Casneuf, Tineke

Abstract Introduction: Multiple Myeloma (MM) is an incurable plasma cell malignancy commonly preceded by the asymptomatic stage smoldering multiple myeloma (SMM). MM is characterized with significant genomic heterogeneity of chromosomal gains and losses (CNVs), translocations, and point mutations (SNVs); alterations that are also observed in SMM patients. However, current SMM risk models rely solely on clinical markers and do not accurately capture progression risk. While incorporating some genomic biomarkers improves prediction, using all MM genomic features to comprehensively stratify patients may increase risk stratification precision in SMM. Methods: We obtained a total of 214 patient samples at SMM diagnosis. We performed whole-exome sequencing on 166 tumors; of these, RNA sequencing was performed on 100. Targeted capture was done on 48 additional tumors. Upon binarization of DNA features, we performed consensus non-negative matrix factorization to identify distinct molecular clusters. We then trained a random forest classifier on translocations, SNVs, and CNVs. The predicted clinical outcomes for the molecular subtypes were further validated in an independent SMM cohort of 74 patients. Results: We identified six genomic subtypes, four with hyperdiploidy (>48 chromosomes, HMC, HKR, HNT, HNF) and two with IgH translocations (FMD, CND) (Table 1). In multivariate analysis accounting for IMWG (20-2-20) clinical risk stages, high-risk (HMC, FMD, HKR) and intermediate-risk (HNT, HNF) genetic subtypes were independent predictors of progression (Hazards ratio [HR]: 3.8 and 5.5, P = 0.016 and 0.001, respectively). The low-risk, CND subtype harboring translocation (11;14) was enriched for the previously defined CD-2 MM signature defined by the B cell markers CD20 and CD79A (FDR = 0.003 ), showed upregulation of CCND1, E2F1, and E2F7 (FDR = 0.01, 0.0004, 0.08), and was enriched for G2M checkpoint, heme metabolism, and monocyte cell signature (FDR = 0.003, 0.003, 0.003, respectively). The FMD subtype with IgH translocations (4;14) and (14;16) was enriched for P53, mTORC1, unfolded protein signaling pathways and plasmacytoid dendritic cell signatures (FDR = 0.01, 0.005, 0.008, respectively). The HKR tumors were enriched for inflammatory cytokine signaling, MYC target genes, T regulatory cell signature, and the MM proliferative (PR) signatures (FDR = 0.02, 0.03, 0.007, 0.02, respectively). The APOBEC mutational signature was enriched in HMC and FMD tumors (P = 0.005), while there was no statistical difference across subtypes in the AID signature. The median follow-up for the primary cohort is 7.1 years. Median TTP for patients in HMC, FMD, and HKR was 3.8, 2.6, and 2.2 years, respectively; TTP for HNT and HNF was 4.3 and 5.2, respectively, while it was 11 years in CND patients (P = 0.007). Moreover, by analyzing the changes in MM clinical biomarkers over time, we found that patients from high-risk subgroups had higher odds of developing evolving hemoglobin and monoclonal protein levels over time (P = 0.01 and 0.002, respectively); Moreover, the absolute increase in M-protein was significantly higher in patients from the high-risk genetic subtypes at one, two, and five years from diagnosis (P = 0.001, 0.03, and 0,01, respectively). Applying the classifier to the external cohort replicated our findings where intermediate and high-risk genetic subgroups conferred increased risk of progression to MM in multivariate analysis after accounting for IMWG staging (HR: 5.5 and 9.8, P = 0.04 and 0.005, respectively). Interestingly, within the intermediate-risk clinical group in the primary cohort, patients in the high-risk genetic subgroups had increased risk of progression (HR: 5.2, 95% CI 1.5 - 17.3, P = 0.007). In the validation cohort, these patients also had an increased risk of progression to MM (HR: 6.7, 95% CI 1.2 - 38.3, P = 0.03), indicating that molecular classification improves the clinical risk-stratification models. Conclusion: We identified and validated in an independent dataset six SMM molecular subgroups with distinct DNA alterations, transcriptional profiles, dysregulated pathways, and risks of progression to active MM. Our results underscore the importance of molecular