Lam, Annette

8037 Background: Both daratumumab in combination with lenalidomide and dexamethasone (DRd) and bortezomib in combination with lenalidomide and dexamethasone (VRd) are endorsed by NCCN guidelines as preferred regimens for the treatment of transplant-ineligible (TIE) patients with newly diagnosed multiple myeloma (NDMM). DRd and VRd have each demonstrated superior efficacy relative to lenalidomide and dexamethasone (Rd) alone in the MAIA and SWOG S0777 trials, respectively, but have not been compared directly in a head-to-head trial. Naive comparisons of efficacy across the 2 trials may be biased because MAIA enrolled only TIE patients (median age 73 years), whereas S0777 enrolled a mixed transplant-not-intended (TNI) population, which included both TIE patients and patients choosing to defer/refuse frontline stem cell transplantation (median age 63 years). Methods: The present study leveraged individual patient-level data (IPD) from both trials to perform an anchored indirect treatment comparison of DRd vs VRd, adjusting for differences in trial eligibility criteria and baseline patient characteristics. Harmonized inclusion criteria (NDMM, age ≥65 years, ECOG performance status ≤2) were applied to both trial populations. Age ≥65 years served as a proxy for TIE status since S0777 enrolled a mixed TNI population. Propensity score (PS) reweighting was used to balance the 2 trial populations on key baseline prognostic factors including age, sex, ISS stage, ECOG performance status, hemoglobin, eGFR, lactate dehydrogenase (LDH) level, and cytogenetic risk. Missing data were addressed with multiple imputation. After alignment of inclusion criteria and PS reweighting, an anchored indirect comparison was performed wherein within-trial progression-free survival (PFS) hazard ratios (HRs) for DRd vs Rd and VRd vs Rd were estimated and used to make indirect inference about PFS for DRd vs VRd. Results: 727 MAIA participants and 198 S0777 participants were eligible for inclusion. After PS reweighting, the trial populations were balanced on the key baseline prognostic factors identified above (standardized difference < 0.1 for each). The adjusted PFS HRs were 0.53 (95% CI: 0.41–0.68; P< 0.0001) for DRd vs Rd in MAIA and 0.88 (0.63–1.23; P= 0.46) for VRd vs Rd in S0777. In the adjusted Rd-anchored indirect comparison, the PFS HR for DRd vs VRd was 0.60 (0.39–0.90; P= 0.02). Conclusions: In this adjusted indirect treatment comparison based on IPD from the MAIA and S0777 trials, PFS was significantly longer for DRd relative to VRd. In the absence of a head-to-head trial comparing DRd and VRd, the present study may help to inform treatment selection in TIE patients with NDMM.

Abstract Background Because patients with newly diagnosed multiple myeloma (NDMM) do not always receive any treatment beyond first-line (1L) therapy, it is imperative that patients receive the best treatment in the 1L setting. However, the optimal initial treatment remains to be identified. We performed a clinical simulation to assess potential outcomes with different treatment sequences. Patients and Methods We used a partitioned survival model to compare overall survival (OS) with (1) daratumumab, lenalidomide, and dexamethasone (D-Rd) in 1L followed by a pomalidomide- or carfilzomib-based regimen in second line (2L) versus (2) bortezomib, lenalidomide, and dexamethasone (VRd) in 1L followed by a daratumumab-based regimen in 2L versus (3) lenalidomide and dexamethasone (Rd) in 1L followed by a daratumumab-based regimen in 2L. Probabilities of transition between health states (1L, 2L+, and death) were based on published clinical data and real-world data from the Flatiron Health database. The proportion of patients discontinuing treatment after 1L (attrition rates) in the base case was estimated with a binomial logistic model using data from the MAIA trial. Results Using D-Rd in 1L conferred a longer median OS compared with delaying daratumumab-based regimens until 2L after VRd or Rd, respectively (8.9 [95% CrI 7.58-10.42] vs. 6.92 [5.92-8.33] or 5.75 [4.50-7.25] years). Results of scenario analyses were consistent with the base case. Conclusion Our simulation, which incorporates clinically representative treatments and attrition rates, supports the use of D-Rd as initial therapy, rather than delaying the use of daratumumab until later lines of therapy, in patients with transplant-ineligible NDMM.

