Abstract: Background: Although MYD88 L265P is highly frequent in WM, by itself is insufficient to explain disease progression since most cases with IgM MGUS also have mutated MYD88. In fact, the percentage of MYD88 L265P in CD19+ cells isolated from WM patients is typically & lt;100%, which questions if this mutation initiates the formation of B-cell clones. Furthermore, a few WM patients have detectable MYD88 L265P in total bone marrow (BM) cells and not in CD19+ selected B cells, raising the possibility that other hematopoietic cells carry the MYD88 mutation. However, no one has investigated if the pathogenesis of WM is related to somatic mutations occurring at the hematopoietic stem cell level, similarly to what has been shown in CLL or hairy cell leukemia. Aim: Define the cellular origin of WM by comparing the genetic landscape of WM cells to that of CD34 progenitors, B cell precursors and residual normal B cells. Methods: We used multidimensional FACSorting to isolate a total of 43 cell subsets from BM aspirates of 8 WM patients: CD34+ progenitors, 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 74x) was performed with the 10XGenomics Exome Solution for low DNA-input due to very low numbers of some cell types. We also performed single-cell RNA and B-cell receptor sequencing (scRNA/BCRseq) 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 progenitors, B cell precursors and germinal center B cells, respectively. Upon immunization, mice from each cohort were necropsied at 5, 10 and 15 months of age and screened for the presence of hematological disease. Results: All 8 WM patients showed MYD88 L265P and 3 had mutated CXCR4. Notably, we found MYD88 L265P in B cell precursors from 1/8 cases and in residual normal B cells from 3/8 patients, which were confirmed by ASO-PCR. In addition, CXCR4 was simultaneously mutated in B cell precursors and WM B cells from one patient. Overall, CD34+ progenitors, B-cell precursors and residual normal B cells shared a median of 1 (range, 0-4; mean VAF, 0.16), 2 (range, 1-5; mean VAF, 0.14), and 4 (range, 1-13; mean VAF, 0.26) non-synonymous mutations with WM B cells. Some mutations were found all the way from CD34+ progenitors to WM B cells and PCs. Interestingly, concordance between the mutational landscape of WM B cells and PCs was & lt;100% (median of 85%, range: 25%-100%), suggesting that not all WB B cells differentiate into PCs. A median of 7 (range, 2-19; mean VAF, 0.39) mutations were unique to WM B cells. Accordingly, many clonal mutations in WM B cells were undetectable in normal cells. Thus, the few somatic mutations observed in patients' lymphopoiesis could not result from contamination during FACSorting since in such cases, all clonal mutations would be detectable in normal cells. Of note, while somatic mutations were systematically detected in normal cells from all patients, no copy number alterations (CNA) present in WM cells were detectable 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. 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+ progenitors 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 MYD88 L265P and in CXCR4, at the B cell progenitor level. Taken together, this study suggests that in some patients, WM could develop from B cell clones carrying MYD88 L265P rather than it 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 Roccaro: Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Transcan2-ERANET: Research Funding; AstraZeneca: Research Funding; European Hematology Association: Research Funding; Transcan2-ERANET: Research Funding; Associazione Italiana per al Ricerca sul Cancro (AIRC): Research Funding; Associazione Italiana per al Ricerca sul Cancro (AIRC): Research Funding; European Hematology Association: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Research Funding; Amgen: 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.

Abstract: 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 & lt;100% (median of 79%, range: 55%-100%), suggesting that not all WM B cells differentiate into PCs. 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.

