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Supplementary Figure from Circulating Tumor and Immune Cells for Minimally Invasive Risk Stratification of Smoldering Multiple Myeloma

Supplementary Data from Circulating Tumor and Immune Cells for Minimally Invasive Risk Stratification of Smoldering Multiple Myeloma

Supplementary Table from Circulating Tumor and Immune Cells for Minimally Invasive Risk Stratification of Smoldering Multiple Myeloma

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

Background The 2/20/20 model to predict risk of transformation in SMM is established at diagnosis and not periodically re-evaluated, mainly because plasma cell (PC) quantification requires invasive bone marrow (BM) aspirates. It could be hypothesized that dynamic monitoring of tumor biomarkers could improve risk-stratification of SMM patients, but this requires minimally invasive methods to replace those performed in BM samples. Such methods should also monitor immune profiles to identify patients with stable tumor burden/genetics, but at risk of progression due to lost immune surveillance. Aim Compare the prognostic value of PC quantification in BM vs the evaluation of circulating tumor cells (CTCs) in peripheral blood (PB), and to define immune signatures predictive of time-to progression (TTP) in SMM. Methods 300 SMM patients are planned to be enrolled in the iMMunocell study that includes 26 sites across 8 European countries. PB samples are collected every 6 months during three years for next-generation flow (NGF) monitoring of CTCs and immune profiling of the T, NK, B and myeloid cell compartments. Cutoffs for risk stratification according to CTCs were calculated using maxstat. A total of 245 SMM patients were enrolled, and we report here a pre-planned interim analysis on the first 150. Results Thus far, 28/150 (19%) patients progressed to MM. Presence of ≤20% vs >20% PCs in BM by morphology failed to predict different TTP(19% vs 32% progressions at 2 years, respectively; p=0.14). Patients with ≤0.73 CTCs/µL had lower risk of transformation vs those with >0.73 CTCs/µL (15% vs 67% progressions at 2 years, respectively; HR: 5.1, p=4.9e-05). Patients with >0.73 CTCs/µL had median TTP of 17.5 months. Patients with low, intermediate and high-risk SMM according to the 2/20/20 model showed significantly different TTP (p=5.14e-06), but those with high-risk had median TTP not reached. Risk stratification using a new 0.7/2/20 (where 0.7 stands for >0.73 CTCs/µL) achieved identical statistical significance (p=5.14e-06), and patients with high-risk SMM had median TTP of 21 months. By applying the gradient boosting algorithm to the T cell dataset, we identified six subsets with an exhausted phenotype CD4+CD28negTIGIT+CD127lo, CD4+CD28+TIGIT+CD127+, CD4+CD28+TIGIT+CD127+CD25+, CD8+CD28negCD127+, CD8+CD28negTIGIT+ and CD8+CD28negTIGIT+PD1+ whose increased frequency was associated with inferior TTP. Patients showing an expansion of these T cell subsets had higher risk of transformation to active MM (HR: 7.33; p=0.002). Conclusions This is the first study performing CTC and immune monitoring every 6 months in PB samples from patients with SMM. Our results suggest that CTC numbers have greater prognostic value than BM PC counts, and that a new 0.7/2/20 model could be dynamically assessed to identify SMM patients at risk of developing active MM. Beyond CTC numbers,this study is also uncovering key immune cell types associated with disease progression.

Background: CTCs may be responsible for MM spreading and accordingly, their numbers in peripheral blood (PB) could be a potential surrogate marker for the rate of dissemination and overall tumor burden in bone marrow (BM). In such case, CTCs may be a powerful biomarker of malignant transformation and disease aggressiveness. Aim: To investigate the clinical significance of CTCs in patients with smoldering (SMM), newly diagnosed (NDMM) and relapsed/refractory MM (RRMM), and to compare the transcriptional profile of CTCs across the disease spectrum. Methods: Next-generation flow (NGF) cytometry was used to assess the percentage of CTCs in PB of 1,157 patients: 316 with SMM, 650 with NDMM and 191 with RRMM. In each disease setting, patients were sub-classified into three groups with undetectable, low and high percentage of CTCs. Cutoffs were defined using maximally selected rank statistics adjusted for time to progression (TTP) in SMM and progression free survival (PFS) in NDMM/RRMM. A subset of SMM patients (n=86) was enrolled in GEM-CESAR. Transplant eligible (n=374) and ineligible (n=276) NDMM, as well as RRMM patients, were homogenously treated according to the GEM2012MENOS65, GEM-CLARIDEX and GEM-KYCYDEX clinical trials, respectively. In 40 patients (2 SMM, 33 NDMM and 5 RRMM) paired CTCs and BM tumor cells were FACSorted and their transcriptional profile was analyzed using RNAseq. Differentially expressed genes were investigated using DESeq2. Results: CTCs were detected in 248/316 (78%), 597/650 (92%) and 170/192 (89%) of SMM, NDMM and RRMM patients. Median CTC frequencies were: 0.001% (0.05 CTCs/µL), 0.01% (0.64 CTCs/µL) and 0.005% (0.22 CTCs/µL), respectively. There were 79 genes differentially expressed between patient-matched CTCs and BM tumor cells (e.g., FLNA, EMP3, LGALS9, MUC1). These were functionally related with TNFα signaling and inflammatory response (enriched in CTCs), as well as to cell cycle and MYC targets (enriched in BM tumor cells). Interestingly, the enrichment of these signatures in CTCs and BM tumor cells was progressively more pronounced from SMM to NDMM and RRMM. Altogether, these data suggest that the CTC-based dissemination potential peaks at the stage of NDMM, which could be related to greater inflammation in BM and cell cycle arrest driving tumor cell egression into PB. There were significant associations between the percentage of CTCs and the 2/20/20 IMWG risk model in SMM, the ISS in NDMM, and high-risk cytogenetics in all three-disease settings. Untreated SMM patients (n=230) with high CTC levels (≥0.02%) showed ultra-high risk of transformation vs those with low and undetectable CTCs (median TTP of 11 months vs not reached [NR] in both; P < .0001). Notably, SMM patients with ≥0.02% CTCs enrolled in GEM-CESAR have not reached a median TTP; thus, early intervention abrogated the poor prognosis of high CTC levels. Transplant-eligible NDMM patients stratified by undetectable, low and high (≥0.2%) CTC levels showed median PFS of NR, 78 