Citations

2004 Background: Vorinostat is a histone deacetylase inhibitor that represents a rational therapeutic target in GBM. Methods: Recurrent GBM patients (pts) having received = 1 prior chemo regimen for progressive disease were eligible. Vorinostat dose was 200 mg bid x 14 days q 3 weeks. We applied a phase II design which had 90% power to declare the regimen active if the study PFS6 rate was 25%, a 15% rate increase over PFS6 in historical controls (10%). Results: 68 patients were treated. Grade 3+ non-hematologic toxicity consisting mainly of fatigue, diarrhea and dehydration occurred in 23% of pts; Grade 3+ hematologic toxicity consisting mainly of thrombocytopenia occurred in 25% of pts. Intra-patient dose escalation to 300 mg bid resulted in higher incidence of grade 3+ thrombocytopenia (45%) and was aborted. Pts receiving enzyme-inducing anticonvulsants (EIAC) had significantly less grade 3 + non-hematologic toxicity (p=0.01) and grade 3/4 thrombocytopenia (p=0.04). Pharmacokinetic analysis showed lower vorninostat C-max and AUC (0–24h) values and higher vorinostat-glucuronide C-max and AUC (0–24h) values in EIAC pts, although this did not reach statistical significance. The trial met the prospectively defined primary efficacy endpoint at the planned interim analysis with 5 of the first 22 patients (23%) being progression-free at 6 mo. RNA array analysis, performed in paired baseline and post-vorinostat treatment samples in a separate subgroup of 5 surgical recurrent GBM pts, who received vorinostat for 6 doses prior to surgery, showed upregulation of E-cadherin (p=0.02), thus indicating a biologic effect of the HDAC inhibitor on the glioblastoma tumors. Histone acetylation, Akt, phospho-Akt, P21Waf1/Cip1 and P27Kip1 expression analysis in all patients and correlation with outcome is ongoing. Conclusions: Vorinostat is well tolerated in recurrent GBM patients. EIAC patients have less toxicity, likely due to increased vorinostat metabolism via glucuronidation. Interim efficacy analysis is indicative of antitumor activity. Final efficacy analysis, RNA array data and correlative tissue analysis will be reported. No significant financial relationships to disclose.

Abstract Purpose: Clear cell ovarian carcinoma (CCOC) is an aggressive disease that often demonstrates resistance to standard chemotherapies. Approximately 25% of patients with CCOC show a strong APOBEC mutation signature. Here, we determine which APOBEC3 enzymes are expressed in CCOC, establish clinical correlates, and identify a new biomarker for detection and intervention. Experimental Designs: APOBEC3 expression was analyzed by IHC and qRT-PCR in a pilot set of CCOC specimens (n = 9 tumors). The IHC analysis of APOBEC3B was extended to a larger cohort to identify clinical correlates (n = 48). Dose-response experiments with platinum-based drugs in CCOC cell lines and carboplatin treatment of patient-derived xenografts (PDXs) were done to address mechanistic linkages. Results: One DNA deaminase, APOBEC3B, is overexpressed in a formidable subset of CCOC tumors and is low or absent in normal ovarian and fallopian tube epithelial tissues. High APOBEC3B expression associates with improved progression-free survival (P = 0.026) and moderately with overall survival (P = 0.057). Cell-based studies link APOBEC3B activity and subsequent uracil processing to sensitivity to cisplatin and carboplatin. PDX studies extend this mechanistic relationship to CCOC tissues. Conclusions: These studies demonstrate that APOBEC3B is overexpressed in a subset of CCOC and, contrary to initial expectations, associated with improved (not worse) clinical outcomes. A likely molecular explanation is that APOBEC3B-induced DNA damage sensitizes cells to additional genotoxic stress by cisplatin. Thus, APOBEC3B is a molecular determinant and a candidate predictive biomarker of the therapeutic response to platinum-based chemotherapy. These findings may have broader translational relevance, as APOBEC3B is overexpressed in many different cancer types.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p<0.01) and D8 (p<0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p<0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI>2, compared to 33% of non-D4 with IPI>2 (p<0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p<0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p<0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Background With lymphoma survival rates increasing and a growing population of long-term survivors, the development of cardiovascular disease (CVD) in this patient population is of increasing importance. Anthracyclines are critical in the management of many lymphoma subtypes. However, there is a risk of developing anthracycline-induced CVD. Here, we estimate the