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8022 Background: Bone marrow (BM) assessment of minimal residual disease (MRD) is being considered as a surrogate endpoint in clinical trials and is prognostic for survival in multiple myeloma (MM). Timing of BM assessment is variable across Chimeric Antigen Receptor T cell (CART) therapy trials and differs from standard of care practice. BM myeloma cell clearance can be detected by month 1 post CART, even before serum immunofixation becomes negative. BM is still hypocellular at month 1, thus prognostic value of MRD negative (MRDneg) at this timepoint is unclear. We examined impact of Day 30 MRD status in patients (pts) who received CART at Mayo Clinic. Methods: Medical records were reviewed retrospectively for MM pts who received CART between 8/2016 and 6/2021. PFS and OS were plotted by Kaplan-Meier method. Results: Sixty MM pts received CART and had BM biopsy at month 1. Median age was 62 yrs, 53% were male, and 78% were BM MRDneg by flow cytometry. Baseline demographics were similar between MRDneg and MRD+ (Table). Overall, 85% (40/47) who were month 1 BM MRDneg had i/u FLC<normal. Patients who achieved CR/sCR had higher rates of BM MRDneg (100% vs 61%, p<0.001) and i/u FLC< normal (89% vs 58%, p<0.001). At month 1, 24/60 (40%) pts had hypocellular BM. Serial BM samples at month 3 (n=35), 6 (n=28) and 12 (n=23) showed MRDneg rate of 93% (25/27), 56% (9/16) 58% (7/12), respectively.. Rate of hypocellularity was 54% (19/35), 32% (9/28) and 30% (7/23), respectively. Among the MRDneg/hypocellular pts at month 1, hypocellular BM was seen in 8/11 (73%) pts at month 3 and 2/4 (50%) pts at month 6 and 12. Compared to MRD+, pts who had BM MRDneg at months1 had longer PFS (Table). PFS was not statistically significantly different between pts who had BM MRDneg and were either hypocellular or not. MRDneg pts with i/u FLC<normal at months1 had better median PFS compared to those who did not. (MRD+:2.9 months (1.2-NR). MRDneg/FLC>normal: 4.9 months (2.3-NR). vs MRDneg/FLC<normal:17.9 months (11.8-NR), p<0.0001). Conclusions: Hypocellular BM is common in the 3 months post CART. Regardless of BM cellularity, BM MRDneg at month 1 correlates with deep response and prolonged PFS. Majority of BM MRDneg pts at month 1 also had FLC<normal. BM MRDneg status and FLC normalization were associated with longer survival. Our data support the continued evaluation of BM early post CART infusion as a prognostic tool. [Table: see text]

AbstractAltered energy metabolism and changes in glycolytic and oxidative phosphorylation pathways are hallmarks of all cancer cells. The expression of select genes associated with the production of various enzymes and proteins involved in glycolysis and oxidative phosphorylation were assessed in the clonal plasma cells derived from patients with newly diagnosed multiple myeloma (NDMM) enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass data set. A scoring system consisting of assigning a point for every gene where their fragments per kilobase of transcript per million (FPKM) was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the glycolytic and oxidative phosphorylation pathways to create a total energy metabolism molecular signature (EMMS) score. This EMMS score was independently associated with worse progression free survival (PFS) and overall survival (OS) outcomes of patients with NDMM. A higher EMMS score was more likely to be present in clonal plasma cells derived from Multiple myeloma (MM) patients than those from patients with monoclonal gammopathy of undetermined significance (MGUS). This was functionally confirmed by the clonal plasma cells from MM patients having a higher rate of mitochondrial and glycolysis‐derived ATP formation than clonal plasma cells from MGUS patients. Thus, this study provides evidence for the effect of energy metabolism within clonal plasma cells on pathogenesis and outcomes of patients with MM. Exploiting the energy‐producing metabolic pathways within clonal plasma cells for diagnostic and therapeutic purposes in MM should be explored in the future.

