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: Knowledge about the mechanism of action (MoA) of monoclonal antibodies (mAb) is required to understand which patients with multiple myeloma (MM) benefit the most from a given mAb, alone or in combination therapy. Although there is considerable research about daratumumab, knowledge about other anti-CD38 mAbs remains scarce. Experimental Design: We performed a comprehensive analysis of the MoA of isatuximab. Results: Isatuximab induces internalization of CD38 but not its significant release from MM cell surface. In addition, we uncovered an association between levels of CD38 expression and different MoA: (i) Isatuximab was unable to induce direct apoptosis on MM cells with CD38 levels closer to those in patients with MM, (ii) isatuximab sensitized CD38hi MM cells to bortezomib plus dexamethasone in the presence of stroma, (iii) antibody-dependent cellular cytotoxicity (ADCC) was triggered by CD38lo and CD38hi tumor plasma cells (PC), (iv) antibody-dependent cellular phagocytosis (ADCP) was triggered only by CD38hi MM cells, whereas (v) complement-dependent cytotoxicity could be triggered in less than half of the patient samples (those with elevated levels of CD38). Furthermore, we showed that isatuximab depletes CD38hi B-lymphocyte precursors and natural killer (NK) lymphocytes ex vivo—the latter through activation followed by exhaustion and eventually phagocytosis. Conclusions: This study provides a framework to understand response determinants in patients treated with isatuximab based on the number of MoA triggered by CD38 levels of expression, and for the design of effective combinations aimed at capitalizing disrupted tumor–stroma cell protection, augmenting NK lymphocyte–mediated ADCC, or facilitating ADCP in CD38lo MM patients. See related commentary by Malavasi and Faini, p. 2946

Abstract: 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.

Abstract: 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.

Abstract: Information on the immunopathobiology of coronavirus disease 2019 (COVID-19) is rapidly increasing; however, there remains a need to identify immune features predictive of fatal outcome. This large-scale study characterized immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection using multidimensional flow cytometry, with the aim of identifying high-risk immune biomarkers. Holistic and unbiased analyses of 17 immune cell-types were conducted on 1,075 peripheral blood samples obtained from 868 COVID-19 patients and on samples from 24 patients presenting with non-SARS-CoV-2 infections and 36 healthy donors. Immune profiles of COVID-19 patients were significantly different from those of age-matched healthy donors but generally similar to those of patients with non-SARS-CoV-2 infections. Unsupervised clustering analysis revealed three immunotypes during SARS-CoV-2 infection; immunotype 1 (14% of patients) was characterized by significantly lower percentages of all immune cell-types except neutrophils and circulating plasma cells, and was significantly associated with severe disease. Reduced B-cell percentage was most strongly associated with risk of death. On multivariate analysis incorporating age and comorbidities, B-cell and non-classical monocyte percentages were independent prognostic factors for survival in training (n=513) and validation (n=355) cohorts. Therefore, reduced percentages of B-cells and non-classical monocytes are high-risk immune biomarkers for risk-stratification of COVID-19 patients.

Abstract: Monoclonal antibodies (mAbs) targeting CD38 are demonstrating remarkable efficacy, particularly when combined with anti-MM agents. Thus, in-depth understanding of the MoA of anti-CD38 mAbs is of utmost importance to design rational treatment combinations. Notably, while there is considerable data about the MoA of Daratumumab, there is virtually no data about the MoA of Isatuximab. Here, we started by analyzing in a selected panel of MM cell lines (RPMI8226, H929, MM1S and OPM2) the effect of Isatuximab on 1) proliferation, 2) direct cell death, 3) complement dependent cytotoxicity (CDC), 4) antibody dependent cell mediated cytotoxicity (ADCC), and 5) antibody dependent cellular phagocytosis (ADPC). Our results show that Isatuximab had no effect on proliferation, and did not directly killed any of the MM cell lines tested. Interestingly, while Isatuximab (similar to drugs such as Cetuximab) binds to C1q, CD38 receptor density in MM cell lines was insufficient to trigger CDC based on the absence of C3 deposition and impact on cell survival. Upon co-culture with human blood leukocytes, Isatuximab induced ADCC particularly on cells showing higher expression of CD38 (P=0.02). Upon co-culture with human-derived M2-like macrophages, we noted a trend (P=.1) towards decreased MM cell expansion; thus, in order to establish the exact contribution of ADCC and ADCP on the efficacy of Isatuximab, we co-cultured RPMI8226 MM cells with human leukocytes and prior to treatment (10µg/mL), used sensitive FACS sorting to remove key immune cell populations such as NK cells, macrophages and T cells from the culture. Our results show that the presence of NK cells was critical for the efficacy of Isatuximab, whereas macrophages and T cell depletion had no effect on MM cell viability in the presence of Isatuximab. We next tested Isatuximab (10µg/mL) in 10 primary patient samples. In the absence of C3 deposition in the majority of samples (ie. no CDC), we observed Isatuximab-induced cell death in all patients tested (P=0.002), along with increased proliferation and activation of NK cells upon Fc recognition. Due to broad expression of CD38 in hematopoietic cells, we also investigated presence of off-target effects of Isatuximab. Importantly, with the exception of CD38bright B cell precursors (P=.0006), there was no toxicity observed in other progenitor and mature hematopoietic cells. We also investigated the potential synergism of Isatuximab in combination with Bortezomib-Dexamethasone; interestingly, the addition of Isatuximab resulted in a (P=0.06) 2.7-fold increment in MM cell death. In order to further understand the direct effect of Isatuximab in both MM and (CD38bright) NK cells, we determined the molecular signature of both subsets in co-culture and upon treatment with Isatuximab. Interestingly (though consistent with the lack of impact in proliferation and direct apoptosis), MM cells showed no gene dysregulation upon treatment with Isatuximab and in the absence of NK cells. By contrast, in the presence of NK cells and respective ADCC, MM cells chemoresistant to Isatuximab showed 184 dysregulated genes, including significant down-regulation of CD38. Noteworthy, Isatuximab binding induced in NK cells up-regulation of genes related to antigen binding, chemokine receptor activity, G-protein coupled peptide receptor or hydrolase activity. One such gene was CD137, which up-regulation was subsequently confirmed at the protein (surface antigen) level. In summary, this critical analysis on Isatuximab's MoA using a comprehensive set of assays, MM cell lines and primary patient samples, suggests that ADCC is the major efficacy contributor for this particular mAb. Thus, drugs that can further stimulate NK cells might be optimal candidates for rational combinations with Isatuximab; in this regard, we showed that Isatuximab induces up-regulation of CD137 which warrants further investigations on the value of combined Isatuximab and anti-CD137 immune therapies. Figure Figure. Disclosures Paiva: Celgene: Honoraria, Research Funding; Janssen: Honoraria; Takeda: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; EngMab: Research Funding; Amgen: Honoraria; Binding Site: Research Funding.