cumulative incidence of CVD in adult lymphoma survivors and investigate risk factors associated with post diagnosis CVD. Methods Participants were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002-2015, the MER offered enrollment to all patients with newly diagnosed lymphoma who are US residents and age >18 years. Participants completed a risk factor questionnaire, and clinical and treatment data were abstracted from medical records. Patients were contacted every 6 months for the first 3 years after diagnosis and annually thereafter to assess disease status, re-treatment and new onset morbidity including CVD. CVD events, including congestive heart failure (CHF), coronary artery disease (CAD), valvular heart disease (VHD), and arrhythmia were identified and validated against medical records. CHF was validated with the Cardiovascular Health Study Criteria and/or the Framingham Criteria. CAD, arrhythmia, and VHD were validated using clinical definitions. We calculated the cumulative incidence of CVD, with death modeled as a competing risk. The association of risk factors and treatments with risk of CVD was estimated using hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression with a competing risk of death. Risk factors included age, sex, diabetes, smoking, body mass index (BMI), and treatment with anthracyclines or radiation therapy. Results The study consisted of 3,063 lymphoma patients after excluding those with chronic lymphocytic leukemia and CVD prior to lymphoma diagnosis. The median age at diagnosis was 59 years (range 18-95), and 56% were males. At a median follow-up was 6.9 years (range 0.8-17.1), 640 patients (21%) had died without CVD and 485 patients self-reported CVD post lymphoma diagnosis, of which 280 (57.7%) were validated. Cardiovascular events included 86 CHF, 78 CAD, 40 VHD, and 164 arrhythmias. The cumulative incidence of CVD (Figure 1) at 5 and 10 years was 6.0% (95% 5.2%-7.0%) and 10.7% (95% CI 9.5%-12.1%), respectively. In multivariable analysis, increasing age (HR=3.93 per 5 years, p<0.001), male sex (HR=1.33, p=0.03), former smoker (HR=1.04, p=0.77), current smoker (HR=1.96, p<0.001), BMI>30 kg/m2 (HR=1.50, p=0.01), and anthracycline treatment (HR=1.49. p<0.001) were all significantly associated with risk of overall CVD, while there was no association with diabetes (HR=0.92, p=0.70) or radiation therapy (HR=1.05, p=0.78) in the multivariable model. Anthracycline use was significantly associated with increased risk of CHF (HR=2.64, p<0.001) and arrhythmia (HR=1.51, p<0.01), but not VHD (HR=0.83, p=0.56) or CAD (HR=1.23, p=0.31) after adjustment for the cardiac risk factors. The number of anthracycline cycles ranged from 0 to 12. 63.2% of individuals that received anthracyclines received 6 cycles. The 5-year cumulative incidence of CVD for 0, 1-5, 6, and >6 anthracycline cycles was 5%, 6.9%, 7.4%, and 7.7%, respectively. Adjusting for cardiac risk factors, the number of anthracycline cycles was significantly associated with increased risk of CVD (1-5 cycles HR=1.34, p=0.11; 6 cycles HR=1.51, p<0.01; >6 cycles HR=2.04, p=0.03). Furthermore, the number of anthracycline cycles was associated with CHF (1-5 cycles HR=2.82, p<0.001; 6 cycles HR=2.46, p<0.001; >6 cycles HR=5.33, p<0.001) and arrhythmia (1-5 cycles HR=1.31, p=0.27; 6 cycles HR=1.60, p<0.01; >6 cycles HR=1.65, p=0.27). Conclusions In the modern treatment era, the risk of new onset CVD in patients with lymphoma without a history of CVD is approximately 1% per year after diagnosis. Arrhythmia and CHF were the most commonly occurring CVD events in this cohort. Both traditional CVD risk factors and treatment with anthracyclines was associated with an increased risk of developing CVD, and anthracyclines were a risk factor for arrhythmia and CHF in particular. Prevention of CVD in lymphoma patients will need to address both treatment and traditional lifestyle factors. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value<0.0001) with OS24 ranging from 18% in patients with TTP <6 months compared to 69% in patients with TTP > 36 months (Figure A). Additionally, age at PD (spline p-value<0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles:Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Introduction. The VES-13 is a simple, self-reported, function based tool originally developed to screen community-dwelling populations to identify persons age 65 years and older at risk of death and functional decline, including death in the next 12 months. The VES-13 items include patient's age, self-rated overall health status, functional limitations in physical activity, and functional disabilities in more complex activities of daily living. As part of the Lymphoma Epidemiology of Outcomes (LEO) cohort study, we collected self-reported VES-13 data at study enrollment on all participants regardless of age, and here we report on the prevalence of vulnerable status defined by the VES-13 and its association with 1-year mortality, overall, stratified at age 65 years, and in the subset of diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy. Methods. From 7/2015 to 6/2017, 3253 participants with NHL were enrolled within 6 months of their diagnosis into the LEO cohort. 