PURPOSEThe study of rare diseases, such as multiple myeloma (MM), often experiences unique research hurdles that can delay or prevent lifesaving discoveries. HealthTree Cure Hub is a first-in-class software program designed to overcome these potential research hurdles.METHODSWe assessed whether HealthTree Cure Hub improved four commonly experienced research hurdles such as (1) small numbers of patient accrual to clinical trials and research studies, (2) shallow and isolated data sets, (3) high costs to answer research questions, and (4) lack of long-term follow-up patient data.RESULTSAs of June 2021, HealthTree Cure Hub, with its unique portal features, has attracted 9,225 patients with MM and diverse demographic backgrounds. While completing an online health profile, patients shared comprehensive data, as well as provided consent to contribute data from electronic medical records. Portal use answered research questions using patient-driven real-world data. This also cultivated relationships with patients and established communication channels that enabled continual patient contact to allow for long-term follow-up.CONCLUSIONThese results suggest that a patient-driven data capture tool such as HealthTree Cure Hub will help alleviate common research hurdles, which, in turn, will accelerate MM research, develop new hypotheses, and ultimately improve survival.

Abstract Background: Fc receptor-homolog 5 (FcRH5) is a type I membrane protein that is expressed exclusively in the B-cell lineage, and at a higher level on myeloma cells than on normal B cells. Cevostamab is a FcRH5xCD3 bispecific antibody (BsAb) that facilitates T cell-directed killing of myeloma cells. Initial data from the dose-escalation phase of the ongoing Phase I study (NCT03275103) of cevostamab monotherapy in patients (pts) with heavily pre-treated RRMM demonstrated promising activity and manageable safety, along with near ubiquitous FcRH5 expression on myeloma cells (Cohen et al. ASH 2020; Sumiyoshi et al. EHA 2021). We present updated safety and efficacy data from a larger cohort of pts, including results comparing Cycle (C) 1 single step-up (SS) and double step-up (DS) dosing for the mitigation of cytokine release syndrome (CRS). Methods: Participants have RRMM for which no established therapy is available or appropriate. Cevostamab (intravenous infusion) is administered in 21-day cycles. In the SS cohorts, the step dose (0.05-3.6mg) is given on C1 Day (D) 1 and the target dose (0.15-198mg) on C1D8. In the DS cohorts, the step doses are given on C1D1 (0.3-1.2mg) and C1D8 (3.6mg), and the target dose (60-160mg) on C1D15. In both regimens, the target dose is given on D1 of subsequent cycles. Cevostamab is continued for a total of 17 cycles, unless progressive disease or unacceptable toxicity occurs. CRS is reported using ASTCT criteria (Lee et al. Biol Blood Marrow Transplant 2019). Results: At data cut-off (18 May 2021), 160 pts had been enrolled (median age: 64 years, range: 33-82 years; male: 58.1%); 21.3% of pts had extramedullary disease. Median number of prior lines of therapy was 6 (range: 2-18). Most pts (85.0%) were triple-class refractory (PI, IMiD, anti-CD38 antibody). 28 pts (17.5%) had received ≥1 prior CAR-T, 13 pts (8.1%) ≥1 prior BsAb, 27 pts (16.9%) ≥1 prior antibody-drug conjugate (ADC), and 54 pts (33.8%) ≥1 prior anti-BCMA targeting agent. Median follow-up in exposed pts was 6.1 months. Almost all had ≥1 adverse event (Table). The most common was CRS (128/160 pts [80.0%]; Grade [Gr] 1: 42.5%; Gr 2: 36.3%; Gr 3: 1.3%). Immune effector cell-associated neurotoxicity syndrome (ICANS) associated with CRS was observed in 21 pts (13.1%) and in 34/211 (16.1%) CRS events (Gr 1: 8.5%; Gr 2: 6.2%; Gr 3: 1.4%). Most CRS events occurred in C1 (87.2%), arose within 24 hours of cevostamab administration (70.5%), and resolved within 48 hours of onset (83.4%). In the pts with CRS, tocilizumab was used for CRS management in 43.8% and steroids in 25.8% (both agents: 18.0%). In SS dose-escalation (68 pts), 3.6mg was chosen as the most effective C1D1 SS dose for limiting CRS in C1, with no target dose-dependent