Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treat-ment individualization based on the probability of a patient achiev-ing undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. Experimental Design: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 trans-plant-eligible patients with active multiple myeloma enrolled in the GEM2012MENOS65 trial. Two external validation cohorts were defined by 76 high-risk transplant-eligible patients with smoldering multiple myeloma enrolled in the Grupo Espanol de Mieloma (GEM)-CESAR trial, and 110 transplant-ineligible elderly patients enrolled in the GEM-CLARIDEX trial. Results: The most effective model to predict MRD status resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (bone marrow plasma cell clonality and circulating tumor cells), and immune-related biomarkers. Accurate predic -ti ons of MRD outcomes were achieved in 71% of cases in the GEM2012MENOS65 trial (n = 214/301) and 72% in the external validation cohorts (n = 134/186). The model also predicted sustained MRD negativity from consolidation onto 2 years main-tenance (GEM2014MAIN). High-confidence prediction of unde-tectable MRD at diagnosis identified a subgroup of patients with active multiple myeloma with 80% and 93% progression-free and overall survival rates at 5 years. Conclusions: It is possible to accurately predict MRD outcomes using an integrative, weighted model defined by machine learning algorithms. This is a new concept toward individualized treatment in multiple myeloma.

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 < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P 5 .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.

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.

Purpose: 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 MMcell 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 CD38(hi) MMcells to bortezomib plus dexamethasone in the presence of stroma, (iii) antibody-dependent cellular cytotoxicity (ADCC) was triggered by CD38(lo) and CD38(hi) tumor plasma cells (PC), (iv) antibody-dependent cellular phagocytosis (ADCP) was triggered only by CD38(hi) 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 CD38(hi) 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 CD38(lo) MM patients.