Citations

Abstract The impact of fluorescence in-situ hybridization (FISH) on outcome in AL amyloidosis remains uncertain. We analyzed the response and overall survival (OS) of 692 AL amyloidosis patients with available FISH testing at diagnosis. First line treatment was categorized as autologous stem cell transplant or three non-transplant regimens (melphalan-based; bortezomib-based, and immunomodulatory drug (IMiD)-based). Forty-nine percent of patients had t(11;14), 37% del 13q/monosomy 13 and 19% had trisomy/tetrasomy. Analysis by abnormality type was restricted to t(11;14) and trisomy/tetrasomy sub-groups as chromosome 13 abnormalities were commonly associated with the other abnormalities. Patients with t(11;14) had lower rate of very good partial response or better (≥VGPR) compared to those lacking t(11;14) (56% vs 70%, P=0.001). By regimen stratification, this difference was maintained for bortezomib-based (52% vs 77%; P=0.004) and IMiD-based therapy (13% vs 54%; P=0.04) (Figure 1). Patients with trisomy/tetrasomy had no difference in the rate of ≥VGPR compared to those who did not (66% vs 63%, P=0.62), with no difference within regimen types. OS was similar between t(11;14)-positive and negative patients (median 43 vs 57 months; P=0.58). However, sub-analysis by regimen type showed a significantly inferior OS among t(11;14)-positive bortezomib-treated patients (median 15 vs 27 months; P=0.05) and t(11;14)-positive IMiD-treated patients (median 12 vs 32 months; P=0.05) (Figure 2). Among t(11;14)-positive bortezomib-treated patients, the OS difference was maintained among favorable-prognosis patients, but was absent for poor-prognosis sub-groups (Table). Trisomy/tetrasomy was associated with inferior OS (median 29 vs 69 months; P=0.001), a difference that was maintained for melphalan (median 15 vs 32 months; P=0.01) and borderline significance for bortezomib (median 14 vs 38 months; P=0.09). Multivariate analysis confirmed an independent effect on OS for trisomy/tetrasomy in the study cohort and for t(11;14) among bortezomib-treated patients. FISH analysis is essential in newly diagnosed AL amyloidosis, as it provides prognostic information among treatment groups and may influence the selection of induction treatment. Patients with t(11;14) should be considered for ASCT at diagnosis. Disclosures Dispenzieri: Celgene: Research Funding; Alnylam: Research Funding; pfizer: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jannsen: Research Funding. Kumar:Glycomimetics: Consultancy; AbbVie: Research Funding; Array BioPharma: Consultancy, Research Funding; Kesios: Consultancy; Onyx: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding; BMS: Consultancy; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Kapoor:Amgen: Research Funding; Takeda: Research Funding; Celgene: Research Funding.

Abstract Background: A recent preclinical study revealed an antiproliferative and apoptotic effect of propranolol on MM cell lines. This study was to investigate if propranolol and other beta blocker intake improved survival in MM patients. Methods: Among patients seen at Mayo Clinic, Rochester from 1993 to 2010, we identified those taking propranolol. Age, year of diagnosis, International Staging System (ISS), Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) were used as case-match variables to match propranolol users to two control populations: no beta blocker and other beta-blocker. Cox proportional hazard modeling was utilized to analyze the effect of propranolol and other beta blockers intake on overall survival (OS) and disease-specific survival (DSS). Results: In 3 groups: propranolol (n=52), no beta blocker (n= 136), or other beta blocker (n=84), transplant eligibility was 63.5%, 66.9%, 66.7% respectively (p=0.90). Median age was 63 years. Median follow-up was 86 months. No statistically differences were seen in age, ISS, mSMART or year of diagnosis in 3 groups (p=0.46).In univariate analysis comparing propranolol vs no beta blocker or other beta blocker cohort, 5-year OS rate was 60.2% vs 47% or 64% (p=0.039), and 5-year DSS rate was 71.7% vs 50.3% or 69.1% (p=0.0029). In multivariate analysis adjusted for age, ISS, transplant eligibility, mSMART and year of diagnosis, compared to propranolol intake, no beta blocker group had poorer OS [HR= 2.081; 95% CI (1.353- 3.200); p=0.0008], and DSS [HR=2.833 (1.709-4.697); p<0.0001], but other beta blocker users had no difference in OS [HR =1.145 (0.728-1.803);p=0.56],and DSS [HR= 1.276 (0.745-2.185); p=0.38]. Conclusion: Propranolol and other beta-blocker intake among MM patients were equally associated with a more favorable OS and DSS. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures Lust: Senesco: PI Other.