Abstract Introduction: Despite recent treatment (tx) advances that have improved overall survival (OS), multiple myeloma (MM) remains a mostly incurable malignancy. Txs become less effective with each successive line of therapy (LOT). The rapidly evolving tx landscape has raised questions about the optimal tx sequence; recent trial data may be useful to develop and test hypotheses about tx sequencing. However, real-world (RW) data have shown that while most patients (pts) with newly diagnosed MM (NDMM) receive first-line (1L) tx, attrition rates are high in later LOT, with up to 50% of transplant ineligible (TIE) pts receiving no tx beyond 1L. As many pts will not have an opportunity to be salvaged with later LOT, achieving the longest possible progression-free survival (PFS) upfront is critical. In the MAIA trial, daratumumab (DARA), lenalidomide, and dexamethasone (D-Rd) demonstrated significant PFS (hazard ratio [HR]=0.53, P&lt;0.0001) and OS (HR=0.68, P=0.0013) benefit vs Rd. In a subgroup of pts ≥65 years old in the SWOG S0777 study, bortezomib, lenalidomide, and dexamethasone (VRd) had no significant OS benefit vs Rd. In the absence of head-to-head studies comparing different tx sequences, clinical trial data can be used to explore different scenarios. Published models suggest that delaying DARA until later LOT is a viable tx strategy. Limitations of these models include basing conclusions on tx sequences other than those recommended in clinical guidelines, not accounting for the attrition rates from 1L to subsequent LOT and relying on inappropriate clinical inputs for second-line (2L) tx and beyond. While addressing those limitations, we examined different clinical scenarios to explore the clinical value of using DARA first vs saving it until a later LOT. Methods: We examined 3 potential clinical tx sequences using a lifetime three-health state clinical simulation to estimate survival outcomes in pts with TIE NDMM. The 3 health states were: 1L (time on tx + any subsequent tx-free interval), 2L (time on 2L + any subsequent LOT), and death. Tx sequences were reflective of clinical guidelines: D-Rd in 1L followed by pomalidomide (POM)- or carfilzomib (CAR)-based regimens in 2L and VRd or Rd in 1L followed by DARA-based regimens in 2L (Figure). Pt characteristics were assumed to be similar to those of pts in MAIA. Time spent in the 1L health state with D-Rd and Rd was based on time to next tx or death (TTNT) curves derived from MAIA; TTNT curves with VRd were derived by applying PFS HRs estimated in the PEGASUS study. The attrition rate for the base case analysis (58.8%) was based on MAIA data. Although similar to published studies, it includes pts who were lost to follow-up and may be an overestimate. A sensitivity analysis evaluated a more conservative estimate (27.2%) excluding censored pts. Attrition rates were assumed to be the same across tx arms. Time spent in the 2L health state with DARA-based and POM- or CAR-based regimens was based on RW OS curves derived from the nationwide EHR-derived deidentified Flatiron Health database. RW data were used as pts in published 2L trials are younger than in MAIA and many have had prior ASCT. We evaluated median OS and 5- and 10-year survival rates. Results: Using D-Rd in 1L improved median OS by 2.5 or 3.5 years compared with delaying DARA-based regimens until 2L after VRd or Rd, respectively (Table). The probability of being alive at 5 and 10 years was higher with D-Rd than with VRd or Rd as initial therapy (Table). Changes in attrition rates impacted the expected median OS, but the absolute difference in median OS was maintained, with an incremental OS benefit consistently &gt;2 years. Conclusions: Unlike previous attempts to examine outcomes with different tx sequences, we incorporated clinically representative sequences for pts with NDMM, included attrition rates, and explored their impact on OS with different tx sequences. Our approach shows that achieving the longest possible PFS in 1L drives OS outcomes. Saving agents until later LOT does not take into account that many pts do not reach 2L, as evidenced by published data on attrition rates in MM. Our findings show that in pts with TIE NDMM, starting with D-Rd may provide up to 3.5 