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Hematopoietic stem and progenitor cells (HSPCs) comprise a continuum of cells with varying differentiation potential and priming toward specific lineages. During both healthy aging and myeloid malignancies, changes occur in the composition and regulation of HSPCs. In this study, we evaluated human HSPCs obtained from young and elderly healthy donors using single-cell RNA sequencing to identify the transcriptional and regulatory alterations associated with aging at single cell resolution. We then applied this knowledge to the study of specific perturbations associated with the development of myeloid pathologies. We isolated >90,000 bone marrow CD34+ cells from 5 young (18-20 y/o), 3 elderly (>65 y/o) healthy donors, 1 patient with myelodysplastic syndrome (MDS) and 1 patient with acute myeloid leukemia (AML), using fluorescence-activated cell sorting. scRNA libraries were prepared with the 10X chromium platform and sequenced. Finally, bioinformatic analysis was performed using available R and Python algorithms such as Seurat, Palantir and Scenic. First, we characterized HSPC subpopulations in young donors by unsupervised clustering and manual annotation. Taking the previous findings as reference, we then classified the elderly and pathological HSPC using elastic-net regularization prediction models (Figure 1A). Comparison of subpopulations in young and elderly donors confirmed the age-related increase in HSC, as well as reduction of lymphoid progenitors and myelomonocytic compartments. Next, we performed differential expression and pathways analysis to uncover age-associated alterations in the transcriptional profile of cells with the same identity. We found a generalized enrichment in elderly HSPC of pathways activated upon stress and inflammation, such as p53, hypoxia and TNF alpha response. This suggests an age-related increased response to the more inflammatory microenvironment of elderly individuals. On the other hand, young HSPC were enriched for cell cycle activation and proliferation pathways, as well as metabolic processes (Figure 1B). Using trajectory analysis, we recovered 6 differentiation paths present in our young donor's data. When compared to the elderly, the greatest changes occurred along the monocytic trajectory. For some genes, expression differed through the whole trajectory, indicating the existence of original transcriptional alterations already at the HSC compartment. On the other hand, expression of myelomonocytic differentiation markers, such as MPO and CD74, reached lower levels in our elderly HSPC data, pointing towards a loss of capacity for monocytic differentiation in progenitors from elderly individuals. Finally, to identify key transcription factors regulating the progression of differentiation routes, we built gene regulatory networks. Overall, we found lower activation levels for transcriptional programs in the early progenitors from elderly donors. In addition, gene ontology enrichment analysis showed that the active networks in the young were enriched for differentiation-related terms, while networks from the elderly were not. These results also indicate an age-associated loss of differentiation capability. We then applied the same computational tools to analyze aberrant hematopoiesis in samples from 2 patients suffering from myeloid malignancies (MDS and AML). On one hand, we subjected the MDS sample to trajectory analysis, focusing on the erythroid lineage. We observed perturbations in the expression dynamics of genes playing a role in erythropoiesis. In the AML sample, we encountered a significant expansion of the most immature cell compartments (HSC, LMPP and MEP). In addition, GRN reconstruction showed up the specific activity of transcription programs activated by factors deregulated during leukemia, such as ZSCAN18 and GFI1. In conclusion, our work described the transcriptional alterations that occur in early hematopoiesis, both during healthy aging and myeloid pathology. We used multiple approaches, such as the study cellular proportions, differentiation trajectories and GRNs. The inclusion of samples from patients with myeloid pathology provided insights into the potential role of single-cell technologies for understanding and treating hematological malignancies. Figure 1 Figure 1. Disclosures Sanchez-Guijo: Gilead: Consultancy, Honoraria; Celgene/Bristol-Myers-Squibb,: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Takeda: Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding. Diez-Campelo: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Valcarcel: BMS: Consultancy, Honoraria, Speakers Bureau; CELGENE: Consultancy, Honoraria, Speakers Bureau; ASTELLAS: Consultancy, Honoraria, Speakers Bureau; AMGEN: Consultancy, Honoraria, Speakers Bureau; NOVARTIS: Consultancy, Honoraria, Speakers Bureau; TAKEDA: Consultancy, Honoraria, Speakers Bureau; JAZZ: Consultancy, Honoraria, Speakers Bureau; SOBI: Consultancy, Honoraria, Speakers Bureau; SANOFI: Consultancy, Honoraria, Speakers Bureau. Romero: 10X Genomics: Current Employment. Prosper: Janssen: Honoraria; Oryzon: Honoraria; BMS-Celgene: Honoraria, Research Funding.

Understanding the regulation of normal and malignant human hematopoiesis requires a comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Recent studies using scRNA-seq technologies have shed light on the organization of the hematopoietic regulatory microenvironment in the mouse. These studies have resolved some of the controversies regarding the overlap of stromal populations, the description of certain discrete stromal cells as professional, hematopoietic cytokine-producing populations, but also helped to delineate the relationship between specific stromal cell types in the murine BM. Nevertheless, these studies are limited by the number of cells sequenced, potentially hampering our ability to resolve the full spectrum of cellular states and differentiation stages that define the stromal BM microenvironment. Further, knowledge on the conservation of the cellular composition in the human BM stroma is in its infancy due to the difficulty of obtaining high-quality samples with sufficient stromal cell numbers from healthy individuals. This leaves us with two outstanding challenges; how to piece together such different fragments towards a comprehensive molecular atlas and to what extent such an atlas in mice is conserved in the human bone marrow. Here, we dissect the intrinsic organization and the heterogeneity within the endothelial (EC) and mesenchymal cell populations (MSC) governing the BM microenvironment in mouse and human. This was accomplished through customized bioinformatics integration of multiple scRNA-seq datasets along with the inclusion of over 50.000 murine and human bone marrow stromal cells. By these means, we were able to identify new subsets of MSC and EC, but more importantly, to define new molecular markers to identify highly specialized subpopulations of cells in the murine BM microenvironment. Pathway enrichment analysis unveiled multiple, potentially transient cell states defined by differential gene expression and the enrichment of specific functional characteristics. Importantly, 14 EC subsets were characterized by enrichment in pathways known to be essential for endothelial homeostasis maintenance, demonstrating a high degree of specialization in the endothelium. Similarly, 11 transient cell states in the MSC compartment were defined and characterized by their differentiation capacity. Importantly, our deep deconvolution of the heterogeneous mesenchymal and endothelial compartments became feasible only by integrating multiple datasets. Furthermore, based on the knowledge generated in the mouse, we were able to describe how much of the information and targets from the mouse can be of interest in human characterization. This analysis identified the expression of the human orthologs to the murine cluster-defining genes with different degrees of enrichment in the endothelium and mesenchyme. Moreover, some of these shared genes in mice and human stromal cells corresponded to the GO-defining genes of the different clusters identified in the mouse. These findings suggest a significant degree of conservation regarding the cellular states that define the stromal microenvironment in mouse and human. Although additional studies and improved processing of human samples will be required for deep characterization of the human BM microenvironment, these preliminary results validate our integrative cross-species approach. Taken together, our study provides a deeper understanding of the composition and specialization of the BM microenvironment and point towards a significant degree of conservation between species. Moreover, we demonstrate the usefulness of the multi-dataset integration and the customized clustering approach used in our study to improve the resolution of complex tissues and organs. This approach promises to aid in the construction of cell atlases by reducing the resources associated with sequencing that a single lab will need to invest in order to obtain meaningful depth in single-cell analysis. Future studies integrating genome, transcriptome, epigenome, proteome, and anatomical positioning together with functional assays to correlate descriptive phenotypes with functional data will help fully resolve the composition, regulation, and connectivity in the BM microenvironment in health and disease. Figure 1 Figure 1. Disclosures Paiva: Adaptive, Amgen, Bristol-Myers Squibb-Celgene, Janssen, Kite Pharma, Sanofi and Takeda: Honoraria; Bristol-Myers Squibb-Celgene, Janssen, and Sanofi: Consultancy; Celgene, EngMab, Roche, Sanofi, Takeda: Research Funding. Saez: Magenta Therapeutics: Patents & Royalties. Prosper: BMS-Celgene: Honoraria, Research Funding; Janssen: Honoraria; Oryzon: Honoraria.