Abstract Background Suppression of uninvolved immunoglobulins is a common finding in multiple myeloma and the preservation of uninvolved immunoglobulins at diagnosis is associated with improved progression-free and overall survival. However, little is known about the impact of myeloma treatment on levels of uninvolved immunoglobulins, and the link between changes in uninvolved immunoglobulins during therapy and treatment response, disease progression and survival. Methods We identified patients who received therapy for newly diagnosed multiple myeloma at our institution between 2001 and 2014, and who had data available on absolute lymphocyte count (ALC) and quantitative uninvolved immunoglobulins (Ig) before commencing treatment. The ALC and levels of uninvolved Ig after 4 cycles of therapy were abstracted from the electronic medical record; patients who switched or stopped treatment, or died, before this time point were excluded. To assess change in ALC, the percentage difference in ALC between baseline and 4 cycles was calculated; for uninvolved Ig, the average of the percentage difference between baseline and 4 cycles for each uninvolved Ig (IgA and IgM for IgG myeloma, IgG and IgM for IgA myeloma, IgG and IgA for IgM and IgD myeloma, and IgG, IgM and IgA for light-chain only myeloma) was calculated. Treatment response at 4 cycles was retrospectively assigned according to International Myeloma Working Group criteria. Time to treatment failure (TTF) was defined as time from start of initial therapy to start of next line of therapy or death (if no additional treatment was received). A landmark analysis was used to calculate overall survival (OS) from the date of follow-up after 4 cycles of therapy. The Kruskal-Wallis, Chi-Square, and log rank tests were used to detect differences in medians, proportions, and survival times respectively. Results A total of 421 patients were included in this analysis. The median age was 63 years (range 33-91), 254 patients (60.3%) were male and median follow-up was 6.5 years (95% CI 5.6-7.3); the vast majority of patients had IgG (n=247 [58.7%]), IgA (n=98 [23.3%]) or light-chain only myeloma (n=68 [16.2%]). First line therapy comprised of pulse-dose dexamethasone (DEX, n=92 [21.9%]), lenalidomide-dexamethasone (RD, n=176 [41.8%]), bortezomib-dexamethasone (VD, n=22 [5.2%]), bortezomib-cyclophosphamide-dexamethasone (VCD, n=84 [20.0%]), and bortezomib-lenalidomide-dexamethasone (VRD, n=47 [11.2%]). Across the entire cohort, the median change in ALC and uninvolved Ig after 4 cycles of treatment was -11.0% (range: -92.7 to +718.8) and +9.0% (-77.7 to +1094.4) respectively; treatment with VCD was associated with the greatest median declines in ALC (DEX: -0.1%; RD: -9.9%; VD: -20.8%; VCD: -40.9%; VRD: -15.3%) and uninvolved Ig (DEX: -0.5%; RD: +15.5%; VD: +44.0%; VCD: -14.0%; VRD: +76.0%, both p<0.001). Conversion from suppression to normalization of the primary uninvolved Ig (IgA in IgG myeloma, and IgG in all other myeloma types) after 4 cycles was seen more frequently with the use of RD (13.1%) and VRD (12.8%) compared to DEX (4.7%), VCD (1.3%), or VD (4.8%), χ2=21.8, p=0.040. When considering only patients in whom the primary uninvolved Ig remained suppressed between baseline and 4 cycles, a ≥25% reduction in the primary uninvolved Ig occurred more frequently with the use of DEX (51.5%) and VCD (34.5%) compared to RD (24.8%), VD (23.5%) or VRD (25.7%), χ2=15.1, p=0.005 (Table 1). Although an average reduction in uninvolved Ig between baseline and 4 cycles (ΔIg<0) was independently associated with a lower likelihood of achieving very good partial response (VGPR) of better on multivariate analysis adjusting for age, sex and treatment regimen (OR=0.40 [0.24-0.63], p<0.001), there were no differences in TTF (2.0yrs vs. 2.0yrs, p=0.783) or OS (8.0yrs vs. 8.0yrs, p=0.721) between patients with ΔIg<0 (n=169) and those with ΔIg≥0 (n=222). Conclusions Myeloma treatments