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.

Simple Summary Bone marrow (BM) aspirates are mandatory for diagnosis and follow-up of patients with multiple myeloma (MM). However, they present two important caveats: Their invasiveness and limited scope to capture the broad tumor heterogeneity. Conversely, circulating tumor cells (CTCs) are detectable in the peripheral blood of patients with precursor and malignant disease states and have strong prognostic value. Moreover, the high genetic and transcriptomic overlap between both plasma cell compartments suggests that CTCs might reflect with notable precision the medullar clone. Furthermore, the study of CTCs could be used as a model to identify mechanisms favoring BM egression and disease spreading. Here, we summarize the state of the art on MM CTCs and provide insights on what they may offer in research and clinical scenarios. Bone marrow (BM) aspirates are the gold standard for patient prognostication and genetic characterization in multiple myeloma (MM). However, they represent an important limitation for periodic disease monitoring because they entail an aggressive procedure. Moreover, recent findings show that a single BM aspirate is unable to reflect the complex MM heterogeneity. Recent advances in flow cytometry, microfluidics, and "omics" technologies have opened Pandora's box of MM: The detection and isolation of circulating tumor cells (CTCs) offer a promising and minimally invasive alternative for tumor assessment and metastasis study. CTCs are detectable in premalignant and active MM states, and their enumeration has strong prognostic value, to the extent that it is challenging current stratification systems. In addition, CTCs reflect with high precision both intra- and extra-medullary disease at the phenotypic, genomic, and transcriptomic levels. Despite this high resemblance between tumor clones in distinct locations, some subtle (not random) differences might shed some light on the metastatic process. Thus, it has been suggested that a hypoxic and pro-inflammatory microenvironment could induce an arrest in proliferation forcing tumor cells to recirculate. Herein, we summarize data on the characterization of MM CTCs as well as their clinical and research potential.

PURPOSE: Early intervention in smoldering multiple myeloma (SMM) requires optimal risk stratification to avoid under- and overtreatment. We hypothesized that replacing bone marrow (BM) plasma cells (PC) for circulating tumor cells (CTC), and adding immune biomarkers in peripheral blood (PB) for the identification of patients at risk of progression due to lost immune surveillance, could improve the International Myeloma Working Group 20/2/20 model.EXPERIMENTAL DESIGN: We report the outcomes of 150 patients with SMM enrolled in the iMMunocell study, in which serial assessment of tumor and immune cells in PB was performed every 6 months for a period of 3 years since enrollment.RESULTS: Patients with >0.015% versus ¿0.015% CTCs at baseline had a median time-to-progression of 17 months versus not reached (HR, 4.9; P<0.001). Presence of >20% BM PCs had no prognostic value in a multivariate analysis that included serum free light-chain ratio >20, >2 g/dL M-protein, and >0.015% CTCs. The 20/2/20 and 20/2/0.015 models yielded similar risk stratification (C-index of 0.76 and 0.78). The combination of the 20/2/0.015 model with an immune risk score based on the percentages of SLAN+ and SLAN- nonclassical monocytes, CD69+HLADR+ cytotoxic NK cells, and CD4+CXCR3+ stem central memory T cells, allowed patient' stratification into low, intermediate-low, intermediate-high, and high-risk disease with 0%, 20%, 39%, and 73% rates of progression at 2 years.CONCLUSIONS: This study showed that CTCs outperform BM PCs for assessing tumor burden. Additional analysis in larger series are needed to define a consensus cutoff of CTCs for minimally invasive stratification of SMM.

PURPOSE Patients with multiple myeloma (MM) may show patchy bone marrow (BM) infiltration and extramedullary disease. Notwithstanding, quantification of plasma cells (PCs) continues to be performed in BM since the clinical translation of circulating tumor cells (CTCs) remains undefined. PATIENTS AND METHODS CTCs were measured in peripheral blood (PB) of 374 patients with newly diagnosed MM enrolled in the GEM2012MENOS65 and GEM2014MAIN trials. Treatment included bortezomib, lenalidomide, and dexamethasone induction followed by autologous transplant, consolidation, and maintenance. Next-generation flow cytometry was used to evaluate CTCs in PB at diagnosis and measurable residual disease (MRD) in BM throughout treatment. RESULTS CTCs were detected in 92% (344 of 374) of patients with newly diagnosed MM. The correlation between the percentages of CTCs and BM PCs was modest. Increasing logarithmic percentages of CTCs were associated with inferior progression-free survival (PFS). A cutoff of 0.01% CTCs showed an independent prognostic value (hazard ratio: 2.02; 95% CI, 1.3 to 3.1; P = .001) in multivariable PFS analysis including the International Staging System, lactate dehydrogenase levels, and cytogenetics. The combination of the four prognostic factors significantly improved risk stratification. Outcomes according to the percentage of CTCs and depth of response to treatment showed that patients with undetectable CTCs had exceptional PFS regardless of complete remission and MRD status. In all other cases with detectable CTCs, only achieving MRD negativity (and not complete remission) demonstrated a statistically significant increase in PFS. CONCLUSION Evaluation of CTCs in PB outperformed quantification of BM PCs. The detection of >= 0.01% CTCs could be a new risk factor in novel staging systems for patients with transplant-eligible MM.

