Abstract: Although light-chain amyloidosis (AL) and multiple myeloma (MM) are characterized by tumor plasma cell (PC) expansion in bone marrow (BM), their clinical presentation differs. Previous attempts to identify unique pathogenic mechanisms behind such differences were unsuccessful, and no studies have investigated the differentiation stage of tumor PCs in patients with AL and MM. We sought to define a transcriptional atlas of normal PC development in secondary lymphoid organs (SLOs), peripheral blood (PB), and BM for comparison with the transcriptional programs (TPs) of tumor PCs in AL, MM, and monoclonal gammopathy of undetermined significance (MGUS). Based on bulk and single-cell RNA sequencing, we observed 13 TPs during transition of normal PCs throughout SLOs, PB, and BM. We further noted the following: CD39 outperforms CD19 to discriminate newborn from long-lived BM-PCs; tumor PCs expressed the most advantageous TPs of normal PC differentiation; AL shares greater similarity to SLO-PCs whereas MM is transcriptionally closer to PB-PCs and newborn BM-PCs; patients with AL and MM enriched in immature TPs had inferior survival; and protein N-linked glycosylation–related TPs are upregulated in AL. Collectively, we provide a novel resource to understand normal PC development and the transcriptional reorganization of AL and other monoclonal gammopathies.

Abstract: The existence of patients with multiple myeloma (MM) and light-chain (AL) amyloidosis who present with a monoclonal gammopathy of undetermined significance (MGUS)–like phenotype has been hypothesized, but methods to identify this subgroup are not standardized and its clinical significance is not properly validated. PATIENTS AND METHODS An algorithm to identify patients having MGUS-like phenotype was developed on the basis of the percentages of total bone marrow (BM) plasma cells (PC) and of clonal PC within the BM PC compartment, determined at diagnosis using flow cytometry in 548 patients with MGUS and 2,011 patients with active MM. The clinical significance of the algorithm was tested and validated in 488 patients with smoldering MM, 3,870 patients with active MM and 211 patients with AL amyloidosis. RESULTS Patients with smoldering MM with MGUS-like phenotype showed significantly lower rates of disease progression (4.5% and 0% at 2 years in two independent series). There were no statistically significant differences in time to progression between treatment versus observation in these patients. In active newly diagnosed MM, MGUS-like phenotype retained independent prognostic value in multivariate analyses of progression-free survival (PFS; hazard ratio [HR], 0.49; P = .001) and overall survival (OS; HR, 0.56; P = .039), together with International Staging System, lactate dehydrogenase, cytogenetic risk, transplant eligibility, and complete remission status. Transplant-eligible patients with active MM with MGUS-like phenotype showed PFS and OS rates at 5 years of 79% and 96%, respectively. In this subgroup, there were no differences in PFS and OS according to complete remission and measurable residual disease status. Application of the algorithm in two independent series of patients with AL predicted for different survival. CONCLUSION We developed an open-access algorithm for the identification of MGUS-like patients with distinct clinical outcomes. This phenotypic classification could become part of the diagnostic workup of MM and AL amyloidosis.

Abstract: Introduction:Renal impairment (RI) is a common complication of multiple myeloma (MM). Almost 20% of patients (pts) present with RI at diagnosis, while approximately 40%-50% of pts will develop RI during the course of their disease. However, there is little information on the renal response of pts with relapsed refractory MM (RRMM) receiving treatment with new drugs in clinical practice. Aims: This is an observational, prospective, multicenter study conducted in pts with RRMM and RI (defined as an estimated glomerular filtration rate [eGFR] 〈 50 mL/min) to evaluate renal response to the administered therapy in pts with moderate (creatinine clearance [CrCl] 30-50 mL/min) or severe (CrCl 〈 30 mL/min) RI. Secondary objectives include MM response rate, overall survival, safety, and health resource utilization. We present results from an interim analysis 4 mos after completion of the inclusion period (cutoff: June 13, 2016). Methods:Renal and MM responses were evaluated according to International Myeloma Working Group criteria. Both eGFR by the Cockroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) formulas were compared to analyze renal response. Results:Overall, 312 pts (mean ± SD age 75 ± 9 yrs, 50% male, 57% in first relapse) were included in the study; 217 (70%) had moderate and 95 (30%) had severe RI, respectively. Anti-myeloma