Abstract: Background: Although great strides were made in the management of MM, our best chances to eradicate this malignancy may lie in preventing its progression.Most current models to predict risk of transformation in SMM are commonly established at diagnosis and not reevaluated over time, because some parameters such as tumor burden or genetic abnormalities require invasive bone marrow (BM) aspirates. It could be hypothesized that periodic monitoring of tumor biomarkers is needed to improve risk-stratification of SMM patients, and so would be new minimally-invasive methods that can replace those performed in BM samples. Such methods should also monitor immune profiles, to identify patients with stable tumor burden/genetics but at risk of progression due to lost immune surveillance. Aim: Determine the level of concordance between the tumor/immune landscape in BM vs peripheral blood (PB) of SMM patients, as well as to evaluate immune profiles together with circulating tumor cell (CTC) numbers and genetic alterations every 6 months in PB, as minimally-invasive methods for identification of SMM patients at risk of developing active MM. Methods: 300 patients are planned to be enrolled in the iMMunocell study that includes 24 sites across 8 European countries. PB samples are collected every 6 months during three years for next-generation flow (NGF) cytometry monitoring of CTCs and immune profiles. Additionally, CTCs and various immune cells are FACSorted to evaluate, every 6 months, their molecular profile in SMM patients with stable vs progressive disease. BM samples are taken at baseline and every 12 months according to patients' choice, in which the same methods described previously for PB are performed. An interim analysis was preplanned to the moment when 150 patients were enrolled. Results: A total of 170 SMM patients were enrolled and we report here data on the first 150. Thus far, 18/150 (12%) patients progressed to MM and according to 20/20/20 criteria, 1 had low, 7 intermediate and 10 had high risk SMM. Only 7/18 cases who progressed had & gt;20% BM plasma cells (PC) by morphology. CTCs were detectable in 107/150 (71%) patients at baseline (median of 0.001% [0% - 0.42%] and 0.03 [0 - 21] CTCs/µL of PB). There was no correlation (or only modestly-significant) between the percentage of CTCs and BMPC by morphology (r=0.156, p=0.065) or flow cytometry (r=0.293, p=0.02). Median CTC counts were 0.02, 0.03 and 0.11 in SMM patients with low, intermediate and high risk disease according to 20/20/20 criteria, respectively (p=0.002). Median CTC numbers were significantly different between cases with stable vs progressive disease (0.02 vs 0.11, p=0.005). As compared to those with ≤1 CTC/µL of PB, patients with & gt;1 CTC/uL showed significantly higher risk of transformation (8% vs 47%, p & lt;0.001) with a median time to progression of 6 months. In addition to the 150 PB samples analyzed at baseline, another 139 specimens were processed at 6, 12 and 18 months. The fluctuation in CTC numbers every 6 months was generally low (median, 0.03 CTCs/uL; IQR, 0.003 - 0.12), though in 10% of patient-samples the absolute variation was & gt;0.5 CTCs/uL. Data on the genetic landscape of CTCs analyzed every 6 months from baseline to disease progression will be shown at the meeting. Immune monitoring in patient-paired PB and BM samples at baseline (n=50) uncovered that 48 of 74 innate and adaptive immune cell types measured by multidimensional flow cytometry had similar distribution. Furthermore, we found significant differences in the distribution of three CD8 T cell subsets defined by differential expression of CD28, CD127, PD1, TIGIT, in PB of SMM patients with stable vs progressive disease. In patients with longitudinal PB samples from baseline until progression to active MM (n=7), there was a significant decrease in helper effector memory CXCR3+CCR4+ and cytotoxic CD127+TIGIT+PD1+ T cells, together with a significant increase in adaptive NK cells and Tγδ CD69+ T cells. Conclusions: This is the first study performing CTC and immune monitoring every 6 months in PB samples from patients with SMM. Our results show a significant correlation between CTC counts and stable vs progressive disease, and suggest that CTC kinetics could be complementary to the 20/20/20 criteria for real-time identification of individual SMM patients at risk of developing active MM. Beyond CTC numbers, this study is uncovering key immune cell types associated with disease progression. Disclosures Terpos: Amgen: Honoraria, Research Funding; Genesis: Honoraria, Other: travel expenses , Research Funding; Janssen: Honoraria, Other: travel expenses , Research Funding; Takeda: Honoraria, Other: travel expenses , Research Funding; Celgene: Honoraria; Medison: Honoraria. Raab:Amgen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees, Research Funding; Heidelberg Pharma: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: 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; Takeda: Membership on an entity's Board of Directors or advisory committees. Ocio:Sanofi: Consultancy, Honoraria; Secura-Bio: Consultancy; Oncopeptides: Consultancy; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria; MDS: Honoraria; GSK: Consultancy; Takeda: Honoraria; Asofarma: Honoraria. Martinez-Lopez:Novartis: Consultancy; Janssen-cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Janssen: Consultancy, Honoraria. de la Rubia:Amgen: Consultancy, Other: Expert Testimony; Celgene: Consultancy, Other: Expert Testimony; Janssen: Consultancy, Other: Expert Testimony; Ablynx/Sanofi: Consultancy, Other: Expert Testimony. Hajek:Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharma MAR: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Oncopeptides: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Ludwig:Celgene: Speakers Bureau; Janssen: Other: Advisory Boards, Speakers Bureau; Bristol Myers: Other: Advisory Boards, Speakers Bureau; Sanofi: Other: Advisory Boards, Speakers Bureau; Amgen: Other: Advisory Boards, Research Funding, Speakers Bureau; Takeda: Research Funding; Seattle Genetics: Other: Advisory Boards. Goldschmidt:Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product:; Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Incyte: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Molecular Partners: Research Funding; Johns Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Mundipharma GmbH: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; University Hospital Heidelberg, Internal Medicine V and National Center for Tumor Diseases (NCT), Heidelberg, Germany: Current Employment; GlaxoSmithKline (GSK): Honoraria; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Merck Sharp and Dohme (MSD): Research Funding. Roccaro:European Hematology Association: Research Funding; AstraZeneca: Research Funding; Transcan2-ERANET: Research Funding; Italian Association for Cancer Research (AIRC): Research Funding; Janssen: Other; Celgene: Other; Amgen: Other. 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: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.