produce differential impacts on immune parameters, with VCD causing the greatest reduction in lymphocytes and uninvolved Ig, implying general targeting of plasma cells, in comparison to lenalidomide, which appeared to be more tumor-specific with relative sparing of lymphocytes and uninvolved Ig. While an average decrease in uninvolved Ig was an independent predictor of a lower likelihood of achieving VGPR or better after 4 cycles of therapy, it was not associated with a shorter TTF or poorer OS. Disclosures Kumar: BMS: Consultancy; Glycomimetics: Consultancy; Onyx: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Kesios: Consultancy; Millennium: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; AbbVie: Research Funding. Dispenzieri:GSK: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees; pfizer: Research Funding; Jannsen: Research Funding; Alnylam: Research Funding; Celgene: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Amgen: Research Funding; Celgene: Research Funding; Takeda: Research Funding. Bergsagel:Amgen, BMS, Novartis, Incyte: Consultancy; Novartis: Research Funding.

Abstract Background: Pomalidomide has demonstrated excellent activity in patients with relapsed, lenalidomide refractory, multiple myeloma (MM). Between November 2007 and March 2012, we enrolled 285 patients with relapsed MM on 5 sequential phase 2 trials; patients received pomalidomide at 2mg or 4 mg daily with weekly dexamethasone (Pom/dex). The approved dose of pomalidomide is 4 mg for 21 of 28 days. We wished to compare efficacy, tolerability and long-term outcomes between cohorts treated with 2 mg or 4mg daily continuously and 4mg daily for 21/28 days. Methods: After excluding two ineligible patients, 283 patients with lenalidomide refractory, relapsed MM from 5 sequential cohorts were analyzed. These patients were divided into 3 groups: Group1 received Pom 2mg for 28/28 day cycle (N= 69), Group 2 received Pom 4 mg for 28/28 day cycle (N= 95) and Group 3 received Pom 4mg for 21/28 day cycle (N= 119). All patients received oral dexamethasone given 40 mg daily on days 1, 8, 15, and 22. Response was assessed by the IMWG Uniform Response criteria. All patients received aspirin 325 mg daily for DVT prophylaxis or full dose anticoagulation. Results: The median age was 63 years (32-85); 35% were female. The median time from diagnosis was 53 months and the median number of prior regimens was 4. 127 (46%) had high-risk molecular markers. Prior therapies (% received) included lenalidomide (100%), thalidomide (46%), bortezomib (78%), autologous stem cell transplant (71%), and allogeneic transplant (4%). The median follow-up is 16.4 months (3.2-64.4). Forty eight percent are alive and 26% remain progression free; 15 patients are continuing to receive treatment. Frequency of AEs by groups are shown in Table 1. The most notable difference is grade 3+ neutropenia seen in 39% of group 1 and 56% and 57% of groups 2 and 3. Confirmed responses of PR or better were seen in 29% (group1), 35% (group2) and 24% (group3). Median duration of response (DOR) was 14.1 months (group1), 14.5 months (group2) and 10.2 months (group3). Median PFS was 5.5 months (group1), 6.9 months (group2) and 4.3 months (group3). Although the dose level cohorts were sequential rather than randomized, we compared OS between the dose levels in an exploratory manner. There was no significant difference in OS between dose levels (p=0.26). Median overall survival (OS) was 16.6 months (group1), 21.9 months (group2) and 16.0 months (group3). Conclusions: Pom/dex is active and well tolerated even in heavily pretreated patients Responses are durable. Response rates and overall toxicity are similar between the 2 mg and 4 mg doses. Neutropenia is more common in those receiving doses of 4mg daily or for 21/28 days compared to those receiving 2 mg daily. Table 1. All Grades Grade 3+ 2mg 28 Day 4mg 28 Day 4mg 21 Day 2mg 28 Day 4mg 28 Day 4mg 21 Day Anemia 68% 58% 