Background: Chimeric Antigen Receptor-modified T cell (CAR-T) therapies have revolutionized cancer immunotherapy, especially in hematological malignancies. Although great results have been achieved during the last years, long-term efficacy is still compromised in some cases and factors behind CAR-T cell disfunction are not fully understood. Recent studies have shown that the control of CAR expression influences CAR-T fitness and antitumoral efficacy 1. Therefore, we hypothesized that CAR density on the membrane of CAR-T cells could directly affect CAR-T cell function. In this study we perform a functional and genomic analysis of FACS-isolated subpopulations of CAR-T cells with different CAR densities (CAR High and CAR Low). Methodology: Second generation CAR-T cells with 4-1BB costimulatory domain targeting BCMA were generated by lentiviral transduction of αCD3/αCD28 activated T cells that were expanded for 12-14 days in the presence of IL-7/IL-15. Phenotypic analyses were performed by flow cytometry before and after coculture with MM cells. Cytotoxic activity and cytokine production were measured by standard procedures. In vivo antitumoral efficacy was evaluated in xenogeneic tumor models in NSG mice. Transcriptomic (RNA-seq) and epigenetic (ATAC-seq) analysis were performed following stablished protocols 2. Single cell analysis was performed using the Chromium Single Cell Immune Profiling solution from 10x Genomic that allows simultaneous analysis of gene expression and paired T-cell receptors from a single cell. Gene Regulatory Network (GRN) analysis was performed using SimiC, a novel computational method that infers regulatory dissimilarities 3. Results: RNA-seq and ATAC-seq analysis revealed completely different profiles between CAR High- and CAR Low-T cells in both CD4 +and CD8 + cell subsets, with >3500 differentially expressed genes (2086 for CD4 + and 1553 for CD8 +) that were related with increased tonic signaling, T cell activation and proliferation in CAR High-T cells. Functional studies at resting state (before antigen encounter) corroborated that CAR High-T cells presented increased tonic signaling, that lead to a higher basal activation and a more differentiated phenotype with skewed presence of CCR7 +/CD45RA +/CXCR3 + T SCM cells. After antigen-driven activation, increased cytotoxicity and cytokine production was observed in CAR High-T cells, that also presented higher percentage of terminally differentiated effector cells (CCR7 -/CD45RA +), along with increased exhaustion (PD1 +/LAG3 +/TIGIT +). This effect was also observed in the infusion products of CARTBCMA-HCB-01 clinical trial for patients with R/R MM (NCT04309981), where products enriched in CAR High-T cells presented increased cytotoxic activity. Although no significant differences were observed in the antitumoral efficacy in vivo, CAR Low-T cells presented increased persistence, suggesting that higher CAR levels could reduce long-term efficacy. Further characterization of CAR-T cells at single cell level (scRNA-seq) showed enrichment of CAR High-T cells in activated CD4 + and exhausted CD8 + cell clusters. The analysis of regulatory dissimilarities driven by different CAR densities with SimiC revealed an increased activity of the regulon associated to NR4A1 transcription factor (a well-known TF driving T cell exhaustion 4) in CAR High-T cells, providing mechanistic insights of the regulatory networks behind differential functionality of CAR High-T cells. Finally, to evaluate the impact of CAR density in the clinical outcome of CAR-T therapies, we developed a gene signature associated to increased CAR density, that was applied to transcriptomic data available from public studies 5. We score the infusion products of several clinical trials testing CTL019 (NCT01029366, NCT01747486 and NCT02640209) and we observed an enrichment on CAR High signature in the products from non-responder patients. Conclusions: Our data demonstrate that CAR density on the membrane of engineered T cells plays important roles in CAR-T activity with a significant impact on clinical outcome. Moreover, the comprehension of regulatory mechanisms driven by CAR densities at the single cell level offer an important tool for the identification of key regulatory factors that could be modulated for the development of improved therapies. Figure 1 Figure 1. Disclosures Rodríguez-Otero: Oncopeptides: 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, Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees; Abbvie: 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; GlaxoSmithKline: Consultancy, 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; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel and other expenses. Paiva: Bristol-Myers Squibb-Celgene, Janssen, and Sanofi: Consultancy; Adaptive, Amgen, Bristol-Myers Squibb-Celgene, Janssen, Kite Pharma, Sanofi and Takeda: Honoraria; Celgene, EngMab, Roche, Sanofi, Takeda: Research Funding. San-Miguel: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Merck Sharpe & Dohme, Novartis, Regeneron, Roche, Sanofi, SecuraBio, Takeda: Consultancy, Other: Advisory board. Prósper: Oryzon: Honoraria; Janssen: Honoraria; BMS-Celgene: Honoraria, Research Funding.