increase in the rate or severity of CRS observed after the C1D8 administration. Likewise, in DS dose-escalation (30 pts), 0.3/3.6mg was identified as the preferred C1D1/C1D8 DS dose for limiting CRS in C1. Notably, the overall rate of CRS was lower in the pts who received the 0.3/3.6mg/target DS regimen than in those who received the 3.6mg/target SS regimen (77.3% [34/44] vs 88.2% [75/85], respectively). The rate of ICANS associated with CRS was also lower in the 0.3/3.6mg/target DS cohort than in the 3.6mg/target SS cohort (4.5% [2/44] vs 21.2% [18/85], respectively). At data cut-off, 158/160 pts were efficacy evaluable. In dose-escalation, responses were observed at the 20-198mg target dose levels, and data suggested a target dose-dependent increase in clinical efficacy. Median time to response was 29 days (range: 20-179 days). Two dose-expansion cohorts were opened: ORR was higher at the 160mg dose level (54.5%, 24/44 pts) than at the 90mg dose level (36.7%, 22/60). At target dose levels &gt;90mg, ORRs in pts with prior exposure to CAR-Ts, BsAbs, ADCs, and anti-BCMA targeting agents were 44.4% (4/9 pts), 33.3% (3/9), 50.0% (7/14), and 36.4% (8/22) respectively. Median follow-up among all responders (n=61) was 8.1 months; estimated median duration of response was 15.6 months (95% CI: 6.4, 21.6). Conclusions: Cevostamab monotherapy continues to show clinically meaningful activity in a large cohort of pts with heavily pre-treated RRMM, with a target dose-dependent increase in ORR, but no increase in CRS rate. Responses appear durable, and are observed in pts with prior exposure to CAR-Ts, BsAbs, and ADCs. Compared with SS dosing, DS dosing at the 0.3/3.6mg level appears to be associated with a trend for an improved C1 safety profile. Figure 1 Figure 1. Disclosures Trudel: Amgen: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Roche: Consultancy; Genentech: Research Funding; Pfizer: Honoraria, Research Funding; BMS/Celgene: Consultancy, Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Sanofi: Honoraria. Cohen: BMS/Celgene: Consultancy; GlaxoSmithKline: Consultancy, Research Funding; Oncopeptides: Consultancy; Novartis: Research Funding; Genentech/Roche: Consultancy; AstraZeneca: Consultancy; Janssen: Consultancy; Takeda: Consultancy. Krishnan: MAGENTA: Consultancy; BMS: Consultancy, Current equity holder in publicly-traded company, Speakers Bureau; JANSSEN: Consultancy, Research Funding; City of Hope Cancer Center: Current Employment; REGENERON: Consultancy; SANOFI: Consultancy; GSK: Consultancy; Amgen: Speakers Bureau. Fonseca: Kite: Consultancy; Juno: Consultancy; Merck: Consultancy; Sanofi: Consultancy; Pharmacyclics: Consultancy; Novartis: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aduro: 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; GSK: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Bayer: Consultancy; Amgen: Consultancy; Mayo Clinic in Arizona: Current Employment; Celgene: Consultancy; BMS: Consultancy; Takeda: Consultancy; Janssen: Consultancy. Spencer: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Berdeja: Bluebird bio, BMS, Celgene, CRISPR Therapeutics, Janssen, Kite Pharma, Legend Biotech, SecuraBio, Takeda: Consultancy; Abbvie, Acetylon, Amgen: Research Funding; EMD Sorono, Genentech: Research Funding; Celularity, CRISPR Therapeutics: Research Funding; GSK, Ichnos Sciences, Incyte: Research Funding; Lilly, Novartis: Research Funding; Poseida, Sanofi, Teva: Research Funding. Lesokhin: Serametrix, Inc: Patents & Royalties; Behringer Ingelheim: Honoraria; Genetech: Research Funding; Iteos: Consultancy; Janssen: Honoraria, Research Funding; pfizer: Consultancy, Research Funding; bristol myers squibb: Research Funding; Trillium Therapeutics: Consultancy. Forsberg: University of Colorado: Current Employment; Karyopharm, Sanofi, Genentech: Research Funding. Costa: Karyopharm: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Rodriguez-Otero: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Regeneron: Honoraria; Clínica Universidad de Navarra: Current Employment. Kaedbey: Takeda, Sanofi: Honoraria; Celgene/BMS, Janssen: Honoraria; Royal