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: 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.

Abstract: From November 2014 to May 2017, 332 patients homogeneously treated with VRD induction, ASCT and VRD consolidation were randomized to receive maintenance therapy with RD (161 patients) vs IRD (171 patients). RD consisted of lenalidomide 15 mg/d from days 1-21 plus dexamethasone 20 mg/d on days 1-4 and 9-12 at 4-weeks intervals while in the IRD arm oral ixazomib at a dose of 4 mg on days 1,8, and 15 was added. MRD negative patients after 24 cycles were discontinued while those who were MRD positive remained on maintenance with RD for 36 more cycles. The MRD negativity from baseline increased from 50.9% to 71.8% with RD and from 59.6% to 72.4% with IRD at 2 years. After a median follow-up of 69 months from the initiation of maintenance, the PFS was similar in both arms, median not reached in either arm with a 6-years PFS rate of 61.3% and 55.6% for RD and IRD, respectively (HR 1.136 [95% CI 0.809 - 1.603]). No significant differences in PFS between RD and IRD were observed in any prognostic subgroup. After 2 years of maintenance, treatment was discontinued in 163 patients who were MRD negative while 63 MRD positive patients were continued on RD therapy. Maintenance discontinuation in MRD negative patients resulted in a low progression rate (17.2% at 4 years), even in patients with high-risk features. In summary, our results show the efficacy of RD maintenance and support the safety of maintenance discontinuation in MRD negative patients at 2 years. This trial is registered at ClinicalTrials.gov (NCT02406144) and EudraCT (2014-00055410).

Abstract: Monitoring of the monoclonal protein (M-protein) by electrophoresis and/or immunofixation (IFE) has long been used to assess treatment response in multiple myeloma (MM). However, with the use of highly effective therapies, the M-protein becomes frequently undetectable, and more sensitive methods had to be explored. We applied IFE and mass spectrometry (EXENT & FLC-MS) in serum samples from newly diagnosed MM patients enrolled in the PETHEMA/GEM2012MENOS65 obtained at baseline (n = 223), and after induction (n = 183), autologous stem cell transplantation (n = 173), and consolidation (n = 173). At baseline, the isotypes identified with both methods fully matched in 82.1% of samples; in the rest but 2 cases, EXENT & FLC-MS provided additional information to IFE with regards to the M-protein(s). Overall, the results of EXENT & FLC-MS and IFE were concordant in & gt;80% of cases, being most discordances due to EXENT & FLC-MS+ but IFE− cases. After consolidation, IFE was not able to discriminate 2 cohorts with different median progression-free survival (PFS), but EXENT & FLC-MS did so; furthermore, among IFE− patients, EXENT & FLC-MS identified 2 groups with significantly different median PFS (P = .0008). In conclusion, compared with IFE, EXENT & FLC-MS is more sensitive to detect the M-protein of patients with MM, both at baseline and during treatment, and provides a more accurate prediction of patients’ outcome. This trial was registered at www.clinicaltrials.gov as #NCT01916252.