74% 14% 15% 27% Lymphopenia 22% 51% 11% 16% 32% 8% Neutropenia 71% 82% 77% 39% 57% 56% Thrombocytopenia 51% 61% 63% 10% 9% 23% Leukopenia 59% 77% 72% 26% 38% 39% Pneumonia 7% 11% 12% 6% 7% 11% Fatigue 51% 65% 60% 9% 5% 8% Neuropathy 28% 32% 28% 0% 3% 0% Elevated Blood Glucose 10% 21% 8% 4% 6% 3% Pneumonitis 3% 2% 3% 3% 1% 1% VTE (Thrombosis) 3% 3% 3% 1% 3% 3% Secondary Malignancy 0% 2% 1% 0% 2% 1% Figure1. Kaplan Meier Overall Survival Curves Figure1. Kaplan Meier Overall Survival Curves Disclosures Lacy: Celgene: Research Funding. Fonseca:Medtronic, Otsuka, Celgene, Genzyme, BMS, Lilly, Onyx, Binding Site, Millennium, AMGEN: Consultancy, patent for the prognostication of MM based on genetic categorization of the disease. He also has sponsored research from Cylene and Onyx Other, Research Funding. Bergsagel:Novartis: Research Funding; Constellation Pharmaceutical: Research Funding; OncoEthix: Research Funding; MundiPharma: Research Funding. Stewart:Novartis: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Array BioPharma: Consultancy; Sanofi: Consultancy; Takeda Pharmaceuticals International Co.: Research Funding. Reeder:Millennium, Celgene, Novartis: Research Funding. Mikhael:Onyx: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Novartis: Research Funding.

Abstract Background: Cytogenetic evaluation using fluorescence in situ hybridization (FISH) at the time of diagnosis is essential for initial risk stratification in multiple myeloma. Despite the absence of cytogenetic high-risk abnormalities a subset of patients does experience poor overall survival comparable high-risk disease (i.e. median overall survival less than three years after diagnosis). We aimed to identify demographic, clinical, and cytogenetic characteristics predicting poor three-year overall survival in patients with standard-risk cytogenetics. Methods: We studied 428 patients who were diagnosed with multiple myeloma between July 2004 and July 2014 at Mayo Clinic Rochester, underwent FISH evaluation within six months of diagnosis, and received treatment with novel agents (immunomodulator, proteasome-inhibitor, or a combination thereof). Patients with high- and intermediate-risk cytogenetics as well as patients lost to follow-up within three years were excluded. High-risk cytogenetics were defined as del(17p), t(14;16), and t(14;20). Intermediate-risk cytogenetics were defined as t(4;14) and gain(1q). Bone marrow aspirates were evaluated for deletions, monosomies, trisomies, and tetrasomies using chromosome- or centromere-specific FISH probes. IGH rearrangements were evaluated using an IGH break-apart probe and evaluating up to five potential partners (FGFR3, CCND1, CCND3, MAF, and MAFB). A multivariable-adjusted logistic regression model was used to assess the associations between the parameters of interest and three-year overall survival. Discrimination and calibration of the model were evaluated using the c-statistic and the Hosmer-Lemeshow test, respectively. The odds ratios of the identified predictors were rounded up to the next integer and assigned as points in a scoring system. Receiver operating characteristic analysis was used to determine the optimal cut-point and associated test performance characteristics. P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis was 65 years (31 - 95), 259 (61%) of the patients were male. The median overall survival was 7.4 years (6.1 - 8.6) for the entire cohort (n = 428), 10.5 years (8.3 - NR) for those who survived at least three years (n = 327, 76%), and 1.6 years (1.2 - 1.7) for those who did not survive three years after diagnosis (n = 101, 24%). The factors associated with poor three-year overall survival and the derived scoring system are summarized in Table 1. Conclusions: Patients with multiple myeloma and standard-risk cytogenetics are a heterogeneous group. One fourth of the patients are experiencing less than three years of overall survival after diagnosis. Stage, age, extent