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.

Normal cell counterparts of solid and myeloid tumors accumulate mutations years before disease onset; whether this occurs in B lymphocytes before lymphoma remains uncertain. We sequenced multiple stages of the B lineage in elderly individuals and patients with lymphoplasmacytic lymphoma, a singular disease for studying lymphomagenesis because of the high prevalence of mutated MYD88. We observed similar accumulation of random mutations in B lineages from both cohorts and unexpectedly found MYD88(L265P) in normal precursor and mature B lymphocytes from patients with lymphoma. We uncovered genetic and transcriptional pathways driving malignant transformation and leveraged these to model lymphoplasmacytic lymphoma in mice, based on mutated MYD88 in B cell precursors and BCL2 overexpression. Thus, MYD88(L265P) is a preneoplastic event, which challenges the current understanding of lymphomagenesis and may have implications for early detection of B cell lymphomas.

The role of decentralized assessment of measurable residual disease (MRD) for risk stratification in acute myeloid leukemia (AML) remains largely unknown, and so it does which methodological aspects are critical to empower the evaluation of MRD with prognostic significance, particularly if using multiparameter flow cytometry (MFC). We analyzed 1076 AML patients in first remission after induction chemotherapy, in whom MRD was evaluated by MFC in local laboratories of 60 Hospitals participating in the PETHEMA registry. We also conducted a survey on technical aspects of MRD testing to determine the impact of methodological heterogeneity in the prognostic value of MFC. Our results confirmed the recommended cutoff of 0.1% to discriminate patients with significantly different cumulative-incidence of relapse (-CIR- HR:0.71, P < 0.001) and overall survival (HR: 0.73, P = 0.001), but uncovered the limited prognostic value of MFC based MRD in multivariate and recursive partitioning models including other clinical, genetic and treatment related factors. Virtually all aspects related with methodological, interpretation, and reporting of MFC based MRD testing impacted in its ability to discriminate patients with different CIR. Thus, this study demonstrated that "real-world" assessment of MRD using MFC is prognostic in patients at first remission, and urges greater standardization for improved risk-stratification toward clinical decisions in AML.

Multiple myeloma (MM) patients undergo repetitive bone marrow (BM) aspirates for genetic characterization. Circulating tumor cells (CTCs) are detectable in peripheral blood (PB) of virtually all MM cases and are prognostic, but their applicability for noninvasive screening has been poorly investigated. Here, we used next-generation flow (NGF) cytometry to isolate matched CTCs and BM tumor cells from 53 patients and compared their genetic profile. In eight cases, tumor cells from extramedullary (EM) plasmacytomas were also sorted and whole-exome sequencing was performed in the three spatially distributed tumor samples. CTCs were detectable by NGF in the PB of all patients with MM. Based on the cancer cell fraction of clonal and subclonal mutations, we found that similar to 22% of CTCs egressed from a BM (or EM) site distant from the matched BM aspirate. Concordance between BM tumor cells and CTCs was high for chromosome arm-level copy number alterations (>= 95%) though not for translocations (39%). All high-risk genetic abnormalities except one t(4;14) were detected in CTCs whenever present in BM tumor cells. Noteworthy, >= 82% mutations present in BM and EM clones were detectable in CTCs. Altogether, these results support CTCs for noninvasive risk-stratification of MM patients based on their numbers and genetic profile.

The reason why a few myeloma cells egress from the bone marrow (BM) into peripheral blood (PB) remains unknown. Here, we investigated molecular hallmarks of circulating tumor cells (CTCs) to identify the events leading to myeloma trafficking into the bloodstream. After using next-generation flow to isolate matched CTCs and BM tumor cells from 32 patients, we found high correlation in gene expression at single-cell and bulk levels (r¿¿¿0.94, P¿=¿10-16), with only 55 genes differentially expressed between CTCs and BM tumor cells. CTCs overexpressed genes involved in inflammation, hypoxia, or epithelial-mesenchymal transition, whereas genes related with proliferation were downregulated in CTCs. The cancer stem cell marker CD44 was overexpressed in CTCs, and its knockdown significantly reduced migration of MM cells towards SDF1-¿ and their adhesion to fibronectin. Approximately half (29/55) of genes differentially expressed in CTCs were prognostic in patients with newly-diagnosed myeloma (n¿=¿553; CoMMpass). In a multivariate analysis including the R-ISS, overexpression of CENPF and LGALS1 was significantly associated with inferior survival. Altogether, these results help understanding the presence of CTCs in PB and suggest that hypoxic BM niches together with a pro-inflammatory microenvironment induce an arrest in proliferation, forcing tumor cells to circulate in PB and seek other BM niches to continue growing.