general population, we stratified the SMRs by patient population, age at diagnosis, and year of diagnosis, and a lack of improvement in excess mortality was observed among older patients (&gt;75 years) in both populations over time (Figure 5). Three- and five-year overall survival estimates were higher in patients seen at MAYO compared to patients followed in SEER, but the survival advantage diminished with increasing follow-up time (Figure 6). On further sub-group analysis within MAYO patients, excess mortality was observed for all International staging system (ISS) stages and patients with high-risk (HR) cytogenetics. Stratifying the SMRs by age and risk group, decreases in excess mortality were most pronounced among older patients, particularly those with indolent disease (Figure 7). Conclusions: Survival outcomes in MM continued to improve in both patient populations. These improvements were more pronounced in MAYO compared to SEER, which may be partially explained by differences in access to specialized care. The most pronounced improvements were observed among younger patients and those with favorable disease characteristics. Future efforts need to focus on improving therapies for patients with unfavorable disease characteristics and on increasing access to specialized care in community settings. Figure 1 Figure 1. Disclosures Kapoor: Glaxo SmithKline: Research Funding; Takeda: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy; Ichnos Sciences: Research Funding; BeiGene: Consultancy; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy. Dingli: Alexion: Consultancy; Apellis: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Lin: Novartis: Consultancy; Celgene: Consultancy, Research Funding; Juno: Consultancy; Takeda: Research Funding; Gamida Cell: Consultancy; Vineti: Consultancy; Merck: Research Funding; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Sorrento: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Alnylam: Research Funding; Pfizer: Research Funding. Kumar: Tenebio: Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Introduction: Despite recent treatment (tx) advances that have improved overall survival (OS), multiple myeloma (MM) remains a mostly incurable malignancy. Txs become less effective with each successive line of therapy (LOT). The rapidly evolving tx landscape has raised questions about the optimal tx sequence; recent trial data may be useful to develop and test hypotheses about tx sequencing. However, real-world (RW) data have shown that while most patients (pts) with newly diagnosed MM (NDMM) receive first-line (1L) tx, attrition rates are high in later LOT, with up to 50% of transplant ineligible (TIE) pts receiving no tx beyond 1L. As many pts will not have an opportunity to be salvaged with later LOT, achieving the longest possible progression-free survival (PFS) upfront is critical. In the MAIA trial, daratumumab (DARA), lenalidomide, and dexamethasone (D-Rd) demonstrated significant PFS (hazard ratio [HR]=0.53, P&lt;0.0001) and OS (HR=0.68, P=0.0013) benefit vs Rd. In a subgroup of pts ≥65 years old in the SWOG S0777 study, bortezomib, lenalidomide, and dexamethasone (VRd) had no significant OS benefit vs Rd. In the absence of head-to-head studies comparing different tx sequences, clinical trial data can be used to explore different scenarios. Published models suggest that delaying DARA until later LOT is a viable tx strategy. Limitations of these models include basing conclusions on tx sequences other than those recommended in clinical guidelines, not accounting for the attrition rates from 1L to subsequent LOT and relying on inappropriate clinical inputs for second-line (2L) tx and beyond. While addressing those limitations, we examined different clinical scenarios to explore the clinical value of using DARA first vs saving it until a later LOT. Methods: We examined 3 potential clinical tx sequences using a lifetime three-health state clinical simulation to estimate survival outcomes in pts with TIE NDMM. The 3 health states were: 1L (time on tx + any subsequent tx-free interval), 2L (time on 2L + any subsequent LOT), and death. Tx sequences were reflective of clinical guidelines: D-Rd in 1L followed by pomalidomide (POM)- or