Citations

Introduction: In recent years, multiple myeloma (MM) research has increasingly focused on unraveling the complex interactions between malignant plasma cells and their surrounding bone marrow microenvironment (BMME). To further elucidate these interactions, we developed a comprehensive single-cell atlas of the MM BMME, integrating both coding and noncoding (ncRNA) transcriptional profiles. By correlating these profiles with specific cytogenetic abnormalities in myeloma cells, we aim to uncover how ncRNAs regulate the phenotypic and functional states of myeloma cells and, consequently, shape the cellular landscape of the BMME. A detailed understanding of how the noncoding transcriptome affects the cellular components and immune landscape of the BMME is essential for advancing our knowledge of MM pathogenesis and identifying novel biomarkers and therapeutic targets. Methods: Our atlas was constructed using single-cell RNA sequencing data from 481 samples of CD138neg cells sorted from bone marrow aspirates of MM patients enrolled in the MMRF CoMMpass study. This dataset includes accompanying whole-genome sequencing and survival data, enabling correlative analysis between transcriptional profiles, cytogenetic abnormalities, and patient outcomes. To generate a combined coding and non-coding transcriptome, we developed an expanded human reference genome by systematically merging mRNA and ncRNA transcripts from the LncBook2.0 and GENECODEv42 reference genomes. The sequencing data was aligned to this expanded genome, followed by quality control processing, batch correction, clustering, and supervised analysis. Results: The resulting coding and non-coding atlas comprises over 1.9 million cells, spanning immune, plasma, and stromal compartments. Remarkably, ncRNAs constituted 46% of the associated genes, including 22,102 long ncRNAs (lncRNAs) and 3,112 small ncRNAs. Subclustering analysis of the major immune compartments (B lymphoid, myeloid, and NK and T lymphoid) yielded 70 subclusters, with ncRNAs representing 24-38% of the top 100 differentially expressed genes within each compartment. This underscores the significant role of ncRNAs in distinguishing immune subpopulations. Moreover, examining the association between specific cytogenetic abnormalities and the immune composition of the BMME, 30 subclusters were found to be differentially abundant (P < 0.05) between cytogenetic abnormalities, supporting our hypothesis that myeloma cell genotypes significantly shape the immune microenvironment. For instance, patients with CCND1 amplification displayed an increase CD4+ central memory T cells with a decrease in CD8+ cytotoxic T cells. Similarly, patients with 1q21 gain exhibited an increase in TGFβ-stimulated monocytes, while a decrease in cytotoxic CD8+ T and CD56dim NK cells was observed in patients with 17p13 deletion. Next, examining which ncRNAs are associated with specific cytogenetic abnormalities, we identified 14 ncRNAs significantly differentially expressed across cytogenetic abnormalities. Increased expression of six of these ncRNAs were associated with poor overall survival both independently and in combination (P < 0.05, HR = 3.7). Notably, three of these ncRNAs have documented roles in cancer progression but have not been previously studied in the context of MM. Subsequently stratifying patients based on their enrichment of these outcome-associated ncRNAs revealed even stronger associations with immune composition, including an increase in CD4+ regulatory T cells (P < 0.05). This further supports our hypothesis that myeloma cell genotypes shape the BMME. Lastly, by constructing a gene regulatory network to predict the interactions between coding and noncoding RNAs, we illustrate the regulatory networks of ncRNAs modulating the phenotypes of myeloma and immune cell subpopulations. Conclusion: In summary, we present the first high-resolution transcriptomic atlas of the MM BMME, integrating both coding and noncoding RNAs. This study reveals distinct immune subpopulations associated with specific myeloma cell genotypes and highlights the central role of ncRNAs in modulating the phenotypic and functional states of both malignant and non-malignant cells in the MM BMME. Our findings emphasize the potential of ncRNAs as therapeutic targets and prognostic markers for MM, offering new avenues for research and clinical intervention.