Background: The immune system reacts to viral infection with cellular and humoral responses. Thus, myelo- and lympho-suppression caused by cancer itself as well as cytotoxic treatment may pose a challenge to COVID-19 patients with solid and hematological tumors, but severe events from initial onset of COVID-19 appear to be more frequent in blood malignancies vs other cancer types. Preliminary data showed lower neutrophil and lymphocyte counts in COVID-19 patients bearing hematological cancer, but there are conflicting results supporting that both worsening of lymphopenia during COVID-19 and its depth prior to infection had a beneficial impact on survival. Thus, greater knowledge on the immune status of hematological patients may be useful to optimize prevention, risk stratification and treatment strategies. Aim: Analyze the immune status of COVID-19 patients with or without solid and hematological cancer. Methods: We use multidimensional flow cytometry (MFC) to analyze immune profiles in peripheral blood samples of 515 COVID-19 patients at presentation. Data was analyzed with a semi-automated pipeline that performs batch-analyses of MFC data to avoid variability intrinsic to manual analysis, and unveils full cellular diversity based on unbiased clustering. In 14 cases, deep immunophenotyping of B- and T-cells was performed and six myeloid- and dendritic-cell subsets were FACSorted for transcriptome analysis using RNAseq. Results: Of the 515 COVID-19 patients, 15 and 10 had solid and hematological tumors, respectively. Those with hematological cancer showed similar frequency of hospitalization than those with solid tumors (90% and 93%, respectively), which was modestly higher to that observed in persons without an active tumor (76%). By contrast, the frequency of hematological cases requiring intensive care (50%) and dying from COVID-19 (30%) was significantly higher to that observed in patients with no active tumor (5.5% and 4%, respectively), or with solid cancer (both 0%). Based on semi-automated analysis of MFC data, we systematically quantified a total of 19 cell types in PB that included 6 myeloid and 13 lymphoid subsets. Patients with hematological malignancies displayed altered immune profiles with significantly decreased absolute numbers of classical and intermediate monocytes, immunoregulatory and cytotoxic NK cells, double-negative, double-positive, CD4 and CD56- γδ T cells, as well as of mature B cells when compared to those with no tumor. Unsupervised hierarchical analysis of RNAseq data from basophils, myeloid and plasmacytoid dendritic cells, classical and non-classical monocytes and neutrophils showed considerable clustering of samples from hematological cases. Furthermore, a variable number of differentially expressed genes was found in all six cell types between COVID-19 patients with or without blood cancer. Genes related to NF-κB and STAT transcription factors as well as genes encoding toll-like receptors and proinflammatory interleukin receptors, all of which described to be implicated in the response and evasion of innate sensing by coronaviruses, were differentially expressed in many of these cell types. Deep phenotypic characterization of T- and B-cell compartments in PB of COVID-19 patients with (N = 4) or without (N = 10) hematological cancer showed that the relative distribution of antigen-dependent maturation stages within the T-cell compartment was generally similar between both groups. However, some hematological cases displayed profound alterations in virtually all of the 16 B-cell subsets analyzed, with a notorious reduction in memory B cells expressing IgG and IgA subclasses. We next compared immune responses from presentation to last follow-up in COVID-19 patients with hematological cancer and favorable (N = 3) vs fatal (N = 3) outcome. Interestingly, we found opposite kinetics in myeloid cell types such as eosinophils and neutrophils, decreasing numbers of various T cell subsets, as well as lower mature B cells and circulating PCs at presentation together with a decrease in B cell counts in deceased cases. Conclusions: Our study exposes for the first time that hematological patients show a constellation of immune alterations that could compromise the response to the infection caused by SARS-CoV-2, suggesting an association between impaired immune responses and poorer outcomes in COVID-19 patients with hematological malignancies. Disclosures Paiva: SkylineDx: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Adaptive: Honoraria; Amgen: Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Kite: Consultancy; Sanofi: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau.

Background: Previous studies showed that MRD- pts after transplant may have detectable monoclonal protein through IFx, creating confusion regarding their prognostication. That said, MRD assessment in these pts was not performed with next generation techniques nor or in later time points. Additional discordances have been identified between multiparameter flow cytometry (MFC) and NGS, which were confirmed in recent analyses comparing NGF vs NGS. Aim: To characterize discordances between flow cytometry vs NGS and IFx through the investigation of immature B cells sharing the same B-cell receptor immunoglobulin (BcR IG) with MM cells. Methods: Progression-free survival (PFS) according to negative vs positive IFx was analyzed in 219 MRD- pts by MFC after transplant, enrolled in the GEM2000 and GEM2005MENOS65 trials. The same comparison was performed in 205 MRD- pts by NGF after consolidation in the GEM2012MENOS65 trial. MRD detection by NGS was compared to MFC or NGF in 140 and 104 cases, respectively. We performed NGS of BcR IG gene rearrangements (mean: 69,975 sequences) and WES (mean depth: 145x) in a total of 68 B cell samples isolated from the bone marrow (BM) of 7 MM MRD- pts by NGF after treatment (GEM2012MENOS65). These were intentionally selected to avoid contamination from MM plasma cells (PCs) during sorting of CD34 progenitors, B cell precursors, mature B cells and normal PCs. We investigated these populations for the presence of clonotypic BcR IG and somatic mutations detected in MM PCs sorted at diagnosis, using T cells as germline control. In another 10 untreated MM pts, we performed scRNA/BcRseq of total BM B cells and PCs (n=52,735), to investigate if the clonotypic BcR IG of MM PCs was detectable in other B cell stages defined by their molecular phenotype. Results: Among 219 MRD- pts by 4 color MFC after transplant, 76 (35%) showed positive IFx and identical PFS to those with negative IFx (medians of 63 vs 66 months, p=0.96). Similarly, 23/205 (11%) MRD- pts by NGF after consolidation showed positive IFx and identical PFS to those with negative IFx (4y rates of 87% vs 78.5%, p=0.35). Thus, albeit the higher sensitivity of NGF and the later time point (consolidation), approximately 1/10 MRD- pts by NGF continued showing positive IFx, and their outcome was as favorable as that of MRD- cases in CR. We then investigated discordances between flow cytometry and NGS. Among 35 MRD- pts by 4 color MFC, 21 (60%) were MRD+ by NGS, whereas 8/44 (18%) MRD- cases by NGF were MRD+ by NGS; only one of the latter 8 pts relapsed so far. Noteworthy, 9/29 MRD- pts by MFC or NGF showed MRD levels ≥10-4 by NGS, suggesting that other factors beyond sensitivity were accounting for the discordances between MRD assessed by MFC/NGF (in the PC compartment) vs NGS (in whole BM samples). NGS of BcR IG gene rearrangements in sorted BM cells from MRD- pts by NGF, uncovered the presence of MM clonotypes in normal PCs (4/7 pts) and in B cells (5/7 pts) at low frequencies (mean of 0.31% in both, range: 0.003% - 9.4%). These findings were confirmed by scRNA/BCRseq, which