Victoria Hospital Lakeshore Hospital: Ended employment in the past 24 months; Jewish General Hospital - McGill University: Current Employment. Richter: Janssen, Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive biotechnologies: Speakers Bureau; BMS, Karyopharm, Antengene: Membership on an entity's Board of Directors or advisory committees; Tisch Cancer Institute: Icahn School of Medicine at Mount Sinai: Current Employment. Mateos: Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene - Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sea-Gen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria; Bluebird bio: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria. Thomas: Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Acerta Pharma: Research Funding; X4 Pharma: Research Funding; Ascentage Pharma: Research Funding; BeiGene: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Wong: Genentech: Current Employment; CTMX, UBX, BMRN: Current equity holder in publicly-traded company. Li: Genentech/Roche: Current Employment, Current equity holder in publicly-traded company. Choeurng: Genentech: Current Employment, Current equity holder in publicly-traded company. Vaze: Roche/Genentech: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Samineni: Genentech: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Sumiyoshi: Genentech: Current Employment, Current equity holder in publicly-traded company, Divested equity in a private or publicly-traded company in the past 24 months. Cooper: Genentech: Current Employment; Roche: Current holder of individual stocks in a privately-held company. Harrison: Haemalogix: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche/Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene/ Juno/ BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Eusa: Consultancy, Honoraria, Speakers Bureau; Terumo BCT: Consultancy, Honoraria. OffLabel Disclosure: Cevostamab is a FcRH5xCD3 bispecific antibody that facilitates T cell-directed killing of myeloma cells. Cevostamab is an investigational agent.

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 Background: trALL is an important secondary primary malignancy (SPM) that has recently been appreciated. While patients (pts) with MM have an 8%-17% risk of developing an SPM while on lenalidomide (R) maintenance following autologous stem cell transplant (ASCT), trALL has been rarely observed. Little is known about the characteristics of trALL in pts with MM compared to pts who had other malignancies prior to the development of trALL. We define tALL as any prior exposure to cytotoxic chemotherapy and/or radiation for another malignancy, and herein, we report a comparative analysis of characteristics and outcomes of pts with trALL with and without MM from the Mayo Clinic Cancer Center (MCCC). Methods: We performed a systematic search in the 3-site MCCC registry and included all pts diagnosed and received at least 1 cycle of ALL-directed therapy and/or underwent allogeneic transplantation (AlloHCT) for ALL between 2007-2020. All cases of trALL in this series are B-ALL. Comparisons of characteristics between trALL patients with and without MM were made using a Wilcoxon rank sum test (continuous variables) or Fisher's exact test (categorical variables). Time-to-event variables were compared using unadjusted Cox proportional hazards regression models. Results: 70 trALL pts (14 MM [20%], 56 non-MM [80%]) were identified during the study period. The median age at trALL diagnosis was 63.9 (range 47-71) for MM pts and 63.1 (range 18.2-84) for non-MM pts. Median follow-up from trALL diagnosis was 1.42 years (range 0.02-10.6 years). The most common prior non-MM malignancies among tALL pts were breast (28.6%), lymphoma (17.9%), myeloid (17.9%), and GU/GYN (14.3%). Amongst the 14 MM pts, 100% (n=10) had ISS-I disease, 8 (57%) had IgG K MM, 1 (n=10) had high risk cytogenetics, 8 (57.1%) received R-based induction , 4 (28.6%) received CyBorD induction, 4(28.6%) received VRD induction, 3 (21.4%) received RD induction. 