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Abstract Introduction Primary testicular lymphoma (PTL) and testicular involvement of systemic lymphoma (secondary testicular lymphoma, STL) are uncommon. Most testicular lymphomas are aggressive B-cell lymphomas and portend a high risk of extranodal relapse, including CNS relapse. As such, when testicular lymphoma is identified, the management strategy is often changed to include CNS prophylaxis. It is, therefore, essential to evaluate testicular involvement in diagnosis and staging. F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a standard imaging modality to stage aggressive lymphoma. Physiologic testicular FDG activity can be challenging to distinguish from involvement by lymphoma given a wide range of normal. An imaging threshold for testicular PET uptake would be clinically relevant and would help guide management. We compare testicular FDG avidity in testicular lymphoma with physiologic testicular FDG avidity. Methods Records of patients (pts) diagnosed with PTL or STL from 2002-2018 enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource were reviewed, yielding 36 pts with testicular lymphoma. Physiologic testicular avidity by FDG PET/CT was determined from analysis of 70 randomly selected patients who received FDG PET/CT from 2013-2018 prior to treatment for non-lymphoma indications including pulmonary nodule, lung carcinoma, head & neck squamous cell carcinoma, gastrointestinal carcinomas, and metastatic melanoma, without known hematologic malignancy, testicular pathology, or history of testicular infection/surgery by medical chart review. Imaging parameters compared in this analysis were SUVmax and SUVmean. All FDG PET/CT exams were reviewed centrally by a nuclear medicine radiologist and a radiology resident. Results Of the 36 pts with testicular lymphoma, 19 had an orchiectomy prior to FDG PET/CT and 7 pts did not have a staging FDG PET/CT scan. Of the 10 pts with intact testes at the time of staging FDG PET/CT, 1 pt with low-grade lymphoma was excluded. 9 pts with aggressive lymphomas were included in this analysis: 7 pts with diffuse large B cell lymphoma (DLBCL), and 1 pt each with Burkitt lymphoma and peripheral T-cell lymphoma, not otherwise specified. Testicular lymphoma was diagnosed by orchiectomy in 3 pts, and by percutaneous biopsy in 3 pts. 3 pts did not have tissue sampling of testes or scrotal contents, but had an overtly FDG-avid testicle highly suggestive of lymphomatous involvement. These 3 pts had biopsy-proven lymphoma in parotid, bone marrow, and stomach, respectively, with mean testicular SUVmax 6.8 among the 3 pts. The median age in this analysis cohort was 62 years (range 35-88). Of the 7 analyzed DLBCL pts, 6 were non-germinal center B-cell, and 1 was germinal center B-cell. The 70 control pts had a median age of 55 years (range 18-90). In known testicular lymphoma cases and control pts, SUVmax and SUVmean were assessed from the most FDG-avid testicle. Median SUVmax for the testicular lymphoma cases was 13.3 (range 5.5-29.9), compared to a median of 3.8 (range 2.1-5.5) for controls (Wilcoxon p <0.0001). Median SUVmean for testicular lymphoma was 9.2 (range 3.3-18.6), compared to a median of 3.1 (range 1.8-4.8) for controls (Wilcoxon p <0.0001) (Figure). Based on this dataset, an SUVmax cutoff of 5.0 has a sensitivity of 100% (95% CI 66.3-100) and a specificity of 98.6% (98% CI 92.3-99.9) for the detection of testicular involvement by an aggressive lymphoma. Conclusions FDG PET/CT shows significantly greater FDG avidity for testicular aggressive lymphomas as compared with physiologic testicular FDG uptake in this single institution study with centralized radiology review of cases and controls. Testicular FDG avidity greater than an SUVmax 5.0 in staging aggressive lymphoma is highly suggestive for testicular involvement, warranting follow-up clinical exam and ultrasound, at a minimum. In addition, testicles with SUVmax <5.0 in a patient with known lymphoma but no symptoms or signs of testicular involvement should be considered negative for the purposes of staging and treatment planning. A pragmatic limitation of this study is that most patients with testicular involvement of lymphoma underwent