and 47 months, respectively (P < .0001). Significant risk stratification was further observed in transplant ineligible NDMM (median PFS: NR, 31 and 14 months, respectively, P = .002) and RRMM (median PFS: NR, 24 and 7 months, respectively, P = .004). In untreated SMM, multivariate analysis of TTP including CTCs, serum M-component (>2 g/dL), sFLC ratio (>20) and BM plasma cells (>20%) selected CTCs as an independent prognostic factor (hazard ratio [HR]: 1.61, P = .015) together with the M-component and sFLC ratio. In NDMM, multivariate analysis of PFS including CTCs, BM plasma cells counts by morphology and flow cytometry, ISS, LDH, cytogenetics and transplant eligibility showed that high CTC levels had independent prognostic value (HR: 1.43, P = .003). Only the achievement of undetectable measurable residual disease (MRD) abrogated the poor prognosis of high CTC levels. Conclusions: This is the largest study investigating the role of CTCs in smoldering and active MM. Our results show that tumor cells are continuously trafficking in PB, possibly through a dynamic mechanism of egression that peaks in NDMM. Evaluation of CTCs in PB outperformed quantification of BM tumor burden in SMM and NDMM, and showed prognostic value in all three-disease stages. Thus, CTC assessment should be part of the diagnostic workup of MM. Early intervention in high risk SMM and undetectable MRD in NDMM may abrogate dismal outcomes associated with high CTC levels. Disclosures Puig: Amgen, Celgene, Janssen, Takeda: Consultancy; Celgene: Speakers Bureau; Celgene, Janssen, Amgen, Takeda: Research Funding; Amgen, Celgene, Janssen, Takeda and The Binding Site: Honoraria. Cedena: Janssen, Celgene and Abbvie: Honoraria. Oriol: GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: 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; Sanofi: 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. Sureda: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GSK: Consultancy, Honoraria, Speakers Bureau; Roche: Other: Support for attending meetings and/or travel; Bluebird: Membership on an entity's Board of Directors or advisory committees; Mundipharma: Consultancy; MSD: Consultancy, Honoraria, Speakers Bureau; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Research Funding, Speakers Bureau. De Arriba: Amgen: Consultancy, Honoraria; Glaxo Smith Kline: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; BMS-Celgene: Consultancy, Honoraria, Speakers Bureau. Moraleda: Pfizer: Other: Educational Grants, Research Funding; Sanofi: Other: Educational Grants, Research Funding; MSD: Other: Educational Grants, Research Funding; ROCHE: Consultancy, Honoraria, Other: Educational Grants, Research Funding; Takeda: Consultancy, Honoraria, Other: Educational Grants, Research Funding; Sandoz: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Other: Educational Grants, Research Funding; Gilead: Consultancy, Honoraria, Other: Educational Grants, Research Funding; Jazz Pharmaceuticals: Consultancy, Honoraria, Other: Educational Grants, Research Funding; NovoNordisk: Other: Educational Grants, Research Funding; Janssen: Other: Educational Grants, Research Funding; Celgene: Other: Educational Grants, Research Funding; Amgen: Other: Educational Grants, Research Funding. Terpos: GSK: Honoraria, Research Funding; Genesis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Novartis: Honoraria; Janssen-Cilag: Consultancy, Honoraria, Research Funding; BMS: Honoraria; Amgen: Consultancy, Honoraria, Research Funding. Goldschmidt: Incyte: Research Funding; Adaptive Biotechnology: Consultancy; BMS: Consultancy, Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding; Celgene: Consultancy, Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding; Chugai: Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding; GSK: Honoraria; Novartis: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding; Johns Hopkins University: Other: Grant; Molecular Partners: Research Funding; MSD: Research Funding; Mundipharma: Research Funding; Dietmar-Hopp-Foundation: Other: Grant; Sanofi: Consultancy, Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy, Honoraria, Other: Grants and/or Provision of Investigational Medicinal Product, Research Funding. Avet-Loiseau: Adaptive Biotechnologies: 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; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: 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; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; 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. Roccaro: AstraZeneca,: Research Funding; Amgen, Celgene, Janssen, Takeda: Membership on an entity's Board of Directors or advisory committees; Associazione Italiana per la Ricerca sul Cancro (AIRC): Research Funding; European Hematology Association: Research Funding; Fondazione Regionale per la Ricerca Biomedica (FRRB), Transcan-2 ERA-NET: Research Funding. Martinez-Lopez: Novartis: 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; Roche: 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; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: 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; 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; 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. Lahuerta: Celgene: Other: Travel accomodations and expenses; Celgene, Takeda, Amgen, Janssen and Sanofi: Consultancy. Ocio: Amgen: Consultancy, Honoraria; Bristol-Myers Squibb/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Honoraria; Karyopharm: Consultancy; MSD: Honoraria; Oncopeptides: Consultancy, Honoraria; Pfizer: Consultancy; Secura-Bio: Consultancy. Rosinol: Janssen, Celgene, Amgen and Takeda: Honoraria. Bladé Creixenti: Janssen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. Mateos: AbbVie: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Honoraria; Oncopeptides: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bluebird bio: Honoraria; 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; 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; Amgen: 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; Celgene - Bristol Myers Squibb: 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; Oncopeptides: Honoraria; Sea-Gen: 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: Adaptive, Amgen, Bristol-Myers Squibb-Celgene, Janssen, Kite Pharma, Sanofi and Takeda: Honoraria; Bristol-Myers Squibb-Celgene, Janssen, and Sanofi: Consultancy; 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.