10 (71.4%) pts were actively on R when they developed trALL, 8 (57.1%) pts received prior ASCT. The median time to develop trALL following ASCT for MM was 46.3 months (m), (95% CI 20.4-67.6), 12 (85.7%) pts received R maintenance with a median duration of 53m (95% CI 16.9-121). The median time to develop trALL after initiation of R maintenance was 61.2m (95% CI 16.9-123.4). The most common trALL induction regimen received was HyperCVAD which was used in 71.4% of MM pts and 51.8% of non-MM pts. In comparison to non-MM pts, MM patients had a significantly lower WBC at diagnosis (Median: 2.0 vs. 5.8, P=0.005),no t(9;22) BCR/ABL1 cytogenetics (0.0% vs. 35.7%, P=0.008), a higher frequency of hypodiploid/near-triploid (Ho-Tri) cytogenetics (42.9% vs. 12.5%, P=0.009), and a lower frequency of prior radiation therapy (7.1% vs. 44.6%, P=0.012). MM pts had a higher frequency of AlloHCT for trALL (71.4% vs. 42.9%, P=0.075) and were more commonly in complete response (CR) at transplant (100.0% vs. 70.8%, P=0.078). No trALL in MM pts were Ph-like ALL. There was a statistically significant difference in time to development of trALL between previous malignancy locations (P=0.018), with GU/GYN (Median: 9 years) having the longest time and myeloid (Median: 3 years) having the shortest time; MM was 6 years. In the overall pt cohort, 56 (80%) pts achieved a complete response after induction and 60% (n=25) were minimal residual disease (MRD)+ after induction. Pts with MM had a significantly lower likelihood of being MRD+ after induction (OR=0.09, P=0.015). Overall, 24.3% of pts relapsed after induction therapy for trALL; 39 (55.5%) died after trALL diagnosis including 43% (n=35) who died after AlloHCT. Although pts with MM tended to have better outcomes compared to non-MM trALL pts in terms of overall survival after trALL diagnosis (Figure 1A), survival after AlloHCT (Figure 1B), relapse rate after trALL diagnosis and non-relapse mortality after AlloHCT, none of these findings were statistically significant in this small cohort (Figure 1C). Conclusion: trALL is a unique SPM that can occur in pts with MM. It is associated with Ho-Tri cytogenetics and is BCR/ABL1 negative. The majority of MM pts that developed trALL had undergone prior ASCT and were on R maintenance when they developed trALL. Despite being more likely to have adverse cytogenetics features, MM-associated trALL has similar survival compared to non-MM-associated trALL which speaks to the poor survival outcomes of this SPM. Further research on trALL in MM pts is merited. Figure 1 Figure 1. Disclosures Litzow: Amgen: Research Funding; Pluristem: Research Funding; AbbVie: Research Funding; Actinium: Research Funding; Astellas: Research Funding; Omeros: Other: Advisory Board; Jazz: Other: Advisory Board; Biosight: Other: Data monitoring committee. Bergsagel: Janssen: Consultancy. Fonseca: Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Bayer: Consultancy; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Novartis: Consultancy; Merck: Consultancy; Sanofi: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy. Dispenzieri: Takeda: Research Funding; Alnylam: Research Funding; Pfizer: Research Funding; Oncopeptides: Consultancy; Sorrento Therapeutics: Consultancy; Janssen: Consultancy, Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Ailawadhi: GSK: Consultancy, Research Funding; Sanofi: Consultancy; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Medimmune: Research Funding; Xencor: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Beigene: Consultancy; BMS: Consultancy, Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy; Ascentage: Research Funding. Foran: actinium: Research Funding; taiho: Honoraria; bms: Honoraria; servier: Honoraria; pfizer: Honoraria; novartis: Honoraria; OncLive: Honoraria; abbvie: Research Funding; boehringer ingelheim: Research Funding; takeda: Research Funding; trillium: Research Funding; aptose: Research Funding; revolution medicine: Honoraria; syros: Honoraria; sanofi aventis: Honoraria; certara: Honoraria; gamida: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: 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 Venetoclax (VTX) is a selective small-molecule inhibitor of BCL-2 that