Abstract: Background: The advent of immunotherapy renewed the interest in immune monitoring to identify determinants of treatment response. Flow cytometry is widely adopted in immunotherapy-based clinical trials, but manual analysis of multiparameter files poses a challenge to capture full cellular diversity and to provide unbiased reporting in large datasets. Methods: Here, we developed a semi-automated pipeline named "FlowCT" which, starting from compensated data obtained with standardized protocols, allows simultaneous analyses of multiple files and automated cell clustering. FlowCT starts with quality control and data normalization followed by an analytical stage with clustering algorithms, dimensional reduction techniques and cluster identification based on antigen expression. Statistical tools are included for immediate analysis of results. Results: As proof-of-concept, we used FlowCT in three different datasets. First, we applied FlowCT to bone marrow (BM) samples from three multiple myeloma (MM) patients stained with 17-color flow cytometry, to determine the increment in the complexity of analyzing 8 and 17 markers, chosen to characterize T cells. Of note, a single combination of CD3, CD4, CD8, CD45RA, CD56, CCR7, PD1 and TIGIT, allowed the identification of 31 lymphocyte subsets using FlowCT, which increased to 39 different clusters with 17 markers and unveiled a novel population of CD3- CD56- CD8+ CD16+ lymphoid cells in the MM immune microenvironment. Secondly, we applied FlowCT to matched peripheral blood (PB) and BM samples from 10 patients with smoldering MM, to objectively assess if PB represents a good surrogate of T-cell distribution in the BM. Using an 8-color combination to characterize CD4 T cells, up to 26 different subsets were identified, including several CD4 T helper (Th) type subsets. Of note, their distribution within PB CD4 T cells was similar to that found in BM, except for CD4 T CXCR3+CCR4+ effector memory and Th17 central memory subsets that decreased in the BM tumor immune microenvironment. Thirdly, we analyzed 30 BM samples from 10 MM patients studied every year during maintenance therapy, monitored with CD4, CD8, CD25, CD45RA, CD127, CCR7, PD1, and TCRγδ to characterize T cells. FlowCT identified 29 different T-cell populations, including 9 CD4 subsets, 14 CD8 subsets, 4 Tγδ cell subsets and 2 distinct Treg subsets. Longitudinal, semi-automated and unbiased analysis unveiled a significant fluctuation of CD4 naïve and transitional memory cells during maintenance, as well as a significant decrease of CD8 CD127- effector memory and transitional effectors cells after 2 years of maintenance. Conclusions: Here, we presented FlowCT, a pipeline optimized for the analysis of large flow cytometry datasets that could be easily implemented by research laboratories to unveil full cellular diversity, singular patterns of antigen expression, and to provide unbiased reporting in large studies, like clinical trials. Disclosures Puig: Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; The Binding Site: Honoraria; Takeda: Consultancy, Honoraria. Borrello:WindMIL Therapeutics: Equity Ownership, Patents & Royalties, Research Funding; Aduro: Patents & Royalties: intellectual property on allogeneic MM GVAX; BMS: Consultancy; Celgene: Honoraria, Research Funding, Speakers Bureau. Rosinol Dachs:Janssen, Celgene, Amgen and Takeda: Honoraria. Mateos:Janssen, Celgene, Takeda, Amgen, GSK, Abbvie, EDO, Pharmar: Membership on an entity's Board of Directors or advisory committees; Janssen, Celgene, Takeda, Amgen, Adaptive: Honoraria; Amgen Inc, Janssen Biotech Inc: Other: Data and Monitoring Committee; Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Takeda Oncology.: Speakers Bureau; AbbVie Inc, Amgen Inc, Celgene Corporation, Genentech, GlaxoSmithKline, Janssen Biotech Inc, Mundipharma EDO, PharmaMar, Roche Laboratories Inc, Takeda Oncology: Other: Advisory Committee. Lahuerta:Takeda, Amgen, Celgene and Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Bladé:Jansen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Paiva:Celgene, Janssen, Sanofi and Takeda: Consultancy; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche and Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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: Background. The number of CTC predicts risk of transformation in smoldering MM and survival in active MM. Growing evidence suggests that as the tumor progresses and the microenvironment becomes hypoxic, clonal plasma cells (PC) constantly invade new regions of the bone marrow (BM) through