carfilzomib (CAR)-based regimens in 2L and VRd or Rd in 1L followed by DARA-based regimens in 2L (Figure). Pt characteristics were assumed to be similar to those of pts in MAIA. Time spent in the 1L health state with D-Rd and Rd was based on time to next tx or death (TTNT) curves derived from MAIA; TTNT curves with VRd were derived by applying PFS HRs estimated in the PEGASUS study. The attrition rate for the base case analysis (58.8%) was based on MAIA data. Although similar to published studies, it includes pts who were lost to follow-up and may be an overestimate. A sensitivity analysis evaluated a more conservative estimate (27.2%) excluding censored pts. Attrition rates were assumed to be the same across tx arms. Time spent in the 2L health state with DARA-based and POM- or CAR-based regimens was based on RW OS curves derived from the nationwide EHR-derived deidentified Flatiron Health database. RW data were used as pts in published 2L trials are younger than in MAIA and many have had prior ASCT. We evaluated median OS and 5- and 10-year survival rates. Results: Using D-Rd in 1L improved median OS by 2.5 or 3.5 years compared with delaying DARA-based regimens until 2L after VRd or Rd, respectively (Table). The probability of being alive at 5 and 10 years was higher with D-Rd than with VRd or Rd as initial therapy (Table). Changes in attrition rates impacted the expected median OS, but the absolute difference in median OS was maintained, with an incremental OS benefit consistently &gt;2 years. Conclusions: Unlike previous attempts to examine outcomes with different tx sequences, we incorporated clinically representative sequences for pts with NDMM, included attrition rates, and explored their impact on OS with different tx sequences. Our approach shows that achieving the longest possible PFS in 1L drives OS outcomes. Saving agents until later LOT does not take into account that many pts do not reach 2L, as evidenced by published data on attrition rates in MM. Our findings show that in pts with TIE NDMM, starting with D-Rd may provide up to 3.5 years of additional OS gain with the currently available 2L tx. This gain could increase with new 2L agents currently in development, reinforcing the importance of using the best agents first, to increase the probability of pts benefitting from tx currently in development. Figure 1 Figure 1. Disclosures Fonseca: Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; GSK: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aduro: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Mayo Clinic in Arizona: Current Employment; BMS: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Bayer: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Juno: Consultancy; Takeda: Consultancy; Merck: Consultancy. Hashim: Johnson and Johnson: Current Employment. Nair: Janssen Research & Development: Current Employment. He: Janssen: Current Employment, Current equity holder in publicly-traded company. Ammann: Janssen: Current Employment, Current equity holder in publicly-traded company. Lam: Janssen: Current Employment, Current equity holder in publicly-traded company. Wildgust: Janssen: Current Employment, Current equity holder in publicly-traded company. Kumar: Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS &lt; 18mo) and non-progressing (NP; PFS &gt; 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val &lt; 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val &lt; 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, 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; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Introduction: Altered cellular metabolism is a hallmark of every cancer cell. Aerobic glycolysis ("The Warburg Effect") is one of the earliest recognized metabolic abnormalities in cancer cells whereby extracellular glucose is preferentially metabolized and eventually processed to generate lactate and energy in the form of ATP before the former is released extracellularly, irrespective of oxygen availability. While extracellular lactate produced and released from cancer cells has traditionally been considered a waste metabolic by-product, recent understanding of cell metabolism suggests that it can also serve as a primary