Kumar, Shaji

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<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 >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 < 18mo) and non-progressing (NP; PFS > 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 < 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 < 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 <0.0001) (Figure D) and 24 vs. 42 months (HR: 1.74; (95%CI: 1.42-2.13); P <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 -> MGUS -> SMM -> 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 <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 <60 (RR: 1.0, P=0.98), daratumumab schedule (every 1/2 weeks vs >2weeks) (RR: 1.0, P=0.97), treatment duration (<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.

Abstract INTRODUCTION POEMS syndrome is a paraneoplastic disorder that results in multi-organ disease, and Vascular endothelial growth factor (VEGF) is associated with disease activity. There is evidence that the cytokine Interleukin- 6 (IL-6) stimulates VEGF production in several cancer cell lines. It has also been suggested that patients with POEMS syndrome experience symptoms improvement when IL-6 levels are low. Our study aims to understand the role of IL-6 and whether it is a contributor to disease activity in POEMS syndrome. METHODS We conducted a retrospective study of patients (pts) seen at Mayo Clinic within 1 year of diagnosis for POEMS syndrome with an IL-6 level within 3 months of diagnosis between 01/01/2011-05/31/2021 were included. Our database comprised 358 pts,7 were excluded because did not have official diagnosis of POEMS syndrome. Two hundred and ninety-nine pts did not have IL-6 testing within 90 days of diagnosis and were excluded. Clinical data was abstracted from our electronic medical record. Descriptive statistics were used for IL-6 levels. The majority of IL-6 laboratory testing for pts utilized the same reference range. A minority of pts had alternate reference ranges; thus, all IL-6 values and reference ranges were fold-corrected to have a unified reference to determine elevation in IL-6. High IL-6 was considered any value above the normal range, also analyzed pts in quartiles and deciles. Hematologic, VEGF and PET response criteria were utilized to group pts into responders or non-responders. Time to progression (TTP) and overall survival (OS) was calculated using the Kaplan Meier method. Differences between curves were by log rank. Statistical analysis was conducted via JMP software package (SAS, Cary, NC, USA). Univariate analysis was done to determine the prognostic value of IL-6. Statistical significance is considered with p-values <0.05. RESULTS 52 patients from among 352 met criteria and were included for analysis. Twenty-one patients (40%) had elevated IL-6 levels (IQR 5.67, 22.6) at the time of diagnosis. The fold elevation about normal was typically not very high, with a median of 3.1-fold elevation (IQR 1.3, 4.4; and range 1.0, 8.3). The percentage of males in the high IL-6 versus the normal Il-6 group were 90% versus 65%, p=0.03. Compared to normal IL-6, those with elevated IL-6 values had more instances of hepatomegaly (43% vs 16%, p=0.03), ascites (28% vs 6%, p=0 .04), abnormal DLCO (26% vs 3%, p=0.03), mixed bone lesions (57% vs 29%, p= 0.04), and lower serum albumin (range 2.3-3.5 vs 2.5-4.5 g/dL, p=0.0008).There was a trend for lower VEGF values among the high IL-6 group, but this did not meet statistical significance (median 250 vs 438 pg/mL). There was no significant difference in the time to progression between pts with high versus normal IL-6 levels (HR 1.36; p=0.456). Overall survival was significantly longer in pts with normal IL-6 compared to pts with high IL-6 levels (Median OS 67.4 vs 47.8 months, p=0.02). Forty-two pts were evaluated for VEGF response after treatment; there was a significant improvement in time to progression (85.5 vs 14.9 months, p=0.03) in pts who were VEGF responders. Among pts evaluable for a hematologic response (n=15), time to progression in pts with baseline high IL6 was longer in hematologic responders compared to non-responders (p=0.02). Conclusion Although OS was longer for pts with normal IL-6, elevated IL-6 at the time of diagnosis does not prevent pts from achieving disease remission. The study demonstrates that response to treatment rather than interleukin-6 levels in pts with POEMS syndrome is more prognostic. This study is an important initial step into understanding the utility of IL-6 in POEMS syndrome. Figure 1 Figure 1. Disclosures Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; 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. Kumar: Oncopeptides: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Merck: Research Funding; Antengene: Consultancy, Honoraria; Tenebio: Research Funding; Novartis: 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; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, 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; 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. Dingli: GSK: Consultancy; Novartis: Research Funding; Janssen: Consultancy; Sanofi: Consultancy; Apellis: Consultancy; Alexion: Consultancy. Lin: Janssen: Consultancy, Research Funding; Merck: Research Funding; Novartis: Consultancy; Legend: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Vineti: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Sorrento: Consultancy; Gamida Cell: Consultancy; Takeda: 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. Dispenzieri: Pfizer: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; 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; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: 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; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; 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; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. 