Buadi, Francis K.

Abstract Introduction Diffuse or polyclonal hypergammaglobulinemia has been associated with multiple medical conditions such as autoimmune diseases, infections, and chronic liver diseases. However, the possibility of differences in gamma globulin levels in different races and its association with environmental and socioeconomic factors is not well studied. Method A randomized sample of 917 adult males (50–74 years) in an urban community in Ghana underwent in-person interview and health examination. Serum from all persons was analyzed by electrophoresis performed on agarose gel. Those found to have a monoclonal protein (n=54) were excluded from further analysis. Gamma globulin levels were measured in the remaining 863 samples. Associations between selected characteristics and gamma globulin levels were assessed by Chi-square test and logistic regression models. Results The median gamma globulin level was 1.86 gm/dl. Seventy three percent of the study population had gamma globulin levels above the 1.6 gm/dl reference level and will be considered to have hypergammaglobulinemia. Surprisingly, 25% had gamma globulin levels above 2.17 gm/dl. In univariate analysis increased gamma globulin levels were associated with older age within the study population. Only 54% of the males aged 60 yrs or younger had gamma globulin levels above 1.6 gm/dl compared to 69% in those older than 60 yrs (p=0.007). Higher education level was associated with a low prevalence of hypergammaglobulinemia 51% compared with 74% in those with less than high school education (p <0.0001). Smoking was not associated (p=0.16) with gamma globulin levels. Although a higher proportion (71%) of people with history of alcohol use had hypergammaglobulinemia compared to the control (63%) this was not statistically significant (p=0.07). Self-reported history of genital warts, genital herpes and meningitis was associated with hypergammaglobulinemia, whereas syphilis, malaria, cholera, gonorrhea, hepatitis, measles and tuberculosis had no association with gamma globulin levels in univariate analysis. Conclusion Three quarters of this randomized sample of Ghanaian adult males has hypergammaglobulinemia based on current laboratory definitions. More work is needed to uncover underlying causes for the observed high gamma globulin levels in this population. Further evaluation of a younger population with less environmental influence may help explain whether this is due to genetic differences, environmental exposures, or a combination.

Background: Primary Systemic Amyloidosis (AL) is a fatal disorder characterized by uncontrolled deposition of light chain derived amyloid with variable survival dependent on extent of organ involvement and degree of individual organ dysfunction. We previously demonstrated that troponin T (cTnT) and N-terminal fragment of brain natriuretic peptide (NT-ProBNP) together can allow prognostic stratification in AL. Uric acid (UA) is a marker of increased mortality in congestive heart failure and we examined its prognostic value in AL. Methods: A cohort 1977 patients with AL seen at our institution and 293 patients with AL undergoing stem cell transplant (PBSCT) were studied retrospectively to examine the value of serum UA. Results: The median OS for those with UA>8mg/dL was 9 months from diagnosis (95% CI; 7.4, 10.6) compared to 20.3 months for those with UA<=8mg/dL (95% CI; 17.8, 22.7), P<0.001 (Figure 1). As previously demonstrated both cTnT≥0.035μg/L and NT-ProBNP≥332ng/L were prognostic for OS. The median OS for patients (n=617) with a cTnT>0.035g/L was 6.7 months (95% CI; 5.0, 8.4) compared to 35.2months (95% CI; 29.4, 41.0) for those with cTnT<=0.035μg/L, P<0.0001. Similarly, the median OS for patients with NT-ProBNP > 332ng/L was 11.8 months (95% CI; 9.4, 14.2) compared to 36.2 months (95% CI; 26.7, 45.8) for those with NT-ProBNP <=332ng/L; P<0.0001. The prognostic value of UA was independent of cTnT and NT-ProBNP and together with these allowed a novel prognostic classification. Patients with none (Stage-I), 1 (Stage-II), 2 (Stage-III) or 3 (Stage-IV) of these risk factors (UA>8mg/dl, cTnT>0.035μg/L and NT-ProBNP>332ng/L) had a median OS of 36.6, 29.2, 11.1, and 3.6 months respectively (P<0.0001) (Figure 2). We also explored various predictors of OS in this large cohort of patients. The parameters that were significant for a shorter median OS included septal thickness>15mm, ejection fraction<50%, alkaline phosphatase>upper limit of normal, serum creatinine>2.0mg/dL, serum albumin<3.0gm/dl, 24-hour urine albumin>5gm, UA>8 mg/dL, cTnT>0.035μg/L and NT-ProBNP>332ng/L. We then performed a multivariate analysis with all these variables and those predicting for shorter OS were cTnT>0.035μg/L, serum uric acid>8mg/dL, and ejection fraction<50%. The stage determined at the time of PBSCT also predicted outcome with a median OS from PBSCT not reached in stages 1 and 2, 55 months for stage 3 and 16 months for stage 4 patients. Interpretation: The data confirms the prognostic value of serum UA in AL and allows development of a novel and simple but powerful, reproducible staging system with applicability at all stages of AL. Figure Figure Figure Figure

Abstract Background: Prior studies suggest that the presence of >5% polyclonal plasma cells (pPCs) among total plasma cells (PCs) within the bone marrow (BM) is associated with a longer progression-free survival, higher response rates, and lower frequency of high-risk cytogenetic abnormalities in patients with newly diagnosed multiple myeloma (MM). However, the incidence and prognostic utility of this factor in patients with relapsed and/or refractory MM has not been previously evaluated. Thus, we evaluated the prognostic value of quantifying the percentage of pPCs among the total PCs in the BM of patients with actively relapsing MM. Methods: We evaluated all MM patients with actively relapsing disease (biochemical and/or symptomatic) seen at the Mayo Clinic, Rochester, from 2012 to 2013, who had BM samples evaluated by seven-color multiparametric flow cytometry. All patients had at least 24 months of follow-up from the date of flow evaluation. Cell surface antigens were assessed by direct immunofluorescence antibodies for CD45, CD19, CD38, CD138, cytoplasmic Kappa and Lambda Ig light chains, and DAPI nuclear stain. The flow cytometry data was collected using the Becton Dickinson FACSCanto II instruments that analyzed 150,000 events (cells); this data was then analyzed by multi-parameter analysis using the BD FACS DIVA Software. PCs were selectively analyzed through combinatorial gating using light scatter properties and CD38, CD138, CD19, and CD45. Clonal PCs were separated from pPCs based on the differential expression of CD45, CD19, DAPI (in non-diploid cases), and immunoglobulin light chains. The percentage of pPCs was calculated in total PCs detected. Survival analysis was performed by the Kaplan-Meier method and differences were assessed using the log rank test. Results: There were 180 consecutive patients with actively relapsing MM who had BM biopsies analyzed via flow cytometry as part of their routine clinical evaluation. The median age of this group was 65 years (range: 40 - 87); 52% were male. At the time of this analysis, 104 patients had died, and the 2-year overall survival (OS) rate for the cohort was 58%. The median number of therapies received was 4 (range: 1 - 15). Of these patients, 61% received a prior ASCT, and almost all (99%) received prior regimens containing either immunomodulators or proteasome inhibitors. There were 55 (30%) patients with >5% pPCs among the total PCs in their BM. The median percentage of pPCs among total PCs in these 55 patients was 33% (range: 5 - 99). The median OS for those with >5% pPCs was not reached compared with 22 months for those with <5% pPCs (P = 0.028; Figure 1). Patients with <5% pPCs PCs had a higher likelihood of high-risk FISH cytogenetics compared with the rest of the patients. In a univariate analysis, increasing number of pPCs was associated with an improved OS, while higher labeling index, number of prior therapies, and the presence of high-risk FISH cytogenetics were associated with a