metabolic fuel for cancer cells via uptake by monocarboxylate transporters (MCTs). Our goal was to evaluate this "Reverse Warburg Effect" phenomenon in multiple myeloma (MM) cells and determine if it can be exploited for therapeutic purposes. Methods: All HMCLs, MM1S, RPMI-8226 and U266, were grown in RPMI-1640 cell culture medium containing 11 mM glucose and supplemented with 10% dialyzed fetal bovine serum (FBS) and 2 mM Glutamine. Primary MM cells were extracted using magnetic bead CD138 positive selection from MM patient bone marrow aspirates. For 13C-labeling experiments, HMCLs and primary MM cells were suspended in RPMI-1640 cell culture media containing 13C-labeled isotopes. Isotopomer analysis of glycolytic and tricarboxylic acid (TCA) cycle metabolites from HMCL and primary MM cell pellets was performed using Agilent Technologies 5975C gas chromatography-mass spectrometry. Small molecule inhibitors, AZD3965 and syrosingopine, were purchased from Selleck Chemicals and Sigma respectively. Cellular viability and proliferation were measured using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrasodium bromide (MTT) and CCK-8 assays respectively. MCT-1 and MCT-4 antibodies for western blotting were utilized to evaluate their cell membrane expression on HMCLs. Results: The HMCLs, MM1S and RPMI-8226 as well as primary CD138+ cells from MM patient bone marrow were cultured in cell culture media containing physiological levels (1 mM) of U-13C-Lactate. The incorporation of extracellular 13C into the intracellular glycolytic and TCA cycle metabolite pool was observed (Fig 1) based on the expected isotopomeric patterns, demonstrating the Reverse Warburg Effect in MM cells. The relative contribution of carbon substrate by extracellular lactate compared to extracellular glucose was assessed in the following HMCLs: MM1S, RPMI-8226 and U266 cells by culturing in cell culture media containing 3-13C-Lactate and U-13C-Glucose. Extracellular lactate (yellow bar) contribution to the formation of TCA metabolites equaled that of glucose (red bar) based on the expected isotopomer patterns, suggesting the relative importance of extracellular lactate as an essential nutrient like glucose (Fig 2). Since MCT-1 and MCT-4 are key bidirectional cell membrane transporters of lactate in and out of cells, we explored the clinical significance of their gene expression level on clinical outcomes using the COMMPASS dataset provided by the Multiple Myeloma Research Foundation (MMRF). When MM patients were dichotomized at above or below the median of the expression levels of fragments per kilobase of transcript per million (FPKM), MCT-1 and MCT-4 overexpression conferred a worse progression free survival and overall survival (Fig 3). The MCT-1/MCT-4 protein expression was detectable across the various HMCLs: MM1S, U266 and RPMI-8226 (Fig 4). Inhibition of MCT-1 by specific inhibitor AZD3965 was able to reduce proliferation but not affect viability of HMCLs at 48 hours (Fig 5). However, dual inhibition of MCT-1/MCT-4 using syrosingopine was able to significantly reduce proliferation and decrease viability of HMCLs in a dose dependent fashion (Fig 6). Finally, dual inhibition of MCT-1/MCT-4 using syrosingopine reduced the utilization of extracellular lactate into the TCA cycle pool by HMCLs in media containing 3-13C-Lactate (Fig 7). Conclusion: Utilization of extracellular lactate via Reverse Warburg Effect phenomenon appears highly active in MM cells. Disrupting the utilization of extracellular lactate by dual inhibition of both MCT-1 and MCT-4 appears therapeutic. In the future, dual inhibition of MCT-1/MCT-4 in combination with other anti-MM therapies should be evaluated to determine synergistic therapeutic potential. Figure 1 Figure 1. Disclosures Kumar: Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Carsgen: Research Funding; Sanofi: Research Funding; Novartis: Research Funding; Antengene: Consultancy, Honoraria; Beigene: Consultancy; Bluebird Bio: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Janssen: Consultancy, 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: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Roche-Genentech: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding.