Abstract: Background: Transplant-eligible MM patients are achieving unprecedented CR rates with frontline therapy. This urges the question about what other tests are informative upon a negative immunofixation (IFx-), as well as if patients with short duration CR continue having dismal survival with modern frontline plus salvage therapies and if so, how to predict risk of unsustained CR. Aim: To provide an optimal definition of unsustained CR and biomarkers to predict it in transplant-eligible MM patients treated with optimal therapy. Methods: A total of 262 patients enrolled in the PETHEMA/GEM2012MENOS65 trial and who were in CR after receiving six induction cycles of bortezomib, lenalidomide and dexamethasone (VRD), autologous transplant and two consolidation cycles of VRD, were included in this study. Afterwards, patients were enrolled in the PETHEMA/GEM2014MAIN trial. Median follow-up of the series was 38 months after consolidation (53 months since diagnosis). Serum free light-chains (sFLC) were measured in 252 cases. MRD was assessment with next-generation flow (NGF) in 257 patients (median limit of detection of 2.8x10-6). FISH was performed in CD138-enriched plasma cells (PCs) from 223 patients at diagnosis [high-risk was defined by the presence of t(4;14), t(14;16) and/or del(17p)]. To understand the relationship between duration of CR and outcome, patients were segmented into 6-monht increments (range, 0 - 48 months) in time since response assessment after consolidation and loss of CR. Results: We first investigated what other tests commonly performed upon the achievement of IFx- were informative, particularly those employed to define stringent CR. The median percentage of PCs by morphology, at the time of IFx-, was 1.7% (range 0-5). Only 4 patients out of 266 (1.5%) with a negative immunofixation had & gt;5% BM PCs and therefore, were not classified as in CR. Almost one-fourth of patients in CR display an abnormal sFLC ratio (56/248, 23%), but their PFS was identical to that of cases with normal sFLC ratio (3 years-PFS rates of 70% vs 72%; P=.6). BM biopsies were not performed in this study to evaluate PC clonality by immunohistochemistry but we noted that in CR patients with persistent MRD, the median percentage of clonal and normal PCs among total PCs identified by NGF was of 3% and 97%, respectively. Thus, the median percentage of normal PCs is 24-fold greater than clonal PCs within the PC compartment, and therefore simple κ/λ ratios measured in ≥100 PCs are unable to detect such low-levels of residual disease. Indeed, persistent MRD was detectable by NGF in 73/252 (29%) CR patients at a median level of 0.03% (range 0.0002% - 0.59%), and resulted in significantly inferior PFS (3-year rates of 49% vs 83% in cases with persistent vs undetectable MRD, P & lt; .00001) and OS (3 years-PFS rates of 84% vs 95%; P=.001). Afterwards, we sought to identify the optimal landmark to define unsustained CR and to identify which biomarkers could identify patients at risk. A duration of CR of & lt;24 months emerged as optimal criteria to identify a numerically relevant subset of patients (39/262, 15%) with dismal OS (median of 48 months vs not reached in patients with duration of CR ≥24 months, P & lt; .00001). We then performed a logistic regression to identify biomarkers with independent value to predict unsustained CR. Elevated LDH levels (HR: 2.9 [1.1 - 8.0], P =.038) and & gt;20% PCs (HR: 2.8 [1.2 - 6.7], P =.020) at diagnosis together with persistent MRD (HR: 4.3 [1.9 - 9.6] , P & lt;.001) had independent value to predict unsustained CR in a multivariable analysis. Thus, a scoring system with the three parameters yielded significant stratification of patients in CR into favorable (no risk factors), intermediate (any risk factor) and dismal (all risk factors) PFS (3-year rates of 92%, 67% and 0%, respectively; P & lt;.0001; Figure 1) and OS (3-year rates of 99%, 90% and 83%, respectively; P=.001). Conclusions: This is the first study evaluating which baseline and response assessments are useful to stratify transplant-eligible patients with unsustained CR in the context of modern therapy; a simple model based on LDH levels and PC counts at diagnosis plus MRD status was identified and can be used broadly. These findings are clinically meaningful because patients with unsustained CR remain a high-risk population despite optimal therapy, and those at risk should be offered alternative treatment strategies before insurmountable disease progression occurs. Figure 1 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. Oriol:Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy; Celgene: Consultancy, Speakers Bureau. Sureda Balari:Roche: Honoraria; Celgene: Consultancy, Honoraria; BMS: Speakers Bureau; Incyte: Consultancy; Janssen: Consultancy, Honoraria; Gilead/Kite: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Merck Sharpe and Dohme: Consultancy, Honoraria, Speakers Bureau; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Speakers Bureau. de la Rubia:Janssen: Consultancy, Other: Expert Testimony; Amgen: Consultancy, Other: Expert Testimony; Celgene: Consultancy, Other: Expert Testimony; Ablynx/Sanofi: Consultancy, Other: Expert Testimony. Moraleda:Takeda: Consultancy, Other: Travel Expenses; Sandoz: Consultancy, Other: Travel Expenses; Novartis: Consultancy, Other: Travel Expenses; Gilead: Consultancy, Other: Travel Expenses; Jazz Pharmaceuticals: Consultancy, Research Funding. Mateos:Pfizer: 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; GlaxoSmithKline: Honoraria; Seattle Genetics: 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; 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; Sanofi: Honoraria, 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; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. San-Miguel:GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: 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; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, 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.