classification in addition to clinical evaluation in better identifying high-risk SMM patients. Moreover, these subgroups may be used to identify tumor vulnerabilities and target them with precision medicine efforts. Figure 1 Figure 1. Disclosures Bustoros: Janssen, Bristol Myers Squibb: Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria. Casneuf: Janssen: Current Employment. Kastritis: Amgen: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Genesis Pharma: Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Walker: Bristol Myers Squibb: Research Funding; Sanofi: Speakers Bureau. Davies: Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Dimopoulos: Amgen: Honoraria; BMS: Honoraria; Takeda: Honoraria; Beigene: Honoraria; Janssen: Honoraria. Bergsagel: Genetech: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; GSK: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Yong: BMS: Research Funding; Autolus: Research Funding; Takeda: Honoraria; Janssen: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; GSK: Honoraria; Amgen: Honoraria. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Getz: IBM, Pharmacyclics: Research Funding; Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

PURPOSE Smoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models. METHODS We used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors. RESULTS We observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway ( KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort. CONCLUSION SMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.

Abstract Premalignant states are known to precede malignancy in many different cancers, including multiple myeloma (MM). Each year, 1% of Monoclonal Gammopathy of Undetermined Significance (MGUS) patients and 10% of Smoldering Multiple Myeloma (SMM) patients transition to symptomatic MM, which remains an incurable disease. By identifying patients at imminent risk (within 2 years) of progression to MM, treatment could begin early and potentially prevent full blown MM and associated morbidity and mortality. Multiple risk-factor models are currently utilized to identify patients at high-risk of developing MM. Commonly cited risk factors include percentage of bone marrow plasma cells, free light chain ratio, serum levels of M-protein, IgG subtype and levels of circulating plasma cells. These tests have individual and combined merit but there remains no consensus for measuring imminent risk of MM progression. To determine whether serum proteomics could help predict progression or provide information on relevant pathways involved in progression to MM, we evaluated 200 MGUS and SMM (187 non-IgM) patient samples on the Myriad DiscoveryMAP(r) platform (250+ serum proteins), supplemented by demographic and clinical information. Of the 187 non-IgM patients, 100 progressed to MM within 2 years (high-risk), and 87 did not progress to MM within 5 years (low-risk). We performed different subset analyses to screen for proteins that were informative and predictive of disease progression from MGUS/SMM to MM. First, a univariate Wilcoxon rank sum test was performed. The top 10 proteins that were found to be differential between high-risk/low-risk for all MGUS and SMM patients were IgM, Progesterone, MCP-2, Kallikrein-5, FGF4, EPO, MCP-4, MIP-1β, MCP-1, and TF. Interestingly, when analyzing MGUS and SMM patients separately, additional proteins that were important in distinguishing high-risk/low-risk patients included IgM, EN-RAGE, BAFF, and several others previously shown to be relevant in MM, as well as a handful of novel proteins with unidentified roles in MM. Second, to determine whether specific combinations of proteins could be utilized together to define high/low-risk, a classification tree was assembled. The combination of 8 features (IgM, Progesterone, Aldose Reductase, E-Selectin, Fas Ligand, Hemoglobin, Age and Creatine kinase-MB) appropriately classified 86% of the 187 patients into high/low-risk. Future analyses will include examining this high/low risk classifier in contrast to current risk factor models, and application to two additional sets of similar patient samples that serve as an independent validation set. Disclosures: No relevant conflicts of interest to declare.