Abstract: Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P & lt; .001). We also determined progression-free survival (HR, 4.09; P & lt; .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.

Abstract: MYD88 L265P occurs in between a normal mutated lymphopoiesis and additional genetic alterations during lymphomagenesis.

Abstract: 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-value 〈 0.01, logFC 〉 1) 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: Patients with multiple myeloma (MM) carrying standard- or high-risk cytogenetic abnormalities (CAs) achieve similar complete response (CR) rates, but the later have inferior progression-free survival (PFS). This questions the legitimacy of CR as a treatment endpoint and represents a biological conundrum regarding the nature of tumor reservoirs that persist after therapy in high-risk MM. We used next-generation flow (NGF) cytometry to evaluate measurable residual disease (MRD) in MM patients with standard- vs high-risk CAs (n = 300 and 90, respectively) enrolled in the PETHEMA/GEM2012MENOS65 trial, and to identify mechanisms that determine MRD resistance in both patient subgroups (n = 40). The 36-month PFS rates were higher than 90% in patients with standard- or high-risk CAs achieving undetectable MRD. Persistent MRD resulted in a median PFS of ∼3 and 2 years in patients with standard- and high-risk CAs, respectively. Further use of NGF to isolate MRD, followed by whole-exome sequencing of paired diagnostic and MRD tumor cells, revealed greater clonal selection in patients with standard-risk CAs, higher genomic instability with acquisition of new mutations in high-risk MM, and no unifying genetic event driving MRD resistance. Conversely, RNA sequencing of diagnostic and MRD tumor cells uncovered the selection of MRD clones with singular transcriptional programs and reactive oxygen species–mediated MRD resistance in high-risk MM. Our study supports undetectable MRD as a treatment endpoint for patients with MM who have high-risk CAs and proposes characterizing MRD clones to understand and overcome MRD resistance. This trial is registered at www.clinicaltrials.gov as #NCT01916252.

Abstract: 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 & gt;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.

Abstract: Benign (ie, IgM MGUS and smoldering WM) clonal B cells already harbor the phenotypic and molecular signatures of the malignant WM clone. Multistep transformation from benign (ie, IgM MGUS and smoldering WM) to malignant WM may require specific copy number abnormalities.