therapies administered were lenalidomide (LEN; 35% of pts), bortezomib (BORT; 21%), different chemotherapy regimens (CT; 22%), and other non-CT treatments (22%). Median follow-up was 7 mos (range, 0-39 mos). To date, 123 pts (39%) have discontinued treatment, 12% due to adverse events (AEs), and 37% have died. The main causes of death were disease progression (8.3%) and infections (6.4%). The mean baseline eGFR according to CG and MDRD formulas was 38.7/41.7 (± 8.5/11.8) mL/min in the moderate RI subgroup and 20.3/20.1 (± 8.0/10.1) mL/min in the severe RI group, with a strong correlation (coefficient 0.91) between the CG and MDRD eGFR. Overall, 13.5% (95% CI, 9.7%-17.2%) of patients had a renal response (5.8% renal complete response [renalCR], 0.3% renal partial response [renalPR] , and 7.4% renal minor response [renalMR]) according to the CG formula while responses measured by the MDRD formula, were 17.3% (9.9% renalCR, 0.3% renalPR, and 7.1% renalMR). Median time to best renal response was 1.8 mos (range, 0.5-8.9 mos). After adjusting for demographic and clinical characteristics, there were no significant differences in GFR improvement between pts receiving LEN- and BORT-based treatments (P = 0.706). Arterial hypertension and female sex were statistically significantly associated with poor renal response. The overall MM efficacy response rate (≥ PR) was 33.4%, achieved after a median of 3.4 mos (range, 0.07-37.8 mos). For pts receiving BORT and LEN, respectively, the overall response rates were 43.5% and 44.8%, whereas only 23% of pts receiving CT achieved at least PR. Progression-free survival was 13.3 mos with LEN-based, 6.8 mos with BORT-based, and 7.5 mos with CT-based therapies (P = 0.006). Conclusions: Preliminary results of this study in pts with RRMM and RI show that LEN- and BORT-based therapies are the regimens most commonly used in clinical practice in these pts. Overall, these therapies can improve RI in approximately 13% of cases, with no differences seen in renal function improvement between LEN- and BORT-based treatments. Disclosures De La Rubia: Amgen, Bristol Myers, Celgene, Janssen: Consultancy. Morales:Celgene: Consultancy. García-Muñoz:Celgene, Roche: Consultancy. Duran:Celgene: Employment.

Abstract: 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-value 〈 0.01, logFC 〉 1) 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.

Abstract: Introduction: In recent years, the multiple myeloma (MM) treatment (Tx) landscape has evolved considerably, with the approval of effective novel agents, such as immunomodulatory drugs (thalidomide, lenalidomide, and most recently, pomalidomide) and proteasome inhibitors (bortezomib and carfilzomib; Chng et al, Leukemia, 2014). However, most patients relapse and patterns of relapse in MM can be heterogeneous (Alegre et al, Haematologica, 2002). Tx rescue for MM relapse should begin at symptomatic clinical relapse (clinR) and probably earlier at asymptomatic biological relapse (BR; specifically when significant paraprotein relapse), according to the International Myeloma Workshop Consensus Panel (Rajkumar et al, Blood, 2011). In the case of asymptomatic BR/progression, rescue Tx could be delayed in a subset of pts (Fernández de Larrea et al, Bone Marrow Transplant, 2014), particularly when M-component and hemoglobin levels are stable. Progressive increases of M-spike blood and/or urine could lead to rescue Tx, even in the absence of clinical symptoms, to avoid complications (ie, renal failure, plasmacytomas, bone lesions; Castelli et al, Clin Med Insights Oncol, 2013). This Spanish registry (EPA-MMBR) is an observational prospective study to describe MM relapse patterns, comparing the impact of Tx decisions (starting Tx at BR vs delaying Tx until clinR). We present the updated preliminary results of this study. Methods: MM pts in (or prior to) first or second BR who had achieved ≥ PR since their last Tx were included. Bi-monthly evaluations were performed. Forty-one Spanish sites are participating after approval from their independent ethics committees, with 410 pts expected to be included. The main objective was to assess time to progression (TTP) from BR in both groups (pts who started Tx at the onset of asymptomatic BR vs pts who started Tx at clinR). Secondary objectives included demographic, clinical, and Tx characteristics; median time from BR to clinR; and response rates. Here we present the results of 138 pts with baseline data (48.4%) of a total of 285 registered. Results: In the cohort evaluated (n = 138), mean age was 67.9 years, and 50.7% were male. MM types were IgG Κ (43.5%), IgG λ (22.1%), IgA Κ (16%), and IgA λ (7.6%). Prognostic stage at diagnosis according to the International Staging System (ISS) was II (32.6%) followed by I and III (22.7% each) and data