ALBERT

Description: During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4+ T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.

Description: Patients with multiple myeloma (MM) carrying high-risk cytogenetic abnormalities (CA) have inferior outcome despite achieving similar complete response (CR) rates when compared to cases with standard-risk CA. This questions the legitimacy of CR as treatment endpoint for high-risk MM, and represents a biological conundrum regarding the nature of tumor reservoirs persisting after therapy in patients with standard- and high-risk CA. Here, we used next-generation flow (NGF) to evaluate measurable residual disease (MRD) in MM patients with standard- (N=300) vs high-risk CA (N=90) enrolled in the PETHEMA/GEM2012MENOS65 trial (NCT01916252), and to identify mechanisms determining MRD resistance in both patient subgroups (N=40). The 36-month progression-free and overall survival rates were higher than 90% in patients with undetectable MRD, with no significant differences (P≥0.202) between cases having standard- vs high-risk CA. Persistent MRD resulted in median progression-free survival of approximately three and two years in patients with standard- and high-risk CA, respectively (P

Description: 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.

Description: Background: Deep understanding of the complexity and diversity of the tumor immune microenvironment (TIME) and its influence on response to therapy is needed to improve the ability to predict, monitor and guide immunotherapeutic responsiveness. Among different cell types in the MM-TIME, granulocytic MDSCs (G-MDSCs) have a prominent role in promoting tumor growth and inducing immune suppression; however, their identification and monitoring is imprecise because the phenotypic profile of MDSCs in the MM-TIME is not well-established. Aim: To provide the detailed phenotypic profile of G-MDSCs based on the immune suppressive potential, gene regulatory network and clinical significance of distinct granulocytic subsets in the MM-TIME. Methods: First, we used multidimensional flow cytometry (MFC) to evaluate the preestablished phenotype of G-MDSCs in bone marrow (BM) samples from controls (n=4) and MM patients (n=5). We then used principal component analysis (PCA) to unbiasedly identify different granulocytic subsets in the MM-TIME, and FACS for in vitro experiments to determine their immune suppressive potential (n=9) and for RNAseq to analyze the molecular profile of G-MDSCs in MM (n=5) vs controls (n=5). Subsequently, the clinical significance of the different granulocytic subsets was investigated by comparing their numbers at diagnosis, in MM patients (n=124) achieving MRD-negativity vs MRD-positivity after treatment with VRD induction (x6) followed by autologous transplant and VRD consolidation (x2) (GEM2012MENOS65 clinical trial). Results: In humans, G-MDSCs have been defined as a unique cluster displaying a CD11b-, CD14-, CD15+, CD33+ and HLADR- phenotype, comprising 1% of total BM nucleated cells in healthy individuals and approximately 25% in MM patients. However, we found that the percentage of cells with a CD11b-CD14-CD15+CD33+HLADR- phenotype was similar in the BM of controls and MM patients (median of 8% in both, P〉.99). Since these cells were not expanded in MM and represented only 24% of total neutrophils, we next used MFC and PCA to unbiasedly identify other cell clusters within neutrophils. Accordingly, 3 major subsets were identified in neutrophils from controls and MM patients, based on homogeneous CD14-CD15+CD33+HLADR- expression but differential reactivity against CD11b, CD13 and CD16: CD11b-CD13lo/-CD16- (19% and 24%), CD11b+CD13lo/-CD16- (46% and 47%) and CD11b+CD13+CD16+ (35% and 29%). Afterwards, we used FACSorting to deplete or isolate individually, each of the 3 neutrophil subsets from the BM MM-TIME and determine its immune suppressive potential in 2 functional assays: 1) the proliferation rate of autologous T cells in presence of CD3/CD28 stimulatory beads and, 2) the cytotoxic potential of autologous T-cells against MM cells using a BCMAxCD3 bispecific antibody. Interestingly, we noted a significant decrease in T cell proliferation when these were stimulated in the presence of CD11b+CD13+CD16+ neutrophils (0.5-fold, p =.03) but not the CD11b-CD13lo/-CD16- and CD11b+CD13lo/-CD16- subsets. In addition, we noted that the cytotoxic potential of T cells engaged by the BCMAxCD3 bispecific antibody significantly increased with the depletion of CD11b+CD13lo/-CD16- and CD11b+CD13+CD16+ subsets (3-fold and 4-fold, respectively; p ≤.04) but not CD11b-CD13lo/-CD16- neutrophils. Furthermore, RNAseq of the 3 subsets in controls and MM patients revealed that genes related with the IL-4, IL-10 and IL-13 immunosuppressive pathways were specifically upregulated in the CD11b+CD13+CD16+ subset. Finally, based on the surrogacy between the achievement of MRD-negativity and prolonged survival, we compared the distribution of the 3 granulocytic subsets in the BM-TIME at diagnosis and observed that patients reaching MRD-negativity (n=56) displayed significantly lower percentages of total neutrophils (46% vs 52%, p =.002), particularly of the CD11b+CD13lo/-CD16- (11% vs 15%, p =.003) and CD11b+CD13+CD16+ (31% vs 35%, p =.07) subsets vs MRD-positive cases (n=68). Conclusions: We have determined the correlation between the phenotypic, molecular and immunosuppressive potential of unique granulocytic subsets. Thus, we have identified optimal markers for monitoring G-MDCSs in patients with MM (ie. CD11b, CD13, CD16) and unveiled that, in contrast to previous findings, the more mature granulocytes are the only stages with immunosuppressive potential. Disclosures Puig: Celgene: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria. Martinez Lopez:Celgene: Research Funding, Speakers Bureau; Bristol Myers Squibb: Research Funding, Speakers Bureau; Novartis: Research Funding, Speakers Bureau; Janssen: Research Funding, Speakers Bureau. Oriol:Celgene: 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; 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. Rios:Amgen, Celgene, Janssen, and Takeda: Consultancy. Rosinol:Janssen, Celgene, Amgen, Takeda: Honoraria. Mateos: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; Amgen: 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; Janssen: 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. 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:Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria. San-Miguel:Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria; BMS: Honoraria; Novartis: Honoraria; Sanofi: Honoraria; Roche: Honoraria.