unveiled in 10/10 pts that clonotypic cells were confined mostly but not entirely within PC clusters. We next performed WES to investigate if genetic abnormalities present in MM PCs at diagnosis were detectable in the same BM cells sorted after treatment in MRD- pts. Surprisingly, 41/201 (20%) somatic mutations present in diagnostic MM PCs were detectable in CD34 progenitors (n=6/7), B-cell precursors (n=4/7), mature B cells (n=5/7) and phenotypically normal PCs (n=4/7). All somatic mutations shared by MM PCs and sorted BM normal cells were non-recurrent, and genes recurrently mutated in MM (ATM, DIS3, KRAS, LTB, MAX,) as well as copy number alterations (CNA) found in MM PCs, were undetectable in normal cells. Conclusions: Albeit more-sensitive NGF, 11% of MRD- pts continue showing positive IFx. This should not be regarded as a false-negative result, since these pts have similar outcome to those in CR and MRD-. Our findings also suggest that, at least in some pts, discordances between NGF and NGS could be attributed to immature clonotypic cells. However, these lack most somatic mutations and CNA found in MM PCs, and therefore cannot drive disease relapse. This would explain the favorable outcome of MRD- pts by NGF despite positive NGS. From a pathogenic standpoint, our study proposes that a mutated and clonally expanded lymphopoiesis precedes secondary driver mutations or CNA leading to the expansion of MM PCs. Disclosures García-Sanz: Janssen: Honoraria, Other: Travel/accommodations/expenses; Novartis: Consultancy; Amgen: Honoraria; Gilead: Other: Research grants, Research Funding; IVS (Biomed 2-Euroclonality): Patents & Royalties: and other intellectual property; Takeda: Consultancy, Honoraria, Other: Travel/accommodations/expenses. Mateos:Janssen: 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; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: 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; 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; Adaptive: 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; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: 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; GlaxoSmithKline: Honoraria. Chatzidimitriou:Janssen: Research Funding. San-Miguel:Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees. Paiva:Amgen: Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Kite: Consultancy; SkylineDx: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Adaptive: Honoraria; Sanofi: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau.

In MM patients relapsing after MRD-negativity, the disease could reemerge from immature cells or from undetectable MRD. However, it remains unknown if immature cells have the same genetic background as MM plasma cells (PCs), as well as the amount of MRD that persists below the limit of detection (LOD) of next-generation techniques. To obtain further insight, we compared the biological landscape of MM PCs at diagnosis to that of CD34 progenitors, B cells and normal PCs isolated from patients with negative MRD by next-generation flow (NGF) after treatment. We performed whole-exome sequencing (WES, mean depth: 90x) with the 10XGenomics Exome Solution for low DNA-input as well as deep NGS of B-cell receptor immunoglobulin (BcR IG) gene rearrangements (mean, 69,975 sequences), in a total of 68 cell-samples isolated from the bone marrow (BM) of 7 MM patients with MRD-negativity by EuroFlow NGF after induction with VRD and auto-transplant (GEM2012MENOS65 trial). Patients with negative MRD were intentionally selected to avoid contamination with MM PCs during sorting of CD34 progenitors, B-cell precursors, mature B cells and normal PCs after induction and transplant. We investigated in these populations the presence of somatic mutations and clonotypic BcR Ig rearrangements detectable in MM PCs sorted at diagnosis, using peripheral blood T cells as germline control. We also performed WES in matched diagnostic MM PCs and MRD cells persisting after VRD induction in 14 cases as control. In another 6 patients with untreated MM, we performed single-cell RNA and BcR IG sequencing (scRNA/BcRIGseq) of total BM B cells and PCs (n=16,380) to investigate before treatment, if the clonotypic BcR IG sequence of MM PCs was detectable in other B cell stages defined by their molecular phenotype. We used multidimensional flow cytometry (MFC) to investigate the frequency of B cell clonality in BM samples from a larger series of 195 newly-diagnosed MM patients, prospectively enrolled in the GEM-CLARIDEX trial. Somatic mutations present in diagnostic MM PCs were detectable in the lymphopoiesis of 5/7 patients achieving MRD-negativity after treatment. In one case, out of 55 mutations present in diagnostic MM PCs, a single mutation in PCSK1N (VAF: 0.30) was detectable in normal PCs. In the other four patients, a total of 85 mutations were present in MM PCs and up to 10 (median VAF, 0.16) were found all the way from CD34 progenitors into B-cell precursors, mature B cells and normal PCs, but not in T cells. Of note, most mutations were reproducibly detected in each cell type after induction and after transplant. All somatic mutations shared by MM PCs and normal cells were non-recurrent, and genes recurrently mutated in MM (eg. ACTG1, ATM, DIS3, FAM46C, KRAS, LTB, MAX, TRAF3) were found in MM PCs but never in normal cells. Copy number alterations (CNA) were found only in MM PCs. By contrast, up to 513/827 (62%) mutations and 48/67 (72%) CNA were detectable in matched diagnostic MM PCs and persistent MRD cells, indicating that the few somatic variants present in normal cells were unlikely related to contaminating MRD below NGF's LOD. Accordingly, MM clonotypic BcR IG rearrangements were detectable in normal PCs (4/7patients) and in immature B cells (5/7 patients) but at much lower frequencies (mean of 0.02% in both). Of note, 9 additional clonotypes (mean 8.4%) were found in MM PCs of 5/7 patients (range, 1-3). scRNR/BcRIGseq unveiled that clonotypic cells were confined mostly but not entirely within PC clusters, and that in 1 patient another clonotype was detectable in mature B cells. Accordingly, using MFC we found in a larger series that 25/195 (13%) of newly-diagnosed MM patients display B-cell clonality (median of 0.7% BM clonal B cells, range 0.02%-6.3%). In conclusion, we show for the first time that MM patients bear somatic mutations in CD34 progenitors that specifically differentiate into the B cell lineage, likely before the disease onset. Because diagnostic, MRD (and relapse) MM PCs display great genetic similarity, these results suggest that undetectable MRD Disclosures Puig: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda, Amgen: Consultancy, Honoraria; The Binding Site: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Martinez-Lopez:BMS: Honoraria, Other: Advisory boards; Janssen: Honoraria, Other: Advisory boards and Non-Financial Support ; Amgen: Honoraria, Other: Non-Financial Support ; Celgene: Honoraria, Other: Advisory boards and Non-Financial Support ; Incyte: Honoraria, Other: Advisory boards; Novartis: Honoraria, Other: Advisory boards; VIVIA Biotech: Honoraria; F. Hoffmann-La Roche Ltd: Honoraria. Lahuerta:Takeda, Amgen, Celgene and Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rosinol Dachs:Janssen, Celgene, Amgen and Takeda: Honoraria. Bladé:Jansen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. Mateos:EDO: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive: Honoraria; Takeda: 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; Pharmamar: Membership on an entity's Board of Directors or advisory committees. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Paiva:Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau.