of bone marrow involvement, karyotype, and platelet count at the time of diagnosis were helpful in identifying patients at risk for poor three-year overall survival. These findings emphasize the importance of further risk stratification in this patient population and warrant external validation. Disclosures Dispenzieri: Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jannsen: Research Funding; pfizer: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Alnylam: Research Funding. Kapoor:Takeda: Research Funding; Amgen: Research Funding; Celgene: Research Funding. Kumar:AbbVie: Research Funding; BMS: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Noxxon: Consultancy, Honoraria; Janssen: Research Funding; Skyline: Consultancy, Honoraria.

Abstract Introduction: A recent study revealed an antiproliferative and apoptotic effect of propranolol on multiple myeloma (MM) cells. Our previous small matched case-control study showed longer survival in patients with propranolol and other beta-blockers (BB) intake than those without. This larger scale study was conducted to confirm the positive association of BB and MM survival. Methods: We identified 1971 newly diagnosed pts seen at Mayo Clinic between 1995 and 2010. Cardiac medication usage after diagnosis of MM was extracted from patient records and categorized based on BB intake. Cause of death was collected with death due to MM as the primary interest event and death due to cardiac disease or other reasons as competing risk events. The primary outcomes were MM disease-specific survival (DSS) and overall survival (OS). Cumulative incidence functions and Kaplan-Meier method were used to estimate the 5-year cumulative incidence rate (CIR) of MM death and OS rate, respectively. DSS and OS were compared by Gray's test and log-rank test, respectively. Multivarable Cox proportional hazard models were used to estimate the adjusted cause-specific HR (HRCSadj.) and hazard ratio (HRadj.) for DSS and OS, respectively, adjusting for demographics, disease characteristics, diagnosis year, and various chemotherapies. Results: 930 (47.2%) of MM patients had no intake of any cardiac medications; 260 (13.2%) had BB only; 343 (17.4%) used both BB / non-BB cardiac medications; and 438 patients (22.2%) had non-BB cardiac drugs. Five-year CIR of MM death and OS rate were shown in table. Superior MM DSS was observed for BB only users, compared to patients without any cardiac drugs (HRCSadj., .53, 95% confidence interval [CI], .42-.67, padj.<.0001) and non-BB cardiac drugs users (HRCSadj., .49, 95% CI, .38-.63, padj.<.0001). Patients received both BB and other cardiac drugs also showed superior MM DSS than non-cardiac drugs users (HRCSadj.., .54, 95% CI, .44-.67, padj.<.0001) and non-BB cardiac drug users. (HRCSadj., .50, 95% CI, .40-.62, padj.<.0001). MM DSS does not differ between BB users with and without other cardiac drugs (padj.=0.90). Multivariable analysis showed the same pattern for OS. None of the MM therapies impacted the differences in DSS and OS among BB intake groups (interaction padj.>.60). Conclusion: MM patients with BB intake showed reduced risk of death due to MM and overall mortality compared to patients who used non-BB cardiac or never used cardiac drugs. The result warrants further investigation for anti-cancer effect of BB in MM. Disclosures Shi: Mayo Clinic: Employment. Kumar:Onyx: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Glycomimetics: Consultancy; Janssen: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; BMS: Consultancy; Kesios: Consultancy. Gertz:NCI Frederick: Honoraria; Celgene: Honoraria; Med Learning Group: Honoraria, Speakers Bureau; Research to Practice: Honoraria, Speakers Bureau; Alnylam Pharmaceuticals: Research Funding; Novartis: Research Funding; Prothena Therapeutics: Research Funding; Ionis: Research Funding; Annexon Biosciences: Research Funding; GSK: Honoraria; Sandoz Inc: Honoraria. Kapoor:Celgene: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Dispenzieri:pfizer: Research Funding; Celgene: Research Funding; Alnylam: Research Funding; Jannsen: Research Funding; GSK: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background: Pomalidomide is an immunomodulatory agent (IMiD®) that has been approved for treatment of relapsed and refractory multiple myeloma (MM). Combinations of IMiDs and proteasome inhibitors offer the potential for deeper and more durable responses due to enhanced efficacy. Preliminary results of a phase 1 study of twice weekly bortezomib with pomalidomide have been reported with promising results (Richardson, ASH 2012). This phase I/II trial was designed to evaluate the maximum tolerated doses (MTD) as well as safety and efficacy of the combination of pomalidomide, once weekly bortezomib and dexamethasone (PVD) in patients with relapsed, lenalidomide refractory, MM. Patients and methods: We included patients with relapsed MM who had 1-4 prior lines of therapy and were resistant or refractory to lenalidomide. In the phase I portion of the trial, dose level 1 consisted of pomalidomide 4 mg days 1-21 PO, bortezomib 1.0mg/m2 days 1,8,15, and 22 IV or SQ and dexamethasone 40 mg days 1,8,15, and 22 PO, given every 28 days. Bortezomib was increased to 1.3mg/m2 for dose level 2 and and was adopted for the phase 2 portion. The primary aim of the phase I cohort was to determine the MTD of the combination, and for the phase II cohort was to evaluate the confirmed response rate (PR, VGPR, or CR) in relapsed refractory MM. Response was assessed by the IMWG criteria and toxicity was graded using the CTCAE v4.0. Results: 50 patients were accrued between March 2012 and July 2014 (dose level (DL)1: 3, DL 2: 6, Phase II: 41). We describe results in 47 patients treated at MTD and phase II. Median age was 66, 51% were female and median time from diagnosis to study was 46 months (15-142). Twenty five percent had mSMART defined high-risk status. Median number of prior regimens was 3. All patients had prior lenalidomide, 68% had stem cell transplant, 17% received thalidomide, 56% had alkylators and 57% had bortezomib. With median follow up of 9 months, 72% remain progression free, 96% are alive and 66% remain on treatment. The most common AEs at least possibly attributable to the combination were anemia, fatigue, leukopenia and thrombocytopenia; however, the majority of these were grade 1-2. Grade ≥3 AEs (regardless of attribution) that occurred in at least 3 patients included neutropenia (29), leukopenia (15), lung infection (6), lymphopenia (8) dyspnea (3) and syncope (3). DVT/PE occurred in one patient. Among the 42 patients who were evaluable, confirmed responses (PR, VGPR, or CR) were seen in 34 (81%) including sCR (3), CR (5), VGPR (8), PR (18). Confirmed responses were seen in 9 of 11( 82%) high risk patients. Median progression free survival was 17.7 months (95%CI: 9.5-NA). Conclusions: PVD is a highly effective combination in patients refractory to lenalidomide with confirmed responses in over 80%. Weekly administration of bortezomib enhanced the tolerability and convenience of this regimen. Toxicities are manageable, mostly consisting of mild cytopenias with no significant neuropathy or DVT. PVD is a highly attractive option in patients with relapsed and refractory MM. Disclosures Lacy: Celgene: Research Funding. Lust:Senesco: PI Other. Fonseca:Medtronic, Otsuka, Celgene, Genzyme, BMS, Lilly, Onyx, Binding Site, Millennium, AMGEN: Consultancy, patent for the prognostication of MM based on genetic categorization of the disease. He also has sponsored research from Cylene and Onyx Other, Research Funding. Stewart:Celgene: Consultancy; Novartis: Consultancy; Array BioPharma: Consultancy; BMS: Consultancy; Millenium: Research Funding. Reeder:Millennium, Celgene, Novartis: Research Funding. Mikhael:Onyx: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Novartis: Research Funding.