induced systemic recirculation. Of note, the frequency of CTCs is typically low and thus, it is conceivable that the dissemination of MM depends on few tumor cells with unique features that induce them to egress the BM and spread the disease through peripheral blood (PB). This hypothesis has not been yet demonstrated because the transcriptional profile of CTCs in MM has not been investigated. Aim. To identify gene regulatory networks related to MM dissemination by comparing the transcriptional profile of CTCs with patient-matched BM clonal PCs. Methods. We used FACS to isolate CTCs and BM clonal PCs of paired PB and BM samples from 34 patients: 24 newly diagnosed MM, 9 relapsed MM and 1 MGUS. Transcriptomes were analyzed using Affymetrix arrays (n =31) and the BD WTA Precise assay was used for single-cell RNA sequencing (scRNAseq, n =3). Data was analyzed using Gene Set Enrichment Analysis (GSEA) and Limma for bulk and Seurat for scRNAseq data. The prognostic value of deregulated genes (FDR 〈 0.1 & logFC 〉 0.5) was investigated using a Cox-regression model in the CoMMpass dataset (n =553, IA11 release). The role of specific deregulated genes was evaluated by shRNA knockdown and blocking using a monoclonal antibody (mAb). Results. Transcriptomic profiling of patient matched CTCs and BM clonal PCs revealed a high correlation in gene expression (r =0.93; p =10-16). Only 45 genes emerged as significantly deregulated in CTCs, and GSEA unveiled biological functions related to inflammatory and interferon response (e.g. CCL5), signaling by IL-6/JAK/STAT3, IL-2/STAT5 and TNF via NFKB (CD44), the epithelial mesenchymal transition (EMP3), mitotic spindle and G2M checkpoints (TOP2A), or E2F targets (BIRC5). A high correlation in gene expression was also observed by scRNAseq (r =0.9; p =10-16), with only 31 genes (e.g. MALAT1, B2M, RHOH, ENAM or DUSP5) differentially expressed (adj.p 〈 0.01). Under the hypothesis that genes deregulated in CTCs could be markers of dissemination and therefore of a more aggressive disease, we evaluated their prognostic significance in CoMMpass, assuming that BM clonal PCs with higher expression of these genes would be enriched in tumor cells with CTC-features. Accordingly, the expression levels of 12/33 upregulated genes in CTCs from bulk RNAseq data were associated with significantly different progression free survival (PFS). In the multivariate analysis including the above mentioned 33 genes and the R-ISS, only FLNA retained independent prognostic value; patients showing higher FLNA expression (n =185/553) had a significantly inferior PFS vs the remaining cases (medians of 22 and 47 months, respectively; p 〈 .001). Of note, patients with higher FLNA expression also displayed significantly higher mutational burden (p =.003). After confirming that genes deregulated in CTCs were clinically meaningful, we selected two genes upregulated in CTCs (FLNA and CD44) to investigate their functional role. Interestingly, knockdown of both genes in the U266 MM cell line significantly altered actin polymerization (p ≤.003) leading to reduce migration (2-fold, p ≤0.01). Furthermore, we evaluated the effect of an anti-CD44 mAb in CB17/Icr-PrKdcscid/Crl immunodeficient mice treated with clodronate to avoid potential phagocytosis. After inoculation of MM1S-GFP intravenously and MM1S-tomato subcutaneously in Matrigel, mice were left untreated (n =11) or treated with a weekly intravenous injection of anti-CD44 (n =11) for one month and monitored weekly by bioluminescence. Overall, there was a significant reduction in the plasmacytoma volume (5-fold, p 〈 .001) and cell engraftment of circulating MM1S cells (42-fold, p =.003) in treated mice. Conclusions. This is the first study analyzing the transcriptional profile of CTCs in MM. Our results reveal that gene expression of CTCs is almost identical to that of patient-matched BM clonal PCs, except for a few genes that are involved in interferon and inflammatory response, hypoxia, cell cycle and migration. Importantly, some of these genes are related to more aggressive disease and therefore, may represent novel therapeutic targets to overcome disease dissemination. Figure. Figure. Disclosures Rios: Amgen, Celgene, Janssen, and Takeda: Consultancy. Martinez-Lopez:Pfizer: Research Funding; Vivia: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; BMS: Research Funding; Novartis: Research Funding. Hajek:Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding. San-Miguel:Sanofi: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Novartis: Honoraria; BMS: Honoraria; Roche: Honoraria.

