Introduction The IFM 2009 study prospectively evaluated the combination of 8 cycles of lenalidomide, bortezomib and dexamethasone (RVd) versus (vs) 3 cycles of RVd plus high dose (HD) melphalan with autologous stem cell transplantation (ASCT) plus 2 consolidation RVd cycles, followed by lenalidomide maintenance for 12 months (mo) in newly diagnosed multiple myeloma (NDMM) patients (Attal M et al, NEJM 2017). RVd with transplant was associated with significantly longer progression-free survival (PFS) (primary endpoint) compared to RVd alone. Overall survival (OS) at 4 years was similar in both groups. In order to evaluate the long-term outcome in the 2 arms and the impact of 2nd line treatments on PFS2 and OS, we performed an extension of patient follow-up (FU) of the IFM 2009 study over a period of 4 years (DB-FU-IFM 2009 / NCT03679351). Methods 700 NDMM patients (pts) (median age 59 years [range, 28.0-65.0]) were randomized between the 2 arms after stratification by ISS stage and FISH analysis. Patient characteristics were well-balanced, notably age, gender, ISS3, and high risk cytogenetics (defined by t(4;14), t(14;16), del(17p)). 100 patients (50 from each arm) who experienced 1st progression were included in the IFM 2009-02 PCD trial (NCT02244125, Garderet L et al, Blood 2018). The choice of 2nd line treatment and decision to perform an ASCT at relapse was based on investigator's discretion. PFS2 was defined as the time from randomization to progression on next line therapy or death from any cause; second PFS as the time from date of 1st progression to progression on next line therapy or death. This trial was previously reported with a median FU of 44 mo (Attal M et al, NEJM 2017). Results As of March 2020, median FU was 93 mo [range, 88-98]. As previously reported, median PFS was significantly longer in the transplant group (TG), at 47.3 mo compared to 35.0 mo in the RVd alone group (RG) (HR (95CI) 0.70 [0.59-0.83] p<0.001). 497/700 pts experienced disease progression, 270 (77.1%) pts in the RG and 227 (64.9%) pts in the TG; 262 and 217 of them respectively initiated a 2nd line. Among these pts, 76.7% (201/262) in RG and 22.6% (49/217) in TG received an ASCT at 1st relapse; 40.1% (105/262) and 46.5% (101/217) respectively received a pomalidomide-based 2nd line. Only 14 and 12 pts received carfilzomib- and daratumumab-based 2nd line regimens. Median PFS2 was similar in both groups, at 95 mo in the RG and not reached (NR) in the TG (HR [95CI] 0.96 [0.76-1.21] p=0.76). This estimate was homogeneous across ISS stage (p-value for interaction 0.26) and cytogenetic group (p-value for interaction 0.90). Median PFS2 in ISS3 and high-risk cytogenetic pts was 73 and 75 mo, and 48 and 47 mo, in the RG and TG, respectively. Second PFS was significantly increased in the RG, at 36 vs 25 mo in TG (HR [95CI] 1.41 [1.11-1.79] p=0.003). Median OS was NR in either group. At 8 years, the OS rate was 60.2% in the RG and 62.2% in the TG (HR [95CI] 1.03 [0.80-1.32] p=0.81). Minimal residual disease (MRD) was a strong predictor of outcome: PFS (HR [95CI] 0.28 [0.22-0.36] p<0.001), PFS2 (HR [95CI] 0.27 [0.20-0.37] p<0.001) and OS (HR [95CI] 0.35 [0.25-0.49] p<0.001) were longer in pts achieving MRD negativity, in comparison with those who did not. The incidence of invasive 2nd primary cancer was not significantly different between the 2 groups (p=0.38); of note, 2nd hematological malignancies were reported in 5/350 and 7/350 pts in RG and TG respectively. Conclusion RVd before and after HD melphalan and ASCT followed by one-year of lenalidomide maintenance is associated with a significantly longer PFS than RVd alone, without a significant increase of 2nd primary malignancy. At 1st relapse, before the systematic use of daratumumab- or carfilzomib-based combinations, almost half of the pts received pomalidomide-based treatment and about 3 quarters of pts who had not received frontline ASCT underwent delayed transplant. With a FU of almost 8 years, median OS was NR and there was no difference between the 2 strategies with respect to PFS2 and OS. MRD appears to predict outcome and might be used after induction to identify those pts who probably do not require a transplant. Quadruplets with anti-CD38 monoclonal antibodies combined with the selective use of HD therapy and transplant, followed by extended maintenance, should be considered in the future to further improve outcome. Such an approach could potentially provide a functional cure for a significant proportion of NDMM pts. Disclosures Perrot: Amgen, BMS/Celgene, Janssen, Sanofi, Takeda: Consultancy, Honoraria, Research Funding. Facon:Celgene, Janssen, Takeda, Amgen, Roche, Karyopharm, Oncopeptides, BMS, Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Clement-Filliatre:BMS/Celgene: Honoraria. Macro:gsk: Honoraria; janssen: Honoraria, Other: travel accomodation, Research Funding; sanofi: Honoraria; takeda: Honoraria, Other: travel accomodation, Research Funding. Karlin:Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, personal fees; Celgene: Other: Personal fees; Sanofi: Honoraria; GlaxoSmithKline: 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, Other: Travel support, personal fees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, personal fees; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support. Touzeau:Abbvie: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Amgen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; GlaxoSmithKline: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Sanofi: Honoraria, Research Funding. Leleu:AbbVie: Honoraria; Sanofi: Honoraria; GSK: Honoraria; Oncopeptide: Honoraria; Amgen: Honoraria; Novartis: Honoraria; Carsgen: Honoraria; Incyte: Honoraria; Merck: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; BMS-celgene: Honoraria. Munshi:C4: Current equity holder in private company; Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Anderson:Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Richardson:Celgene/BMS, Oncopeptides, Takeda, Karyopharm: Research Funding. Moreau:Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Takeda: Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Novartis: Honoraria. Attal:BMS/Celgene, Sanofi: Consultancy, Honoraria, Research Funding.