2004 were evaluable on VES-13, and 1183 (59%) completed it before the initiation of therapy. VES-13 scores range from a minimum of 0 (low risk for decline) to a maximum of 10 (greatest risk for decline), and a score ≥3 was classified as vulnerable. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants were contacted every 6 months for the first three years and then annually thereafter to identify outcomes. Medical records were reviewed by LEO clinicians to classify cause of death according to a standard protocol. Therapy was determined by the treating physician, and this was independent of knowledge of the VES-13 score. The association of VES-13 with 1-year mortality from date of diagnosis was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, which also provided model c-statistics. Results. The median age of the 2004 participants in this analysis was 62 years (range 18-94); 57% were male; 54% were ≥65 years; and 28% had a normal body mass index (BMI), 1% were underweight, 35% were overweight, and 36% were obese. Clinically, 59% of participants had an aggressive subtype, 65% were stage III-IV, 24% had B-symptoms, and 11% had a performance status (PS) of ≥2. Overall, 28% of participants were classified as vulnerable (95% CI 26%-30%), with a higher prevalence among those completing the survey after initiation of therapy (38%, 95% 34%-41%) versus before initiation of therapy (22%, 95% CI 20%-24%), and a higher prevalence for those ≥65 years (32%, 95% CI 29%-34%) versus <65 (25%, 95% CI 22%-28%). During the first year of follow-up, there were 147 deaths, with 61% due to lymphoma, 14% therapy-related, 12% due to another malignancy, 3% other causes, and 10% unknown. Significant univariate predictors of 1-year mortality included vulnerable status, older age, PS 2+, stage III-IV, aggressive subtype, lower quality of life score, lower BMI, and 1 or more comorbidities, and were included the backward stepwise modeling. Vulnerable status (OR=3.26, 95% CI 2.15-4.96) was retained in the final model that also included age, PS, stage, and NHL subtype (Table), with a model c-statistic of 0.81. As shown in the Table, these associations were similar whether VES-13 was assessed before (c-statistic 0.77) or after (c-statistic 0.81) initiation of therapy. After adjustment for age, PS, stage and NHL subtype, vulnerable status predicted 1-year mortality in participants ≥65 years (OR=1.96, 95% CI 1.15-3.36; model c-statistic 0.80) as well as <65 years (OR=6.38, 95% CI 3.07-13.3; model c-statistic 0.83). Finally, in the DLBCL patients treated with immunochemotherapy (N=521), vulnerable status (38% of patients) also predicted 1-year mortality (OR=2.11, 95% CI 1.05-4.26) after adjusting for age, PS and stage (model c-statistic 0.78). Conclusion. Based on a self-administered VES-13, 28% of newly diagnosed NHL patients in the LEO cohort were classified as vulnerable, including 25% of those <65 years, and vulnerable status was a strong predictor of 1-year-mortality, even in the subset of DLBCL patients treated with presumed curative intent. These results highlight the potential value of screening for vulnerable status overall and across a wider age spectrum in order to optimize patient management and reduce early mortality. Disclosures Martin: Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Casulo:Gilead: Honoraria, Other: Travel, accommodation, expenses; Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses. Cohen:Takeda Pharmaceuticals North America, Inc.: Research Funding; Bristol-Meyers Squibb Company: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Gilead/Kite: Consultancy; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Kahl:Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Maurer:Nanostring: Research Funding; Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Spectrum: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding. Rutherford:Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Flowers:National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Bayer: Consultancy; Spectrum: Consultancy; Karyopharm: Consultancy; Acerta: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; AstraZeneca: Consultancy; Optimum Rx: Consultancy; V Foundation: Research Funding; Celgene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Denovo Biopharma: Consultancy; Gilead: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding.

Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

Introduction: Axicabtagene Ciloleucel (axi-cel), a CD19 chimeric antigen receptor (CAR) T-cell therapy, was approved for the treatment of relapsed/refractory aggressive B-cell non-Hodgkin lymphoma in October, 2017. In the ZUMA-1 trial leading to axi-cel FDA approval, patients (pts) with prior or active secondary central nervous system (CNS) lymphoma involvement were excluded. A recent publication of 8 pts with secondary CNS lymphoma who underwent Tisagenlecleucel CAR T cell therapy was reported (Frigault M.J.2019). Since the two FDA approved CAR T cell products have a different neurotoxicity profile, understanding outcomes of axi-cel in this setting is important. We report here the real-world experience of 17 pts treated with axi-cel who had a history of secondary CNS involvement or had active CNS disease at time of CAR T infusion. Methods: Seventeen academic centers from the US Lymphoma CAR T Consortium contributed data independently from the manufacturer. Data regarding secondary CNS involvement, management, and outcome were obtained in addition to CAR T therapy outcome for pts who were identified as having active, secondary CNS involvement at the time of evaluation for CAR-T therapy. Nine centers reported data on 17 cases with CNS involvement. Follow-up data was missing for one pt in the CNS cohort. Lee criteria or the modified Lee grading scale were used for cytokine release syndrome (CRS). CTCAEv4 or CARTOX grading were used for immune effector cells associated neurotoxicity syndrome (ICANS). All leukapheresed pts were included in the intention to treat (ITT) analysis for response rate and event-free survival (EFS). EFS was defined as date of leukapheresis until progression or death due to any cause. EFS was evaluated using Kaplan Meier curves with log-rank test. Differences in clinical characteristics and response between CNS and non-CNS pts were not formally tested due to small sample size and multiple comparison concerns. Results: With a data cut-off of 4/30/2019, 300 pts underwent leukapheresis with intention to manufacture standard of care axi-cel. By the time of leukapheresis, 17 (6%) had secondary CNS involvement (4 parenchymal disease, 10 leptomeningeal/CSF, 3 data not available). Compared to the non-CNS cohort, baseline demographics were comparable (Panel A). Manufactured axi-cel was within specification for 100% of the CNS cohort. There was a higher rate of bridging therapy use in the CNS cohort 82% (1 steroids only, 2 radiation therapy, 12 systemic therapy) vs 52% in non-CNS cohort; p=0.022. Time from leukapheresis to CAR T infusion was 3.5 days longer in the CNS cohort as opposed to the non-CNS cohort: median time of 29.5 (range 20-76) vs. 26 days (range 5-67), (p=0.029), respectively. The CAR T infusion rate was 88% for the CNS cohort (15/17) compared to 93% (262/283) in the non-CNS cohort. Among the 15 infused pts in the CNS cohort, 10 had resolution of CNS involvement, and 5 had persistent active CNS disease at time of CAR T infusion. After axi-cel infusion, the incidence of CRS and ICANS, of any grade or grade 3 or higher, were comparable between the CNS and non-CNS cohorts. Tocilizumab and steroid use were comparable between the two groups (Panel B). No seizures or cerebral edema were noted in the CNS cohort. With a median follow-up of 10.1 months from leukapheresis (range 7.6-12.6), the ITT best overall response rates (CR+PR) and ongoing responses at month 6 between CNS and non-CNS cohorts were 75% vs. 59%, and 41% vs. 31%, respectively (Panel B). In the 5 pts with active CNS disease at time of CAR T infusion, the response of CNS disease were 2 CR, 1 PR and 2 PD as best response. In the 10 pts with resolved CNS disease at time of CAR T infusion, 2 PD were seen and both occurred systemically. EFS from leukapheresis was not statistically significantly different between CNS and non-CNS cohorts (6 months EFS: CNS cohort, 36%; non-CNS cohort 57%. HR=1.58, 95% CI: 0.83-3.01, p=0.16, Panel C). Six month EFS from the date of infusion for the CNS cohort was 49.9% (Panel D). Conclusions: Pts attempting CAR T therapy with secondary CNS disease in the real world setting had similar rates of CAR T infusion, toxicity, and outcomes when compared to patients without CNS disease. Small sample size and limited follow-up caution the strength of conclusions for application to clinical practice, but these results support further investigation of CAR T in pts with history of or active secondary CNS lymphoma. Figure Disclosures Bennani: Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board. Maurer:Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Bayer: Honoraria; Spectrum: Honoraria; TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Jain:Kite/Gilead: Consultancy. Chavez:Novartis: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees. Cashen:Seattle Genetics: Other: Speaker's Bureau; Novartis: Other: Speaker's Bureau; Celgene: Other: Speaker's Bureau. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. McGuirk:Novartis: Research Funding; Fresenius Biotech: Research Funding; Astellas: Research Funding; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; ArticulateScience LLC: Other: Assistance with manuscript preparation; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Deol:Novartis: Other: Advisory board; Kite: Other: Advisory board; Agios: Other: Advisory board. Sehgal:Juno/Celgene: Research Funding; Merck: Research Funding; Kite/Gilead: Research Funding. Goy:COTA: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: leadership role for profit healthcare company; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Astrazenca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; University of Nebraska: Research Funding; Hakensackumc: Research Funding; Takeda: Other: Grants outside of the submitted work; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work; Pharmacyclics/Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding; Genentech: Other: Grants outside of the submitted work, Research Funding; Hackensack University Medical Center, RCCA: Employment; Acerta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants outside of the submitted work, Research Funding. Hill:Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; TG therapeutics: Research Funding; AstraZeneca: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celegene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Research Funding; Takeda: Research Funding; Amgen: Research Funding. Vu:Celgene: Other: Stock. Andreadis:Genentech: Equity Ownership, Other: Spouse is Employee; Novartis: Honoraria, Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Jazz Pharmaceuticals: Consultancy; Bayer: Consultancy. Munoz:Incyte: Research Funding; Portola: Research Funding; Celgene: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Bayer: Consultancy, Speakers Bureau; AstraZeneca: Speakers Bureau; Pharmacyclics LLC an AbbVie Company: Consultancy, Research Funding, Speakers Bureau; Kite Pharma: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Fosunkite: Speakers Bureau; Kyowa: Consultancy, Honoraria, Speakers Bureau. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Miklos:Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Locke:Kite: Other: Scientific Advisor; Novartis: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy. Neelapu:Unum Therapeutics: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Consultancy; Incyte: Consultancy; Poseida: Research Funding; Allogene: Consultancy; Merck: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Karus: Research Funding; Precision Biosciences: Consultancy; Cell Medica: Consultancy; Acerta: Research Funding; Cellectis: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy. Lin:Novartis: Membership on an entity's Board of Directors or advisory committees; Juno: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; BlueBird Bio: Research Funding; Celgene: 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; Sorrento: Membership on an entity's Board of Directors or advisory committees.

Introduction: Advances in the understanding of disease biology, the introduction of new drugs, and better supportive care have improved outcomes in multiple myeloma (MM). Most improvements have been observed in clinical trial and tertiary referral center populations but questions remain about the generalizability of these findings to patients treated in the community. Methods: We studied all patients diagnosed with MM between 01/01/2001 and 12/31/2015 who had complete demographic and overall survival (OS) data available and were seen at Mayo Clinic (MAYO) or followed in the Surveillance, Epidemiology, and End Results Program (SEER, 18 registry data 2000 - 2016, 11/2018 submission). We retrieved age at diagnosis, sex, date of diagnosis, date of last follow-up, and OS for all patients. OS was defined as the time from diagnosis to death from any cause. Patients who were alive at the end of follow-up (12/31/ 2016) were censored. OS estimates were calculated using the Kaplan-Meier method. Age- and sex-adjusted Cox models were used to assess the association between the 5-year interval of diagnosis and OS. Expected OS estimates were calculated based on United States general population rate tables (Human Mortality Database) using a conditional approach. Standardized mortality ratios (SMR) were calculated by dividing the number of observed deaths by the number of expected deaths in age- and sex-matched general United States population controls. Linear regression models were fit to test for linear trends in early mortality and SMR over time (per 5-year interval). P-values below 0.05 were considered statistically significant. Results: The median