Introduction Newly-diagnosed multiple myeloma (MM) patients often share tumor genetic abnormalities associated with a high risk of progression and poor survival. We hypothesized that single-cell transcriptomic characterization of the tumor immune microenvironment (TIME) may be combined with tumor-based risk stratification to uncover novel disease biology mechanisms associated with clinical outcomes and improve risk stratification. Methods We performed single-cell RNA sequencing of 361 CD138 negative sorted bone marrow mononuclear cells (BMMCs) samples from 263 MM patients enrolled at time of diagnosis in the Multiple Myeloma Research Foundation CoMMpass study (NCT01454297). Specimens were analyzed at baseline (n=263) across a network of five collaborating academic research centers following assay harmonization. Genetic factors that determine risk were defined as one or more of the following gain- or loss-of-function events derived from baseline genomic sequencing data: del17p14, translocations of the Ig locus with the MAF/A/B or WHSC1/MMSET/NSD2 loci, and gain of chromosome 1q21. Statistical modeling strategies were used to assess single-cell populations or RNA signatures associations with known risk factors or disease progression (FDR<0.05). Results The study generated high-quality single-cell profiles of 731,643 cells from 123 high-risk and 107 standard-risk MM patients based on genetics. Based on distinct transcriptome profiles, BMMCs formed 106 clusters corresponding to 5 major compartments: NK and T cells, B cells, erythrocytes, myeloid cells, and plasma cells. Our preliminary differential abundance analysis comparing high- vs standard-risk patients at baseline uncovered significant differences in the TIME associated with genetic risk. The high-risk group was identified as enriched in effector CD8+, and CD4+ T cells. In contrast, the high-risk group exhibited depletion of naive CD8+ and interferon-associated CD4+ and CD8+ T as compared to the standard-risk group. Within subclusters in the T and NK cells compartment, a higher cytotoxicity signature was observed in high-risk patients, characterized by the expression of granzymes and perforin. Finally, our preliminary survival and progression analysis associated a higher frequency of IDO1-expressing macrophages in worst progression patients, while better outcomes were associated with CD8+ T and B cells expressing androgen response and IL6/STAT3/EMT programs, respectively. Conclusions Current approaches for risk stratification are based on bulk measurements of tumor's genetic or epigenetics features, which do not capture the diversity and dynamics of the TIME. Through single-cell transcriptomics, we were able to capture, in high granularity, the TIME and correlate specific immune cell populations and phenotypes with relapse risk and poor prognostic outcomes. These results suggest that immune subpopulations may be an essential novel aspect for improving current risk stratification models.

Introduction: BCMA-directed CAR-T and Bispecific T-cell engager (BiTE) therapies have shown remarkable overall response rates in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM). This has led to the approval of idecabtagene vicleucel, ciltacabtagene autoleucel and teclistamab by the FDA, EMA and other regulatory agencies. We performed a global survey on accessibility, practice patterns, and toxicity management of commercially available CAR-T and BiTE therapies. Methods: An online survey was completed by clinicians attending the International Myeloma Society's Immune Effector Cell Therapies in Multiple Myeloma Workshop, in-person or virtually, made available for 30 days after the meeting held in March 2023, Boston, MA. Results: 87 clinicians from 16 countries completed the survey, most practicing in an academic practice (86%) and in an urban environment (89%). Sixty-four percent of the respondents practice in North America followed by Europe (25.3%) including Germany, United Kingdom, Ireland, Italy, Netherlands, Spain, France, Greece, and Cyprus, and 6.9% from Asia and 3.4% from Australia. Seventy-seven percent reported CAR-T cell therapy was approved in their country with 44.8% treating 1-5 patients and 14.9% treating >5 patients per month; 67% reported BiTE therapy was approved in their country with 14.9% treating >5 patients per month. Of evaluable responses (n = 40), 62% administered CAR-T therapy inpatient and 15% administered CAR-T therapy outpatient ≥ 50% of the time. Length of time between decision to treat with CAR-T therapy to infusion was 6-12 weeks for 42% of responders and <6 weeks for only 20%. Most (75%) reported inpatient administration of BiTE therapy step-up dosing; 17.5% reported