Abstract BACKGROUND: While hematologic cancers comprise only 10% of all malignancies, they are divided into >100 distinct World Health Organization subtypes. It is known that higher volume of care is generally associated with better clinical outcomes. However, such a volume-outcome relationship in the medical management of hematologic cancers has not been rigorously explored. The American Society of Clinical Oncology (ASCO) National Census of Oncology Practices shows that the majority of hematologist-oncologists in the United States (US) have a combined hematology-oncology practice (J Oncol Pract 2013). In this study, we estimated the annual number of new and established patients with major hematologic cancers seen on average by a hematologist-oncologist in the US. METHODS: We estimated the number of hematologist-oncologists working in the US using the ASCO workforce information system data from 2011. We utilized statistics from the Surveillance Epidemiology and End Results (SEER) Program to determine the incidence and 37-year limited prevalence of hematologic cancers in 2011. We used 'first malignant tumor per site' statistics as the tumor inclusion method. For potentially curable hematologic cancers (acute lymphocytic leukemia, acute myeloid leukemia, Burkitt lymphoma, diffuse large b-cell lymphoma, Hodgkin lymphoma, and marginal zone lymphoma), we used the estimated 1-5 year survival rates from SEER and excluded patients who survived >5 years, since relapses are rare afterwards. Because prevalence estimates of chronic myelomonocytic leukemia, myelodysplastic syndromes, and certain subtypes of non-Hodgkin lymphoma are unavailable, we were unable to calculate the number of annual established cases. For myeloproliferative neoplasms, we obtained the prevalence estimate from Mehta J, et al (Leuk Lymphoma 2014). We derived the distribution of major non-Hodgkin lymphoma subtypes from the National Cancer Data Base (NCDB) Participant User File. RESULTS: The ASCO workforce information reported a total of 13,084 hematologist-oncologists working in the US in 2011. The Table summarizes the average number of specific hematologic cancer cases seen per hematologist-oncologist in 2011. CONCLUSION: Hematologic cancers are relatively rare but complex. In the US, a hematologist-oncologist on average cares for only 1-2 new patients of any subtype of hematologic cancers annually. The number of established patients is correspondingly low. These numbers are expected to vary by practice setting and disease specialization. As the diagnosis and management of hematologic cancers becomes more sophisticated, future research should explore the potential of a volume to clinical outcome relationship for these providers. Table. Hematologic Cancer Average Annual Number of Cases per Hematologist-Oncologist in the US New Cases Established Cases All Cases Acute lymphocytic leukemia 0.4 1.4 1.8 Acute myeloid leukemia 1 1.5 2.5 Chronic lymphocytic leukemia 1.1 10.7 11.8 Chronic myeloid leukemia 0.4 2.7 3.1 Chronic myelomonocytic leukemia 0.1 - - Hodgkin lymphoma 0.7 2.7 3.4 Multiple myeloma 1.6 6.3 7.9 Myelodysplastic syndromes 1.2 - - Myeloproliferative neoplasms 0.6 22.2 22.8 Non-Hodgkin lymphoma 5.1 - - Anaplastic large cell 0.1 - - Burkitt 0.1 0.3 0.4 Diffuse large B-cell 2 6 8 Follicular 1.1 - - Hairy cell leukemia 0.1 - - Lymphoplasmacytic 0.1 - - Mantle-cell 0.3 - - Marginal zone 0.5 2.1 2.6 Peripheral T-cell, not otherwise specified 0.1 - - Disclosures No relevant conflicts of interest to declare.