age at diagnosis was 3 years lower in patients at MAYO (64 years, 15% ≥ 75 years, 60% male, n = 3293) compared to SEER (67 years, 29% ≥ 75 years, 55% male, n = 61779). After a median follow-up of 2.8 years the median OS was longer in MAYO compared to SEER (5.4 versus 4.0 years, HR 0.82, 95% CI 0.78 - 0.86, p < 0.001) and remained statistically significant after adjusting for age and sex (HR 0.91, 95% CI 0.86 - 0.95, p < 0.001). Early mortality (1-year mortality) decreased between 2001-2005 and 2011-2015: From 20% to 11% at MAYO and from 26% to 19% in SEER. When grouping OS by year of diagnosis (in 5-year-intervals) improvements were seen in both populations (A) and remained statistically significant after adjusting for age and sex. The relative improvements were similar comparing the 5-year period after the introduction of the novel therapies (2006 - 2010) to 2001 - 2005 and more pronounced in MAYO for the most recent 5-year interval (2011 - 2015, A). This trend was reflected in a steady temporal improvement in 5-year OS estimates in MAYO including the most recent 5-year interval (2011 - 2015, B bottom left). In SEER there was a comparable increase between the first two 5-year intervals but a lesser improvement in more recently (2011 - 2015, B bottom left). A diagnosis of MM remained associated with significant excess mortality in all age groups over time in both populations (B top). There was a decrease in excess mortality over time at MAYO (SMR decline per 5-year interval 1.3, 95% CI 0.9 - 1.8, p < 0.001) while there was little change in SEER (SMR decline 0.0, 95% CI -0.3 - 0.3, p = 0.917, B bottom right). Further stratifying by age at diagnosis, the decrease in excess mortality was observed mostly in patients < 75 years at MAYO (SMR decline per 5-year interval 1.7, 95% CI 1.5 - 2.0, p < 0.001, C bottom left) and to a lesser extent in older patients (SMR decline 0.4, 95% CI 0.2 - 0.6, p < 0.001, C bottom right). No such trends towards improvement were observed in either age group in SEER (C bottom). In older patients, early mortality remained approximately 30% in both populations despite continued improvements, while the 5-year OS estimates for the most recent 5-year interval (2011 - 2015) were 37% at MAYO and 26% in SEER (C top). Conclusions: Both early mortality and long-term survival have improved over time. Reductions in excess mortality were largely confined to younger patients with access to specialized care. Patients ≥ 75 years represent more than a quarter of all patients in the community, a third of them died within one year of the diagnosis, and only one in four was alive five years later. Older patients with MM remain a vulnerable population and have derived only limited benefit from recent advances in the field. Safe and effective therapies for older patients with MM remain an unmet need. Figure Disclosures Gertz: Ionis/Akcea: Consultancy; International Waldenstrom Foundation: Research Funding; 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; DAVA oncology: 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. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Celgene: Research Funding. Lacy:Celgene: Research Funding. Maurer:Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Kapoor:Takeda: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Glaxo Smith Kline: Research Funding; Cellectar: Consultancy. Leung:Takeda: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

SummaryNon‐endemic Burkitt lymphoma (BL) is a rare germinal centre B‐cell‐derived malignancy with the genetic hallmark of MYC gene translocation and with rapid tumour growth as a distinct clinical feature. To investigate treatment outcomes, loss of lifetime and relapse risk in adult BL patients treated with intensive immunochemotherapy, retrospective clinic‐based and population‐based lymphoma registries from six countries were used to identify 264 real‐world patients. The median age was 47 years and the majority had advanced‐stage disease and elevated LDH. Treatment protocols were R‐CODOX‐M/IVAC (47%), R‐hyper‐CVAD (16%), DA‐EPOCH‐R (11%), R‐BFM/GMALL (25%) and other (2%) leading to an overall response rate of 89%. The two‐year overall survival and event‐free survival were 84% and 80% respectively. For patients in complete remission/unconfirmed, the two‐year relapse risk was 6% but diminished to 0·6% for patients reaching 12 months of post‐remission event‐free survival (pEFS12). The loss of lifetime for pEFS12 patients was 0·4 (95% CI: −0·7 to 2) months. In conclusion, real‐world outcomes of adult BL are excellent following intensive immunochemotherapy. For pEFS12 patients, the relapse risk was low and life expectancy similar to that of a general population, which is important information for developing meaningful follow‐up strategies with increased focus on survivorship and less focus on routine disease surveillance.