outpatient step-up dosing administered ≥ 50% of the time. Use of institutional or practice SOPs for CAR-T and BiTE therapy patient selection was reported by 85% and 58%, respectively, with the factors influencing SOP outlined in Table 1. Of available CAR-T therapies, 53% prefer cilta-cel and 23% reported CAR-T therapy preference depended most on patients' co-morbidities/frailty (89%), timing of next available apheresis (67%), age (56%), CNS disease (44%) and other factors. Maintenance therapy after CAR-T therapy was reportedly used by 25% and 5% of respondents in 1-10% and 11-50% of their patients, respectively, to “maintain level of response achieved” (50%) or “achieve a deeper level of response” (25%). About half (43%) of the respondents reported using an SOP or particular criteria for secondary Hemophagocytic Lympho-Histiocytosis/Macrophage Activation Syndrome/CAR-related-HLH (sHLH/MAS/CarHLH) management that varied between the use of ASTCT Working Group IEC associated HLH-like syndrome Criteria (47%) (Hines 2023), CARTOX HLH Criteria (24%) (Neelapu 2017) and HLH-2004 criteria (6%). Tocilizumab was used as cytokine release syndrome (CRS) prophylaxis only by one responder. Keppra neurologic prophylaxis was used by 47.5% and 15% of responders for CAR-T and BiTE therapy, respectively. Delayed neurotoxicity with CAR-T was reported in 1-5% of patients by 57.5% of responders and 6-10% of patients by 15% of responders; most common manifestations were other movement/neuromuscular disorder (62%), nerve palsy (55%), and Parkinsonism (45%). Infectious prophylaxis was used to protect against HSV (92.5%), PJP (90%), bacterial pathogens (55%), fungal pathogens (60%), and IVIG (77.5%). However, criteria for infectious prophylaxis use varies widely incorporating IgG levels, infectious history, CD4+ count, B-cell aplasia and cytopenias (48.1 - 88.9%). Most respondents (46.3 - 86.5%) reported completing an infectious work-up including CMV, EBV, Hep B/C, and HIV ( Table 2). Conclusions: Trends of CAR-T and BiTE therapy use and patterns of concurrent prophylactic and supportive care in MM vary widely among academic centers across the globe, demonstrating a need for further understanding of toxicity pathogenesis, toxicity management and consistent guidelines. This survey also demonstrates the great unmet need for access beyond urban centers among the international myeloma community. Prospective studies and claims data analysis should be utilized to evaluate the longitudinal cost of care, quality of life, duration of response and management of infectious disease, neurological or related complications of CAR-T and bispecific TCE therapies.

Abstract BACKGROUND Standard of care (SOC) and patient survival in glioblastoma have changed little in the past 17 years. We evaluated in a phase 3 trial whether adding an autologous tumor lysate-loaded dendritic cell vaccine (murcidencel) to SOC extends survival. Patients and METHODS Newly diagnosed glioblastoma patients were randomized 2:1 to either murcidencel or placebo. Under a crossover design, all patients could receive murcidencel following tumor recurrence. All parties remained blinded regarding treatments before recurrence. Patients thus received murcidencel at new diagnosis (nGBM) or at recurrence (rGBM) following crossover from placebo. The primary and secondary endpoints compare overall survival (OS) with contemporaneous, matched external controls. Four sets of analyses were conducted to ensure rigorous matching of the controls, reduce biases, and confirm the robustness of the results. RESULTS 331 patients were enrolled. With the crossover, 89% received murcidencel. Median OS (mOS) for nGBM patients (n = 232) was 19.3 months from randomization (22.4 months from surgery) with murcidencel vs. 16.5 months from randomization in the controls (HR = 0.80, p = 0.002). Survival at 48 months from randomization was 15.7% vs. 9.9%, and at 60 months was 13% vs. 5.7%. For rGBM (n = 64), mOS was 13.2 months from relapse vs. 7.8 months in the controls (HR = 0.58, p < 0.001). Survival at 24 months post-recurrence was 20.7% vs. 9.6%, and at 30 months post-recurrence was 11.1% vs 5.1%. In nGBM patients with methylated MGMT (n = 90), mOS was 30.2 months from randomization (33 months from surgery) with murcidencel vs. 21.3 months from randomization in the controls (HR = 0.74, p = 0.027). The treatment was well tolerated, with only 5 serious adverse events deemed at least possibly related to the vaccine. CONCLUSION Clinically meaningful and statistically significant survival extension was seen in both nGBM and rGBM patients treated with murcidencel and SOC compared with contemporaneous, matched external controls who received SOC alone.