No abstract

PURPOSE Multiple myeloma (MM) is characterized by copy number abnormalities (CNAs), some of which influence patient outcomes and are sometimes observed only at relapse(s), suggesting their acquisition during tumor evolution. However, the presence of micro-subclones may be missed in bulk analyses. Here, we use single-cell genomics to determine how often these high-risk events are missed at diagnosis and selected at relapse. MATERIALS AND METHODS We analyzed 81 patients with plasma cell dyscrasias using single-cell CNA sequencing. Sixty-six patients were selected at diagnosis, nine at first relapse, and six in presymptomatic stages. A total of 956 newly diagnosed patients with MM and patients with first relapse MM have been identified retrospectively with required cytogenetic data to evaluate enrichment of CNA risk events and survival impact. RESULTS A total of 52,176 MM cells were analyzed. Seventy-four patients (91%) had 2-16 subclones. Among these patients, 28.7% had a subclone with high-risk features (del(17p), del(1p32), and 1q gain) at diagnosis. In a patient with a subclonal 1q gain at diagnosis, we analyzed the diagnosis, postinduction, and first relapse samples, which showed a rise of the high-risk 1q gain subclone (16%, 70%, and 92%, respectively). In our clinical database, we found that the 1q gain frequency increased from 30.2% at diagnosis to 43.6% at relapse (odds ratio, 1.78; 95% CI, 1.58 to 2.00). We subsequently performed survival analyses, which showed that the progression-free and overall survival curves were superimposable between patients who had the 1q gain from diagnosis and those who seemingly acquired it at relapse. This strongly suggests that many patients had 1q gains at diagnosis in microclones that were missed by bulk analyses. CONCLUSION These data suggest that identifying these scarce aggressive cells may necessitate more aggressive treatment as early as diagnosis to prevent them from becoming the dominant clone.