Introduction The differentiation status plasticity of Multiple Myeloma (MM) plasma cells (PCs) is an adaptive strategy that might confer a specific fitness to tumour cells, enabling their interaction with an evolving microenvironment. Therefore, proliferation rates of MM clones are not constant, and the immunophenotypic profile of the most skilled MM PCs might be the expression of specific genetic and genomic programs, that emerge under the therapeutic pressure and promote tumour development. However, the genomic background that supports any diverse plasma cell differentiation phenotypes has not yet been inferred. Aim To correlate the genetic and genomic background with the immunophenotypic profile of MM clones at diagnosis, in order to stratify patients (pts) according to a Maturation Index, and ultimately to evaluate the impact of this stratification on the disease outcome. Patients and Methods 117 newly diagnosed MM (NDMM) pts, mostly treated up-front with a proteasome inhibitor (PI) -based treatment, were included in the study. For each pts, both neoplastic PCs and CD19+ B cells compartments were characterized by 8-color multi-parameter flow cytometry analysis, combining CD138-PE, CD38-PE-Cy7, CD56-APC-Cy7, CD20-APC, CD19-FITC, CD27-APC, CD45-PerCp, CD28-APC, CD117-FITC, CD28-APC, CD81-APC, IgK-APC and IgL-FITC. In a subgroup of pts, both a SNP array profile for copy number alterations (CNAs) and a 25-genes targeted mutational panel were assessed in CD138+ PCs. A custom ddPCR assay was also employed to evaluate through a 10-Hh gene signature the self-renewal status of PCs. Results In order to define a Maturation Index, pts were evaluated at different levels of heterogeneity for: a) differential expression of CD19/CD81 markers; b) level of chromosomal instability (CIN) and point mutations; d) self-renewal status. Based on the co-expression of CD19 and CD81 markers, pts were stratified in 3 different subgroups, recapitulating a progressive PC maturation process: the most immature, which included pts with CD19+/CD81+ PCs (18/117 = 15%), the intermediate, including CD19-/CD81+ PCs (42/117 = 36%) and the most mature, including CD19-/CD81- PCs (57/117 = 49%). The advanced PC differentiation status characterizing this latter subgroup was further confirmed by the high expression of CD28 and CD44, and the reduced expression of CD20, CD27 and CD45, commonly associated with less mature stages of the disease (p CIN was defined according both to the total CNAs count and to the number of chromosomes' breakpoints (BPs). Pts with an advanced differentiation status displayed a higher number of total CNAs and BPs (median tot. CNAs = 550 vs. 105, and median BPs nr. = 18.5 vs. 8 in CD19-81- and CD19+81+ pts, respectively, p Finally, a more mature PC clone was significantly related to a higher prevalence of unfavourable features at baseline (e.g. ≥ 3 PET lesions, ≥ 100 k/l ratio, ISS III disease stage p Conclusions A Maturation Index defined NDMM pts with an advanced differentiation status both at immunophenotypic and molecular level, finding lastly associated with a prevalence of poor prognostic features. Chromosomal instability, together with cellular phenotypic plasticity, represents an important, yet poorly defined, mechanism by which MM clones accelerate their own evolution and survival. Acknowledgements: AIRC_IG2014-15839, RF-2016-02362532. Disclosures Mancuso: Celgene: Honoraria, Speakers Bureau; Amgen: Honoraria; Takeda: Honoraria; Janssen: Honoraria. Zamagni:Sanofi: Honoraria, Other: Advisory Board, Speakers Bureau; Celgene Corporation: Honoraria, Other: Advisory board, Speakers Bureau; Janssen: Honoraria, Other: Advisory board, Speakers Bureau; Amgen: Honoraria, Other: Advisory board, Speakers Bureau; BMS: Honoraria, Other: Advisory Board, Speakers Bureau; Takeda: Honoraria, Speakers Bureau. Tacchetti:Oncopeptides: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau; BMS: Honoraria; Celgene: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Janssen: Honoraria. Zinzani:CELGENE: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; PORTOLA: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ROCHE: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GILEAD: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; JANSSEN-CILAG: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SERVIER: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SANDOZ: Membership on an entity's Board of Directors or advisory committees; IMMUNE DESIGN: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; EUSAPHARMA: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KYOWA KIRIN: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SANOFI: Consultancy; MSD: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CELLTRION: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; VERASTEM: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Cavo:janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; bms: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; AbbVie: 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, Speakers Bureau; takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; novartis: Honoraria; sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