orchiectomy prior to FDG PET/CT scan, which decreased our sample size. Larger, pooled studies would be helpful to validate these clinically relevant findings. Figure. Figure. Disclosures Witzig: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Background: PTLD is the most common malignancy, other than non-melanoma skin cancer, complicating solid organ transplantation (SOT) and has been one of the most commonly observed fatal consequences in SOT. The clinical presentations, management strategies, histologies, causes of death and outcomes are diverse (Dierickx D, Habermann TM. N Engl J Med 2018;378:549-62). The 2017 WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues has redefined the categories as non-destructive PTLDs (plasmacytic hyperplasia, infectious mononucleosis, and florid follicular hyperplasia), polymorphic PTLD, monomorphic PTLD: B-cell neoplasms (diffuse large B-cell lymphoma, Burkitt lymphoma, high-grade B-cell lymphoma, plasmablastic lymphoma, plasma cell myeloma, plasmacytoma, and other) and T-cell neoplasms (peripheral T-cell lymphoma NOS, hepatosplenic T-cell lymphoma, other), and classic Hodgkin lymphoma (CHL) PTLD. We report the outcomes and long-term follow-up of patients from a single institution based on these categories whose pathology was retrospectively reviewed and reclassified based on the WHO 2017 classification. Methods: Patients with SOT who were diagnosed with PTLD at Mayo Clinic (Rochester, MN) were identified through the Mayo Clinic Lymphoma Data base and the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). The histology was re-reviewed in 80% of the cases (RLK) and classified according to the WHO Classification of Tumours of Haematopoietic an Lymphoid Tissues 2017. Cases were considered "PTLD, unclassified" if the histology could not be confidently classified based on pathology material available for review. Indolent small B-cell lymphomas were not included among the PTLDs except for EBV-positive marginal zone lymphoma. Cox proportional hazards models were used to assess the association of clinical factors in overall survival (OS). Results: 233 patients diagnosed with PTLD between 1987 and 2017 were identified. The median age at the time of diagnosis of PTLD was 54 years (range 16 to 84) with 85 patients (36%) over the age of 60. 156 (67%) were male. The transplanted organs were kidney (41%), kidney/pancreas (5%), liver (29%), heart (9%), lung (7%), and other (8%). PTLD occurred late (more than one year after transplantation) in 66%. There were 69 stage I, 19 stage II, 8 stage III, and 128 stage IV patients. 84% presented with extranodal disease. 21% had involvement of the engrafted organ. 64% of the patients developed a PTLD that was EBV positive by in situ hybridization. Initial approaches to management included reduction of immunosuppression (N=55), chemotherapy/immunochemotherapy (N=71), reduction in immunosuppression with rituximab (N=58), single agent rituximab (N=14), and radiation therapy (N=5). At a median follow-up of 87 months (range 9-289), 139 (60%) patients had died. All six CHL-PTLD patients are alive, two of whom had an event. The median overall survival (OS) was 85 months (95% CI: 39-144) in 177 monomorphic B-cell lymphoma PTLD, 95.5 months (95% CI: 40-not reached) in 24 polymorphic PTLD, and 60 months in 8 non-destructive PTLD cases. In contrast, the median overall survival was 12 months (95% CI: 4-200) in 8 monomorphic T cell and 3 months (95% CI: 1-unreached) in 9 unclassified PTLD cases. Conclusion: PTLD is a heterogeneous group of immunodeficiency-associated lymphoproliferative disorders. The overall survival in non-destructive, polymorphic, and monomorphic PTLD were similar. Monomorphic T/NK cell types had inferior outcomes. Figure. Figure. Disclosures Maurer: Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Ansell:Celldex: Research Funding; Merck & Co: Research Funding; Takeda: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Diffuse large B cell lymphoma post-transplant lymphoproliferative disorder (DLBCL-PTLD) represents a fatal complication after a solid organ transplant (SOT). Approximately 50% of DLBCL-PTLD can be related to Epstein Barr Virus (EBV) infection. However, the EBV status is not taken into account in the