Abstract Introduction: Multiple gene expression profile assays have successfully prognosticated the overall survival (OS) outcomes of patients with Newly Diagnosed Multiple Myeloma (NDMM) and are now available for use in clinical practice. The genes included in these profiles encompass several aspects of cancer biology broadly such as cell-cycle biology, energy metabolism, migration, adhesion, immune evasion etc. Since altered energy metabolism is one of the hallmarks of all cancer cells, our goal was to take a deep-dive into specific genes associated with intracellular central carbon energy metabolism (CCEM) pathways such as oxidative phosphorylation (OxPhos) as well as glycolysis and lactate fermentation (Glyco-Lac) in MM cells to determine their impact on the pathogenesis and prognosis of MM patients. Methods: Gene expression level data via RNA sequencing from the COMMPASS dataset (made available by the Multiple Myeloma Research Foundation (MMRF)) was utilized. The expression levels of all genes associated with the OxPhos (N = 118) and Glyco-Lac (N = 34) pathways were evaluated in CD138+ cells derived from patients with NDMM. Each gene was dichotomized into low and high expression groups -based on their expression level above and below the median fragments per kilobase of transcript per million (FPKM), respectively. The prognostic significance of dichotomizing each of the 152 genes in this NDMM cohort was evaluated by levels of their hazard ratios (HRs) for OS determined by the log-rank method. Genes from the OxPhos and Glyco-Lac pathways were selected based on their significant negative impact on OS, wide representation of their biologic activity throughout the pathway and high independence from each other. This resulted in the selection of 12 distinct genes of interest. A scoring system consisting of assigning a point for every gene where their FPKM was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the OxPhos and Glyco-Lac pathways to create an energy metabolism score (EMS). Patients were then categorized into "low" (0-8) or "high" (9-12) EMS groups. In addition, patients were designated as having high-risk cytogenetics if they had any of the following: t(4;14), t(14;16), t(14;20), del17p, and Amp 1q. Survival analysis was performed using the Kaplan-Meier survival analysis and compared via the log-rank method. Finally, the distribution of the EMS score among patients within a spectrum of plasma cell disorders such as, monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma and MM as well as normal plasma cells was assessed using a previously published transcriptome dataset (GSE6477). Results: A total of 766 patients were included in this study. The selected genes included in this scoring system and their biological relevance in the OxPhos and Glyco-Lac pathways are depicted in Figure A. The distribution of low and high EMS groups across ISS stage and cytogenetic risk are depicted in Figure B and C respectively. The median OS (mOS) and PFS for the high (N = 248) vs. low (N = 518) EMS groups was 55 vs. 94 months (HR: 2.41; (95%CI: 1.85-3.14); P &lt;0.0001) (Figure D) and 24 vs. 42 months (HR: 1.74; (95%CI: 1.42-2.13); P &lt;0.0001). Patients with ISS 2 stage disease categorized as having a high or low EMS score had a similar mOS to patients with ISS 3 and ISS 1 stage disease respectively (Figure E). Similarly, the mOS was similar among patients with standard risk and high-risk cytogenetics when categorized by low and high EMS (Figure F). Finally, the relative expression of most of the 12 select CCEM genes (Figure G) and the probability of a high EMS score increased among patients along the spectrum of plasma cell disorders (Normal plasma cell -&gt; MGUS -&gt; SMM -&gt; MM) (Figure H). Conclusions: The expression levels of specific genes associated with the activity of intracellular CCEM pathways such as glycolysis, lactate fermentation and oxidative phosphorylation in plasma cells provides evidence for the impact of cellular energy metabolism on the pathogenesis and survival outcomes of MM. This provides an opportunity for developing future studies aimed at understanding the energy metabolism pathways of plasma cell malignancies in greater detail so as to exploit them for diagnostic and therapeutic purposes in MM. Figure 1 Figure 1. Disclosures Kumar: Novartis: Research Funding; Amgen: Consultancy, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dispenzieri: Alnylam: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: AbbVie: Research Funding; Takeda: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding. Dingli: Apellis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Alexion: Consultancy. Lin: Gamida Cell: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Juno: Consultancy; Novartis: Consultancy; Legend: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy; Merck: Research Funding; Vineti: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Fonseca: Aduro: Consultancy; Kite: Consultancy; Juno: Consultancy; Merck: Consultancy; Takeda: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; GSK: Consultancy.