Multiple Myeloma (MM) maintenance therapy is a low intensive, prolonged treatment, commonly administered to newly diagnosed patients (pts) at the end of fixed-time, front-line regimens. Lenalidomide (LEN) is considered the best available maintenance option for MM able to prolong pts survival. However, the actual benefits or disadvantages of a LEN-based continuous maintenance and its potential role as selective pressure able to induce genomic changes are still unclear. The identification of specific therapy-driven modifications has been so far prevented by the scarce number of homogeneously-treated cohorts of pts with paired samples (diagnosis, D and relapse, R). In this study we estimated the role of LEN maintenance therapy in eliciting genomic changes in a cohort of MM pts homogeneously up-front treated with PI-based regimens. Whole genome Copy Number Aberrations (CNAs) landscape was obtained by SNPs array in 54 pts whose BM-CD138+ were collected both at D and at first R. Pts had an high-risk (HR) disease, as defined both by the presence of HR cytogenetic aberration at baseline in 81% of pts and by the 29 m median TTP. RawCopy 1.10 was used for the segmentation of SNPs array signals; samples' purity was adjusted by using manually reviewed ASCAT solutions. A custom gene-level CN calling approach was set up, to match every gene's CN value at D and R, thus generating genomic evolutive patterns based on changes of these values. Finally, high-risk genomic loci were computed using GISTIC 2.0_v7 to derive focal regions with oncogene and/or tumor suppressor genes relevant for MM biology. After induction therapy, 31/54 pts were treated with HD chemotherapy followed by either single or double ASCT; LEN maintenance therapy was then offered both to 20/31 auto-transplanted and to 6/23 not auto-transplanted pts. Three main evolutive trajectories (linear L, drift D, and branching B) were defined according to the CN changes' direction, reflecting a putative positive, negative, or both positive and negative selective pressure, respectively. A fourth, stable (S) trajectory was also observed, characterized by the absence of CN changes between D and R. Overall, 29, 15 and 10 pts relapsed with B/D, L and S pattern, respectively; at R, all LEN-treated pts quantitively and qualitatively changed their sub-clonal architecture: a B/D evolutive pattern characterized 70% of pts. By contrast, genome remained mostly stable in 61% of not-treated pts. We then focused on CN changes of specific chromosomal regions and/or genomic loci, whose prognostic role has been already established in MM (i.e. CN losses of TP53 on chr17p, RB1 on chr13q, CYLD on chr16q, CDKN2C and FAM46C on chr1p; CN gain of CKS1B on chr1q). When present at D these CNAs tended to persist throughout the disease course regardless of whether pts received or not maintenance. The emersion of any of these CNAs at R was widely observed both in pts receiving or not maintenance, whereas a negative selective pressure over them was more likely to occur in pts receiving maintenance, as compared to the others (50% vs 11% of B/D trajectories in LEN-treated vs not-treated pts, respectively). Strikingly, in LEN-treated pts, the extension of both TTP and OS was favored by the extinction and/or negative selection of the HR CNAs (B/D patterns), and shortened by their stability or positive selection (L/S patterns) (median TTP 46 vs 32 m HR=3,6 CI 1,2-10,6, p=0.01; median OS 111 vs 63 m HR 5,7 CI 1,1-32,4, p=0.04 in B/D vs L/S pts, respectively). On the contrary, the absence of maintenance selective pressure seemed to affect neither the evolution trajectory, nor the clinical course of not-treated pts (fig 1). The extinction of sub-clones carrying HR lesions in pts receiving maintenance therapy is likely to be associated to the negative selection exerted by the therapy itself. This might explain the extended survival of pts receiving maintenance and relapsing with B/D evolution patterns, as compared to that of pts whose genomic landscape either tended to remain stable or let the prevalent emersion of HR genomic features. On the contrary, the sub-clonal architecture of pts not receiving maintenance seemed to randomly evolve, due to the absence of a specific therapy-related selective pressure. Overall, information on the genomic changes occurring throughout the disease course might be relevant for the recognition of pts most benefitting from a continuous therapy. Thanks to AIRC_IG2014-15839, RF-2016-02362532 Disclosures Zamagni: Takeda: Honoraria, Speakers Bureau; Sanofi: Honoraria, Other: Advisory Board, Speakers Bureau; Janssen: Honoraria, Other: Advisory board, Speakers Bureau; Amgen: Honoraria, Other: Advisory board, Speakers Bureau; BMS: Honoraria, Other: Advisory Board, Speakers Bureau; Celgene Corporation: Honoraria, Other: Advisory board, Speakers Bureau. Tacchetti:BMS: Honoraria; Takeda: Honoraria, Speakers Bureau; Janssen: Honoraria; Oncopeptides: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Celgene: Honoraria, Speakers Bureau. Mancuso:Celgene: Honoraria, Speakers Bureau; Janssen: Honoraria; Takeda: Honoraria; Amgen: Honoraria. Petrucci:Janssen-Cilag: 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; Bristol-Myers Squibb: 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; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Liberati:Incyte: Consultancy; Servier: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol & Mayer: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Rossi:Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; Mundipharma: Honoraria; BMS: Honoraria; Sandoz: Honoraria. Boccadoro:Bristol-Myers Squibb: Honoraria, Research Funding; Mundipharma: Research Funding; Sanofi: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; AbbVie: Honoraria. Cavo:amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; novartis: Honoraria; sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; bms: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel accommodations, Speakers Bureau; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees.