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. Significance: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

Abstract Multiple Myeloma (MM) is a hematologic malignancy marked by uncontrolled clonal expansion of plasma cells. Previous research has examined single-cell transcriptome profiles of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) and found that natural killer (NK) cell abundance is elevated in the early stages and correlated with altered chemokine receptor expression. This study suggested the critical role of immune cells on myeloma progression from asymptomatic MGUS to symptomatic MM. Up to date, however, there are no published studies comprehensively comparing tumor and immune populations differences between MM NON-progressors (NPs) and FAST-progressors (FPs) and investigating how clonal plasma cells affect disease progression in a large cohort. Therefore, understanding how tumor and immune cells influence disease progression within symptomatic MM is of great interest.Here, we subjected CD138-negative Bone Marrow Mononuclear cells (BMMC) samples from 418 MM patients to scRNA-seq. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Based on time to progressive disease (TTPD), we classified patients into 2 categories. Patients with TTPD less than 18 months were classified as FAST-progressors (FPs), whereas patients with TTPD more than 5 years were classified as NON-progressors (NPs). By analyzing patient genomic alterations and its association with progression, we found that there was a significant association of slow MM progression with t(11;14) (p = 0.048), consistent with previous study. In our preliminary analysis, we profiled 83 CD138-sorted MM bone marrow samples using scRNA-seq. Interestingly, we found plasma cells from samples with the same genetic alterations tend to cluster together, highlighting the important role of genetic drivers in transcriptome profiles of plasma cells. Moreover, integrated analysis of bone marrow samples from 83 MM patients and 4 healthy donors revealed an atypical naïve-B cell subset with enrichment of cells from fast-progressors and partial expression of MS4A1. Differentially expressed genes for this naïve-B cell subset includes KLF9, BCL2L11, JOSD1, and IRS2, etc. Overall, as part of MMRF immune profiling research, this study will help to interrogate how genetic alterations and disease progression interplay MM tumor and TME and provide a sufficiently broad and valuable dataset for systematically characterizing MM at single-cell resolution. Hopefully, this study could identify novel targets for MM immunotherapies, and ultimately identify patients with high risk of fast progression for early intervention in the clinic. Citation Format: Lijun Yao, Tianjiao Wang, Kazuhiro Sato, Reyka Jayasinghe, I-Ling Chiang, Darwin D'souza, William Pilcher, Edgar Gonzalez-Kozlova, Yered Pita-Juarez, Taxiarchis Kourelis, Deon Bryant Doxie, Beena Thomas, Brian Lee, Swati Sharma Bhasin, Upadhyaya Bhaskar, Mark Fiala, Julie Fortier, Travis Dawson, John Leech, Shaji Kumar, Hearn Cho, Seunghee Kim-Schulze, Bee Raj, Stephen Oh, the MMRF Immune Profiling Research Team, John Dipersio, Ravi Vij, Adeeb Rahman, Ionnis Vlachos, Shaadi Mehr, Mark Hamilton, Daniel Auclair, Surendra Dasari, David Avigan, Madhav Dhodapkar, Sacha Gnjatic, Manoj Bhasin, Li Ding. Single-cell transcriptome profiling of multiple myeloma bone marrow samples suggests that disease progression interplays with tumor and tumor microenvironment in The MMRF CoMMpass Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1729.

PURPOSE CARTITUDE-1, a phase Ib/II study evaluating the safety and efficacy of ciltacabtagene autoleucel (cilta-cel) in heavily pretreated patients with relapsed/refractory multiple myeloma, yielded early, deep, and durable responses at 12 months. Here, we present updated results 2 years after last patient in (median follow-up [MFU] approximately 28 months), including analyses of high-risk patient subgroups. METHODS Eligible patients had relapsed/refractory multiple myeloma, had received ≥ 3 prior lines of therapy or were double refractory to a proteasome inhibitor and immunomodulatory drug and had received prior proteasome inhibitor, immunomodulatory drug, and anti-CD38 therapy. Patients received a single cilta-cel infusion 5-7 days after lymphodepletion. Responses were assessed by an independent review committee. RESULTS At a MFU of 27.7 months (N = 97), the overall response rate was 97.9% (95% CI, 92.7 to 99.7); 82.5% (95% CI, 73.4 to 89.4) of patients achieved a stringent complete response. Median duration of response was not estimable. Median progression-free survival (PFS) and overall survival (OS) were not reached; 27-month PFS and OS rates were 54.9% (95% CI, 44.0 to 64.6) and 70.4% (95% CI, 60.1 to 78.6), respectively. Overall response rates were high across all subgroups (95.1%-100%). Duration of response, PFS, and/or OS were shorter in patients with high-risk cytogenetics, International Staging System stage III, high tumor burden, or plasmacytomas. The safety profile was manageable with no new cilta-cel–related cytokine release syndrome and one new case of parkinsonism (day 914 after cilta-cel) since the last report. CONCLUSION At approximately 28 months MFU, patients treated with cilta-cel maintained deep and durable responses, observed in both standard and high-risk subgroups. The risk/benefit profile of cilta-cel remained favorable with longer follow-up.