Abstract INTRODUCTION: Belantamab mafodotin (BLMF) is a first-in-class, antibody-drug conjugate targeting B-cell maturation antigen on myeloma cells. BLMF was approved for use in patients (pts) with advanced multiple myeloma (MM). Due to the risk of ocular toxicity, it is made available through a restricted program under a Risk Evaluation and Mitigation Strategy. It is speculated that monomethyl auristatin F (MMAF), the cytotoxic payload of BLMF may be internalised by the corneal epithelium via an off-target mechanism, leading to epithelial changes. In the pivotal DREAMM-2 trial, only 1% of pts permanently discontinued treatment due to keratopathy. Data regarding BLMF-induced ocular toxicity following its commercial availability since August 2020, are sparse, and in this context, we evaluated our clinical experience with BLMF to determine the impact of its ocular toxicity on outcomes when utilized outside of stringent clinical trial settings. METHODS: We reviewed records of pts with advanced MM who were evaluated at Mayo Clinic between 09/01/2020-07/01/2021 and initiated on BLMF monotherapy following a baseline ophthalmic examination that was also performed prior to each dose, and as needed for worsening ocular symptoms. Data pertaining to ocular symptoms, best corrected visual acuity (BCVA) and corneal examinations were collected and analyzed with respect to patient outcomes. Adverse events were graded using the National Cancer Institute Common Toxicity Criteria for Adverse Events v5.0. RESULTS: Thirty-four pts, with median age of 67 (range:49-90) years who received BLMF at the approved 2.5 mg/kg dose as their first infusion and completed at least one cycle were included. All patients were instructed to use preservative-free lubricant eye drops at least 4 times a day, starting with the initial infusion and continuing until the end of therapy, and avoid contact lenses. The median number of prior lines of therapy was 8 (range: 2-15). The median time to BLMF initiation from the diagnosis of MM was 7.5 (range: 1.3-19.7) years. The median follow-up from initiation of BLMF was 7 [95% confidence interval (CI): 5.7-8.6] months, and the median number of BLMF doses administered was 2 (range: 1-6). Twenty-seven (79%) pts experienced ocular toxicity, comprising keratopathy [n=25 (74%)] and/or decreased visual acuity [n=21 (62%)]. Table 1 outlines the keratopathy-related details. Thirteen pts (38%) developed symptomatic xerophthalmia. A clinically significant decrease in BCVA of equal or worse than 20/40 in the better-seeing eye was observed in 12 (35%) pts at a median of 1.7 (95%CI: 1.4-2.2) months of commencing BLMF. In 7 (58%) pts, it improved to better than 20/40 within a median of 3 (range: 0.7-4) months from the time of documentation of impaired BCVA following withholding treatment (n=5), with or without subsequent dose reduction or its permanent discontinuation (n=2). Treatment was permanently discontinued in 5 (15%) pts after a median of 3 doses due to grade ≥2 keratopathy. It was withheld in 9 (26%) due to grade ≥2 keratopathy. Four (12%) pts required dose reduction due to keratopathy (Table 1). Of 14 pts in whom BLMF was held or permanently discontinued due to keratopathy, 10 (77%) had disease progression within a median of 1 month (range 0.4-2.5) following BLMF interruption. Among 11 pts (32%) responded to BLMF, 45% developed grade 2 or higher keratopathy after the first dose compared to 13% of pts who were refractory to BLMF (n=23). The median duration of response was 4 (95%CI: 0.7-NR) months. Among responders, 2 pts (18%) had to permanently discontinue BLMF due to keratopathy, with subsequent progression from partial remission at 1.4 and 4.6 months following its discontinuation. Additionally, 73% of responders required dose withholding due to keratopathy and of those, 50% had evidence of disease progression within a median of 1.2 month (range: 0.7-1.7) following BLMF interruption. DISCUSSION & CONCLUSIONS : Despite efficacy in pts with advanced MM, in routine practice, ocular toxicity of BLMF is a major challenge and a deterrent to its continuous use. While frequent ocular toxicity-related interruptions were observed at expected rates, permanent treatment discontinuation due to keratopathy was observed in a higher proportion of pts than that encountered in the DREAMM-2 trial. Larger prospective studies are warranted to accurately discern the effects of ocular toxicities on outcomes of pts treated with BLMF. Figure 1 Figure 1. Disclosures Patel: Emmecell: Consultancy; GlaxoSmithKline: Consultancy; Senju Pharmaceuticals: Consultancy; Santen, Inc.: Consultancy. Kumar: Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Ailawadhi: AbbVie: Consultancy; Xencor: Research Funding; Medimmune: Research Funding; Genentech: Consultancy; Ascentage: Research Funding; Takeda: Consultancy; Sanofi: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; BMS: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; GSK: Consultancy, Research Funding; Karyopharm: Consultancy; Beigene: Consultancy. Bergsagel: Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria. Gertz: Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Kapoor: Karyopharm: Consultancy; AbbVie: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding.