Introduction: plasticity is a hallmark of Multiple Myeloma (MM) clone(s), where both quiescent MM cells, acting as tumor-initiating cells, and proliferative MM cells, able to invade and disseminate, might co-exist. Aim of the study is to stratify patients (pts) according to both the level of chromosomal instability (CIN) and their plasma cells (PCs) differentiation stages, and to evaluate the impact of this stratification on the disease outcome. Patients & Methods: 145 newly diagnosed MM pts were included in the study. Whole-genome copy number alterations (CNAs) were analysed by SNP array both in the CD138+PCs and CD19+B-cells. In each pts, both the CD138+/CD38high PCs and CD19+B-cells compartments were characterized by 6-color multi-parameter flow cytometry analysis, combining CD138-PE, CD38-PE-Cy7, CD20-APC, CD19-APC-Cy7, CD27-FITC, CD45-FITC, CD28-APC, CD44-FITC, CD54-APC, CD81-PerCP-Cy5.5, CD56-APC. Results: According to the CD138+ PCs’ CIN, as described both by the total amount of CNAs and by the portion of genome changed (GC), three major pts subgroups were identified: the most representative, including 21/64 pts, was characterized by a higher CIN (median CNAs: 550 %GC ≥ 25) as compared to the others (intermediate and low CIN, median CNAs: 220 10≤ GC%≥25). Hyperdyploidy, but also high-risk features [i.e. 17p del (TP53)] mainly characterized CD138+ PCs with high CIN. On the contrary, in the same pts, the CD19+B-cells display a quite simple karyotype with very few microalterations (>50kb), mostly involved in the signal transduction pathway (loss on KRAS, chr12p12.1 and on SIRPB1, chr20p13). According to the the co-expression of CD19/CD81, describing the MM clone(s) differentiation status, PCs with a high level of CIN resulted more mature CD19-/CD81-. Both the high expression of CD28 and CD44 and the reduced expression of CD20, CD27 and CD45 confirmed the advanced differentiation status. Finally, although baseline clinical features of pts with more mature, genomically instable PCs, are associated to bad prognosis (e.g. PET lesions, k/l ratio, ISS III, β2-microglobulin; p Conclusion: High level of genomic complexity correlates with advanced PCs differentiation stages, and this is lastly associated with a prevalence of poor prognosis features. Both CIN and phenotypic pliancy represent important, yet poorly defined, mechanisms by which MM clone(s) accelerate their own evolution and survival. Acknowledgements: AIRC, AIL, HARMONY, Berlucchi Citation Format: Marina Martello, Rosalinda Termini, Enrica Borsi, Vincenza Solli, Andrea Poletti, Lucia Pantani, Serena Rocchi, Katia Mancuso, Elena Zamagni, Paola Tacchetti, Mario Arpinati, Gabriella Chirumbolo, Nicoletta Testoni, Giulia Marzocchi, Giovanni Martinelli, Michele Cavo, Carolina Terragna. Higher levels of genomic complexity correlates with an advanced plasma cell differentiation status in newly diagnosed multiple myeloma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 473.