Abstract Background: There has been major improvement in the survival of patients diagnosed with multiple myeloma (MM) over the past two decades. Such improvement has been attributed to the broader use of hematopoietic progenitor cell transplantation and novel drugs that prolong initial remission and more successfully treat relapsed disease. Early mortality (EM), here defined as death in the first year after diagnosis, is uncommon in modern MM trials. The magnitude of EM in unselected patients with MM and its impact on overall outcomes in this disease is unknown Methods: We analyzed EM in MM utilizing data from the Surveillance Epidemiology and End Result Program (SEER-13). Patients diagnosed with MM as first malignant neoplasm between 1993 and 2010 (follow up until end of 2011) were included. Cases reported from death certificate or autopsy only were excluded, along with patients recorded as alive but with no survival time (<1% of total). Relative EM rates (1 – relative survival) used data from the US population in 2000 as reference. We described EM for different population groups, changes over time and performed multivariate analysis (binary logistic regression) to assess risk factors for EM in MM. To assess the impact of factors other than reduction in EM in the increased survival of MM, we performed a landmark survival analysis including only those patients who survived at least 1 year after diagnosis. Results: There were 30,324 patients in the analysis, 11,439 diagnosed at age < 65 and 18,885 at age ≥ 65. Absolute EM was 17.6% and 35.3% while relative EMwas 16.9% and 31.7% for the younger and the older groups respectively. There was decline of relative EM rates that occurred in both age groups but only became evident among older patients after 2005 (Figure 1). In multivariate analysis, factors associated with increased risk of EM were male sex, age ≥65 years , diagnosis in 1993-2001, presence of primary plasma cell leukemia, low household income and residence in the coverage area of certain SEER registries (Table 1). Landmark analysis of 1-year survivors demonstrated increased survival from 1993-2001 to 2002-2010 for patients < 65 (median 58 vs. 85 months, P<0.001) and for patients ≥ 65 (median 38 vs. 48 months, P<0.001, Figure 2). Conclusions: Reduction in EM partially explains population improvements in MM overall survival. EM remains high in MM and a major barrier for continued improvement in survival outcomes. Table 1 Factor Reference OR 95% C.I. P Female sex Male 0.90 0.85-0.95 <0.001 Year of diagnosis 1993-2001 2002-2010 1.16 1.10-1.22 <0.001 Plasma Cell Leukemia Multiple Myeloma 5.62 4.17-7.58 <0.001 Age ≥65 years < 65 years 2.56 2.42-2.72 <0.001 Race-ethnicity <0.001 NHB NHW 0.97 0.90-1.04 0.39 Hispanic NHW 1.04 0.95-1.14 0.41 AIAN NHW 0.90 0.63-1.28 0.56 API NHW 0.84 0.75-0.94 0.004 Median household income# 0.91 0.87-0.95 <0.001 SEER registry <0.001 Alaska Los Angeles 1.37 0.62-3.02 0.43 Atlanta Los Angeles 1.003 0.89-1.14 0.96 Connecticut Los Angeles 0.83 0.75-0.93 0.002 Detroit Los Angeles 1.06 0.96-1.16 0.23 Hawaii Los Angeles 1.21 1.01-1.45 0.04 Iowa Los Angeles 0.89 0.80-0.99 0.03 New Mexico Los Angeles 0.88 0.77-1.01 0.07 Rural Georgia Los Angeles 1.10 0.76-1.58 0.63 San Francisco-Oakland Los Angeles 0.99 0.86-1.11 0.87 San Jose-Monterey Los Angeles 1.05 0.87-1.12 0.51 Seattle-Puget Sound Los Angeles 0.91 0.83-1.01 0.08 Utah Los Angeles 1.13 0.98-1.29 0.08 NHB= non-Hispanic Blacks;, NHW= non-Hispanic Whites; AIAN= American Indian, Alaska Natives; API= Asian and Pacific Islanders; #Per US$ 10,000 Figure 1 Figure 1. Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.