not available (22%). Pts' cytogenetic risk included standard risk (20.8%), high risk (8.5%), and no cytogenetic alterations (32.3%). More than half of pts (51.5%) had received autologous stem cell transplant, 13.2% consolidation and 21.1% maintenance. After first-line Tx, pts had achieved sCR (16.0%), CR (33.6%), VGPR (28.8%), or PR (21.6%). Pts with BR had a median time from diagnosis to BR of 2.66 years. Tx was delayed until clinR in 51.2% of pts, and 48.8% had started Tx at BR (73.3% of whom had significant paraprotein relapse; Figure); there were no relevant differences in demographic and clinical characteristics between the 2 groups. Median time to BR was 31.97 months. Median time from BR to clinR was 105.0 days. Among pts who started Tx at BR, most pts received lenalidomide-based Tx (75% at first relapse and 70.6% at second relapse). At the time of the analysis, only 3 pts had disease progression (1 in each group). In pts who started Tx at BR, first-line Tx after diagnosis was mainly bortezomib-based (73.3%). sCR was achieved in 20.0% of pts, CR in 33.3%, PR in 26.7%, and VGPR in 20.0%. Since the beginning of the study, Tx was prescribed in 54 pts (39.1%). Since only 3 pts had progressed, further follow-up data, particularly in terms of TTP and survival, are needed to identify differences between these 2 strategies. Conclusions: To our knowledge, this is the first prospective study in MM that evaluates the effects of starting Tx at BR vs starting Tx at clinR. In this updated cohort, we find that almost half of pts started Tx at BR (48.8%). We also found that a higher percentage of pts started Tx at BR with significant paraprotein relapse (73.3%), which seems to be a clear factor guiding Tx decision for the physician. Further follow-up and a complete study are needed to identify the differences between these 2 strategies and to define patients who could benefit from early Tx. Figure 1. Percentage of pts who started Tx at clinR, BR, or significant paraprotein relapse (n = 138). Figure 1. Percentage of pts who started Tx at clinR, BR, or significant paraprotein relapse (n = 138). Disclosures Gironella: Celgene Corporation: Consultancy, Honoraria. Fernández de Larrea:Celgene Corporation: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Lahuerta:Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cabañas:Amgen: Membership on an entity's Board of Directors or advisory committees. Lostaunau:Celgene SLU: Employment. Vilanova:Celgene SLU: Employment. Alegre:Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.

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: Background: Within the spectrum of monoclonal gammopathies, there are various subgroups with unique biological and clinical profiles. Namely, the presence of multiple myeloma (MM) and light-chain amyloidosis (AL) pts with MGUS-like phenotype has been hypothesized, but the criteria to identify this subgroup are poorly defined and lack clinical validation. Aim: Develop an algorithm based on a large flow cytometry dataset across the spectrum of monoclonal gammopathies, for automated identification of MM and AL pts with MGUS-like phenotype. Methods: This study included 5,114 pts with monoclonal gammopathies and available flow cytometry data on the frequency of bone marrow (BM) plasma cells (PC) and the percentages of normal and clonal PC within the BM PC compartment, at diagnosis. An algorithm to classify pts with MGUS-like phenotype was developed based on these three parameters, obtained from 548 MGUS, 393 smoldering MM (SMM) and 2,011 MM pts. Newly diagnosed MM pts were homogeneously treated according to the GEM2000 (n = 486), GEM2005MENOS65 (n = 330), GEM2005MAS65 (n = 239), GEM2010MAS65 (n = 230), GEM2012MENOS65 (n = 450) and CLARIDEX (n = 276) protocols. The prognostic value of the MGUS-like phenotype was validated in 96 SMM pts studied in Arkansas and 1,859 MM pts treated outside clinical trials in Czech Republic. The clinical significance of the algorithm was investigated in two independent series of Spanish (n = 102) and Italian (n = 105) AL pts. Results: The frequency of BM PC and of normal and clonal PC within the BM PC compartment were used to plot MGUS, SMM and MM pts in a principal component analysis (PCA). Lines defining 1.5 standard deviations of MGUS and MM pts were used as reference to classify each of the 5,114 cases. Once plotted against the dataset, individual pts were classified as MGUS-, intermediate- or MM-like, if their location in the PCA fell inside the MGUS, the overlapping or the MM reference lines, respectively. In the training SMM series, patient classification into MGUS-, intermediate- and MM-like phenotype resulted in significantly different rates of disease progression (0%, 54% and 66% at 5y, respectively; P & lt; .001). These results were validated