Abstract: 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 ( 〈 2x10-6), 3-year progression-free survival (PFS) rates were similar for those with SR FISH, t(4;14) and del(17p) (90%, 100% and 89%; p 〉 .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 〈 .001). Since clearance of MRD notably lowered the risk of relapse and persistence of MRD significantly shortened the PFS in each cytogenetic group (p ≤.001), we investigated the unique features of MRD cells persisting after VRD-induction by comparing their transcriptome to that of patient-matched tumor cells at diagnosis (n=40). Accordingly, MRD cells showed 763 genes significantly deregulated (Padj 〈 .05), including a cluster of proteasome subunits and proteasome related genes (i.e. PSMB5, PSMC3IP, BTRC, HUWE1, FBXL20 and TRIM69). Gene set enrichment analysis unveiled biologic determinants of MRD resistance such as the IL6-JAK-STAT signaling pathway in SR patients and the ROS pathway in HR patients (FDR 〈 0.1). Interestingly, the number of genes deregulated in MRD cells of SR patients was 9-fold higher than HR cases suggesting that, whereas in SR MM, a few tumor cells with specific gene regulatory networks may have higher probability to persist VRD induction, the presence of HR cytogenetic alterations is associated per se, with a transcriptional program that allows a few MRD cells to persist treatment. Conclusions: This is one of the largest studies integrating patients' cytogenetics and MRD status. Our results, based on intensive treatment and MRD monitoring using NGF, unveil that achieving MRD-negativity may overcome the poor prognosis of HR cytogenetics. By contrast, persistent MRD significantly reduces PFS rates, particularly in patients with del(17p). Interestingly, MRD cells from SR and HR patients may have different transcriptional mechanisms leading to VRD resistance, and further understanding of these could provide knowledge on how to eradicate MRD in both patient subgroups. Disclosures Puig: Takeda: Consultancy, Honoraria; Celgene: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Garcia-Sanz:Affimed: Research Funding. Martinez-Lopez:BMS: Research Funding; Pfizer: Research Funding; Vivia: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Novartis: Research Funding. Oriol:Celgene: 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; Takeda: 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. Rios:Amgen, Celgene, Janssen, and Takeda: Consultancy. De La Rubia:Ablynx: Consultancy, Other: Member of Advisory Board. Mateos: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; GSK: Consultancy, 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; Celgene: 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; 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. 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:Janssen: Honoraria. San-Miguel:Amgen: Honoraria; BMS: Honoraria; Novartis: Honoraria; Sanofi: Honoraria; Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria.