worse OS. In a multivariate analysis, only the increasing number of pPCs (P = 0.006), higher labeling index (P = 0.0002) and number of prior therapies (P = 0.003) retained statistical significance. Conclusion: Quantitative estimation of the percentage of pPCs among the total PCs in the BM of patients with actively relapsing MM was determined to be a predictor of worse OS. As such, this parameter is able to identify a group of patients with MM with actively relapsing disease who have a particularly poor outcome. Further studies evaluating its biological significance are warranted. Figure 1 Kaplan-Meier curve comparing OS between patients with ≥5% pPCs and <5% pPCs among the total PCs in their BM. Figure 1. Kaplan-Meier curve comparing OS between patients with ≥5% pPCs and <5% pPCs among the total PCs in their BM. Disclosures Kapoor: Celgene: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Gertz:Prothena Therapeutics: Research Funding; Novartis: Research Funding; Alnylam Pharmaceuticals: Research Funding; Research to Practice: Honoraria, Speakers Bureau; Med Learning Group: Honoraria, Speakers Bureau; Celgene: Honoraria; NCI Frederick: Honoraria; Sandoz Inc: Honoraria; GSK: Honoraria; Ionis: Research Funding; Annexon Biosciences: Research Funding. Kumar:AbbVie: Research Funding; Noxxon Pharma: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Research Funding; Kesios: Consultancy; Glycomimetics: Consultancy; BMS: Consultancy.

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: The combination of MPD and amyloidosis is rare, and this patient population's clinical outcome is not well studied. Methods: Pts with a MPD and amyloidosis were identified via an clinical note search engine that searches through the electronic medical records of patients seen at Mayo Clinic Rochester, Jacksonville, and Scottsdale between 1990 and 2016. Terms used included amyloid or amyloidosis, chronic myelogenous leukemia or CML, essential thrombocytopenia or ET, and polycythemia vera or PV. Demographic and clinical data were abstracted from the medical record. Pts with both disorders were analyzed and their mortality rates along with median time to death were calculated. Prevalences at the Mayo Clinic were calculated for the years 2014 and 2015. Results: Twenty-three pts diagnosed with both a MPD and amyloidosis were identified. Thirteen (56.5%) were male, 10 (43%) were female. Eleven (47.8%) were initially diagnosed at the Mayo Clinic. Types of amyloidosis were as follows: Eleven (47%) had immunoglobulin light-chain (AL), four had localized (17%), two (8.7%) had wild-type transthyretin (ATTR), one (4.3%) had mutant ATTR, and five (21.8%) were unknown. Types of MPD were as follows: Seven (30%) had polycythemia vera (PV), seven (30%) had chronic myelogenous leukemia (CML), five (22%) had myelofibrosis, and four (17%) had essential thrombocytosis (ET). Fifteen (65%) were initially diagnosed with a MPD. Median time to last follow-up from second diagnosis was 1.7 years, and median time to death following second diagnosis was 1.4 years. The mortality rate was 87% in the total population. The median time to death for AL and PV was 2.7 years, AL and CML 1.0 years, and AL and ET 1.17 years. Myelofibrosis did not occur with AL. The most common combination was AL and PV, which accounted for five (22%) of the cases. Treatment regimens for the patients with AL were varied. Multiple drugs were combined with dexamethasone including melphalan, velcade, pomalidomide, lenalidomide, doxorubicin, and revlimid. Cyclophosphamide, bortezomib, and dexamethasone (CyBorD) were used in two cases. The mean time to diagnosis of amyloidosis from symptom onset was 10.3 months. Prevalences of AL and a MPD in 2014 and 2015 at the Mayo Clinic were 2.8% and 1.5%, respectively. Conclusions: The mortality rate for the combined diagnoses is high. The most common combination of diagnoses was that of AL and PV, which was associated with a mean time to death of 2.7 years. Disclosures Al-Kali: Celgene: Research Funding; Onconova Therapeutics, Inc.: 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.