Deregulation of long non-coding RNAs (lncRNAs) is emerging as a common feature of different human tumors and their investigation may uncover novel biomarkers and oncogenic mechanisms. Previous studies have suggested that the alteration of some lncRNAs may play an important role in the pathogenesis of multiple myeloma (MM); however, the complete expression landscape of lncRNAs has not been elucidated. In the present work we characterized the lncRNAs transcriptome of MM and determined the potential involvement of lncRNAs in the pathogenesis and clinical behavior of MM. To characterize the MM transcriptome, we performed paired-end strand-specific RNA sequencing (ssRNA-seq) in 38 purified plasma cell (PC) samples from MM patients and in 3 bone marrow PCs (BMPCs) of healthy donors, as well as in distinct normal B-cell populations (Naïve, Centroblasts, Centrocytes, Memory and Tonsilar PCs). We identified 40,511 novel lncRNAs that were expressed, accounting for more than half of MM transcriptome (56%). This group of novel lncRNAs together with previously annotated lncRNAs comprised most (82%) of the MM transcriptome. We studied the transcriptional heterogeneity in MM samples and observed that lncRNAs showed a much more heterogeneous expression than coding genes, suggesting that these elements could contribute to the biological heterogeneity of the disease. Moreover, to determine differentially expressed genes, each MM patient was compared to normal BMPCs, detecting 19,886 lncRNAs deregulated (10,351 overexpressed and 9,535 downregulated) in more than 50% of patients. We then analyzed the transcriptional dynamics of MM considering the different stages of B-cell differentiation and focused on a group of 989 lncRNAs that were upregulated specifically in plasma cells from MM in comparison with the rest of B-cell stages (MM-specific lncRNAs). Next, we aimed to determine whether upregulation of MM-specific lncRNAs in MM was under epigenetic control so we analyzed the distribution of six histone modifications with non-overlapping functions (H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K27me3, and H3K9me3) of within the lncRNAs of interest by ChIP-seq in MM cases as compared to normal B cell subtypes. We detected 89 lncRNAs with de novo epigenomic activation. These data suggest an epigenetic rewiring in MM where the loci of most MM-specific lncRNAs are in an inactive state in normal cells and become active in MM. We focused on a specific lncRNA, LINC-SMILO, de novo epigenetically active and expressed in MM cells to determine whether upregulation of this lncRNA could play a role in the pathogenesis of the disease. Knockdown of LINC-SMILO in 3 different MM cell lines (MM.1S, MM.1R and KMS-11) using two different shRNAs, resulted in reduced proliferation and induction of apoptosis of myeloma cells. Using low input RNA-seq (MARS-seq), we found that inhibition of LINC-SMILO was associated with activation of ERVs (Endogenous retroviruses) and increase in interferon (IFN) induced genes and activation of IFN pathways, essential for MM cells survival. Finally, we aimed to determine whether the use of specific lncRNAs could improve the current prognostic stratification of MM patients using the IA11 release of CoMMpass data. We analyzed the prognostic value of lncRNAs using COX regression analysis and Backward elimination of Stepwise regression analysis, obtaining that the overexpression of the lncRNA PDLIM1P4 together with 1q amplification and 17p deletion stratified MM patients in three different risk groups (Figure 1). In summary, our study shows that the lncRNA transcriptome is widely altered in MM and suggests that some of the identified lncRNAs have marked prognostic influence and can be used as potential therapeutic targets for MM. Disclosures Paiva: Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Melnick:KDAc Therapeutics: Membership on an entity's Board of Directors or advisory committees; Constellation Pharmaceuticals: Consultancy; Epizyme: Consultancy; Janssenn: Research Funding.