exhibits antitumoral activity against MM cells presenting lymphoid features and those with translocation t(11;14). Despite its impressive clinical activity, VTX therapy for a prolonged duration can lead to drug resistance. Therefore, it is important to understand the underlying mechanisms of resistance in order to develop strategies to prevent or overcome resistance. In the present study, we established four VTX resistant human myeloma cell lines (HMCLs) from four sensitive HMCLs, including three with t(11;14), in culture with a stepwise increase in treatment dose with VTX. To identify the molecular basis of acquired VTX resistance, whole exon sequencing (WES), mRNA-sequencing (mRNAseq), and protein expression assays were performed in the four isogenic VTX-sensitive/resistant HMCLs and three MM patients with samples collected before VTX administration and after clinical resistance to the drug. Compared with sensitive cell lines and patient samples collected before VTX administration, mRNAseq analysis identified downregulation of BIM and upregulation of BCLXL in both resistant cell lines and MM cells from relapse patients. Other transcriptional changes detected included upregulation of AURKA, BIRC3, BIRC5, and IL32. Enrichment analysis of differentially expressed genes suggested involvement of PI3K and MAPK signaling, likely associated with cytokines, growth factors (EGF, FGF and IGF family members), and receptor tyrosine kinase (EGF and FGF). Western blot analysis was performed to compare BCL2 family expression in resistant cell lines versus sensitive cell lines and it showed upregulation of BCL2 survival members (such as MCL-1 and BCLXL), and downregulation of pro-apoptotic BH3 members (such as BIM and PUMA). BIM expression was completely lost in one resistant cell line, and introduction of exogenous BIM into this cell line enhanced VTX sensitivity. Interestingly, BCL2 was upregulated in some resistant cell lines generated after a long-term treatment with VTX, suggesting BCL2 expression level may not be suitable as a marker of VTX sensitivity for acquired resistance. Unlike in CLL, BCL2 mutations were not identified through WES in any resistant cell lines or primary patient sample harvested after relapse. While 8 genes were mutated in two resistant samples , no clear mutational pattern emerged . Based on the above, we further tested some specific inhibitors in in vitro or ex vivo cell models to help understanding resistant mechanism and identify strategies to overcome VTX resistance. We found that inhibition of MCL-1, with the compound S68345, substantially enhanced VTX sensitivity in three resistant HMCLs and in primary cells from one relapsed MM patient. A BCLXL inhibitor (A155463) only significantly enhanced VTX sensitivity in one resistant cell line after co-treatment with VTX. Co-treatment of the other three resistant cell lines with VTX, S68345 and A155463 resulted in the most synergistic anti-myeloma activity, suggesting those cell lines are co-dependent on MCL-1, BCLXL, and BCL2 for survival, although they are more dependent on MCL-1. We also found that inhibition of PI3K signaling, IGF1, RTK (EGF and FGF) and AURKA significantly increased VTX sensitivity, partially through downregulation of MCL-1, and BCLXL, and upregulation of BIM. Conventional anti-MM drugs such as dexamethasone, bortezomib and lenalidomide, were shown to have little activity on augmenting VTX sensitivity in most resistant cell lines. In summary, we find that acquired resistance to VTX in MM is largely associated with BCL2 family regulation, including upregulation of survival members such as MCL-1, BCLXL, BCL2, and downregulation of pro-apoptotic members, especially BIM. Our study also indicates that upstream signaling involved in BCL2 family regulation during acquired resistance is likely related to cytokine, growth factor, and/or RTK-induced cell signaling such as PI3K. Co-inhibition of MCL-1, or BCLXL, as well as the upstream PI3K, RTK (FGF and EGF), IGF-1 mediated signaling were effective in overcoming VTX resistance. Disclosures Fonseca: Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees.