Abstract: Background: Since survival in AL mainly depends on the extent of organ involvement of patients at presentation, early diagnosis and risk stratification are key to improve patients' outcome. Therefore, together with surrogates of organ involvement, biomarkers identifying patients with MGUS or MM at greater risk of developing AL would be highly valuable to prevent organ damage, to maximize therapeutic efficacy and to improve outcomes in AL. Aim: To investigate the value of multidimensional flow cytometry (MFC) for simultaneous fast diagnostic screening of plasma cell (PC) clonality and risk stratification, as well as to identify immunophenotypic markers useful for the selection of patients with monoclonal gammopathies candidates for monitoring of pre-symptomatic organ damage related to AL. Methods: We used MFC to characterize a large series of patients with newly-diagnosed (ND) AL (N=94) vs MGUS (N=20) and NDMM (N=52), as well as age-matched healthy adults (HA, N=30). For each patient with AL, automated risk stratification was performed using principal component analysis (PCA) based on the relative frequency of bone marrow (BM) PCs, plus the percentage of clonal and normal PCs within the whole BM PC compartment, vs a database containing information on the same three parameters from a total of 1,774 patients, including 497 MGUS and 1,227 NDMM. In parallel, immunophenotypic protein expression profiles (iPEP) of AL patients were clustered using t-SNE, and the comparison between the iPEP of clonal PCs from patients with AL vs MGUS and MM cases was performed using canonical-correlation analysis (CCA). To identify additional immunophenotypic hallmarks of AL, the BM cellular composition in HA, MGUS, AL and MM patients was compared using 2-dimensional minimum spanning tree (MST) force-directed classification to determine the distance among individual cases. Results: PC clonality was detected by MFC in 93/94 (99%) AL patients, whereas an M-component was detectable in 96% of cases by electrophoresis, immunofixation and sFLC. PCA as defined above, identified AL patients displaying an MM-like (n=6) and an MGUS-like (n=38) signature, as well as 49 cases with an intermediate signature between the MGUS and MM reference datasets. Multivariate analysis of baseline prognostic factors for survival, including patients' age, number of organs involved, Mayo staging, the percentage of BM PCs based on cytomorphology and eligibility for ASCT, showed that having an intermediate- or an MM-like profile had an independent adverse effect on patients' progression-free (PFS) and overall survival (OS) (HR:3.4; P≤.02). t-SNE based on the iPEP of clonal PCs revealed two major clusters of AL patients with significantly different PFS, defined by opposite patterns of expression for CD45, CD56 and CD138 (P≤.02). CCA of tumor iPEP showed partial overlap between AL vs MGUS and MM, with progressively higher percentages of cases with a CD38lo, CD45-ve, CD81-ve and CD138lo iPEP being observed from MGUS to AL and MM. In contrast, AL patients displayed significantly lower reactivity for CD56 (P≤ .03). Further characterization of the BM cellular composition allowed the systematic assessment of 16 cell populations and 18 phenotypic parameters that, by MST, mapped AL in between MGUS and MM. Of note, while AL patients displayed a predominantly-clonal PC compartment in the absence of an MM-like tumor PC expansion, the percentage of B-cell precursors was consistently lower in AL patients than in HA, MGUS and MM (P=.004). Thus, using optimal cut-off values to discriminate between AL vs MGUS and MM, we built a scoring model based on the presence of 〈 100% CD56+ve clonal PCs, 〈 0.1% B-cell precursors, 〉 80% clonal PCs within total BM PCs and 〈 2% BM PCs. Overall, a significant (P 〈 .001) association was found between a progressively higher score and the diagnosis of AL, with a 74% accurate classification based on ROC analysis (AUC of 0.74; 95% CI = 0.66 - 0.82; P 〈 .001) of the performance of the scoring model. Conclusions: We demonstrate the value of MFC for fast diagnostic screening of PC clonality in AL and simultaneous automated patient risk-stratification, based on the BM tumor burden and PC phenotype. In addition, our results also provide new immunophenotypic markers for the identification of patients with monoclonal gammopathies that are candidates for monitoring of pre-symptomatic organ damage related to AL. Disclosures Puig: Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Celgene: Honoraria, Research Funding. Ocio:Array Pharmaceuticals: Research Funding; Sanofi: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Pharmamar: Consultancy; BMS: Consultancy; AbbVie: Consultancy; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy; Mundipharma: Research Funding; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Oriol:Takeda: 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; Amgen: 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. De La Rubia:Ablynx: Consultancy, Other: Member of Advisory Board. Martinez Lopez:Janssen: Research Funding, Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Novartis: Research Funding, Speakers Bureau; Bristol Myers Squibb: Research Funding, Speakers Bureau. Mateos:Amgen: 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; Abbvie: 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; Janssen: 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. Lahuerta:Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria.