years of additional OS gain with the currently available 2L tx. This gain could increase with new 2L agents currently in development, reinforcing the importance of using the best agents first, to increase the probability of pts benefitting from tx currently in development. Figure 1 Figure 1. Disclosures Fonseca: Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; GSK: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aduro: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Mayo Clinic in Arizona: Current Employment; BMS: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Bayer: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Juno: Consultancy; Takeda: Consultancy; Merck: Consultancy. Hashim: Johnson and Johnson: Current Employment. Nair: Janssen Research & Development: Current Employment. He: Janssen: Current Employment, Current equity holder in publicly-traded company. Ammann: Janssen: Current Employment, Current equity holder in publicly-traded company. Lam: Janssen: Current Employment, Current equity holder in publicly-traded company. Wildgust: Janssen: Current Employment, Current equity holder in publicly-traded company. Kumar: Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Background: Light chain (AL) amyloidosis is a rare disease in which deposits of insoluble amyloid derived from immunoglobulin light chains accumulate and cause dysfunction of organs including the heart and kidneys. In order to reverse organ damage and improve overall survival, treatment must provide rapid, deep, and sustained hematologic responses. Although therapies approved for the treatment of multiple myeloma have demonstrated some efficacy, there are currently no health authority-approved treatments for AL amyloidosis. A retrospective observational study was undertaken to characterize patients and describe treatment patterns in patients with AL amyloidosis from real-world data in the US. Methods: Data were extracted from Optum Electronic Health Records (EHR) and Optum Clinformatics Data Mart (claims) databases. Eligible patients had (1) a diagnosis of AL amyloidosis (ICD-10 code E85.81) on or after January 1, 2008 or (2) a diagnosis of amyloidosis (ICD-9 codes 277.30, 277.39; ICD-10 codes E85.89, E85.9) on or after January 1, 2008 and at least one line of therapy (LOT) comprising one or more of the following treatments: bendamustine bortezomib, carfilzomib, cyclophosphamide, daratumumab, dexamethasone, doxorubicin, elotuzumab, etoposide, interferon alfa-2a, interferon alfa-2b, ixazomib, lenalidomide, melphalan, panobinostat, pomalidomide, prednisone/prednisolone, thalidomide, vincristine, or autologous stem cell transplant. Patients were required to have continuous medical enrollment during the 365 days prior to the index amyloidosis diagnosis date (claims data) or have first active date at least 365 days prior to index amyloidosis date (EHR data), must not have had prior cancer in the 365-day period prior to the index amyloidosis diagnosis date, and must have been age ≥18 years at time of amyloidosis diagnosis. Patients treated with dexamethasone only or dexamethasone and prednisolone only for ≤90 days and those treated with prednisone/prednisolone only irrespective of duration were excluded. LOTs were defined by a start date, an end date, and a distinct regimen made up of ≥1 drugs. Patients may have multiple sequential LOTs during the available follow-up period. Comorbidities were scored according to the Charlson Comorbidity Index (CCI). A score of 0 reflects no comorbidities; higher scores reflect increased mortality risk and the maximum score is 24. Descriptive statistics are provided for patient characteristics at index diagnosis date for patients who had ≥1 LOT, distribution of regimens in each LOT, treatment attrition, and duration of treatment in each line of therapy and by regimen. Results: Data from 1688 patients (Optum claims, n=624; Optum EHR, n=1064) were included in the analysis (Figure). Median age at diagnosis was 67 years and 55.7% of patients were male. Almost 70% of patients had an index diagnosis date between 2013 and 2019. The median CCI score at index diagnosis date (based on comorbidities in the 365-day pre-index diagnosis period) was 1 and 10.9% of patients had a stem