BACKGROUND MM patients (pts) are characterized by a prevalence of aneuploidies and Copy Number Alterations (CNAs) broadly scattered over the whole genome. Along with translocations and/or single nucleotide variants, they account for the MM-distinctive genomic heterogeneity, which characterizes the onset of symptomatic MM and identifies each pts. AIM. To deeply explore the genomic landscape of newly diagnosed, homogeneously treated MM pts, to assess the prognostic relevance of whole genome aneuploidies, CNAs, and structural aberrations. PATIENTS AND METHODS 344 highly purified BM CD138+ samples were profiled by SNPs array. R scripts were employed to analyse genomic data. Pts, enrolled in the EMN02 phase III trial, were randomized to receive VMP (121 pts) or high-dose MEL and autologous stem cell transplantation (223 pts), after bortezomib-based induction. At a median follow-up of 46 months (m), the estimated PFS and OS rates were 55% and 83%, respectively. RESULTS To evaluate the contribution of each genomic variables to the overall pts variability, a Principal Component Analysis of the whole genome’s numerical and structural aberrations was performed, highlighting that a “common ground” of at least 4 chromosomal aberrations (hyperdiploidy, H, 13qCN loss, IgH-t and 1qCN gains) was sufficient to describe and resume the MM overall genomic heterogeneity. Therefore, since 13qCN loss and 1qCN gains co-segregate, 3 pts sub-types might be identified, partially overlapping, even though well-defined by the presence of either H, or IgH-t or 13qCN loss and 1qCN gain. Outcomes of the latter MM sub-type was explored. Pts carrying 13qCN loss and 1qCN gain were characterized by the presence of several small deletions in MM-critical genes (YAP1, TRAF3, CYLD), and by the markedly de-regulated expression of CCND2 and CCND1 (as evaluated by the analysis of CoMMpass-derived RNAseq expression dataset). Their 46-m PFS and OS estimates were shorter as compared to that of patients carrying either just one or any of these CNAs (PFS: 39%, 44% and 73%, respectively; p=0.0001. OS: 68%, 83% AND 91%; p=0.0001). PFS and OS hazard ratios (HR) of pts carrying 13qCN loss and 1qCN gain (PFS: 1.76; OS: 2.24) were comparable to that of pts carrying either del(17p) (PFS: 2.1; OS: 2.1) or t(4;14) (PFS: 1.80; OS: 1.7), resulting as an independent factor predicting both PFS and OS in a Cox multivariate analysis. Survival data were confirmed on additional SNPs array data derived from 151 unrelated MM pts not included in the EMN02 trial, as well as on 700 pts, whose CNAs data were extrapolated from the MMRF CoMMpass Study dataset. CONCLUSIONS We propose a simple way to stratify MM pts, according to the detection of 2 CNAs, which is able to select pts with high (25%) and with low (40%) risk of progression and/or death, allowing to predict the clinical outcome in most MM pts. Supported by AIRC, Fond. del Monte Bo-Ra, Fond. Berlucchi, BO-AIL, Harmony Citation Format: Carolina Terragna, Marina Martello, Andrea Poletti, Vincenza Solli, Enrica Borsi, Rosalinda Termini, Lucia Pantani, Elena Zamagni, Giulia Marzocchi, Paola Tacchetti, Nicoletta Testoni, Serena Rocchi, Luca Cifarelli, Luca Dozza, Giovanni Martinelli, Michele Cavo. Chromosomal instability and bad prognosis both connote a multiple myeloma (MM) sub-type carrying 13qCN loss and 1qCN gain [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 470.

The inter- and intra-patients (pts) genetic heterogeneity of MM is the result of a competition between subclones harboring different genomic background, under the selective pressure of both microenvironment constrains and therapeutic treatment. Aim: to describe the clonal architecture changes driven by PI-based regimens in a cohort of homogeneously treated MM pts, by means of a genome-wide approach The study included 50 MM pts up-front treated with PI-based regimens. SNPs array data, derived from the BM CD138+ enriched cell fractions, as collected both at diagnosis (D) and at relapse (R), were analyzed with TAPS and Raw Copy R packages. In addition novel and personalized R-based scripts were designed, aimed at the detailed description of the whole genome CN changes. To be able to analyze the whole genome's changes between D and R, we first translated the signal derived from each of the 2,7x106 markers of the array (Cytoscan HD, Affymetrix) into gene-specific signals, therefore being able to describe CN changes of 20,172 genes. To highlight specific chromosomal regions affected by PI-based selective pressure, we than categorised the overall genomic CN changes, assuming that a limited number (9) of alternative genes' transitions might be observed between D and R. We therefore obtained dynamic profiles describing in details each genes' CNAs transitions, thus being able to highlight the following chromosomal regions particularly involved: chr 1p, 2p, 5, 15p, 19q (negative selection/extinction against CNs gains); chr 2q, 4 and 1p (positive selection/emersion of CNs losses and gains); chr13 and chr19p (mostly stable CN losses and gains). Each gene's CNAs transitions were also evaluated per pts, in order to describe the pts-specific evolutionary trajectories, thus highlighting an extremely high heterogeneity among pts, which might be roughly resumed in four main clonal evolution patterns: 32% of pts showed a stable, whereas 68% a branching evolution pattern. Among the branching patterns, three different behaviours were shown: 8% of pts showed a prevalent positive selection of CNAs, 24% showed a prevalent extinction of CNAs, whereas the remaining showed a blend of both behaviours. Of note, any correlation was observed between the presence of particular CNAs and the propensity to particular evolution patterns. Nevertheless, pts with stable evolution patterns had a slightly shorter median TTP, as compared to pts with branching patterns (22 vs 30 months, range 4-122 and 5-65, respectively). We concluded that the selective pressure of PI-based regimens mostly causes branching trajectories, even if any peculiar CNAs seem to guide at baseline this process. Overall, the clonal evolution patterns in MM remain still unpredictable, being probably driven by a combination of different factors, not yet fully depicted. Thanks to AIRC (MC), FB and FM (CT), AILBologna. Citation Format: Carolina Terragna, Andrea Poletti, Vincenza Solli, Marina Martello, Barbara Santacroce, Rosalinda Termini, Enrica Borsi, Chiara Benni, Lucia Pantani, Elena Zamagni, Paola Tacchetti, Giovanni Martinelli, Michele Cavo. Whole-genome analysis of CNAs identifies four main evolution trajectories in multiple myeloma (MM) patients front-line treated with PI-based regimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2176.