Abstract: Chromosomal abnormalities (CA) play a pivotal role in predicting MM patients risk. Accordingly, well-defined CA have been associated with higher risk of transformation in smoldering MM (SMM), have been recently incorporated to the revised ISS, and may even guide treatment decisions; furthermore, because high-risk CA can be present in minor subclones at diagnosis it is recommended to reassess them at the time of relapse. Altogether, this implies that throughout their clinical course MM patients go through several bone marrow (BM) aspirates for cytogenetic risk assessment, and less invasive methods to screen for CA [ie. peripheral blood (PB) liquid biopsies] would be mostly welcomed providing they are fully comparable to its assessment in BM clonal plasma cells (PCs). Here, using the recently developed and sensitive next generation flow (NGF) method to monitor MRD in MM (median limit of detection, 3x10-6), we assessed the applicability of screening for CA in the PB, by determining the frequency of MGUS and MM patients with detectable CTCs and their respective absolute numbers. Using NGF, we detected CTCs in 22/37 (60%) MGUS cases, 9/12 (75%) SMM, 24/25 (96%) newly diagnosed MM, and 22/26 (85%) relapsed MM. Median numbers of CTCs/µL in the PB of MGUS, SMM, newly diagnosed and relapsed MM were 0.04, 0.1, 0.5 and 0.6, respectively. Thus, we noted highly significant differences (P 〈 .001) between benign and malignant disease stages, with a 12.5-fold increment in CTCs/µL between MGUS and MM, and 5-fold between SMM and MM. By using whole exome sequencing, we reported recently that the concordance in CA between CTCs and BM clonal PCs was typically 〈 50% due to the limited amount of DNA extracted from low numbers of CTCs, which required whole genome amplification. Accordingly, we investigated in 18 patient paired BM and PB (~5mL) samples (2 MGUS, 2 SMM and 14 MM) whether after NGF sorting and using the Affymetrix CytoScan HD (which requires significantly less DNA to provide molecular karyotypes), we could determine the pattern of CA in BM clonal PCs by screening that of CTCs. In 10 patients in which CTCs were successfully characterized, we obtained 100% concordance between BM clonal PCs and CTCs at the chromosome level (including trisomies of chromosomes 3, 5 and 21 which have been described to modulate the prognosis of patients with high-risk MM), for chromosomal arms, and for interstitial gains or losses (Panel A). Importantly, there was also 100% concordance with FISH performed on CD138+ BM clonal PCs for del(1p), +1q, del(13q) and del(17p) CA. In the remaining cases, ultra-low numbers of sorted CTCs (≤5.000) precluded their successful characterization, which could be readily overcome by using larger volumes of PB (ie. 〉 5mL). Because the CytoScan HD does not assess the t(4;14), we included FGFR3 into the NGF panel of mAb in order to detect its surface expression in patients harboring the t(4;14). Similarly to that described above, we observed 100% concordance between FGFR3 expression in CTCs and the presence of t(4;14) in CD138+ BM clonal PCs, as confirmed by FISH (Panel B). Since gene expression profiling (GEP) has also been used to predict risk in MM, we investigated in 12 patient paired BM and PB samples whether after NGF sorting and using the Affymetrix Human Gene 2.0 ST Array, we could determine patients risk by screening the GEP of CTCs. Unsupervised analysis showed correct clustering between GEP of BM clonal PCs and CTCs in 9/12 cases, demonstrating that individual patient signatures typically outweighed GEP of MM clones located in different tissues (Panel C). Thus, CTCs could be used to predict risk according to well-established GEP signatures. In conclusion, by using sensitive NGF we showed that CTCs are present in 60% of MGUS and 87% of MM, which unravels high feasibility for liquid biopsying these patients. By also using NGF to sort CTCs, as well as commercially available, cost-effective and standardized arrays to determine CA and gene expression profiles, we demonstrated for the first time that the genetic features of BM clonal PCs can be determined with great accuracy by studying CTCs. Altogether, this combined approach could offer to many MM patients a less invasive cytogenetic characterization at certain stages of their clinical course. 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.