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 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: Ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T (CAR-T) cell therapy with 2 B-cell maturation antigen (BCMA) targeting single-domain antibodies, demonstrated early, deep, and durable responses in the phase 1b/2 CARTITUDE-1 study in patients (pts) with relapsed/refractory multiple myeloma (RRMM) who had been heavily pretreated (Berdeja, Lancet, 2021). After a median follow-up of 12.4 months, the overall response rate (ORR; as assessed by independent review committee) was 97%, with 67% of pts achieving stringent complete response (sCR). The 12-month progression-free survival (PFS) and overall survival (OS) rates were 77% and 89%, respectively. Here, we report updated results from CARTITUDE-1 with longer duration of follow-up (median 18 months). Methods: Eligible pts had MM and received ≥3 prior therapies or were refractory to a proteasome inhibitor (PI) and an immunomodulatory drug (IMiD), and had received a PI, IMiD, and an anti-CD38 antibody. After apheresis, bridging therapy was permitted. Pts received a single cilta-cel infusion (target dose 0.75×10 6 CAR+ viable T cells/kg; range 0.5-1.0×10 6) 5-7 days (d) after lymphodepletion (300 mg/m 2 cyclophosphamide, 30 mg/m 2 fludarabine daily for 3 d). Primary objectives were to characterize cilta-cel safety, confirm the recommended phase 2 dose (phase 1b), and evaluate cilta-cel efficacy (phase 2). Response was assessed per IMWG criteria by an independent review committee and minimal residual disease (MRD) negativity at 10 -5 by next-generation sequencing. In this combined phase 1b and phase 2 analysis, cytokine release syndrome (CRS; per Lee et al Blood 2014 grading criteria) and neurotoxicity (by CTCAE v5.0) were mapped to the ASTCT criteria for CRS and immune effector cell-associated neurotoxicity (ICANS), respectively. Results: As of Feb 11, 2021, 97 pts (58.8% male; median age 61.0 years [range 43-78]) received cilta-cel. Pts had received a median of 6 (range 3-18) prior lines of therapy; 83.5% were penta-drug exposed, 87.6% were triple-class refractory, 42.3% were penta-drug refractory, and 99.0% were refractory to last line of therapy. ORR was 97.9% (95% CI: 92.7-99.7); 80.4% of pts achieved sCR, and 94.8% achieved very good partial response or better (Figure). The median time to first response was 1 month (range 0.9-10.7), median time to best response was 2.6 months (range 0.9-15.2), and median time to complete response or better was 2.6 months (range 0.9-15.2). The median duration of response was 21.8 months (95% CI: 21.8-not estimable). Of 61 pts evaluable for MRD, 91.8% were MRD negative at the 10 -5 threshold; MRD 10 -5 negativity was sustained for ≥6 months in 44.3% (27/61) of pts and ≥12 months in 18% (11/61) of pts. The 18-month PFS and OS rates were 66.0% (95% CI: 54.9-75.0) and 80.9% (95% CI: 71.4-87.6), respectively. 18-month PFS rates in pts who achieved sustained MRD for ≥6 months and ≥12 months were 96.3% (95% CI: 76.5-99.5) and 100%, respectively. The most common grade 3/4 hematologic AEs in ≥25% of pts were neutropenia (94.8%), anemia (68.0%), leukopenia (60.8%), thrombocytopenia (59.8%), and lymphopenia (49.5%). There were no fatalities related to cytopenias, and no new safety signals with longer follow-up. CRS occurred in 94.8% of pts (mostly grade 1/2); median time to onset was 7 d (range 1-12), and CRS resolved within 14 d in 98.9% of pts. There was no new CAR T-cell neurotoxicity since the previous report. Post cilta-cel infusion, 21 deaths occurred during the study: 0 within first 30 d, 2 within 100 d, and 19 more than 100 d post infusion. Ten deaths were due to disease progression, 6 were treatment-related (as assessed by the investigator), and 5 were due to AEs unrelated to treatment. One patient was retreated with cilta-cel (for progressive disease) and had stable disease (per computerized algorithm) post-retreatment with no incidence of CAR T-cell neurotoxicity. Conclusions: At a longer median follow-up of 18 months, a single cilta-cel infusion led to early, deep, and durable responses in heavily pre-treated pts with MM. Follow-up is ongoing and updated data will be presented. Cilta-cel demonstrated a manageable safety