The pliancy of Multiple Myeloma (MM) plasma cells differentiation status acts as adaptive strategy to exogenous stress (e.g. in response to therapy). However, the genomic background that supports any diverse plasma cell differentiation phenotype has not yet been inferred. Aim: to correlate the genomic background with the phenotypic plasticity of MM clone(s) at diagnosis, in order to stratify patients (pts) according to both the level of chromosomal instability (CIN) and their PC differentiation stages, and ultimately to evaluate the impact of this stratification on the disease outcome. 64 newly diagnosed pts were included in the present study. Most pts (54/64) received a PI-based treatment as front-line therapy. Each patient was characterized by 6-color multiparametric flow citometry analysis, combining CD138-PE, CD38-PE-Cy7, CD20-APC, CD19-APC-Cy7, CD27-FITC, CD45-FITC, CD28-APC, CD44-FITC, CD54-APC, CD81-PerCP-Cy5.5, CD56-APC and SHH-PE, as functional marker of Hedgehog pathway activation (Miltenyi Biotech). Whole CNAs characterization of CD138+ purified BM PCs was carried out by SNPs array ( Cytoscan HD ,Affymetrix). According to the detected CIN, as described both by total CNAs and portion of genome changed (GC), 3 major subgroups were identified: the first one with high CIN (21 pts; medium tot. CNAs = 550, % GC ≥ 25%); the second one with a intermediate CIN (25 pts; medium tot. CNAs = 315, % GC = 10-25%) and the third one with low CIN (18 pts; medium tot. CNAs = 105, % GC ≤ 10%). As expected, in pts with high CIN, hyperdiploidy explains more than a quarter of unstable genome; however, they were also characterized by a higher prevalence of high-risk features, such as 1p deletion (FAF1), 16q deletion (WWOX, FANCA) and 17p deletion (TP53), which were almost exclusively associated to high CIN pts (p A detailed immunophenotypic analysis of the three subgroups of pts showed that high CIN background mainly characterized mature PCs (17/21 = 81%), as described by: a) a significant deregulation of both CD19 and CD81; b) a higher expression of CD28 and CD44; c) a reduced expression of CD20, CD27 and CD45. Finally, the presence of more mature PCs with high CIN characterizes pts carrying baseline clinical features associated to bad prognosis (e.g. PET lesions, k/l ratio, ISS III, β2-microglobulin; p In conclusion, a high level of genomic complexity correlates with an advanced PCs differentiation stages in newly diagnosed MM patients; this is associated with a prevalence of poor prognosis features. Chromosomal instability, together with cellular phenotypic pliancy, represents an important, yet poorly defined, mechanism by which MM clone(s) accelerate their own evolution and survival. Acknowledgements: AIRC, AIL, Fond. Berlucchi. Citation Format: Marina Martello, Rosalinda Termini, Barbara Santacroce, Enrica Borsi, Vincenza Solli, Chiara Benni, Andrea Poletti, Lucia Pantani, Beatrice Zannetti, Serena Rocchi, Elena Zamagni, Paola Tacchetti, Francesca Ulbar, Mario Arpinati, Gabriella Chirumbolo, Nicoletta Testoni, Giulia Marzocchi, Giovanni Martinelli, Michele Cavo, Carolina Terragna. The pliancy of plasma cell differentiation status conceals a gradient of chromosomal instability in newly diagnosed multiple myeloma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3387.

Intro: Multiple Myeloma (MM) is a biologically complex disease, whose genetic plasticity favors the coexistence of genetically heterogeneous subclones, selected in a Darwinian fashion throughout the disease course. Therapy might represent a major selective pressure over the different subclones, thus supporting an evolutionary model of the disease. Aim: To explore the existence of different clonal evolution patterns in MM, eventually driven by therapeutic selective pressure. P&M The study included 33 pts with symptomatic MM, up-front treated either with combination regimens including a proteasome inhibitor (28), or with cyclophosphamide. For each pts, paired BM samples were collected both at diagnosis and at relapse. SNPs array analyses were performed on the CD138+ enriched cell fractions. Results: Two approaches were applied: a) monitoring the variations of macro CNAs; b) focusing on changes of CNAs frequencies, as observed in 27 genes of interest. Both approaches were consistent in highlighting three major evolution patterns: in 7/33 (21%) pts, the genomic background at relapse was almost identical to that of diagnosis. In 13/33 (39%) pts, an overall increase in the frequencies of the same CNAs as observed at diagnosis was detected at relapse. Finally, in 13/33 (39%) pts, either increased or decreased frequencies of several CNAs, as well as several differences in the CNAs type’s prevalence were observed at relapse, as compared to diagnosis. Of interest, even if an overall CNAs median number increase was observed from diagnosis to relapse (226 vs 507, respectively) - supported by acquisition of CNAs either commonly described as secondary genomic events (i.e. del17p13, amp1q21, del1p23), or associated to the resistance to bortezomib (i.e. del8p21) - any peculiar CNAs resulted significantly prevalent in the 3 identified subgroups of pts. A high rate (92%) of achievement of VGPR or better quality of response to upfront therapy characterized the third subgroup of pts, whereas the rate of VGPR in the remaining pts was only 20% and PR or SD were observed in 9 and 7 pts, respectively. Finally, the median time to first progression of this subgroup of pts was significantly shorter as compared to that of pts with branching evolution (24 vs 35 months, range 4-41 and 7-123 months, respectively, p=0,01). Conclusion: The genomic architecture