Description: 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.

Description: Granulocytic myeloid-derived suppressor cells (G-MDSCs) promote tumor growth and immunosuppression in multiple myeloma (MM). However, their phenotype is not well established for accurate monitoring or clinical translation. We aimed to provide the phenotypic profile of G-MDSCs based on their prognostic significance in MM, immunosuppressive potential, and molecular program. The preestablished phenotype of G-MDSCs was evaluated in bone marrow samples from controls and MM patients using multidimensional flow cytometry; surprisingly, we found that CD11b+CD14−CD15+CD33+HLADR− cells overlapped with common eosinophils and neutrophils, which were not expanded in MM patients. Therefore, we relied on automated clustering to unbiasedly identify all granulocytic subsets in the tumor microenvironment: basophils, eosinophils, and immature, intermediate, and mature neutrophils. In a series of 267 newly diagnosed MM patients (GEM2012MENOS65 trial), only the frequency of mature neutrophils at diagnosis was significantly associated with patient outcome, and a high mature neutrophil/T-cell ratio resulted in inferior progression-free survival (P 〈 .001). Upon fluorescence-activated cell sorting of each neutrophil subset, T-cell proliferation decreased in the presence of mature neutrophils (0.5-fold; P = .016), and the cytotoxic potential of T cells engaged by a BCMA×CD3-bispecific antibody increased notably with the depletion of mature neutrophils (fourfold; P = .0007). Most interestingly, RNA sequencing of the 3 subsets revealed that G-MDSC–related genes were specifically upregulated in mature neutrophils from MM patients vs controls because of differential chromatin accessibility. Taken together, our results establish a correlation between the clinical significance, immunosuppressive potential, and transcriptional network of well-defined neutrophil subsets, providing for the first time a set of optimal markers (CD11b/CD13/CD16) for accurate monitoring of G-MDSCs in MM.

Description: 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