cell transplant on or after the index diagnosis date. The three most common comorbidities were hypertension (50.8%), renal failure (26.2%), and congestive heart failure (24.6%). Median follow-up period was 28 months. Treatment information for regimens used by ≥5% of patients for LOT1 is summarized in the Table. The most common treatment regimen for LOT1 was a proteasome inhibitor (PI), an alkylating agent, and a steroid (n=392, 23.2%); this combination was used for an average of 111 days. Of patients treated with this combination, the most common regimen was bortezomib, cyclophosphamide, and dexamethasone (VCd), used by 358 patients. Steroid only (dexamethasone for ≥90 days) regimens accounted for 16.4% and a PI ± steroid for 13.2% of LOT1. A limitation of the study is that assumptions were used to classify AL amyloidosis vs other types of amyloidosis. Conclusions: VCd is the most commonly used regimen in patients with newly-diagnosed AL amyloidosis. This is consistent with clinical guidelines and real world data from Europe (Palladini et al, EHA 2020), supporting this regimen as the current standard of care for treatment of patients with newly-diagnosed AL amyloidosis. Disclosures Dispenzieri: Celgene: Research Funding; Pfizer: Research Funding; Alnylam: Research Funding; Intellia: Research Funding; Janssen: Research Funding; Takeda: Research Funding. Zonder:Intellia, Amgen, Takeda, Janssen, Regeneron, Alnylam, Caelum, Oncopeptides: Consultancy; BMS, Celgene: Research Funding. Hoffman:Loxo: Current equity holder in publicly-traded company; Celgene: Honoraria, Speakers Bureau. Wong:Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Fortis: Research Funding; Roche: Research Funding; GSK: Research Funding; Amgen: Consultancy; Bristol Myers Squibb: Research Funding. Liedtke:Adaptive: Membership on an entity's Board of Directors or advisory committees; Caelum: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria. Abonour:BMS: Consultancy, Research Funding; Takeda: Consultancy; Janssen: Honoraria, Research Funding; Celgene: Consultancy. D'Souza:Amgen, Merck, TenoBio: Research Funding; Akcea, Imbrium, Janssen, Pfizer: Consultancy. Lee:Janssen: Current Employment. Nair:Janssen: Current Employment. Potluri:SmartAnalyst Inc.: Current Employment. Weiss:Janssen: Current Employment, Current equity holder in publicly-traded company. Vermeulen:Janssen: Current Employment, Current equity holder in publicly-traded company. Lam:Janssen: Current Employment. Mehra:Janssen: Current Employment.

Abstract Daratumumab in combination with lenalidomide‐dexamethasone (D‐Rd) recently received FDA approval for the treatment of transplant‐ineligible patients with newly diagnosed multiple myeloma (NDMM). The present PEGASUS study compared progression‐free survival (PFS) in patients treated with D‐Rd in the MAIA trial and patients treated with common standard‐of‐care regimens from the Flatiron Health electronic health record‐derived deidentified database, which has data from patients treated primarily at community‐based oncology practices in the United States. Individual‐level patient data from both data sources were used to perform an anchored indirect treatment comparison (ITC) of D‐Rd to bortezomib‐lenalidomide‐dexamethasone (VRd) and bortezomib‐dexamethasone (Vd); lenalidomide‐dexamethasone (Rd) was the common anchor for the ITC. Hazard ratios (HRs) reflecting direct comparisons of PFS within MAIA (D‐Rd vs Rd) and Flatiron Health (VRd vs Rd; Vd vs Rd) were used to make ITCs for D‐Rd vs VRd and Vd, respectively. After application of MAIA inclusion/exclusion criteria and propensity‐score weighting, the Flatiron Health patients resembled the MAIA trial population on measured baseline characteristics. Based on the direct comparison within MAIA, treatment with D‐Rd was associated with a significantly lower risk of progression or death compared to Rd (HR 0.54; 95% CI 0.42, 0.71). Based on the ITCs, D‐Rd was associated with a significantly lower risk of progression or death compared to VRd (HR 0.68; 95% CI 0.48, 0.98) and Vd (HR 0.48; 95% CI 0.33, 0.69). In the absence of head‐to‐head trials comparing D‐Rd to VRd or Vd, the present ITC may help inform treatment selection in transplant‐ineligible patients with NDMM.

Introduction: Daratumumab, a CD38-directed monoclonal antibody, in combination with lenalidomide and dexamethasone (D-Rd), recently received FDA approval for the treatment of patients with newly diagnosed multiple myeloma (NDMM) ineligible for autologous stem cell transplantation (ASCT). FDA approval was based on results of the prespecified interim analysis of a pivotal phase III trial (MAIA) in which D-Rd was compared to Rd among patients with NDMM who were ineligible for ASCT. In the MAIA trial, D-Rd significantly reduced the risk of progression or death compared to Rd. However, no trials have compared D-Rd to other standard-of-care (SOC) regimens. The present study compared progression-free survival (PFS) of D-Rd from the MAIA trial database to common SOC regimens from the Flatiron Health EHR Database, a de-identified nationwide US database of records primarily from community-based oncology practices. Methods: Patient-level data were obtained from the MAIA trial and the Flatiron database. Patients in the MAIA trial were enrolled between March 2015 and January 2017. To be included in MAIA, patients had to be deemed ineligible for high-dose chemotherapy with ASCT due to either age (≥65 years) or presence of comorbidities, have Eastern Cooperative Oncology Group performance status (ECOG PS) ≤2, creatinine clearance ≥30mL/min, and adequate bone marrow reserve. In the Flatiron Database, patients had to be diagnosed with MM between January 2011 and April 2019, age ≥65 years at initiation of first line treatment (LOT1), could not have received stem cell transplant as part of LOT1, and met MAIA inclusion/exclusion criteria. Regimens received by fewer than 10% of patients in Flatiron were excluded due to sample size and statistical power considerations. Bortezomib-lenalidomide-dexamethasone (VRd), bortezomib-dexamethasone (Vd), and Rd were the most commonly used regimens in non-transplant NDMM patients, representing 67% of regimens used in this setting. Multiple imputation by chained equations was used to address missing data in Flatiron. Eligible Flatiron patients treated with VRd, Vd, or Rd as LOT1 were reweighted to resemble the MAIA trial population on measured baseline characteristics using propensity-score (PS) weighting. An anchored adjusted indirect treatment comparison (ITC) was performed to compare D-Rd to VRd and D-Rd to Vd, using a stratified Cox proportional hazards regression model with Rd as the common anchor across both data sources. The model calculates hazard ratios (HRs) for direct comparisons within MAIA (D-Rd vs Rd) and within Flatiron (VRd vs. Rd, Vd vs. Rd) to estimate the HRs indirectly for D-Rd vs. VRd and D-Rd vs. Vd. Results: After application of MAIA inclusion criteria and PS-reweighting, the Flatiron VRd, Rd, and Vd patients resembled the MAIA trial population on baseline patient demographics and clinical characteristics (Table 1). D-Rd statistically significantly reduced the risk of progression or death compared to Rd (HR=0.55; 95% CI, 0.41-0.72) based on the direct comparison with D-Rd using the MAIA trial data; D-Rd statistically significantly reduced the risk of progression or death compared to VRd (HR=0.66; 95% CI, 0.46-0.95) and Vd (HR=0.47; 95% CI, 0.33-0.67) based on the anchored ITC (Figure 1). Sensitivity analyses will be performed and updated analysis using longer follow-up data from the MAIA study will be presented at the meeting. Conclusion: This analysis demonstrated that D-Rd reduced the risk of progression or death compared to other SOCs VRd and Vd for patients with NDMM who are transplant-ineligible. In the absence of head-to-head trials, this type of patient-level analysis provides important information that can help optimize treatment for patients with NDMM who are transplant-ineligible. Disclosures Durie: Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Usmani:Amgen, Celgene, Janssen, Sanofi, Takeda: Speakers Bureau; Amgen Array Biopharma, Bristol-Myers Squibb, Celgene, Janssen, Merck, Pharmacyclics, Sanofi, Takeda: Other: Research Grant; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, SkylineDX, Takeda: Other: Consultant/Advisor. Ammann:Janssen: Employment, Equity Ownership. Lam:Janssen: Employment, Equity Ownership. Kobos:Janssen: Employment. Maiese:Janssen: Employment, Equity Ownership. Facon:Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees.