Abstract Purpose: Multiple myeloma (MM) is a plasma cell malignancy characterized by a collection of cytogenetic abnormalities that drive clinical presentation, response to treatment, and prognosis. The most common immunoglobulin heavy chain (IGH) translocation found in MM, t(11;14), results in CCND1 overexpression. Although t(11;14) is considered a standard risk cytogenetic abnormality, heterogeneity in patient outcomes and response to therapy within the t(11;14) group has been reported. In contrast to t(11;14) mantle cell lymphoma, where most cases (73%) have a typical, balanced t(11;14) rearrangement, only 47% of t(11;14) MM have a typical, balanced t(11;14) FISH pattern; the remaining 53% of MM are characterized by an atypical t(11;14) FISH result (Dalland, Genes Chromosomes Cancer, 2021). Here, we compared the genetic characteristics of typical vs. atypical t(11;14) MM and the association with other genomic abnormalities, CCND1 expression, IGH breakpoint location and patient outcomes. Methods: We performed a retrospective study of newly diagnosed t(11;14) MM patients seen at Mayo Clinic (N=411) or enrolled in the MMRF CoMMpass trial (N=167). The t(11;14) FISH results of the Mayo Clinic samples were segmented into two groups: typical CCND1/IGH signal pattern (n=179, 3 red, 3 green, 2 fusion signals) and atypical CCND1/IGH signal pattern (n=232, any FISH pattern deviating from typical). The CoMMpass cases had both RNAseq and long-insert WGS data and were analyzed to detect gene expression, structural and copy number variants and IGH breakpoint locations. These data were used to classify the t(11;14) as typical (n=63) or atypical (n=104) and whether there was a single t(11;14) fusion (n=107) or a gain of the t(11;14) fusion (n=60), and whether these cases were associated with changes in gene expression or breakpoint location. Differences between the groups were measured using χ2 and Wilcoxon tests. Survival curves were estimated using Kaplan Meier and compared using the Log-Rank test. Statistical significance was determined at p&lt;0.05. Results: Of the Mayo t(11;14) cohort, 232/411 (56%) had an atypical t(11;14) FISH pattern and 179/411 (44%) had a typical t(11;14) FISH pattern. Although an atypical t(11;14) FISH pattern was not significantly associated with monosomy 17/17p deletion, MYC rearrangements, 1q gain or ISS stage III, it was significantly associated with the presence of any trisomy in comparison to typical t(11;14) cases (40/222=18% vs.12/166=7%, p=0.002). Atypical t(11;14) cases had an increase in CCND1 gene expression compared to typical t(11;14) cases (median 375.0 RPKM vs. 266.4 RPKM, p&lt;0.0001) and cases with a gain of the t(11;14) fusion were associated with even greater CCND1 expression compared to cases with a single t(11;14) fusion (median 459.4 RPKM vs. 263.2 RPKM, p&lt;0.0001). We next evaluated whether the t(11:14) FISH pattern was associated with differences in IGH breakpoint locations. Nearly 80.0% of atypical t(11;14) cases had a breakpoint within the constant region of the IGH locus commonly reported for MM, while only 61.0% of the typical cases had constant region breakpoints (p=0.035); the remaining cases had breakpoints involving the VDJ region. We next evaluated the impact of atypical t(11;14) FISH on OS. In Mayo cases, OS was significantly shorter in patients with atypical FISH patterns compared to typical t(11;14) FISH patterns (6.9 years [95% CI: 5.1-7.8] vs. 9.0 years [95% CI: 7.0-11.6], p=0.019). Although atypical t(11;14) was associated with increased risk of death (RR 1.45, p=0.021), this finding was not retained in a multivariate model including high risk abnormalities such as age, ISS stage, 17p deletion and 1q gain demonstrating atypical t(11;14) is not an independent predictor of poor outcome when treated with standard therapies. Conclusions: Atypical t(11;14) FISH in MM is found in approximately half of t(11;14) cases, is associated with trisomies, higher CCND1 expression and is more likely to have a breakpoint involving the constant region of the IGH locus. The enrichment of VDJ breakpoints within typical t(11;14) MM is consistent with enrichment of VDJ IGH breakpoints in MCL, which are mostly characterized by typical t(11;14) FISH patterns. While not an independent predictor of outcome with standard therapies, it remains to be evaluated whether patients with typical or atypical t(11;14) have a differential response to novel agents, such as venetoclax. Disclosures Fonseca: Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Merck: Consultancy; Juno: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Aduro: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; AbbVie: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Takeda: Consultancy; Bayer: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees. Bergsagel: Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse. Kumar: Merck: Research Funding; Carsgen: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Celgene: 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; Novartis: Research Funding; Adaptive: 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; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Sanofi: Research Funding.