Abstract Background: MASS-FIX is a screening method for serum and urine monoclonal proteins in multiple myeloma and related plasma cell disorders, which uses immunoglobulin enrichment coupled with matrix-assisted laser desorption ionization time-of-flight mass-spectrometry (MALDI-TOF). In addition to superior sensitivity over conventional gel-based techniques, MASS-FIX can distinguish therapeutic monoclonal antibodies (MoAb) from patient's M protein. As the utilization of therapeutic MoAbs increases, it is essential to understand the persistence pattern of these therapeutic antibodies in the serum. We designed this study to evaluate the duration of daratumumab detection by MASS-FIX in the serum of treated patients. Methods: We used a prospectively maintained database at Mayo clinic to identify patients with multiple myeloma and related plasma cell disorders who were treated with a daratumumab-containing regimen anytime during their disease course and had serial MASS-FIX data available after discontinuation of daratumumab. A univariate analysis was performed to assess for factors that may impact the clearance of daratumumab. Results: We included 125 patients with plasma cell disorders who received daratumumab as first or subsequent line of treatment between March 15 th, 2016, and March 4 th, 2020. The median age was 60.2 years and 57% were male. The most common diagnoses were multiple myeloma (70%) and light chain amyloidosis (18%). Daratumumab-based treatments were initiated after a median of 28.8 (IQR: 6.4-76.3) months from initial diagnosis. The most common regimen used was daratumumab, bortezomib and dexamethasone (23%); 26% underwent transplant after daratumumab-based induction. The median duration of treatment with a daratumumab-based regimen was 208 (IQR: 99-479) days. The median follow-up from the time of daratumumab discontinuation was 457 (95% CI: 346-NR) days. By last follow up, daratumumab was not detected by MASS-FIX in 93 (74%) patients but remained detectable in 32 (26%) patients. The median time from daratumumab discontinuation to disappearance of daratumumab by MASS-FIX was 160 (IQR: 107-233) days. On univariate analysis, the presence of ≥0.5 grams of urine protein was associated with earlier disappearance of daratumumab on MASS-FIX [risk ratio (RR): 2.0, P=0.02). The median time from daratumumab discontinuation to disappearance of daratumumab on MASS-FIX was 116 (95%CI: 76-160) days in patients with urine protein ≥0.5 grams and 203 (95%CI: 162-216) days in patients with urine protein &lt;0.5 grams (P=0.02). There was no association between the time to disappearance of daratumumab by MASS-FIX and old age ≥70 (RR: 0.9, P=0.81], male gender (RR: 0.9, P=0.60), eGFR &lt;60 (RR: 1.0, P=0.98), daratumumab schedule (every 1/2 weeks vs &gt;2weeks) (RR: 1.0, P=0.97), treatment duration (&lt;200 days vs ≥200 days) ( RR: 1.0, P=0.95), or transplantation status (RR: 1.0, P=0.98). Conclusion: The therapeutic monoclonal antibody daratumumab remains detectable in the serum of treated patients by MASS-FIX for several months after discontinuation and the duration varies between individual patients. This data has implications for diagnostic and monitoring testing and may provide guidance for reuse of daratumumab in clinical trials and practice. Proteinuria is associated with earlier disappearance of daratumumab by MASS-FIX and may have implications in patients with amyloidosis and monoclonal immunoglobulin deposition disease (MIDD). Further studies are needed to identify additional factors associated with the timing of disappearance. Disclosures Murray: Mayo Clinic: Other: Has received patents for the Mass-Fix technology which has been licensed to the Binding Site with potential royalties.. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; AbbVie: Research Funding. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring. Dingli: Alexion: Consultancy; Novartis: Research Funding; Apellis: Consultancy; Janssen: Consultancy; Sanofi: Consultancy; GSK: Consultancy. Kumar: Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Merck: Research Funding; Roche-Genentech: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.