Introduction : Daratumumab (DARA) is a human CD38 antibody approved in combination with standard-of-care (SoC) regimens in pts with NDMM who are TIE. DARA-based treatments showed significantly improved progression-free survival (PFS) in pts with TIE NDMM in ongoing phase 3 studies ALCYONE (DARA + bortezomib/melphalan/prednisone [D-VMP] vs VMP) and MAIA (DARA + lenalidomide/dexamethasone [D-Rd] vs Rd continuous). An NMA allows estimation of the relative efficacy of regimens not compared in head-to-head trials. A previous NMA examined D-VMP and D-Rd vs comparators (Facon T et al., EHA 2019), excluding the SWOG S0777 study (bortezomib/lenalidomide/dexamethasone [VRd]; Durie BG, et al. Lancet 2017) as it enrolled both transplant-eligible (TE) and TIE pts, and data for pts who are TIE only (~50% [aged ≥65 y and frail]) were unavailable. In this NMA, D-Rd and D-VMP had the highest probabilities of being more effective than reference treatment Rd continuous in terms of PFS. Recently, VRd received EMA approval for treatment of TIE NDMM based on SWOG S0777, but results specific to TIE pts are still unavailable. Recent NMAs of TIE pts (Blommestein HM et al., Haematologica, 2019; Weisel et al., EHA 2019; Cao Y et al., Clin Lymphoma Myeloma Leuk, 2019) have included data from the the intent-to-treat (ITT) population of SWOG S0777, though its inclusion violates the similarity assumption. Here, we present a sensitivity analysis of the previously-conducted NMA including all pts who received VRd in SWOG S0777 for a comprehensive view of the comparative effectiveness of DARA-based treatments in TIE NDMM. Methods: VRd was included as part of a sensitivity analysis of the previous NMA based on the ITT population, reporting PFS and overall survival (OS). The Guyot method was used to estimate OS hazard ratios (HRs) for pts aged ≥65 y (proxy for TIE) from SWOG S0777 (Durie BG et al., ASH 2018). Median follow-up in ALCYONE and MAIA was 27.8 months and 28.0 months, respectively. Based on a systematic literature review conducted through January 2019, both PFS and OS were extracted and synthesized in Bayesian NMAs. Choice of fixed- or random-effects (FE or RE) model was based on lowest deviance information criterion (DIC) and/or presence of heterogeneity in the network. Rd continuous was selected as reference, as it was commonly included in guidelines across regions. For PFS and OS, HR <1 indicates the comparison is not in favor of Rd continuous. In addition to the previous NMA, we also report an NMA sensitivity analysis including only comparators approved by the EMA that are relevant for the European, Middle East and/or Africa (EMEA) region (EMEA NMA). Results: Global NMA: RE model was used for both PFS and OS. Compared with Rd continuous, the PFS HR for VRd in the sensitivity analysis was 0.74 (95% credible interval [CrI], 0.42-1.30), which was similar to SWOG S0777 (0.71; [96% Wald confidence interval [CI], 0.56-0.91]) and higher than HRs for D-Rd (0.55; 95% CrI, 0.31-0.97) and D-VMP (0.58; 95% CrI, 0.20-1.62; Table). For OS, the SWOG S0777 HR estimated by Guyot algorithm for pts aged ≥65 y was 0.80 (95% CI, 0.56-1.16). Compared with Rd continuous, the OS HR for VRd in the sensitivity analysis was 0.80 (95% CrI, 0.51-1.26), which was similar to SWOG S0777 and higher than D-Rd (0.78; 95% CrI, 0.51-1.18). EMEA NMA: FE model was used for both PFS and OS. Compared with Rd continuous, the PFS HR for VRd in the sensitivity analysis was 0.74 (95% CrI, 0.60-0.92), which was higher than HRs of D-Rd (0.55; 95% CrI, 0.43-0.71) and D-VMP (0.64; 95% CrI, 0.41-1.02). Compared with Rd continuous, the OS HR for VRd in the sensitivity analysis was 0.80 (95% CrI, 0.55-1.17), which was higher than for D-Rd (0.78; 95% CrI, 0.56-1.09). These results are consistent with the Global NMA, but differ slightly as the EMEA NMA has fewer evidence-loops (only EMEA SoC regimens were included). Conclusions: This study demonstrated favorable efficacy outcomes for DARA-based regimens vs other relevant frontline options, such as VRd, for pts with NDMM who are TIE. Limitations of the sensitivity analysis include lack of comparative trial data and demographic differences between pts from SWOG S0777 and trials of frontline MM treatments for pts who are TIE included in our network. The majority of SWOG S0777 pts are TE (<65 y), who often have better prognoses than TIE pts (≥65 y), and thus violates the similarity assumption of NMAs. However, DARA-based regimens still showed a comparative clinical advantage. Disclosures Facon: Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Usmani:Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, SkylineDX, Takeda: Other: Consultant/Advisor; Amgen Array Biopharma, Bristol-Myers Squibb, Celgene, Janssen, Merck, Pharmacyclics, Sanofi, Takeda: Other: Research Grant; Amgen, Celgene, Janssen, Sanofi, Takeda: Speakers Bureau. Dimopoulos:Sanofi Oncology: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Mateos:Adaptive: Honoraria; EDO: Membership on an entity's Board of Directors or advisory committees; Pharmamar: Membership on an entity's Board of Directors or advisory committees; Takeda: 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; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees. Cavo:Janssen, Celgene, Amgen, Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, Janssen, Amgen, BMS, Abbvie, Takeda: Honoraria; Janssen, Celgene: Other: Travel Accommodations; Janssen, Celgene: Speakers Bureau. Heeg:Ingress-Health: Employment. Van Beekhuizen:Ingress-health: Consultancy. Pisini:Janssen: Employment, Equity Ownership. Nair:Janssen: Employment, Equity Ownership. Lam:Janssen: Employment, Equity Ownership. Slavcev:Janssen: Employment, Equity Ownership. He:Janssen: Employment, Equity Ownership.