profile with no new safety signals observed with longer follow-up. Further investigations of cilta-cel are ongoing in earlier lines of therapy (CARTITUDE-2 [NCT04133636], CARTITUDE-4 [NCT04181827], and CARTITUDE-5 [NCT04923893]) and in outpatient settings. Figure 1 Figure 1. Disclosures Martin: GlaxoSmithKline: Consultancy; Janssen: Research Funding; Sanofi: Research Funding; Amgen: Research Funding. Usmani: Takeda: Consultancy, Research Funding, Speakers Bureau; SkylineDX: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Janssen Oncology: Consultancy, Research Funding; Bristol-Myers Squibb: Research Funding; EdoPharma: Consultancy; Array BioPharma: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding, Speakers Bureau; GSK: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau; Abbvie: Consultancy. Berdeja: Lilly, Novartis: Research Funding; Poseida, Sanofi, Teva: Research Funding; GSK, Ichnos Sciences, Incyte: Research Funding; Bluebird bio, BMS, Celgene, CRISPR Therapeutics, Janssen, Kite Pharma, Legend Biotech, SecuraBio, Takeda: Consultancy; Celularity, CRISPR Therapeutics: Research Funding; Abbvie, Acetylon, Amgen: Research Funding; EMD Sorono, Genentech: Research Funding. Jakubowiak: Amgen: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Gracell: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Cohen: Novartis: Research Funding; BMS/Celgene: Consultancy; Takeda: Consultancy; Genentech/Roche: Consultancy; Oncopeptides: Consultancy; AstraZeneca: Consultancy; Janssen: Consultancy; GlaxoSmithKline: Consultancy, Research Funding. Hari: Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Karyopharm: Consultancy; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Adaptive Biotech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau; Celgene-BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding, Speakers Bureau. 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. Deol: Kite, a Gilead Company: Consultancy. Lesokhin: bristol myers squibb: Research Funding; Iteos: Consultancy; pfizer: Consultancy, Research Funding; Behringer Ingelheim: Honoraria; Trillium Therapeutics: Consultancy; Serametrix, Inc: Patents & Royalties; Genetech: Research Funding; Janssen: Honoraria, Research Funding. Munshi: Adaptive Biotechnology: Consultancy; Karyopharm: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Legend: Consultancy; Novartis: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Amgen: Consultancy; Pfizer: Consultancy; Bristol-Myers Squibb: Consultancy. O'Donnell: Onocopeptide: Consultancy; Karyopharm: Consultancy; Janssen: Consultancy; Bristol Myer Squibb: Consultancy; Adaptive: Consultancy; Takeda: Consultancy. Stewart: Oncopeptides: Honoraria; Janssen: Honoraria; GSK: Honoraria; BMS: Honoraria; Amgen: Honoraria; Skyline diagnostics: Consultancy; Genomcs England: Membership on an entity's Board of Directors or advisory committees; Tempus Inc.: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; PikSci Inc.: Current holder of individual stocks in a privately-held company, Patents & Royalties; Sanofi Aventis: Honoraria. Schecter: Janssen: Current Employment, Current holder of stock options in a privately-held company. Goldberg: Janssen: Current Employment, Current holder of individual stocks in a privately-held company. Jackson: Janssen: Current Employment; Memorial Sloan Kettering Cancer Center: Consultancy. Yeh: Janssen: Current Employment. Banerjee: Janssen: Current Employment, Current holder of individual stocks in a privately-held company. Allred: Janssen: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Zudaire: Janssen: Current Employment. Deraedt: Janssen: Current Employment. Olyslager: Janssen: Current Employment. Pacaud: Legend Biotech: Current Employment. Lin: Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Vineti: Consultancy; Gamida Cell: Consultancy; Sorrento: Consultancy; Legend: Consultancy; Merck: Research Funding; Janssen: Consultancy, Research Funding. Jagannath: Legend Biotech: Consultancy; Karyopharm Therapeutics: Consultancy; Janssen Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Sanofi: Consultancy; Takeda: Consultancy. OffLabel Disclosure: At the time of abstract submission, cilta-cel is being investigated for the treatment of multiple myeloma but is not yet approved.