Background: The transformation from a normal to a cancer cell is driven by the multistep acquisition of genetic alterations. Recently, shared mutations between clonal B cells in MBL/CLL and CD34+ hematopoietic progenitor cells (HPC) have been identified. Similarly, a HPC origin of BRAFV600E mutations in hairy cell leukemia (HCL) has been uncovered, strengthening the notion that at least a fraction of somatic mutations may occur in CD34+ HPC before the malignant transformation of some B cell neoplasms. Since almost all WM patients have mutated MYD88L265P, it is worthy to investigate if this disease follows a similar pathogenic process than that of MBL/CLL or HCL. Aim: Define the cellular origin of WM by comparing the genetic landscape of WM cells to that of CD34+ HPC, B cell precursors and residual normal B cells. Methods: We used FACSorting to isolate 57 cell subsets from bone marrow (BM) aspirates of 10 WM patients: CD34+ HPC, B cell precursors, residual normal B cells (if detectable), WM B cells, plasma cells (PCs) and T cells (germline control). Whole-exome sequencing (WES, mean depth 79x) was performed with 10XGenomics Exome Solution for low DNA-input due to limited numbers of some cell types. Single-cell RNA and B-cell receptor sequencing (scRNA/BCRseq) was performed in total BM B cells and PCs (n=32,720) from 3 IgM MGUS and 2 WM patients. Accordingly, the clonotypic BCR detected in WM cells was unbiasedly investigated in all B cell maturation stages defined according to their molecular phenotype. In parallel, MYD88p.L252P (orthologous position of the human L265P mutation) transgenic mice were crossed with conditional Sca1Cre, Mb1Cre, and Cγ1Cre mice to selectively induce in vivo expression of MYD88 mutation in CD34+ HPC, B cell precursors and germinal center B cells, respectively. Upon immunization, mice from each cohort were necropsied at 5, 10 and 15 months. Results: All 10 WM patients showed MYD88L265P and 3 had mutated CXCR4. Notably, we found MYD88L265P in B cell precursors from 1/10 cases and in residual normal B cells from 4/10 patients, which were confirmed by ASO-PCR and ddPCR. Indeed, these more sensitive methods detected MYD88L265P in B cell precursors from 6/10 cases and in residual normal B cells from 6/10 patients. CXCR4 was simultaneously mutated in B cell precursors and WM B cells from one patient. Overall, CD34+ HPC, B-cell precursors and residual normal B cells shared a median of 2 (range, 0-45; mean VAF, 0.13), 3 (range, 1-44; mean VAF, 0.168), and 6 (range, 1-56; mean VAF, 0.29) somatic mutations with WM B cells; some being found all the way from CD34+ HPC to WM B cells and PCs. Interestingly, concordance between the mutational landscape of WM B cells and PCs was A median of 18 mutations (range, 3-26; median CCF and range, 0.72 [0.07 - 1]) were unique to WM cells. Importantly, clonal mutations in WM B cells were undetectable in normal cells. Thus, the few WM subclonal mutations observed in patients' lymphopoiesis could not result from contamination during FACSorting since in such cases, WM clonal mutations would become detectable in normal cells. Furthermore, copy number alterations (CNA) present in WM cells were undetectable in normal cells. scRNA/BCRseq unveiled that clonotypic cells were confined mostly within mature B cell and PC clusters in IgM MGUS, whereas a fraction of clonotypic cells from WM patients showed a transcriptional profile overlapping with that of B cell precursors. scRNA/BCRseq also uncovered transcriptional differences between clonal B cells from IgM MGUS vs WM patients (eg, proliferation, metabolism). In mice, induced expression of mutated MYD88 led to a moderate increase in the number of B220+CD138+ plasmablasts and B220-CD138+ PCs in lymphoid tissues and BM, but no signs of clonality or hematological disease. Interestingly, such increment was more evident in mice with activation of mutated MYD88 in CD34+ HPC and B-cell precursors vs mice with MYD88 L252P induced in germinal center B cells. Conclusions: We show for the first time that WM patients have somatic mutations, including MYD88L265P and CXCR4 at the B cell progenitor level. Taken together, this study suggests that in some patients, WM could develop from B cell clones carrying MYD88L265P rather than being the initiating event, and that other mutations or CNA are required for the expansion of B cells and PCs with the WM phenotype. Disclosures Motta: Roche: Honoraria; Janssen: Honoraria. Rossi:Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Other: Advisory board; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Alexion: Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria; Jazz: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Garcia-Sanz:Takeda: Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Gilead: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Amgen: Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Self: Patents & Royalties: BIOMED-2 PRIMERS FOR CLONALITY ASSESSMENT; IVS technologies: Consultancy, Patents & Royalties; Novartis: Research Funding. Roccaro:Transcan2-ERANET: Research Funding; European Hematology Association: Research Funding; Amgen: Other; AstraZeneca: Research Funding; Celgene: Other; Janssen: Other; Italian Association for Cancer Research (AIRC): Research Funding. San-Miguel:Amgen, BMS, Celgene, Janssen, MSD, Novartis, Takeda, Sanofi, Roche, Abbvie, GlaxoSmithKline and Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees. Paiva:Sanofi: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; SkylineDx: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Adaptive: Honoraria; Amgen: Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Kite: Consultancy.

Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis. Genetic alterations do not fully explain the molecular pathogenesis of the disease, indicating that other types of lesions, such as transcriptional aberrations, may play a role in its development. Moreover, MDS prevalence is almost exclusive to older patients, suggesting that elderly-related alterations may predispose to the development of this clinical entity. Thus, study of the transcriptional lesions occurring in the aging-MDS axis could shed some light of the molecular bases of the disease. To characterize the transcriptional profile of HSCs in aging and MDS, we isolated CD34+, CD38-, CD90+, CD45RA- cells from 11 untreated MDS patients with unilineage and multilineage dysplasia (median of 75 y/o), as well as from 16 young and 8 elderly healthy donors (median of 21 and 70 y/o, respectively), and their expression profile was analyzed using MARS-seq. Unsupervised principal component analysis demonstrated that the three groups of HSCs clustered separately, indicating that different expression profiles characterize healthy young and elderly, and MDS-associated HSCs. To better understand the gene expression deregulation of HSCs, we analyzed the transcriptional dynamisms along the aging-MDS axis, detecting groups of genes following different patterns of expression. Some gene clusters showed exclusive alteration either in aging or in the progression from elderly HSCs to MDS-HSCs, other groups of genes presented a continuous alteration along the axis, and some displayed opposite regulation in aging and in the transition to MDS (Figure 1). Genes showing specific downregulation in aging were involved in DNA damage sensing and repair, and in cell cycle regulation, whereas genes overexpressed in this process were enriched in apoptosis regulators and in cancer-associated genes, including AML-related factors. These findings indicate that transcriptional changes in aging may predispose for MDS and AML, and potentially other malignancies. Interestingly, we detected a group of genes in which the age-mediated upregulation of gene expression was reversed to that of young HSCs in MDS, indicating a "rejuvenation" profile of malignant HSCs. These genes were