Abstract Introduction: Daratumumab is a human CD38 IgGκ monoclonal antibody that demonstrated significant activity and a manageable safety profile in combination with bortezomib and dexamethasone. In a randomized phase 3 study, daratumumab in combination with bortezomib and dexamethasone (DVd) significantly prolonged progression-free survival (PFS) versus bortezomib and dexamethasone alone (Vd) in a pre-specified interim analysis of patients (pts) with relapsed or refractory multiple myeloma (RRMM; Palumbo A. N Engl J Med 2016; in press). Herein, we examine subgroups from this study to compare the efficacy of DVd vs Vd in bortezomib-naive and bortezomib-experienced pt populations. In addition, the efficacy of DVd vs Vd in pts who were refractory to lenalidomide at last prior line of therapy was also evaluated. Methods: Pts who received ≥1 prior line of therapy were randomized (1:1) to 8 cycles (q3w) of Vd (bortezomib: 1.3 mg/m2 SC on Days 1, 4, 8, 11; dexamethasone: 20 mg PO on Days 1, 2, 4, 5, 8, 9, 11, 12) with or without daratumumab (16 mg/kg IV qw in Cycles 1-3, Day 1 of Cycles 4-8, then q4w until progression). Pts who were refractory to bortezomib were not eligible. The primary endpoint was PFS. Bone marrow aspirate samples that had been prepared with Ficoll were evaluated for minimal residual disease (MRD) using three different thresholds (10-4, 10-5, and 10-6) based on the ClonoSEQ assay (Adaptive Biotechnologies, Seattle, WA, USA). Results: Median follow-up was 7.4 months. Among bortezomib-naive pts (DVd, n=89; Vd, n=83), PFS was significantly improved with DVd vs Vd (median: not reached [NR] vs 7.5 months; HR, 0.25; 95% CI, 0.13-0.47; P<0.0001); estimated 12-month PFS rates were 72% vs 28%, respectively. ORR was 88% with DVd vs 70% with Vd (P=0.0040), with ≥very good partial response (VGPR) rates of 72% vs 42% (P<0.0001), and ≥complete response (CR) rates of 30% vs 20% (P=0.1199), respectively. For pts who previously received a bortezomib-containing regimen (DVd, n=162; Vd, n=164), PFS was also significantly longer with DVd vs Vd (median: 12.3 vs 6.7 months; HR, 0.46; 95% CI, 0.32-0.66; P<0.0001). Estimated 12-month PFS rates were 55% vs 27%, respectively. ORR was significantly higher with DVd vs Vd (80% vs 60%; P=0.0001), along with significantly higher rates of ≥VGPR (52% vs 22%; P<0.0001) and ≥CR (13% vs 3%; P=0.0019). Among pts who were refractory to lenalidomide at the last prior line of therapy (DVd, n=45; Vd, n=60), PFS was significantly longer in DVd vs Vd (median: 10.3 vs 4.4 mo; HR, 0.37; 95% CI, 0.21-0.65; P=0.0004; Figure). Within this subgroup, ORR was significantly higher for DVd vs Vd (81% vs 50%; P=0.0021), and the same trends were observed for rates of ≥VGPR (54% vs 12%; P<0.0001) and ≥CR (20% vs 5%; P=0.0261). Updated efficacy and safety data, including MRD analyses across different sensitivity thresholds (10-4, 10-5, and 10-6), will be presented at the meeting. Conclusions: These analyses confirm that addition of daratumumab to Vd significantly improves outcomes for RRMM pts regardless of prior treatment with bortezomib. Importantly, this treatment benefit of DVd vs Vd was maintained in pts who were refractory to lenalidomide at the last prior line of therapy. These data lend further support to adding daratumumab to a standard-of-care regimen in RRMM. Figure Progression-free Survival in Patients Refractory to Lenalidomide at the Last Prior Line of Therapy Figure. Progression-free Survival in Patients Refractory to Lenalidomide at the Last Prior Line of Therapy Disclosures Lentzsch: BMS: Consultancy; Foundation One: Consultancy; Celgene: Consultancy, Honoraria. Quach:Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen Cilag: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Ovilla:Janssen: Consultancy. Qi:Janssen: Employment. Deraedt:Janssen: Employment, Equity Ownership. Schecter:Janssen: Employment, Equity Ownership. Amin:Janssen: Employment. Qin:Janssen: Employment. Casneuf:Johnson & Johnson: Equity Ownership; Janssen R&D, Beerse, Belgium: Employment. Chiu:Janssen: Employment. Sasser:Johnson & Johnson: Equity Ownership; Janssen Pharmaceuticals R&D: Employment. Sonneveld:Karyopharm: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding.