in the Arkansas series (8%, 27% and 71% at 5y, respectively; P & lt; .001). Only 5% of SMM pts with high-risk disease according to Mayo or PETHEMA criteria had an MGUS-like phenotype, and these had virtually no risk of progression at 5y. In the training MM series, pts with MGUS-like phenotype showed significantly longer progression free (PFS) and overall survival (OS) vs the remaining pts. Median PFS was 10y vs 3y (hazard ratio [HR]: 0.46, P & lt; .001) and median OS was not reached (NR) vs 6.5y (HR: 0.48, P & lt; .001), respectively. These results were validated in the Czech Republic series with significant differences in PFS (HR: 0.45, P & lt; .001) and OS (HR: 0.38, P & lt; .001) between MGUS-like vs other MM pts. MGUS-like classification in the training MM series retained independent prognostic value in multivariate analyses of PFS (HR: 0.48, P & lt; .001) and OS (HR: 0.54, P = .033), together with ISS, LDH, cytogenetics, induction regimen, transplant-eligibility and complete remission (CR). MGUS-like pts showed similar PFS (P = .932) and OS (P = .285) regardless of having standard vs high risk cytogenetics. Notably, MGUS-like transplant-eligible MM pts treated with proteasome inhibitors, immunomodulatory drugs and corticoids during induction showed PFS and OS rates at 5y of 86% and 96%, respectively. Differences in PFS among MGUS-like MM pts achieving ≥CR vs & lt;CR were not significant (median of 13y vs 9y, respectively; P = .122), which suggests that attaining CR is not mandatory to reach long-term survival in this subgroup of pts, treated with fixed-duration regimens. Classification of AL pts into the MGUS-, intermediate- and MM-like phenotype resulted in significantly different PFS in the Spanish (median of 28, 20 and 1 months, respectively; P = .001) and Italian (median 32, 11 and 3 months, respectively; P & lt; .001) cohorts. Conclusions: We developed an algorithm that can be readily installed in clinical flow cytometry software, and requires three parameters that are routinely assessed at screening. Patient' automated classification using the algorithm was validated in large series across the spectrum of monoclonal gammopathies. Because pts with MGUS-like phenotype have a distinct clinical behavior, their identification could become part of the diagnostic workup in SMM, MM and AL. Disclosures Cedena: Janssen, Celgene and Abbvie: Honoraria. Milani: Celgene: Other: Travel support; Janssen-Cilag: Honoraria. Cordon: Cytognos SL: Research Funding. Oriol: Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy. de la Rubia: Amgen, Bristol Myers Squibb,: Honoraria, Speakers Bureau; Celgene, Takeda, Janssen, Sanofi: Honoraria; Ablynx/Sanofi: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; AbbVie: Consultancy; Bristol Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Accommodations; GSK: Consultancy; Takeda: Consultancy; Sanofi: Membership on an entity's Board of Directors or advisory committees. De Arriba: Amgen: Consultancy, Honoraria; Glaxo Smith Kline: Consultancy, Honoraria; BMS-Celgene: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau. Cabañas: Janssen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Sanofi: Honoraria. Gonzalez De La Calle: Celgene-BMS, Janssen, Amgen: Honoraria. Rodríguez-Otero: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Consultancy, 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; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel and other expenses. Hajek: Pharma MAR: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Jimenez-Zepeda: BMS, Amgen, Takeda, Janssen: Honoraria. Palladini: Janssen Global Services: Honoraria, Other: advisory board fees; Pfizer: Honoraria; Siemens: Honoraria. Rosinol: Janssen, Celgene, Amgen and Takeda: Honoraria. Bladé Creixenti: Janssen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. Martínez-López: Janssen, BMS, Novartis, Incyte, Roche, GSK, Pfizer: Consultancy; Roche, Novartis, Incyte, Astellas, BMS: Research Funding. Mateos: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Regeneron: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene - Bristol Myers Squibb: 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; Sea-Gen: 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; AbbVie: Honoraria; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bluebird bio: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria; Oncopeptides: Honoraria. San-Miguel: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Merck Sharpe & Dohme, Novartis, Regeneron, Roche, Sanofi, SecuraBio, Takeda: Consultancy, Other: Advisory board. Paiva: Bristol-Myers Squibb-Celgene, Janssen, and Sanofi: Consultancy; Adaptive, Amgen, Bristol-Myers Squibb-Celgene, Janssen, Kite Pharma, Sanofi and Takeda: Honoraria; Celgene, EngMab, Roche, Sanofi, Takeda: Research Funding.