involved in response to inflammation, to different types of stress conditions such as hypoxia or radiation, and to cytokines. Elderly HSCs may upregulate such genes in response to the known inflammatory microenvironment of elderly bone marrow. Intriguingly, the decrease in expression detected in MDS suggests that malignant HSCs lose the ability of reacting to such stimuli, possibly favoring their survival in a hostile microenvironment. Finally, the analyses performed allowed for the identification of genes showing MDS-specific deregulation. Genes specifically overexpressed in MDS compared to normal (both young and elderly) HSCs, we enriched in transcriptional and epigenetic regulators, and among them, we detected the presence of DDIT3/CHOP, a member of the CCAAT/enhancer-binding protein (C/EBP) family of transcription factors. To determine its potential effects on hematopoietic deregulation, DDIT3 was exogenously overexpressed in healthy HSCs. Notably, its upregulation produced an erythroid bias in an ex-vivo differentiation system, with an increase in the percentage of erythroblasts and a decrease in granulocytes and monocytes compared to HSCs transduced with the empty vector. Transcriptomic analysis of transduced HSCs not subjected to differentiation demonstrated how DDIT3 overexpression produced an erythroid-prone state of HSCs, suggesting it may act as a pioneer factor in MDS-HSCs. Furthermore, gene set enrichment analysis showed that DDIT3 overexpression produced an MDS-like transcriptional profile, suggesting this factor may be key in the acquisition of the disease. Altogether, our results demonstrate that HSCs undergo transcriptional changes in the aging-MDS axis that may alter their intrinsic functions as well as their response to the microenvironment, ultimately contributing to the acquisition of the disease. In particular, our data show that DDIT3 may be a potential driver of MDS transformation. Disclosures Paiva: Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Díez-Campelo:Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Background: Despite significant improvements in the treatment of MM, the outcome of patients with HR cytogenetics remains poor despite similar complete remission (CR) rates as compared to SR cases. Relapses among patients in CR are attributed to the persistence of MRD, but knowledge about the impact of MRD in patients with SR and HR cytogenetics, treated with modern therapies and monitored with next-generation techniques, is limited. Similarly, there is virtually no data about in vivo mechanisms of resistance in SR and HR MM; however, since MRD represents those very few cells that are resistant to treatment, it could be hypothesized that profiling MRD cells may shed light into the mechanisms of resistance in both SR and HR patients. Aim: To determine the clinical impact of MRD in MM patients with SR vs HR cytogenetics, and to identify transcriptional mechanisms determining MRD resistance by investigating the transcriptome of MRD cells in both patient subgroups. Methods: This study was conducted in a series of 390 patients enrolled in the PETHEMA/GEM2012 trial (6 induction cycles with VRD followed by ASCT and 2 courses of consolidation with VRD). FISH was analyzed on CD138 purified PCs at diagnosis. MRD was predefined to be prospectively assessed following induction, transplant and consolidation, using next-generation flow (NGF) according to EuroFlow. In 40 patients [28 with SR and 12 with HR cytogenetics: i.e., t(4;14), t(14;16) and/or del(17p)], diagnostic and MRD tumor cells persisting after VRD-induction were isolated by FACS according to patient-specific aberrant phenotypes. Due to the small number of sorted MRD cells (median of 25,600) we used a 3' end RNAseq method optimized for generating libraries from low-input starting material (MARSeq). Differential expression analyses were performed with DESeq2 R package. Results: At the latest time-point in which MRD was assessed, MRD-positive rates progressively increased (p =.006) from SR patients (148/300, 49%) to cases with t(4;14) (24/42, 57%) and del(17p) (29/38, 76%). Furthermore, MRD levels were significantly superior in patients with del(17p) compared to SR FISH (0.02% vs 0.006%, p =.009), while MRD levels in patients with t(4;14) (0.004%) were similar to those in SR MM. Only 10 patients had a t(14;16) and 4 were MRD-positive. Among patients achieving MRD-negativity (.05). Conversely, 3-year PFS rates for MRD-positive patients decreased from those having SR FISH to those with t(4;14) and del(17p) (59%, 46% and 24%, respectively), with statistically significant differences between the first and the latest subgroups (p Since clearance of MRD notably lowered the risk of relapse and persistence of MRD significantly shortened the PFS in each cytogenetic group (p ≤.001), we investigated the unique features of MRD cells persisting after VRD-induction by comparing their transcriptome to that of patient-matched tumor cells at diagnosis (n=40). Accordingly, MRD cells showed 763 genes significantly deregulated (Padj Conclusions: This is one of the largest studies integrating patients' cytogenetics and MRD status. Our results, based on intensive treatment and MRD monitoring using NGF, unveil that achieving MRD-negativity may overcome the poor prognosis of HR cytogenetics. By contrast, persistent MRD significantly reduces PFS rates, particularly in patients with del(17p). Interestingly, MRD cells from SR and HR patients may have different transcriptional mechanisms leading to VRD resistance, and further understanding of these could provide knowledge on how to eradicate MRD in both patient subgroups. Disclosures Puig: Takeda: Consultancy, Honoraria; Celgene: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Garcia-Sanz:Affimed: Research Funding. Martinez-Lopez:BMS: Research Funding; Pfizer: Research Funding; Vivia: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Novartis: Research Funding. Oriol:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Rios:Amgen, Celgene, Janssen, and Takeda: Consultancy. De La Rubia:Ablynx: Consultancy, Other: Member of Advisory Board. Mateos:GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Lahuerta:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: 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; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Bladé:Janssen: Honoraria. San-Miguel:Amgen: Honoraria; BMS: Honoraria; Novartis: Honoraria; Sanofi: Honoraria; Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria.

Background: MM and AL are the two most common malignant monoclonal gammopathies. Both diseases result from the accumulation of clonal PCs, but their clinical behavior is significantly different suggesting fundamental differences in disease biology. Previous attempts to identify genetic hallmarks that could explain such differences have been unsuccessful. Furthermore, it is unknown if MM and AL arise from the same or different normal PC counterparts. Aim: To define a transcriptional atlas of the normal PC development in peripheral blood (PB) and bone marrow (BM) for comparison with the transcriptional programs of clonal PCs in MM and AL. Methods: A total of 93 subjects were studied. In 7 healthy adults (HA), PB PCs were phenotypically sorted according to heavy-chain isotypes (IgG, IgA and IgM). In addition, 5 different BM PCs subsets were isolated based on the differential expression of CD19, CD39, CD81 and CD56, due to their ascribed role in dissecting unique BM PC differentiation states. Clonal PCs from patients with MM (n=38) and AL (n=41) were isolated by FACS according to patient-specific aberrant phenotypes. Due to small numbers of PCs sorted from each subset in HA and clonal PCs in AL patients, we used an RNAseq method optimized for limited cell numbers. Differential expression across all pairwise comparisons between groups was analyzed with Deseq2 