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. 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: Smoldering multiple myeloma (SMM) prognostication models routinely used in clinical practice were developed for use at diagnosis. However, retrospective studies have shown that in SMM patients the risk of progression to multiple myeloma (MM) decreases over time. Therefore, this study assessed whether the Mayo 2018 and IMWG 2020 scores could be used dynamically to risk stratify patients post-diagnosis, and whether they could identify SMM patients with evolving disease. Methods: We retrospectively studied 704 SMM patients diagnosed between January 2000 to January 2020. Patients with a baseline FLCr ≥100 and involved FLC ≥10 mg/dL or baseline bone marrow plasma cells ≥60% were excluded. We collected serial laboratory data and available pathology data and re-applied the Mayo 2018 and IMWG 2020 SMM risk stratification models at annual landmark timepoints up to 5 years post SMM diagnosis. Survival analyses were performed using the Kaplan-Meier method. Time to progression (TTP) was defined as time from diagnosis or landmark timepoint to treatment initiation for MM or systemic AL amyloidosis. Cox proportional hazards models were used to estimate hazard ratios. A two-sided p-value <0.05 was considered statistically significant. Results: At SMM diagnosis, 271 (38%) patients were low risk, 228 (32%) were intermediate risk, and 205 (29%) were high risk per the Mayo 2018 score. Applying the IMWG 2020 score at diagnosis, 90 (34%) of patients were low risk, 111 (42%) were low-intermediate risk, 54 (21%) were intermediate risk, and 9 (3%) were high risk. The Mayo 2018 and IMWG 2020 risk scores was re-assessed annually post-diagnosis in patients without progression (respective sample sizes: n=430 and n=197 at year 1 landmark, n=326 and n=143 at year 2 landmark, n=260 and n=106 at year 3 landmark, n=203 and n=73 patients at year 4 landmark). The Mayo 2018 and IMWG 2020 models reliably stratified patients based on progression risk post diagnosis (as shown in Figure 1 and Figure 2, respectively). As shown in Figure 1, if only diagnostic values were used in risk categorization with the Mayo 2018 model, the TTP between risk categories became less distinct over time. However, when follow-up laboratory values were used to re-stratify patients, the TTP between risk categories was more consistent over time. The respective 2-year progression risk in Mayo 2018 high-risk versus IMWG 2020 intermediate-high risk patients was 51% versus 62% at the 1-year landmark, 60% versus 65% at the 2-year landmark, 47% versus 62% at the 3-year landmark, and 47% versus 45% at the 4-year landmark. To assess the prognostic significance of an increase in SMM risk category, we stratified patients based on whether the SMM risk category at follow-up had increased versus was stable or decreased compared to the baseline SMM risk stratification. We found that patients evolving to a risk category during follow up consistently had an increased risk of progression compared to patients with a stable/decreased risk categorization using both the Mayo 2018 (HR 3.34, 95% CI 2.04-5.48, p<0.001 at 2-year landmark; HR 3.41, 95% CI 2.05-5.68, p<0.001 at 3-year landmark; HR 2.69, 95% CI 1.34-5.38, p=0.005 at 4-year landmark) and IMWG 2020 (HR 2.32, 95% CI 1.42-3.77, p<0.001 at 1-year landmark; HR 3.10, 95% CI 1.69-5.70, p<0.001 at 2-year landmark; HR 2.50, 95% CI 1.30-4.78, p=0.004 at 3-year landmark; HR 2.26, 95% CI 0.91-5.60, p=0.072 at 4-year landmark) risk models. However, patients categorized as Mayo 2018 high-risk at follow-up had a similar risk of progression regardless of whether the baseline risk categorization was low-intermediate versus high-risk (HR 0.87, 95% CI 0.51-1.48, p=0.606 at 2-year landmark; HR 1.01, 95% CI 0.59-1.73, p=0.972 at 3-year landmark; HR 0.75, 95% CI 0.34-1.65, p=0.467 at 4-year landmark). Conclusions: Our findings support the use of the Mayo 2018 and IMWG 2020 scores post-diagnosis. We showed that patients migrating to a higher risk category have an increased risk of progression. This suggests that if patients evolve to a high-risk score during follow-up, they should be considered for an early intervention treatment approach. Figure 1 Figure 1. Disclosures Kapoor: Sanofi: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Karyopharm: Consultancy; Cellectar: 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. Dispenzieri: Pfizer: Research Funding; Janssen: Consultancy, Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy. Gertz: Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Ionis Pharmaceuticals: Other: Advisory Board. Dingli: Janssen: Consultancy; Sanofi: Consultancy; Apellis: Consultancy; Novartis: Research Funding; Alexion: Consultancy; GSK: Consultancy. Kumar: Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: 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; BMS: Consultancy, Research Funding; Bluebird Bio: Consultancy; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; KITE: 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; Oncopeptides: Consultancy; Beigene: Consultancy; 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 Background: The use of immunotherapeutic agents in multiple myeloma (MM) has shown improvements in clinical outcomes, emphasizing the role the host immune system plays in disease control. In recognition of this relationship, efforts aimed at identifying biomarkers of immune surveillance in MM have identified prognostic value in the peripheral absolute lymphocyte (ALC) and absolute monocyte (AMC) counts at various stages of disease and following autologous stem cell transplant (ASCT), with lower ALC and AMC serving as a surrogate for immune dysregulation and correlating with inferior progression free (PFS) and overall survival (OS). With ASCT remaining the standard of care in the treatment of MM, we sought to examine whether CD3 content of the apheresis product during stem cell collection could be used as a biomarker for immune competence and whether it influences outcomes. Methods: A retrospective study was conducted on 1086 MM patients who underwent stem cell (CD34) collection with available CD3 data and subsequent ASCT at our institution. We recorded the total absolute CD3 and CD34 cell count upon completion of mobilization, with a higher CD3 count serving as a surrogate for immune competence. In addition to investigating the absolute CD3 count, we calculated the CD3/CD34 ratio given variation in the CD34 goals. Patients were dichotomized based on whether their absolute CD3 and CD3/CD34 ratio values were above or below medians. A Kaplan-Meier model was used to compare median PFS and OS between groups. A cox proportional hazards model was used to determine its prognostic impact, adjusting for ISS stage 3, high risk FISH, achievement of CR near day 100, and maintenance therapy received post-ASCT. Results: The median length of follow-up from date of ASCT for the entire cohort (N=1086) was 34.1 months (range: 0.26-157 months) and the median time from stem cell collection to ASCT was 9 days (range: 3-3143 days). The most common mobilizing regimen was Neupogen and plerixafor (N=425, 39%) and 694 (64%) patients received ASCT in the first line setting. The median absolute total CD3 count was 4.3 x 10^6/kg (range: 0.1-21.9) with 542 patients above and 544 patients at or below the median. The median absolute total CD34 count was 8.53 x 10^6/kg (range: 0.2-37.0) and median CD3/CD34 ratio was 0.50 (range: 0.01-15), with 536 patients above and 550 patients at or below the median ratio. The median PFS among patients with a CD3 count > 4.25 x 10^6/kg was 23.1 vs. 16.9 months among patients with CD3 count ≤ 4.25 x 10^6/kg (p<0.0001) and median OS was 65.3 months vs. 41.6 months (p<0.0001), respectively. A similar trend was observed when dividing patients based on CD3/CD34 ratio, with median PFS of 22.4 vs. 17.5 months (p=.0003) and median OS of 61.5 vs. 45.7 months (p=.0001), both favoring the cohort with a CD3/CD34 ratio > 0.5 (Figure 1). The prognostic value among patients with a CD3/CD34 ratio > 0.5 was retained after adjusting for ISS stage III, high risk FISH features, and use of maintenance therapy as follows: PFS HR: 0.73 (95% CI: 0.62-0.86; p=0.0002); OS 0.71 (95% CI: 0.59-0.88; p=0.001). Conclusions: Our study demonstrates that patients who have higher CD3 content in their apheresis product have better PFS and OS following ASCT. These findings reveal a possible role for using absolute CD3 and CD3/CD34 ratios in the apheresis product as a surrogate marker for immune competence and in predicting clinical outcomes. Figure 1 Figure 1. Disclosures Gertz: 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; Ionis Pharmaceuticals: Other: Advisory Board. Dingli: GSK: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Alexion: Consultancy; Apellis: Consultancy; Novartis: Research Funding. Dispenzieri: Takeda: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Kapoor: Sanofi: Research Funding; Takeda: Research Funding; Ichnos Sciences: Research Funding; Glaxo SmithKline: Research Funding; Regeneron Pharmaceuticals: Research Funding; AbbVie: Research Funding; Karyopharm: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding. Kumar: Novartis: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Tenebio: Research Funding; Amgen: Consultancy, 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; Bluebird Bio: Consultancy; BMS: Consultancy, Research Funding; Oncopeptides: Consultancy; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Abbvie: 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; Beigene: Consultancy; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.