Abstract Background: Each additional line of therapy is associated with lower rates of deeper responses, shorter durations of treatment-free intervals, and increased rates of toxicities/comorbidities in patients with multiple myeloma (MM; Yong K, et al. Br J Haematol 2016. 175[2]:252-264), all of which are factors that contribute to treatment discontinuation. In order to characterize the newly diagnosed MM (NDMM) patient population in the United States not treated with a stem-cell transplant based on the number of subsequent lines of therapy (LOT) received, we examined baseline patient characteristics and pre-existing comorbidities, frontline treatment patterns, and attrition rates for these patients according to whether subsequent LOT was received or not received. Methods: NDMM patients were identified from 3 US patient-level data sources: the Surveillance, Epidemiology, and End Results (SEER)-Medicare Linked database (from January 2007 through December 2014), the OPTUM™ Commercial Claims database (from January 2000 through March 2017), and the OPTUM™ Electronic Medical Records (EMR) database (from January 2007 through March 2016). Patients included in the assessment had 1) an index diagnosis of MM on or after 1 January 2007, 2) known gender, 3) medical prescription coverage in place at diagnosis, 4) a 1-year look-back period prior to the index diagnosis, 5) no prior malignancies in the 1-year period prior to index diagnosis, and 6) received ≥1 LOT. The analysis was limited to patients who did not receive stem cell transplantation at any time during their follow-up. Descriptive and logistic regression analyses were conducted to characterize and compare patients who received or did not receive a subsequent LOT. Results: The analysis included a total of 7,724 patients (1 LOT only: n = 3,906; 2+ LOT: n = 3,818). A summary of baseline patient characteristics and pre-existing comorbidities by LOT is summarized in Table 1. Patients who received a subsequent LOT vs those that did not were significantly younger (median age 72.0 vs 74.0 years; P <0.0001) and had significantly lower mean Charleston Comorbidity Index (CCI) score due to lower occurrence of renal, cardiac, and hepatic comorbidities (Table 1). Older age at diagnosis and presence of renal, congestive heart failure (CHF), hepatic, and pulmonary comorbidities were associated with higher attrition. The regimens used in frontline treatment included bortezomib/dexamethasone (26%), lenalidomide/dexamethasone (21%), bortezomib/lenalidomide/dexamethasone (11%), a bortezomib/alkylator ± steroid combination (6%), bortezomib monotherapy (10%), lenalidomide monotherapy (6%), thalidomide monotherapy (4%), and other (17%). The proportions of patients progressing to each subsequent line of therapy are shown in Table 2. In total, 49% of patients went on to receive a second line of therapy, and 55% of patients receiving a second line of therapy went on to receive a third line of therapy. The incidence of patients not receiving subsequent lines of therapy due to death or being lost to follow up by LOT is summarized in Table 2. Conclusions: A majority of non-transplant patients with NDMM in the study did not progress to subsequent lines of therapy, with attrition as high as 50% per line of therapy and only <8% of patients reaching LOT 5. This high level of attrition is associated with older age and poor comorbidity status of non-transplant patients with NDMM. Therefore, utilizing the most optimal regimens upfront (rather than reserving them for later lines) is warranted. Disclosures Fonseca: Mayo Clinic: Employment; Amgen: Consultancy. Usmani:Abbvie, Amgen, Celgene, Genmab, Merck, MundiPharma, Janssen, Seattle Genetics: Consultancy; Amgen, BMS, Celgene, Janssen, Merck, Pharmacyclics,Sanofi, Seattle Genetics, Takeda: Research Funding. Mehra:Janssen Global Services, LLC: Employment. Slavcev:Janssen Global Services, LLC: Employment. Ukropec:Janssen Scientific Affairs, LLC: Employment. Lam:Janssen Global Services, LLC: Employment. Potluri:SmartAnalyst Inc.: Employment.