of a subgroup of relapsed MM pts, up-front responsive to new drugs-based combination therapies, resulted overall different from that of diagnosis, suggesting a branching evolution of the disease, sustained by the shrinking of the most prevalent clone (therapy-sensitive), as well as by the expansion of subclones (therapy-resistant) not already evident at diagnosis. This observation raises the question whether re-treatment of relapsed pts should be appropriate in the case of branching evolution. Acknowledgements: AIRC (MC), Fondazione Berlucchi (CT), FUV (EB). Citation Format: Carolina Terragna, Marina Martello, Barbara Santacroce, Vincenza Solli, Lucia Pantani, Elena Zamagni, Paola Tacchetti, Beatrice Zannetti, Katia Mancuso, Giulia Marzocchi, Nicoletta Testoni, Gaia Ameli, Rosalinda Termini, Angela Flores Dico, Enrica Borsi, Giovanni Martinelli, Michele Cavo. A branching evolution model at relapse characterizes multiple myeloma patients who responded to upfront combination therapy including new drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3936. doi:10.1158/1538-7445.AM2017-3936

Background Multiple Myeloma (MM) is a biologically complex disease, whose genetic plasticity favors the coexistence of genetically heterogeneous subclones, selected in a Darwinian fashion throughout the disease course. Based on a disease evolution model, it is likely that therapy acts like an evolutionary bottleneck, applying a selective pressure on the clones' genomic background. However, a formal correlation between the modulation of intra-clonal heterogeneity and the treatment has yet to be demonstrated. Aim To explore the existence of different clonal evolution patterns in MM, eventually driven by therapeutic selective pressure, we longitudinally analyzed a cohort of patients (pts) by using a high-throughput technology, able to finely dissect the genomic intra-clonal changes occurring in each pts, across disease progression. Patients and methods Thirty-three pts with symptomatic MM were included in this study. Most pts (28) were up-front treated with regimens including a proteasome inhibitor (PI), either in combination with an immunomodulator (IMiD) (thalidomide or lenalidomide) or with cyclophosphamide. The remaining pts (5) were up-front treated with IMiDs-based regimens, not including PI. For each pts, paired bone marrow samples were collected both at diagnosis and at relapse. SNPs array analyses were performed on the CD138+ enriched cell fractions (Affymetrix 6.0 and CytoscanHD) and data were analyzed with ChAS v3.1 and Nexus software, to obtain Copy Number Alterations (CNAs) results. Results: The genomic landscape's modulations were evaluated in details in each pair of samples included in the study: by one side, by monitoring the variations of macro CNAs types; by the other, by focusing on changes of CNAs frequencies, as observed in 27 genes of interest (selected according to their recognized role in MM pathogenesis). Both approaches were consistent in highlighting three major evolution patterns: in 7/33 (21%) pts, the genomic background at relapse was almost identical to that of diagnosis, suggesting that no evolution affected the diagnosis' genomic architecture. In 13/33 (39%) pts, an overall increase in the frequencies of the same CNAs as observed at diagnosis was detected at relapse, suggesting a linear evolution of the diagnosis' predominant clone. Finally, in 13/33 (39%) pts, either increased or decreased frequencies of several CNAs, as well as several differences in the CNAs type's prevalence were observed in samples collected at relapse, as compared to those collected at diagnosis, suggesting a branching evolution of sub-clones not-detectable at diagnosis. Of interest, even if an overall increase in the median number of CNAs was observed in the CD138+ cell fractions collected from the whole population of newly diagnosed and relapsed pts (226 vs 507, respectively) - supported by acquisition of CNAs either commonly described as secondary genomic events (i.e. del17p13, amp1q21, del1p23), or associated to the resistance to bortezomib (i.e. del8p21) - nevertheless any peculiar CNAs resulted significantly prevalent in the three identified subgroups of pts. The subgroup of pts who underwent a branching evolution was characterized by a high rate (92%) of achievement of VGPR or better quality of response to upfront therapy, regardless of incorporation of PI into induction regimens. On the contrary, although pts who experienced either linear or no evolution were mostly treated with highly effective PI-based triplet combinations (19/20 pts), the rate of VGPR in this subgroup was only 20% and PR or SD were observed in 9 and 7 pts, respectively. Finally, the median time to first progression of this subgroup of pts was significantly shorter as compared to that of pts with branching evolution (24 vs 35 months, range 4-41 and 7-123 months, respectively, p=0,01). Conclusion The genomic architecture of a subgroup of relapsed MM pts, who were up-front responsive to new drugs-based combination therapies, resulted overall different from that of diagnosis, suggesting a branching evolution of the disease, sustained by the shrinking of the most prevalent clone (therapy-sensitive), as well as by the expansion of subclones (therapy-resistant) not already evident at diagnosis. This observation raises the question whether re-treatment of relapsed pts should be appropriate in the case of branching evolution. Acknowledgments: AIRC (MC), Fondazione Berlucchi (CT), FUV (EB). Disclosures Cavo: Takeda: Honoraria; Amgen: Honoraria; Bristol-Myers Squibb: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding.