Lin, Yi

Background: The overall participation of cancer patients in interventional clinical trials in the United States remains very low, with ~5% of patients being enrolled in clinical trials nationwide. The outcomes of patients with MM have improved significantly over the past decade, but available data suggest that the participation rates for patients with MM is comparable to other cancers. The drivers of participation and the potential impact of clinical trial participation have not been systematically studied in MM. Patients and Methods: We identified 228 patients who were enrolled into clinical trials for initial therapy of newly diagnosed MM between 2004 and 2018, and 4 controls for each of these patients. Controls were patients with NDMM, who were diagnosed closest in time to the index patients and did not participate in an interventional treatment trial. Various baseline characteristics as well as overall survival were compared between the two groups. Results: The baseline characteristics of the two groups are as shown in the Table. Patients who entered clinical trials were more likely to be female, resided closer to the clinic, and were more likely to have a prior history of MGUS. They were more likely to have higher ISS Stage, and a higher serum LDH, but there was no difference in the FISH risk status. Other indices of disease burden such as lower hemoglobin and platelets, higher serum creatinine were all seen more often in the control group, but may have been influenced by the trial entry criteria. Looking at the outcomes, the overall survival was longer among those enrolled into clinical trials compared to those who did not [median 103 (95% CI; 86, 136) vs. 63 (95% CI; 53, 69) months, p<0.001 (Figure). Conclusions: The current study provides important clues regarding demographic determinants of trial participation and disease biology related features that reflect likelihood of trial participation. Overall survival was significantly longer among the trial participants, which likely represent a mix of reasons including baseline status of patients, intensity of monitoring and efficacy of novel treatment approaches. Table Disclosures Kapoor: Janssen: Research Funding; Celgene: Honoraria; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding; Takeda: Honoraria, Research Funding. Dispenzieri:Alnylam: Research Funding; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Akcea: Consultancy; Intellia: Consultancy. Gertz:Ionis: Honoraria; Alnylam: Honoraria; Prothena: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Spectrum: Honoraria, Research Funding. Lacy:Celgene: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy. Leung:Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees. Russell:Imanis: Equity Ownership. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

Background: In the SCHOLAR-1 study, the largest published retrospective analysis of outcomes in patients with refractory large B-cell lymphoma (LBCL), the objective response rate (ORR) was 26% and complete response (CR) rate was 7% with available salvage therapies in the pre-chimeric antigen receptor (CAR) T cell therapy era (Crump et al. Blood. 2017). These results served as a benchmark for assessing novel therapies. ZUMA-1 (NCT02348216) is the pivotal, multicenter, single-arm Phase 1/2 study evaluating axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 CAR T cell therapy, in patients with refractory LBCL that supported the approval of axi-cel in the United States and Europe. At a median follow-up of 27.1 months, the ORR in ZUMA-1 was 83% and the CR rate was 58% (Locke et al. Lancet Oncol. 2019). Here, we describe comparative analyses of outcomes in ZUMA-1 and SCHOLAR-1 after adjusting for potential imbalances in refractory status between the 2 studies. Methods: Patients in both studies had refractory LBCL defined as stable disease of ≤ 6 months with ≥ 4 cycles of frontline or ≥ 2 cycles of later-line therapy, progressive disease as best response, or relapse ≤ 12 months post autologous stem cell transplant (SCT). To address potential imbalances between studies in refractory status which could affect response and survival outcomes, standardized analyses were performed which equally weighted the proportions of patients by refractory categorization (primary refractory, refractory to ≥ second-line therapy, or relapse after SCT) and presence of post-refractory SCT in each study. Stratified Cochran-Mantel-Haenszel (CMH) tests and Cox models were used to compare the odds ratio for response and hazard ratio (HR) for survival between ZUMA-1 and SCHOLAR-1. P values were descriptive and were not adjusted for multiplicity. Results: Axi-cel was administered to 101 patients in the pivotal Phase 2 portion of ZUMA-1. In the SCHOLAR-1 analysis, 508 patients were evaluable for response and 497 were evaluable for survival. The median follow-up in ZUMA-1 was 2.3 years, and the median follow-up from the overall SCHOLAR-1 study ranged from 7.6 to 14.8 years across different cohorts. In general, ZUMA-1 patients were more heavily pretreated with more patients receiving ≥ 3 lines of therapy as compared with SCHOLAR-1 (69% vs 23%). ZUMA-1 also had more patients who were refractory to second- or later-line therapy compared with SCHOLAR-1 (76% vs 62%). Fewer patients in ZUMA-1, however, were classified as primary refractory vs those in SCHOLAR-1 (26% vs 45%), and a similar proportion of patients between studies relapsed within 1 year of SCT (21% vs 18%). After standardization, the ORR and CR rate in ZUMA-1 vs SCHOLAR-1 were 72% and 54% vs 22% and 7%, respectively. The odds ratios for ORR and CR rate were 7.2-fold and 11.5-fold higher, respectively in ZUMA-1 vs SCHOLAR-1 (CMH test; P < .0001 for both ORR and CR rate). The 2-year survival rate after standardization was 50% (95% CI, 40% - 59%) in ZUMA-1 and 12% (95% CI, 9% - 15%) in SCHOLAR-1, which translated to a 73% reduction in the risk of death in ZUMA-1 relative to SCHOLAR-1 (HR, 0.27; P < .0001). Conclusions: This standardized analysis of the ZUMA-1 and SCHOLAR-1 studies indicates that treatment with axi-cel in this selected population results in 11.5-fold higher odds of CR and a 73% reduction in the risk of death compared with standard salvage regimens in an unselected population. Although limited by retrospective evaluation and cross-study comparisons, these results support the previous literature indicating that axi-cel is a highly effective treatment option for patients with refractory LBCL. Disclosures Neelapu: Pfizer: Consultancy; BMS: Research Funding; Cellectis: Research Funding; Precision Biosciences: Consultancy; Allogene: Consultancy; Acerta: Research Funding; Cell Medica: Consultancy; Celgene: Consultancy, Research Funding; Karus: Research Funding; Novartis: Consultancy; Poseida: Research Funding; Unum Therapeutics: Consultancy, Research Funding; Incyte: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Merck: Consultancy, Research Funding. Locke:Novartis: Other: Scientific Advisor; Kite: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy. Bartlett:Genentech, Inc.: Research Funding; Gilead: Research Funding; Autolus: Research Funding; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Affimed: Research Funding; Immune Design: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Kite Pharma: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Miklos:Celgene-Juno: Consultancy; Novartis: Consultancy; Adaptive Biotechnologies: Consultancy, Research Funding; Janssen: Consultancy; Miltenyi: Consultancy, Research Funding; AlloGene: Consultancy; Kite, A Gilead Company: Consultancy, Research Funding; Pharmacyclics: Consultancy, Patents & Royalties, Research Funding; Becton Dickinson: Consultancy; Precision Bioscience: Consultancy. Jacobson:Pfizer: Research Funding; Celgene: Consultancy, Other: travel support; Humanigen: Consultancy, Other: travel support; Precision Biosciences: Consultancy, Other: travel support; Bayer: Consultancy, Other: travel support; Novartis: Consultancy, Honoraria, Other: travel support; Kite, a Gilead Company: Consultancy, Honoraria, Other: travel support. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. Siddiqi:BeiGene: Research Funding; Celgene: Research Funding; TG Therapeutics: Research Funding; Kite, A Gilead Company: Research Funding; Seattle Genetics: Speakers Bureau; Janssen: Speakers Bureau; Juno Therapeutics: Consultancy, Other: travel support, Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Research Funding, Speakers Bureau. Crump:Kite/Gilead: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Servier: Consultancy. Kuruvilla:Seattle Genetics: Honoraria; Roche: Honoraria; Novartis: Honoraria; Merck: Honoraria; Karyopharm: Honoraria; Janssen: Honoraria; Gilead: Honoraria; Celgene: Honoraria; BMS: Honoraria; Astra Zeneca: Honoraria; Amgen: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Gilead: Consultancy; Abbvie: Consultancy; BMS: Consultancy; Janssen: Research Funding; Roche: Research Funding. Van Den Neste:Gilead: Other: travel support. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Navale:Kite, A Gilead Company: Employment, Equity Ownership; Gilead: Equity Ownership; Allogene: Equity Ownership; Cellectis: Equity Ownership; Bluebird Bio: Equity Ownership; Amgen: Equity Ownership; Organesis: Equity Ownership; Jounce: Equity Ownership; Editas: Equity Ownership; Intellia: Equity Ownership. DePuy:Kite, A Gilead Company: Consultancy, Other: travel support. Kim:Kite, A Gilead Company: Employment.

Background: A variety of risk factors have been described in multiple myeloma and current risk assessment incorporates ISS stage with specific FISH results and serum LDH (R-ISS). However, this model does not include all the current abnormalities described as prognostic for survival in multiple myeloma. Importantly, the impact of many of these high-risk abnormalities are not uniform. We examined if we can better integrate FISH results into a risk assessment tool to better predict the outcomes of newly diagnosed MM. Patients and methods: We studied a cohort of 1316 patients with FISH done within 6 months of diagnosis of MM, in whom results for commonly observed abnormalities were available. We specifically examined the individual impact of common translocations involving chromosome 14, MYC rearrangements, chromosome 1q gain (single or multiple duplication) and del13q/monosomy 13. A risk assessment system was developed, weighting each abnormality according to their Risk Ratio and integrating ISS stage and serum LDH into the final model construction. Overall survival was calculated from diagnosis, with those alive at last follow up being censored. Results: We first examined the impact of each of the above FISH abnormalities: 1) high risk translocations [t(4;14), t(14;16), or t(14,20)], 2) trisomies, 3) t(11;14), 4) MYCrearrangements, 5) del13q/monosomy 13, and 6) 1q gain . Each of the abnormalities, except for t(11;14), was prognostic for survival (Table 1 with the risk ratios). For 1q gain, the median OS was NR, 105 mos and 79 mos respectively for no abnormality, duplication of 1 copy and duplication of multiple copies, (p<0.001). On multivariate analysis, t(11;14) and trisomies were no longer prognostic for overall survival (Table 1). The cumulative impact of abnormalities demonstrated worsening survival in the presence of increasing numbers of abnormalities (Figure 1). Including ISS stage 3 and LDH > ULN as additional variables for prognostication indicated both were individually prognostic for OS. In a multivariate analysis, including these two and FISH abnormalities, 1q gain and LDH were not independently prognostic. The final model consisted of HR translocations, MYCrearrangements, del17p/monosomy 17, del13q/monosomy 13, and ISS stage 3. Each of these variables was weighted using their risk ratio and a composite score was developed using 998 patients for whom all variables were available (range: 0-7.9; median 1.8). Three patient groups were characterized: group 1 (0; 32%), group 2 (1-4; 58%) and group 3 (>4; 10%) with a median OS of 53 mos, 106 mos, and NR, respectively, p <0.001 (Figure 2). Conclusion: Using the most relevant FISH and laboratory factors, in a large cohort of patients, we refined the current system to develop a risk stratification system that predicts survival in patients with newly diagnosed MM treated with contemporary treatment regimens. This needs validation in future studies. Disclosures Kumar: Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. Gertz:International Waldenstrom Foundation: Research Funding; Annexon: Consultancy; Medscape: Consultancy, Speakers Bureau; Amyloidosis Foundation: Research Funding; Abbvie: Other: personal fees for Data Safety Monitoring board; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents & Royalties; Physicians Education Resource: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Appellis: Consultancy; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Amgen: Consultancy. Dispenzieri:Akcea: Consultancy; Janssen: Consultancy; Intellia: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Lacy:Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Cellectar: Consultancy. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy.

Introduction: Waldenström macroglobulinemia (WM) is characterized by circulating monoclonal IgM and bone marrow infiltration with lymphoplasmacytic lymphoma (LPL) cells. Presently, there is limited data to guide prognosis or treatment options of WM based on the proportion of lymphocytes or plasma cells in the LPL population on the pathology. In this study, we aim to look at differing clinical features and outcomes of patients with WM based on the plasma cell (PC) percentage in the diagnostic bone marrow biopsy. Methods Patients with WM seen at Mayo Clinic, Rochester, MN between January 2001 and December 2017, with a bone marrow biopsy and a corresponding serum free light chain (FLC) examination at diagnosis of WM were included. The percentage of lymphocytes and PCs in the marrow biopsy was determined based on morphology ± immunohistochemistry. On immunohistochemistry, CD138 and/or MUM-1 positivity was used to quantify PCs, whereas CD20 and/or PAX5 were used to quantify lymphocytes. Patients with ≥10% PC component in the lymphoplasmacytic (LPL) cells on bone marrow biopsy were considered to have WM with excess plasma cells (WMEPC), while those with &lt;10% PCs were considered to have lymphocytic WM (LWM). Continuous variables were compared using Wilcoxon test and categorical variables were compared using Chi square test or Fisher's exact test. Time-to-next therapy (TTNT) was defined as the period from initiation of first line therapy to start of second line therapy or death due to any cause before second line therapy. Time-to-event analyses were conducted using Kaplan Meier method. Results: Of 228 evaluable patients, 147 (64%) had ≥ 10% PCs in the initial marrow biopsy. The median follow-up was 3.4 years (95% CI: 2.3-4.3 years) for LWM and 4.3 years (95% CI: 3.4-4.8 years) for WMEPC (p=0.33). The baseline characteristics of WMEPC versus LWM are depicted in Table 1. Patients with WMEPC had lower bone marrow involvement by LPL (30%) compared to LWM (60%; p&lt;0.001) at diagnosis, but the rate of symptomatic hyperviscosity was higher in WMEPC during the disease course (Table 1). The TTNT was shorter for WMEPC [2.6 years (95% CI: 1.8-4.5 years)] compared to LWM [4.5 years (95% CI 3.2-8.2)], although it did not reach statistical significance (p=0.09, Figure 1). There was a trend towards shorter OS for WMEPC [Not reached (95% CI 7.2- NR)] compared to LWM [10.7 years (95% CI 7.3 -12.1 years); p=0.28], Figure 2. Of the 228 patients, 79% (n=182) had data available for systemic therapy. The rate of PR or better with frontline therapy was higher in rates in LWM compared to WMEPC (61 % vs 42% p=0.02, Table 1). Among WMEPC cohort, patients receiving either a proteasome inhibitor (PI)-based or an immunomodulatory (IMiD)-based combination as frontline therapy (n=16) had higher rates of partial response or better (68%) compared to non-PI/non-IMiD based therapy (n=114), p=0.04. Conclusion: WM with excess plasma cells at diagnosis is associated with a trend towards shorter TTNT and OS compared to WM with &lt;10% plasma cells in the lymphoplasmacytic population at diagnosis. Our preliminary data shows higher rates of partial response or better with the use of PI/IMiD based therapy, but the small cohort size limits the strength of this finding which needs to be validated externally. Disclosures Ansell: Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding. Gertz:Spectrum: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; Prothena: Honoraria; Alnylam: Honoraria; Ionis: Honoraria. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Dispenzieri:Alnylam: Research Funding; Celgene: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy. Lacy:Celgene: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Kapoor:Janssen: Research Funding; Cellectar: Consultancy; Glaxo Smith Kline: Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria.

Background: Two-year follow-up from ZUMA-1 trial for axicabtagene ciloleucel (axi-cel) CD19 chimeric antigen receptor T-cell (CART) in aggressive non-Hodgkin lymphoma (NHL) demonstrated that patients (pts) achieving complete remission (CR) as their best response have the longest progression free survival (PFS), while PFS remained poor for others. Majority of the best response are achieved by month 3 post-infusion. Moreover, a subset of pts who do not achieve CR by month 1 may still achieve CR while others will progress within months. Having a clinically available test to predict for durable response can assist clinicians in selecting pts who can be observed for continued response vs. those who may need intervention earlier. Peak CAR-T expansion has been correlated with best clinical response to axi-cel. However there is no clinically available test to quantify CART cells post treatment. Given the prolonged lymphotoxic effect of the lymphodepletion (LD) chemotherapy administered before CART infusion, we hypothesize that the peak expansion of absolute lymphocyte count (ALC, ALCpeak) after infusion likely corresponds to CART expansion. We examined for correlation between ALCpeak and clinical response at month 3 post-infusion for pts who received axi-cel. Methods: Data was collected from pts who received axi-cel between June 2016 and March 2019 at Mayo Clinic Rochester (NCT02348216, NCT03153462, and standard of care). All infused pts were included in the intention to treat analysis for response and event-free survival (EFS). Lugano 2014 criteria were used for clinical response. EFS was defined as the date of infusion until progression or death due to any cause and evaluated using Kaplan Meier curves with log-rank test. Comparison between subgroups was investigated using Fisher's exact test and Wilcoxon test for categorical and continuous variables, respectively. Statistical analysis performed using JMP Pro 14.1. Results: Of the thirty-six NHL pts treated with axi-cel, the median age was 56 yrs (range 26-65); 78% (28) were male; 44% (16) had DLBCL; the median number of prior therapies was 3 (2-6), 47% (17) had stem cell transplant. Nineteen (53%) pts received bridging therapy after leukapheresis. All pts received fludarabine and cyclophosphamide for LD chemotherapy and achieved ALC nadir by the day of CART infusion (median 0.03 x 10^9/L, range 0.01-0.11). As CART expansion were found to peak within the first two weeks in ZUMA-1 trial, we examined ALC trend over 15 days post infusion and found that ALCpeak generally happened within the same timeframe (median ALCpeak day: 9, range 6-15). At 3 months post infusion, 33.3% (12/36) of pts achieved CR, 8.3% (3/36) pts were in partial remission (PR). No correlation was found between CR response and clinical characteristics: age >60 years, stage III/IV, B symptoms, elevated LDH, IPI score 3-4, number of previous treatments, use of bridging therapy, peak CRP and ferritin. However, the ALCpeak was significantly different between CR and non-CR pts (median, range: 1.09, 0.35-2.55; 0.67, 0.15-1.03; p=0.04). Using receiver operator curve (ROC)-derived area under the curve (AUC) estimates, ALCpeak 1.08 x 10^9/L was identified as the best threshold to discriminate patients at different likelihood to achieve CR at month 3. Pts achieving ALCpeak ≥ 1.08 showed a strong increased likelihood of achieving CR at month 3 (OR 2.8; 95% CI, 1.12-6.99; P=0.02). There was no statistical difference between the ALCpeak high and low groups in terms of use of bridging therapy, occurrence of any grade or higher grade cytokine release syndrome or neurotoxicity, or the use of tocilizumab or steroid. Interestingly, for pts who had changes in clinical response from month 1 to month 3, all 3 pts who converted to CR by month 3 had ALCpeak≥1.08, while the 13 of the 15 pts who had progressed by month 3 had ALCpeak<1.08. In addition, EFS was significantly different between pts who had ALCpeak above and below 1.08 (6 months EFS rate 75% vs. 25%, respectively, Figure 1). Conclusions: We found that the ALC expansion occurred in the first 15 days post axi-cel infusion, consistent with the known time frame for CART expansion. Higher ALCpeak level in this timeframe correlated with higher likelihood of achieving CR, and is associated with conversion to CR. This study is limited by the small sample size and limited follow-up, but the results are promising for further prospective investigation in a larger cohort of patients. Disclosures Paludo: Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Bennani:Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Karyopharm: Research Funding; Rigel: Consultancy. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

INTRODUCTION: Primary plasma cell leukemia (pPCL) is the most aggressive form of multiple myeloma (MM). Recently, pPCL has been defined by as few as >5% circulating plasma cells (cPCs) detected morphologically on a peripheral blood smear. However, this technique may not be sufficiently sensitive and is dependent on inter-observer bias to properly identify and quantify cPCs. Multiparametric flow cytometry (MFC) provides a readily available and highly sensitive method to identify and quantify cPCs. However, an optimal quantitative cutoff for cPCs by MFC to define the presence of pPCL has not been established to date. Thus, this is the first study to date that determines an optimal cutoff of cPCs/ microliter that could identity patients with pPCL at diagnosis. METHODS: We retrospectively evaluated all newly diagnosed MM patients seen at the Mayo Clinic, Rochester from January 2007 to December 2017 who had their peripheral blood samples evaluated morphologically by peripheral blood smear and immunophenotypically by MFC prior to beginning therapy. Patients with a peripheral blood smear detecting >5% cPCs were classified as having pPCL. Six-color MFC was performed on peripheral blood mononuclear cells by isolated by Ficoll gradient, and stained with antibodies to CD45, CD19, CD38, CD138 and cytoplasmic Kappa and Lambda immunoglobulin light chains. The data was collected using Becton Dickinson FacsCanto II instruments collecting 150,000 events and analyzed using the BDFacs DIVA Software. The gating strategy employed first used the expression of CD38, CD138, and cytoplasmic immunoglobulin light chains to identify all plasma cells in the specimen. The cPCs were then discriminated from polyclonal/normal plasma cells based on differential CD19 and CD45 expression. The cPCs detected were reported as the number of clonal events/150,000 collected total events and converted to the absolute number of cPCs/microliter using their absolute lymphocyte count and monocyte count determined at the time of the blood draw for cPC MFC assessment. A receiver operator curve (ROC) was performed to determine the absolute cPCs/microliter by MFC that best identified those patients with >5% cPC on their peripheral blood smear. Survival analysis was performed by the Kaplan-Meier method and differences assessed using the log rank test. RESULTS: A total of 591 patients were included in this analysis of which 59% were male and the median age was 66 years (range: 27 - 95) with 22% being 75 years of age or older. From this cohort, 133 (24%) patients had high risk FISH cytogenetics, 172 (30%) were classified as ISS 3 at diagnosis, 83 (15%) had an elevated LDH level and 61 (17%) had a plasma cell labeling index (PCLI) of 2% or greater. There were 65 (11%) patients with any amount of cPCs detectable by peripheral blood smear of which only 22 (4% of the total population) met criteria for pPCL with >5% cPCs. With MFC, 403 (68%) patients had detectable cPCs. The median number of cPCs per microliter was 0.81 (0 - 10,455) with 273 (46%) having >1 cPC/microliter. A ROC analysis determined that the presence of >50 cPCs/microliter by MFC was the optimal cutoff to identify patients with >5% cPCs by blood smear with approximately a specificity of 94% and a sensitivity of 86%. The presence of >50 cPCs/microliter by MFC was associated with higher presence of high risk FISH cytogenetics, ISS 3 classification, elevated LDH and PCLI > 2%. The median time to next treatment (TTNT) for patients with and without >5% cPCs by peripheral blood smear was 18 months vs. 30 months (P < 0.001). The median TTNT for patients with and without >50 cPCs/microliter by MFC was 19 months vs. 30 months (P < 0.01). The median overall survival (OS) for patients with and without >5% cPCs by peripheral blood smear was 25 months vs. 71 months (P < 0.001; Fig 1A). The median OS for patients with and without >50 cPCs/microliter by MFC was 38 months vs. 71 months (P < 0.01; Fig 1B). CONCLUSION: This is the largest study that attempts to identify an optimal cutoff of cPCs/microliter by MFC for diagnosing pPCL patients. The presence of >50 cPCs/microliter is an optimal cutoff by MFC that best predicts for the presence of >5% cPCs by peripheral blood smear. However, there remains discordance in the OS outcomes between the two cutoffs by different methodologies for pPCL. This could signify the presence of biological processes associated with the presence of >5% cPCs when detected by peripheral blood smear that are not captured by MFC. Disclosures Dispenzieri: Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy; Alnylam: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Lacy:Celgene: Research Funding. Kapoor:Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Cellectar: Consultancy; Janssen: Research Funding; Celgene: Honoraria. Leung:Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Russell:Imanis: Equity Ownership. Gertz:DAVA oncology: Speakers Bureau; Ionis/Akcea: Consultancy; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Medscape: Consultancy, Speakers Bureau; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Other: Development of educational programs and materials; Springer Publishing: Patents & Royalties; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Abstract Background: Modern clinical practice places time restrictions on the amount of information and processing that can be done in a typical office visit. Meeting patient needs within the healthcare system is challenging, contributing to patient (pt) and clinician dissatisfaction and potential suboptimal outcomes. We developed an electronic point-of-care case management system for cancer patients. The system's goal is to obtain patient-reported quality of life (PROQOL) outcomes and provide clinicians with information regarding QOL issues to address. We tested this system in a prospective randomized study against usual care (UC). The primary objective was to determine if PROQOL system would improve mean QOL over time. Methods: Eligible pts were adults with multiple myeloma (MM) or light amyloidosis (AL), seen at Mayo Clinic. Pts were randomized 2:1 to PROQOL system or usual care. PROQOL system was offered prior to every visit. Pts select from various categories about their single biggest concern, and receive a printed list of actionable resources based on selected concern. Clinicians also receive the PROQOL results to review with pts. Providers and pts randomized to PROQOL completed a "was it worth it survey" (WIWI). An 8 item Linear Analogue Self-Assessment was used to assess QOL, the primary endpoint. Secondary endpoints included distribution of choices of most important concern and their relationship to demographic and clinical variables. The study was powered to detect a 0.5 standard deviation difference in QOL between groups. Results: There were 233 patients enrolled (171 MM, 62 AL) in this study between July 1, 2016 and April 17, 2018. Median age was 65 years (range 31-87), and 59% were male. Median time from diagnosis to study consent was 37 months (PROQOL 36.3 versus UC 39.6). Median follow up for all patients was 15 months (range: 0.7-22 months), and median follow up for PROQOL pts was 16 months and 14 months for UC pts (p=0.027). There were 25 pts that have died since enrollment and 208 alive. No statistical significant difference in QOL between PROQOL and UC (median QOL 7 vs 7, p=0.75). The most prevalent main concerns selected were Physical Health (37%) and Cancer and Diagnosis (27%), the pattern remained even when pts were given the option to pick a second major concern 26% and 20% respectively. No other concern was listed more than 8% at baseline. All category selections and percentages are shown in the Figure. The average time to complete the instrument was 6 minutes. Pts found the PROQOL system was worth their time (75%), they would choose it again (87%) and recommend it to others (82%). Eighty-two percent of clinicians did not think the PROQOL tool negatively impacted their practice and 75% believed they may see improvement in pt wellbeing. Discussion: The PROQOL system was well received by pts and clinicians but did not improve QOL relative to UC in this study. The PROQOL demonstrates that majority of patients strongly desire more information regarding ongoing therapy, clinical trials, prognosis and toxicity. Further investigation is needed to understand whether communication is limited by time or mechanism of information delivery, such as verbal or written, or whether it should be undertaken in a separate allied health care visit. Figure. Figure. Disclosures Gertz: spectrum: Consultancy, Honoraria; Abbvie: Consultancy; Research to Practice: Consultancy; annexon: Consultancy; Medscape: Consultancy; Physicians Education Resource: Consultancy; Amgen: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; Apellis: Consultancy; Ionis: Honoraria; celgene: Consultancy; Prothena: Honoraria. Kumar:Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Lacy:Celgene: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.

Abstract Introduction Axi-cel is an autologous anti-CD19 CAR T-cell therapy approved by the US FDA 10/18/2017, for the treatment of adults with relapsed or refractory (r/r) large B-cell lymphoma (DLBCL), primary mediastinal large B-cell lymphoma (PMBCL), transformed lymphoma (tFL), and high-grade B-cell lymphoma (HGBCL) who have failed at least two prior systemic lines of therapy. In the pivotal ZUMA-1 trial, 108 patients (pts) with r/r DLBCL were treated with axi-cel: the best overall response rate (ORR) was 82% and complete response (CR) rate was 58%. At a median follow-up of 15.4 months, 42% of the pts had ongoing remission (Neelapu and Locke et al. NEJM 2017). Grade 3 or higher cytokine release syndrome (CRS) by Lee criteria and neurologic events (NEs) occurred in 13% and 31% of the pts, respectively. Here, we evaluated the real world outcomes of pts treated with standard of care axi-cel under the commercial FDA label. Methods and Results Seventeen US academic centers contributed data to this effort independently of the manufacturer. As of 6/30/2018, 211 pts were leukapheresed with intention to manufacture commercial axi-cel. Of these, 165 (78%) pts completed axi-cel infusion as of 6/30/18 and a further 23 (11%) pts are scheduled for axi-cel infusion in July 2018. Of the 23 remaining pts, 7 (3%) received axi-cel therapy on ZUMA-9 expanded access trial (NCT03153462) due to non-conforming cell therapy product, 15 (7%) pts died before axi-cel infusion (14 from lymphoma progression and 1 from sepsis) and 1 (1%) patient attained CR from bridging therapy and was not infused. Safety was evaluable in 163 pts receiving axi-cel. Grade ³3 CRS and NEs occurred in 7% and 31% of pts. Tocilizumab was administered in 62% of pts and 57% received corticosteroids. Outside of lymphoma progression, 3 deaths occurred post-axi-cel; 1 due to HLH, 1 due to systemic candidiasis, and a third due to septic shock. There were no grade 5 NEs observed. Response assessment was done for pts infused with axi-cel and who were re-staged at day 30 and/or day 100, or were deemed to have clinical progression. Of 112 pts evaluable at day 30, ORR was 79% with 50% CR, 29% PR, 6% SD and 14% with PD. Of 39 pts evaluable at day 100, 59% of pts had ongoing response (CR 49%, PR 10%). At the time of abstract submission, more detailed patient characteristics and treatment course data were available in 134/165 pts infused. Median age was 59 (range 21-82) with 57% male. Performance status (PS) was ECOG 0-1 (81%), ECOG 2 (16%) and ECOG 3 (3%). By histology, 61% of pts had DLBCL including HGBCL, 31% had tFL and 8% PMBCL. Thirty-one percent had a prior autologous stem cell transplant. Bridging therapy between apheresis and infusion was given in 56% of patients, the majority of which consisted of chemotherapy. Sixty-six of 134 (49%) would not have met eligibility criteria for ZUMA-1 at the time of leukapheresis. Common criteria that would have made these patients ineligible for ZUMA-1 included ECOG PS >1 (n = 22), platelets <75k (n = 17), GFR <60 (n =12), active DVT/PE (n = 13), liver enzyme abnormalities (n = 10), a history of CNS lymphoma (n = 8), recent checkpoint inhibitor therapy (n = 7), ejection fraction <50% (n = 4), prior CD19 CAR T therapy (n = 4), and prior allogeneic transplant (n=2). Median time from leukapheresis to start of conditioning chemotherapy was 21 days and median time from leukapheresis to axi-cel infusion was 26 days. Median hospitalization period was 14 days. Conclusions This multicenter retrospective study delineates the real world outcomes of axi-cel CAR T-cell therapy for r/r aggressive B-cell lymphoma when used as a standard of care. Though limited by relatively short follow up, 30 day responses in the real world setting are comparable to the best responses observed on the pivotal ZUMA-1 clinical trial (Table 1). Importantly, safety appears comparable to the ZUMA-1 trial despite nearly half the pts failing to meet ZUMA-1 eligibility criteria. Updated results including PFS and OS will be presented at the meeting. L.J.N. and M.D.J. contributed equally; D.B.M., S.S.N. and F.L.L. contributed equally. Disclosures Nastoupil: Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; TG Therappeutics: Research Funding; Karus: Research Funding; Genentech: Honoraria, Research Funding; Janssen: Research Funding; Gilead: Honoraria; Juno: Honoraria; Novartis: Honoraria; Spectrum: Honoraria. Lunning:Celgene: Consultancy; Spectrum: Consultancy; Portola: Consultancy; Genzyme: Consultancy; Janssen: Consultancy; Seattle Genetics: Consultancy; Kite: Consultancy; Juno: Consultancy; AbbVie: Consultancy; Astra-Zeneca: Consultancy; Genentech: Consultancy; Gilead: Consultancy; Genentech: Consultancy; Bayer: Consultancy; TG Therapeutics: Consultancy; Verastem: Consultancy. Reagan:Seattle Genetics: Research Funding. McGuirk:Gamida Cell: Research Funding; Astellas Pharma: Research Funding; Fresenius Biotech: Research Funding; Bellicum Pharmaceuticals: Research Funding; Novartis Pharmaceuticals Corporation: Honoraria, Other: speaker, Research Funding; Kite Pharma: Honoraria, Other: travel accommodations, expenses, speaker ; Pluristem Ltd: Research Funding. Deol:Novartis: Consultancy; Kite Pharmaceuticals: Consultancy. Hill:Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees. Andreadis:Gilead: Consultancy; Genentech: Consultancy, Employment; Astellas: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Seattle Genetics: Consultancy; Pharmacyclics: Consultancy; Juno: Research Funding; Kite: Consultancy; Novartis: Consultancy, Research Funding. Munoz:Pfizer: Consultancy; Bayer: Consultancy, Speakers Bureau; Juno: Consultancy, Honoraria; Janssen: Consultancy; Genentech: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy; Pharmacyclics: Consultancy, Honoraria; Kite: Consultancy, Honoraria, Speakers Bureau; Alexion: Consultancy; Gilead: Speakers Bureau. Westin:Novartis Pharmaceuticals Corporation: Membership on an entity's Board of Directors or advisory committees; Celgen: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Apotex: Membership on an entity's Board of Directors or advisory committees. Vose:Kite Pharma: Research Funding; Acerta Pharma: Research Funding; Celgene: Research Funding; Novartis: Honoraria, Research Funding; Roche: Honoraria; Seattle Genetics, Inc.: Research Funding; Legend Pharmaceuticals: Honoraria; Merck Sharp & Dohme Corp.: Research Funding; Abbvie: Honoraria; Epizyme: Honoraria; Bristol Myers Squibb: Research Funding; Incyte Corp.: Research Funding. Miklos:Genentech: Research Funding; Kite - Gilead: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pharmacyclics - Abbot: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy, Research Funding. Locke:Kite Pharma: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy; Novartis Pharmaceuticals: Other: Scientific Advisor.

