Sasser, A. Kate

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.

Abstract Treatment of myeloma has benefited from the introduction of more effective and better tolerated agents, improvements in supportive care, better understanding of disease biology, revision of diagnostic criteria, and new sensitive and specific tools for disease prognostication and management. Assessment of minimal residual disease (MRD) in response to therapy is one of these tools, as longer progression-free survival (PFS) is seen consistently among patients who have achieved MRD negativity. Current therapies lead to unprecedented frequency and depth of response, and next-generation flow and sequencing methods to measure MRD in bone marrow are in use and being developed with sensitivities in the range of 10−5 to 10−6 cells. These technologies may be combined with functional imaging to detect MRD outside of bone marrow. Moreover, immune profiling methods are being developed to better understand the immune environment in myeloma and response to immunomodulatory agents while methods for molecular profiling of myeloma cells and circulating DNA in blood are also emerging. With the continued development and standardization of these methodologies, MRD has high potential for use in gaining new drug approvals in myeloma. The FDA has outlined two pathways by which MRD could be qualified as a surrogate endpoint for clinical studies directed at obtaining accelerated approval for new myeloma drugs. Most importantly, better understanding of MRD should also contribute to better treatment monitoring. Potentially, MRD status could be used as a prognostic factor for making treatment decisions and for informing timing of therapeutic interventions. Clin Cancer Res; 23(15); 3980–93. ©2017 AACR.