Multiple myeloma (MM) is a hematological malignancy characterized by the accumulation of tumor plasma cells (PCs) in the bone marrow (BM). Despite considerable advances in terms of treatment, patients’ prognosis is still very heterogeneous. Cytogenetics and minimal residual disease both have a major impact on prognosis. However, they do not explain all the heterogeneity seen in the outcomes. Their limitations are the result of the emergence of minor subclones missed at diagnosis, detected by sensible methods such as single-cell analysis, but also the non-exploration in the routine practice of the spatial heterogeneity between different clones according to the focal lesions. Moreover, biochemical parameters and cytogenetics do not reflect the whole complexity of MM. Gene expression is influenced by a tight collaboration between cytogenetic events and epigenetic regulation. The microenvironment also has an important impact on the development and the progression of the disease. Some of these determinants have been described as independent prognostic factors and could be used to more accurately predict patient prognosis and response to treatment.

Cytogenetics abnormalities (CA) are known to be the preponderant prognostic factor in multiple myeloma (MM). Our team has recently developed a prognostic score based on 6 CA, where del(1p32) appears to be the second worst abnormality after del(17p). The aim of this study was to confirm the adverse impact of 1p32 deletion on newly-diagnosed multiple myeloma (NDMM) patients. Among 2551 NDMM patients, 11% were harboring del(1p32). Their overall survival (OS) was significantly inferior compared to patients without del(1p32) (median OS: 49 months vs. 124 months). Likewise, progression-free survival was significantly shorter. More importantly, biallelic del(1p32) conferred a dramatically poorer prognosis than a monoallelic del(1p32) (median OS: 25 months vs. 60 months). As expected, the OS of del(1p32) patients significantly decreased when this abnormality was associated with other high-risk CA (del(17p), t(4;14) or gain(1q)). In the multivariate analysis, del(1p32) appeared as a negative prognostic factor; after adjustment for age and treatment, the risk of progression was 1.3 times higher among patients harboring del(1p32), and the risk of death was 1.9 times higher. At the dawn of risk-adapted treatment strategies, we have confirmed the adverse impact of del(1p32) in MM and the relevance of its assessment at diagnosis.

Primary plasma cell leukemia (pPCL) is an aggressive form of multiple myeloma (MM) that has not benefited from recent therapeutic advances in the field. Because very rare and heterogeneous, it remains poorly understood at the molecular level. To address this issue, we performed DNA and RNA sequencing of sorted plasma cells from a large cohort of 90 newly diagnosed pPCL, and compared to MM. We observed that pPCL presents a specific genomic landscape with a high prevalence of t(11;14) (about half) and high-risk genomic features such as del(17p), gain 1q, del(1p32). In addition, pPCL displays a specific transcriptome when compared to MM. We then aimed at specifically characterize pPCL with t(11;14). We observed that this sub-entity displayed significantly fewer adverse cytogenetic abnormalities. This translated into better overall survival when compared to pPCL without t(11;14) (39.2 months vs 17.9 months, p=0.002). Finally, pPCL with t(11;14) displayed a specific transcriptome, including differential expression of BCL2 family members. This study is the largest series of patients with pPCL reported so far.

No abstract

Myeloma therapeutic strategies have been adapted to patients’ age and comorbidities for a long time. However, although cytogenetics and clinical presentations (plasmablastic cytology; extramedullary disease) are major prognostic factors, until recently, all patients received the same treatment whatever their initial risk. No strong evidence allows us to use a personalized treatment according to one cytogenetic abnormality in newly diagnosed myeloma. Retrospective studies showed a benefit of a double autologous transplant in high-risk cytogenetics according to the International Myeloma Working Group definition (t(4;14), t(14;16) or del(17p)). Moreover, this definition has to be updated since other independent abnormalities, namely gain 1q, del(1p32), and trisomies 5 or 21, as well as TP53 mutations, are also prognostic. Another very strong predictive tool is the response to treatment assessed by the evaluation of minimal residual disease (MRD). We are convinced that the time has come to use it to adapt the strategy to a dynamic risk. Many trials are ongoing to answer many questions: when and how should we adapt the therapy, its intensity and duration. Nevertheless, we also have to take into account the clinical outcome for one patient, especially adverse events affecting his or her quality of life and his or her preferences for continuous/fixed duration treatment.