Abstract Introduction Waldenstrom macroglobulinemia (WM) is a rare disease accounting for only 1-2% of all hematologic malignancies and with an overall annual, age-adjusted incidence of approximately 3.5 to 5.5 cases per million-person years. With approximately 13,000 active hematologist/oncologists in the US, most of who are in the community oncology setting, it's estimated that a hematologist/oncologist will only diagnose a WM case approximately every 8 years. Therefore, the clinical experience of a general hematologist/oncologist in the management of WM is likely very limited compared to more prevalent malignances. Previous studies in the setting of rare cancers suggest a correlation between higher volume of care and improved outcomes. Therefore, we explored the volume-outcome relationship in WM using the National Cancer Data Base (NCDB). Methods Patient-level data from the NCDB, a nationwide, facility-based, database covering approximately 70% of all newly diagnosed cancer cases in the US, was queried for all new WM cases diagnosed between 2004 and 2014. Only patients requiring treatment were included. Treatment facilities were divided into quartiles based on the average annual volume of newly diagnosed cases of WM seen. Cox regression was used to analyze the association between facility WM volume and survival, adjusted by demographics (sex, age, race), socioeconomic status (income, education, insurance type), geography (area of residence, treatment facility type, travel distance), comorbidity factors (Charlson-Deyo score), and year of diagnosis. Time-to-event analysis was calculated from frontline therapy initiation date using the Kaplan-Meier method and the log-rank test. Results A total of 3,732 patients with WM treated in 831 facilities were included. The median age at diagnosis was 70 years and 75% of the patients were treated within 20 miles from their residency zip code. Patient characteristics per treatment facility volume quartile are shown in table 1. The median annual facility volume was 1 new WM patient/year (range 0.1 to 21). The median follow-up from frontline treatment was 5 years. The unadjusted median OS by facility volume was: Q1: 6.5 years (95% CI: 5.7-7.4), Q2: 7 years (95% CI: 6.3-8.2), Q3: 8.2 years (95% CI: 7.1-8.9), and Q4: NR (95% CI: 8.5-NR), p<0.0001 (figure 1). The estimated 5-year OS by facility volume was: Q1: 56%, Q2: 61%, Q3: 64%, Q4: 71%, p<0.0001. Multivariate analysis including all variables showed in Table 1 demonstrated that facility volume was independently associated with all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower-quartiles facilities had a higher incremental risk of death (Q3 hazard ratio [HR], 1.07 [95% CI: 0.88 to 1.29] p=0.46; Q2 HR, 1.34 [95% CI: 1.11 to 1.63] p=0.002; Q1 HR, 1.52 [95% CI: 1.23 to 1.88] p=<0.0001). Conclusion Our results suggest that a volume-outcome relationship exits in WM as patients treated initially at higher-volume facilities had a lower risk of mortality. Although differences in the underlying disease biology, referral patterns after initial therapy, or cumulative treating-physician experience could not be assessed, these potential biases would only underestimate the magnitude of the volume-outcome relationship reported. Disclosures Ansell: Celldex: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding. Gertz:Medscape: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Ionis: Honoraria; Physicians Education Resource: Consultancy; Prothena: Honoraria; Teva: Consultancy; Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; annexon: Consultancy; Apellis: Consultancy. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Ailawadhi:Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding; Celgene: Consultancy; Takeda: Consultancy. Reeder:Affimed: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Abstract 448 Background: POEMS syndrome is a rare form of plasma cell dyscrasia that presents with polyneuropathy and various other systemic findings. Patients with 1–3 lesions and bone marrows without clonal plasma cells have been given targeted radiation as the initial and potentially definitive therapy. Outcomes of patients treated thus have not been systematically studied. Patients and Methods: One hundred and forty-six patients with POEMS syndrome were seen at the Mayo Clinic in Rochester, MN between January 1999 and September 2011. Charts of patients with research authorization were reviewed with IRB approval. Patients who had been given targeted radiation as their initial primary therapy were eligible for this study. Thirty-eight patients satisfied criteria and comprise the study population. Three patients had either short or poorly documented follow-up and were excluded from the response and time to next treatment analyses. Patients were categorized by systems that were involved at diagnosis, time of radiation, response after radiation, and the need for further therapy. Statistical analyses were performed using JMP statistical software (SAS, Carey, NC). Results: The median age was 50, with 76% of the population being male. Median time from diagnosis to radiation was 0.7 months, ranging from 0 to 9.4 months. 20, 14, 11 and 2 patients had immunosuppressive therapy with corticosteroids, IVIG, plasmapheresis, or rituximab, respectively, directed at a prior presumptive, but incorrect, diagnosis of CIDP. Five patients had one cycle or less of alkylator, lenalidomide, or bortezomib, prior to receiving what was hoped to be definitive radiation therapy. The median number of bone lesions was 1, range 1–6. In total 55 lesions of the possible 64 were irradiated being mostly found in the pelvic bones including the sacrum, ileum, ischium, and pubis. The median dose of radiation given was 45 Gy range 35 Gy to 54 Gy. Forty-nine percent had clinical improvement, 26% had a mixed clinical picture, 3% merely stabilized, and 23% had a clinical worsening. 29% had a complete hematologic response, 9% had a very good partial hematologic response, 9% had a partial hematologic response, 26% had no hematologic response, and the remaining were either non- or oligo-secretory or undocumented. 11% had a complete FDG-PET response, 11% had a partial FDG-PET response, 11% had no FDG-PET response, and the remaining had no documented FDG-PETs to analyze. 11% had a complete VEGF response, 3% had partial VEGF response, 14% had no VEGF response, and the rest were either not documented or unevaluable. The median follow up of living patients is 30 months, and the 4-year OS is 95%. Forty-eight percent of patients went on to receive additional therapy after their radiation (Figure). The median time to next therapy (TTNT) for these patients was 7.6 months (range 2.2 –73 months). Subsequent therapies included high dose chemotherapy with autologous hematopoietic stem cell transplant (n=10), low dose alkylator (n=2), thalidomide/lenalidomide (n=3), and more radiation (n=3). None of the baseline characteristics predicted for whether a patient would receive (or require) subsequent therapy, including the number of bone lesions at baseline. Conclusion: The results show that radiation is sufficient to produce clinical improvements and hematologic and radiographic responses approximately 50% of patients, even those with as many as 3 lesions. Disclosures: No relevant conflicts of interest to declare.