Background:Patients (pts) with multiple myeloma associated with high cytogenetic risk abnormalities have poor outcomes. In CASTOR, D‐Vd prolonged progression‐free survival (PFS) vs bortezomib and dexamethasone (Vd) alone, and exhibited a manageable safety profile in pts with RRMM. We conducted a subgroup analysis of D‐Vd vs Vd in CASTOR, based on cytogenetic risk.Aims:The purpose of this analysis was to determine the efficacy and safety of D‐Vd in CASTOR based on cytogenetic risk status.Methods:Eligible pts received ≥1 prior line of therapy. Cytogenetic risk was based on a combined analysis of next‐generation sequencing (NGS) and fluorescence in situ hybridization (FISH)/karyotype testing. High‐risk pts had t(4;14), t(14;16), or del17p abnormalities. Standard (std)‐risk pts were confirmed negative for all 3 abnormalities. Minimal residual disease (MRD; 10–5) was assessed via NGS using clonoSEQ® assay V2.0.Results:In CASTOR (D‐Vd, n = 251; Vd, n = 247), high‐risk was confirmed in 26.7% and 25.9% of pts in the D‐Vd and Vd groups, respectively. At a median follow up of 40.0 months (mo), D‐Vd prolonged PFS vs Vd in pts with high‐ (median 13.4 vs 7.2 mo; HR, 0.40 [95% CI, 0.24–0.65]; P = 0.0002) or std‐risk (median 18.4 vs 6.8 mo; HR, 0.28 [95% CI, 0.20–0.37]; P <0.0001). Higher ORR was seen with D‐Vd vs Vd (high risk: 84.8% vs 60.0%; P = 0.0226; std risk: 85.4% vs 65.0%; P <0.0001), including deep responses of ≥CR (high risk: 33.3% vs 8.3%; std risk: 29.9% vs 10.2%) and ≥VGPR (high risk: 65.2% vs 35.0%; P = 0.0049; std risk: 64.3% vs 28.0%; P <0.0001). Higher rates of MRD negativity (high risk: 17.9% vs 0%; P = 0.0003; std risk: 13.3% vs 2.4%; P = 0.0003), and sustained MRD negativity for ≥6 mo (high risk: 16.4% vs 0%; P = 0.0006; std risk: 6.1% vs 1.8%; P = 0.0859) and ≥12 mo (high risk: 7.5% vs 0%; P = 0.0581; std risk: 1.8% vs 0%; P = 0.2477) were seen with D‐Vd vs Vd. D‐Vd significantly prolonged PFS vs Vd in pts with one prior line of therapy only (high risk: median 20.1 vs 8.4 mo; HR, 0.30 [95% CI, 0.14–0.64]; P = 0.0012; std risk: median 32.6 vs 7.9 mo; HR, 0.18 [95% CI, 0.11–0.29]; P <0.0001; Figure). Additionally, D‐Vd significantly prolonged PFS2 (high risk: median 27.9 vs 18.6 mo; HR, 0.59 [95% CI, 0.37–0.94]; P = 0.0258; std risk: median 40.1 vs 21.6 mo; HR, 0.43 [95% CI, 0.32–0.59]; P <0.0001) regardless of cytogenetic risk status.Additional data including safety analyses will be presented.Summary/Conclusion:Adding daratumumab to Vd demonstrates significant efficacy in pts with high‐risk RRMM including PFS2. Among high‐risk RRMM pts, MRD negativity was only achieved with D‐Vd. These findings support use of D‐Vd for high‐risk RRMM. NCT02136134image