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PURPOSE Patients with newly diagnosed multiple myeloma (NDMM) show heterogeneous outcomes, and approximately 60% of them are at intermediate-risk according to the Revised International Staging system (R-ISS), the standard-of-care risk stratification model. Moreover, chromosome 1q gain/amplification (1q+) recently proved to be a poor prognostic factor. In this study, we revised the R-ISS by analyzing the additive value of each single risk feature, including 1q+. PATIENTS AND METHODS The European Myeloma Network, within the HARMONY project, collected individual data from 10,843 patients with NDMM enrolled in 16 clinical trials. An additive scoring system on the basis of top features predicting progression-free survival (PFS) and overall survival (OS) was developed and validated. RESULTS In the training set (N = 7,072), at a median follow-up of 75 months, ISS, del(17p), lactate dehydrogenase, t(4;14), and 1q+ had the highest impact on PFS and OS. These variables were all simultaneously present in 2,226 patients. A value was assigned to each risk feature according to their OS impact (ISS-III 1.5, ISS-II 1, del(17p) 1, high lactate dehydrogenase 1, t(4;14) 1, and 1q+ 0.5 points). Patients were stratified into four risk groups according to the total additive score: low (Second Revision of the International Staging System [R2-ISS]-I, 19.2%, 0 points), low-intermediate (II, 30.8%, 0.5-1 points), intermediate-high (III, 41.2%, 1.5-2.5 points), high (IV, 8.8%, 3-5 points). Median OS was not reached versus 109.2 versus 68.5 versus 37.9 months, and median PFS was 68 versus 45.5 versus 30.2 versus 19.9 months, respectively. The score was validated in an independent validation set (N = 3,771, of whom 1,214 were with complete data to calculate R2-ISS) maintaining its prognostic value. CONCLUSION The R2-ISS is a simple prognostic staging system allowing a better stratification of patients with intermediate-risk NDMM. The additive nature of this score fosters its future implementation with new prognostic variables.

INTRODUCTION: There is expectation of using biomarkers to personalize treatment in MM. Yet, a successful treatment selection cannot be confirmed before 5 or 10 years of progression-free survival (PFS). Treatment individualization based on the probability of an individual patient to achieve undetectable MRD with a singular regimen, could represent a new model towards personalized treatment with fast assessment of its success. This idea has not been investigated previously. AIM: Develop a machine learning model to predict undetectable MRD in newly-diagnosed transplant-eligible MM patients, treated with a standard of care. METHODS: This study included a total of 278 newly-diagnosed and transplant-eligible MM patients treated with proteasome inhibitors, immunomodulatory drugs and corticosteroids. The training (n=152) and internal validation cohort (n=60) consisted of 212 active MM patients enrolled in the GEM2012MENOS65 trial. The external validation cohort was defined by 66 high-risk smoldering MM patients enrolled in the GEM-CESAR trial, which treatment differed only by the substitution of bortezomib by carfilzomib during induction and consolidation. RESULTS: We started by investigating patients' MRD status after VRD induction, HDT/ASCT and VRD consolidation according to their ISS and R-ISS, LDH levels, and cytogenetic alterations. Surprisingly, neither the ISS nor the R-ISS predicted significantly different MRD outcomes. Indeed, high LDH levels and del(17p13) were the only parameters associated with lower rates of undetectable MRD. Because these two features are relatively infrequent at diagnosis, we next aimed to evaluate other disease features and develop integrative, weighted and more effective models based on machine learning algorithms. Of 37 clinical and biological parameters evaluated, 17 were associated with MRD outcomes. These were subsequently modeled using logistic regression for machine learning classification, where the sum of the weighted coefficients multiplied by its input variable, is transformed into a probability outcome that ranges from 0 to 1 using a logit sigmoid function. The most effective model resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (plasma cell [PC] clonality in bone marrow and CTCs in blood) and immune related (myeloid precursors, mature B cells, intermediate neutrophils, eosinophils, CD27 negCD38 pos T cells and CD56 brightCD27 neg NK cells) biomarkers. Of note, immune biomarkers displayed the highest coefficient weights and were determinant to predict patients' MRD status in this model. Data obtained for an individual patient can be substituted into our formula, which results in a numerical probability of achieving undetectable (>0.5) vs persistent (0.685 or Patients predicted to achieve undetectable MRD using standard and high-confidence values showed longer PFS and overall survival (OS) than those with probability of persistent MRD. In fact, patients with >0.687 probability of achieving undetectable MRD showed 86% PFS and 94% OS at five years, whereas those in whom persistent MRD was predicted ( CONCLUSION: We demonstrated that it is possible to predict patients' MRD status with significant accuracy, using an integrative, weighted model based on machine learning algorithms. Although immune biomarkers are not commonly used, the raw data from which these can be developed is generally obtained in diagnostic laboratories using flow cytometry to screen for PC clonality. Furthermore, we made the model available to facilitate its use in clinical practice at www.MRDpredictor.com. Disclosures Puig: Celgene, Janssen, Amgen, Takeda: Research Funding; Celgene: Speakers Bureau; Amgen, Celgene, Janssen, Takeda: Consultancy; Amgen, Celgene, Janssen, Takeda and The Binding Site: Honoraria. Cedena: Janssen, Celgene and Abbvie: Honoraria. Oriol: Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, 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; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. De Arriba: Amgen: Consultancy, Honoraria; Glaxo Smith Kline: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; BMS-Celgene: Consultancy, Honoraria, Speakers Bureau. Martínez-López: Janssen, BMS, Novartis, Incyte, Roche, GSK, Pfizer: Consultancy; Roche, Novartis, Incyte, Astellas, BMS: Research Funding. Lahuerta: Celgene: Other: Travel accomodations and expenses; Celgene, Takeda, Amgen, Janssen and Sanofi: Consultancy. Rosinol: Janssen, Celgene, Amgen and Takeda: Honoraria. Bladé Creixenti: Janssen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. Mateos: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria; Bluebird bio: Honoraria; GSK: Honoraria; Sanofi: 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; 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; Adaptive Biotechnologies: 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; Oncopeptides: 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; Oncopeptides: Honoraria; Janssen: 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. San-Miguel: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Karyopharm, Merck Sharpe & Dohme, Novartis, Regeneron, Roche, Sanofi, SecuraBio, and Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. 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.

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 < .001). These results were validated in the Arkansas series (8%, 27% and 71% at 5y, respectively; P < .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 < .001) and median OS was not reached (NR) vs 6.5y (HR: 0.48, P < .001), respectively. These results were validated in the Czech Republic series with significant differences in PFS (HR: 0.45, P < .001) and OS (HR: 0.38, P < .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 < .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 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 < .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.