R package followed by k-means clustering of genes in R. Single-cell RNAseq (scRNAseq, 10xGenomics) was performed in a total of 35,910 PCs from 3 HA, 2 MM and 2 AL. We used Seurat R package to remove batch effect followed by canonical correlation to perform an integrated analysis of all single PCs from HA, MM and AL subjects. Results: Principal component analysis of RNAseq data unveiled two major clusters of normal PCs: those in PB and those in BM (with some transcriptional diversity between CD19+ and CD19- PCs), whereas the CD19+CD39+CD81+CD56- BM subset co-localized with PB and CD39- BM PCs (Panel A). Clonal PCs from MM and AL patients clustered together, and both displayed some transcriptional variance related to the spatial location of normal PCs (i.e. PB or BM). In total, 2174 genes were found significantly deregulated after cross-comparing the 10 PC groups (adj.p-value1) and semi-supervised k-means clustering unveiled 8 transcriptional modules (Panel B). Namely, the transition from PB into BM PCs was characterized by genes related to proliferation (clusters 1 & 2), whereas CD39+ and CD39- BM PC subsets differed on the expression of genes associated with proliferation, homing, and metabolism (1, 2, 4 & 6). Thus, CD19+CD39+CD81+CD56- BM PCs emerged as a novel subset that bridges new-born PB with long-lived (CD39-) BM PCs. Interestingly, clonal PCs from MM and AL shared transcriptional programs related to quiescence (5 & 6) with long-lived BM PCs; however, skewing of polyclonal immunoglobulin gene expression (3) and active gene transcription (8) emerged as hallmarks of the neoplastic transformation from normal, long-lived PCs into clonal PCs. That notwithstanding, the later displayed expression levels of the proliferation and homing transcriptional modules (1 & 4) similar to new-born PB and CD39+ BM PCs. Of note, a small transcriptional cluster of genes related to ribosome biogenesis (7) was significantly more expressed in MM than AL. These findings led us to integrate scRNAseq profiles of normal and clonal BM PCs from MM and AL patients, to define PC clusters based on their transcriptional program rather than their normal vs malignant status (Panel C). This strategy unveiled 11 different PC clusters with unequal distribution between groups. Thus, more than half of clonal PCs in MM and AL were assigned to a cluster that is also predominant in normal PCs (1). By contrast, other clusters with a transcriptional program similar to that of new-born PCs (2 & 5) became rarer in MM and AL. Furthermore, a cluster of PCs with an immature-like phenotype (6) was detectable in MM but almost absent in AL. Conclusions: This is the first integrated analysis of the transcriptional programs of normal PC subsets and clonal PCs in MM and AL, both at the bulk and single-cell levels. Our results unveil shared and exclusive transcriptional states in normal and clonal PCs, together with unique differences between clonal PCs in MM and AL. Thus, we provide here a fundamental resource to understand normal PC development and the cellular origin of both malignant monoclonal gammopathies. Figure Figure. Disclosures Puig: Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Ocio:Pharmamar: Consultancy; AbbVie: Consultancy; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy; BMS: Consultancy; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Sanofi: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Mundipharma: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Array Pharmaceuticals: Research Funding. Oriol:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Martinez Lopez:Bristol Myers Squibb: Research Funding, Speakers Bureau; Janssen: Research Funding, Speakers Bureau; Novartis: Research Funding, Speakers Bureau; Celgene: Research Funding, Speakers Bureau. Mateos:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lahuerta:Takeda: 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; Celgene: 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. San-Miguel:Sanofi: Consultancy; Takeda: Consultancy; Novartis: Consultancy; MSD: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Brystol-Myers Squibb: Consultancy; Amgen: Consultancy; Roche: Membership on an entity's Board of Directors or advisory committees.

Abstract 1520 Histone deacetylases (HDACs) have been identified as therapeutic targets due to their regulatory function in chromatin structure and organization. Here we analyzed the therapeutic effect of LBH589 or panobinostat, a class I-II HDAC inhibitor, in acute lymphoblastic leukemia (ALL). In vitro, LBH589 induced a significant dose-dependent increase in cell apoptosis and a markedly inhibition of cell proliferation, which were associated with increased H3 and H4 histone acetylation. While apoptosis of ALL cells was detected between 12 and 24 hours after treatment with LBH589, changes in acetylated H3 and H4 were detected as early as 2 hours. Phosphorylation of H2AX, as an early marker of DNA damaged, was detected 12 to 24 hours after in vitro treatment with LBH589. These results suggest that H3 and H4 acetylation precede DNA damaged and induction of apoptosis indicating that inhibition of HDAC is likely to be responsible at least in part for LBH589 induced apoptosis and inhibition of cell proliferation. The in vivo activity of LBH589 was initially examined in a subcutaneous ALL mouse model. The ALL cell lines TOM-1 and MOLT-4 were transplanted (1×106 cell per animal) subcutaneously into the left flanks of 6-week-old female BALB/cA-Rag2−/−γc−/−. These cell lines develop into a rapidly growing tumor. Treatment with 5mg/kg of LBH589 was initiated 24 hours after injection of the leukemic cells, included 3 cycles of 5 consecutive days of LBH589 with two days rest between cycles and animals were monitored for 24 days. A significant inhibition of tumor growth was demonstrated in animals treated with LBH589 compared with control animals (P Using primary ALL cells, a xenograft model of human leukemia in BALB/c-RAG2−/−γc−/− mice was established, allowing continuous passages of transplanted cells to several mouse generations. A total of 10 million cells from a patient with T-ALL (ALL-T1) and a patient with B-ALL (ALL-B1) were administered intravenously into the tail vein of 6-week-old immunodeficient female BALB/cA-Rag2−/−γc−/− mice. Kinetics of engraftment of leukemic cells was monitored in PB and BM by phenotyping while organ infiltration was analyzed by immunohistochemistry. There were no significant differences in the genome, methylome or transcriptome between the original sample and the samples obtained after multiple generations on mice. To determine the efficacy of LBH589 alone or in combination with drugs currently used for treatment of ALL, BALB/cA-RAG2−/−γc−/− mice engrafted with ALL-T1 and ALL-B1 cells were treated with LBH589, Vincristine and Dexamethasone or a combination of LBH589 with Vincristine and Dexamethasone. Treatment was initiated when disease could be detected in PB by FACS (24 hours after injection of cells for ALL-T1 and between day 17 and 21 after injection for ALL-B1). LBH589 was administered i.p. on days 1–5, 8–12 and 15–19, Vincristine i.v. on days 1, 8 and 21 and Dexamethasone daily until day 21 i.p. and survival was analyzed. Treatment of mice engrafted with T or B-ALL cells with LBH589 induced an in vivo increase in the acetylation of H3 and H4, which was accompanied with prolonged survival of LBH589-treated mice in comparison with those receiving Vincristine and Dexametasone. Notably, the therapeutic efficacy of LBH589 was significantly enhanced in combination with Vincristine and Dexametasone. Our results demonstrate the therapeutic activity of LBH589 in combination with standard chemotherapy in pre-clinical models of ALL and suggest that this combination may be of clinical value in the treatment of patients with ALL. Disclosures: No relevant conflicts of interest to declare.