Abstract Objective: To fully contextualize the benefit of novel agents such as daratumumab (DARA) monotherapy for the treatment of patients with heavily pre-treated and highly refractory multiple myeloma (MM), it is critical to understand the real-world outcomes of this patient population on current standard of care (SOC) therapies. The objective of this study was to perform adjusted comparisons to determine the comparative effectiveness of DARA monotherapy versus real-world SOC therapies. Methods: Data for patients treated with DARA 16 mg/kg monotherapy were available from clinical trials MMY2002 (n=106) and GEN501 (n=42), while patients treated with SOC therapies were derived from the International Myeloma Foundation (IMF) chart review of patients with MM who had ≥3 prior lines of therapy and were double refractory to a proteasome inhibitor (PI) and an immunomodulatory drug (IMiD) (n=543) (Kumar et al., ASH 2016; submitted). The pooled DARA studies demonstrated a median overall survival (OS) of 20.1 months versus 13.0 months for SOC based on the IMF cohort (Usmani et al., Blood 2016; Kumar et al., ASH 2016; submitted). The relative treatment effect of DARA versus SOC was estimated using two adjusted comparison methodologies, propensity score matching (PSM) and multivariate Cox regression analyses. Both methodologies utilized individual patient data to compare OS. Modeled covariates for the PSM were age, gender, prior lines of therapy, albumin, and refractory status to bortezomib (BOR), carfilzomib (CAR), lenalidomide (LEN), and pomalidomide (POM). PSM was performed using caliper matching with a caliper width 25% of the standard deviation of the logit-transformed propensity score, using sampling without replacement. For the regression analysis, the covariates included in the multivariate proportional hazards regression model were age, gender, prior lines of therapy, albumin, beta-2 microglobulin, prior exposure to POM and CAR, and PI/IMiD refractory status. Clustering of observations at the treatment-line level within patients was controlled for using the robust sandwich estimate for the covariance matrix, making confidence intervals (CIs) more conservative. For both PSM and regression, statistical significance testing was performed using a two-tailed p-value of <0.05, and all comparisons between treatment groups were reported with hazard ratios (HRs) and 95% CIs. Results: Prior to PSM, imbalances between the DARA and SOC groups were significant for prior lines of therapy and proportions of patients refractory to POM, CAR, BOR, and LEN. After PSM, the DARA and SOC groups were well balanced for all covariates included in propensity score calculations. After PSM, comparisons found significant improvement in favor of DARA relative to SOC for OS (HR=0.44 [95% CI 0.31-0.63]) (Figure 1). Regression analyses revealed consistent results. After adjustment for differences in all covariates included in regression between the DARA and SOC groups, results showed significant improvement in favor of DARA compared with SOC for OS (HR=0.43 [95% CI 0.32-0.59]) (Figure 2). Conclusions: Findings from both PSM and regression analyses were consistent and suggest that DARA is associated with significant gains in OS compared with SOC therapies for patients with heavily pre-treated and highly refractory MM. References: 1. Usmani SZ, Weiss BM, Plesner T, Bahlis NJ, Belch A et al. (2016) Clinical efficacy of daratumumab monotherapy in patients with heavily pretreated relapsed or refractory multiple myeloma. Blood 128 (1): 37-44. 2. Kumar SK, et al. (2016) Natural history of relapsed myeloma, refractory to immunomodulatory drugs and proteasome inhibitors: a multicenter IMWG study. The 58th Annual Meeting of the American Society of Hematology: submitted. Disclosures Kumar: Celgene: Consultancy, Research Funding; Noxxon: Consultancy, Honoraria; Janssen: Research Funding; AbbVie: Research Funding; BMS: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Skyline: Consultancy, Honoraria. Durie:Amgen: Consultancy; Takeda: Consultancy; Janssen: Consultancy. Su:Janssen: Research Funding. Diels:Johnson & Johnson: Employment, Equity Ownership. Hutton:Essai Canada: Consultancy; Cornerstone Research Group: Consultancy; Janssen: Research Funding. Lam:Janssen: Employment. Tetsuro:Johnson & Johnson: Equity Ownership; Janssen: Employment.