DLBCL-PTLD categorization, although increasing evidence suggests its critical role in biological, clinical and prognostic aspects of the disease. In this single center retrospective study, we compared clinical and histological features, as well as outcomes of DLBCL-PTLDs by EBV status. Methods This study focused on patients with SOT who were diagnosed with DLBCL-PTLD at Mayo Clinic (Rochester, MN). Patients were identified through the Mayo Clinic Lymphoma Data base and the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER), between 1989 and 2017. The histology was re-reviewed (RLK) and classified according to the WHO Classification or Tumours of Haematopoietic and Lymphoid Tissues 2017. Cell of origin (COO) was performed by Hans algorithm. EBV RNA in situ hybridization (EBER) was performed. Cox proportional hazards models were used to assess the association of clinical factors in overall survival (OS). Results 148 DLBCL-PTLD were identified. The median age at SOT and at the time of PTLD diagnosis were 50 years (range: 15-71) and 56 years (range: 18-82), respectively. The median time to PTLD diagnosis from SOT was 38 months (range: 1-499). The types of SOT included renal (44%), liver (36%), cardiac (9%), renal/pancreas (5%), pancreas (8%), lung (7%) and multiorgan (4%). 62% were stage III-IV, 30% had elevated LDH and 67% had an IPI >1. 88% of patients presented with extranodal disease, and 16% had involvement of the grafted organ. Eighty-three (56%) cases were EBV positive. The COO information was available on 35 cases. The non-GCB subtype was identified in 24 cases and GCB subtype in 11 cases. The initial treatment was as follows: reduction in immunosuppression (12%), immunosuppression reduction with rituximab (26%), rituximab alone (5%), chemotherapy with or without rituximab (35%). Patients with EBV positive PTLD were more likely to have grafted organ involvement (20% vs. 8%, p=0.047) and late PTLD (occurring after 12 months from SOT - 60% vs. 8%, p<0.001) than patients with EBV negative PTLD. There was no association between EBV status and age, sex, stage, LDH, and extranodal site involvement. At a median follow-up of 80 months (range 9-269), 83 patients (56%) had died. The 5-year survival rate from PTLD diagnosis was 56% (95% CI: 48-65%). The median OS was 85 months (95% CI: 37-148). There was no difference in OS in patients with EBV positive PTLD (median OS=84.7 months, HR=1.21, 95% CI: 0.77-1.92) versus patients with EBV negative DLBCL-PTLD (median OS=85.4 months, p=0.41). Conclusion Our study assessed the impact of EBV status on the presentation and clinical outcome of DLBCL-PTLD in a 148-patient single-institution cohort. 56% of cases were EBV positive. EBV positivity was associated with grafted organ involvement and late PTLD diagnosis. Patients with EBV-negative and EBV-positive disease had similar survival from PTLD diagnosis. Disclosures Maurer: Celgene: Research Funding; Morphosys: Research Funding; Nanostring: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Background: Lymphoma survivors are at risk for long term and late effects which may negatively impact quality of life (QOL). Few studies have assessed the impact of change in health behaviors (physical activity (PA), alcohol and smoking) on QOL, specifically in lymphoma survivors. Methods: Patients were prospectively enrolled <9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource cohort. At baseline and 3-year follow-up (FU3), patients self-reported QOL, smoking, alcohol use and PA. At FU3, survivors reported change in health behaviors since diagnosis. The Godin Leisure Score Index (LSI) was calculated as a measure of PA (Pophali et al. ASH 2017); modeled by tertile. QOL was measured via FACT-G; the total and sub-scales [emotional (EWB), functional (FWB), physical (PWB), and social/family well-being (SFWB)] scores were transformed to a 0-100 scale. Patients who completed <80% of QOL questions were excluded. The association of QOL with health behaviors was evaluated using Kruskal-Wallis test (categorical) and Spearman's rank correlation (continuous). Results: At baseline, 2805 participants were evaluable for QOL of which 2025 were evaluable at FU3. The median age at diagnosis was 62 (IQR 52-69) years. Majority were males (N=1148, 56.8%), with advanced stage III-IV disease (N=941, 62%) and ECOG performance status <2 (N=1935, 96%). At baseline, smoking status was current (N=237), former (N=802), never (N=1310) or not available (NA, N= 456). Current smokers had a significantly lower median QOL scores compared to former and never smokers for EWB (75 vs 79 vs 79, p<0.01), FWB (71 vs 79 vs 79, p<0.01), PWB (86 vs 89 vs 89, p=0.02), SFWB (86 vs 90 vs 89, p=0.02) and total FACT-G (67 vs 82 vs 82, p<0.01). The median number of pack years smoked among current &former smokers was 15.6 (range 0.5-126). There was a significant negative correlation of pack years smoked with FWB (p=0.05), and total FACT-G (p=0.04). At FU3, current smokers had significantly lower median scores compared to former and never smokers for FWB (80 vs 86 vs 86, p=0.01), PWB (89 vs 93 vs 93, p<0.01) and total FACT-G (82 vs 86 vs 87, p<0.01). EWB (83 vs 88 vs 88, p=0.12) and SFWB (83 vs 86 vs 86, p=0.12) were also lower than never and former smokers but not statistically significant. Smoking status was decreased (N=30), increased (N=0) and unchanged (N=1439) at FU3. There was no association of change in smoking with change in QOL. At baseline, alcohol use status was current (N=1189), former (N=506), never (N=518) and NA (N=592). Current alcohol users had the highest FWB (79 vs 75 vs 75, p<0.01) vs former and never users. Former users vs current and never users had the lowest PWB (86 vs 89 vs 89, p<0.01), SFWB (88 vs 89 vs 92, p<0.01) and total FACT-G (80 vs 83 vs 82, p<0.01). EWB was similar in all 3 groups. Among current & former alcohol users, the average number of drinks/week was 3.4. There was no association of the number of drinks/week with QOL except positive correlation with PWB (p=0.01). At FU3, alcohol use was reported as increased (N=40), decreased (N=312) and no change (N=875). There was no association between change in alcohol use and change in QOL. At baseline, participants with PA in the high vs low LSI tertile had significantly better median scores for FWB (79 vs 75, p=0.01), SFWB (93 vs 89, p<0.01) and total FACT-G (83 vs 82, p<0.01). At FU3, participants in the high vs low LSI tertile had significantly better scores on all subscales and total FACT-G with a significant positive correlation between the continuous LSI and QOL (FACT-G total and subscales). PA was reported as increased (N=107), decreased (N=394) and no change (N=495). There was a significant association of increase in PA with change in PWB (p<0.01), EWB (p=0.01), FWB (p<0.01) and total FACT-G (p<0.01) from baseline to FU3, but not with change in SFWB (p=0.06). In a sensitivity analysis, QOL was modeled for health behaviors in the subset of patients with QOL assessed prior to treatment; results were consistent with the QOL in all patients. Conclusions: Health behaviors in lymphoma survivors are significantly associated with QOL: better QOL is associated with not smoking, more PA, and moderate alcohol use. Current alcohol consumption, as opposed to former alcohol use, may be related to overall health status. An increase in PA after lymphoma diagnosis is associated with an increase in the QOL. Thus, modification of health behaviors has the potential to impact QOL in lymphoma survivors. Figure. Figure. Disclosures Cerhan: Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Monoclonal gammopathies, including multiple myeloma (MM), represent a group of plasma cell (PC) disorders that comprise of mostly incurable hematopoietic malignancies with an increasing incidence in the US. Previous epidemiological studies demonstrated a 2-3 fold higher incidence of monoclonal gammopathy of undetermined significance (MGUS) and a similarly higher incidence of MM along with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). This suggests a possible ancestral-associated genetic predisposition of AAs to the development of monoclonal gammopathies (Landgren, Blood, 2006). When access to care is equal, AAs have a better overall survival compared to EAs suggesting that AAs may have a genetic predisposition that renders them better responders to treatment or have more favorable cytogenetic subtypes of MM (Waxman, Blood, 2010). Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry. We hypothesize that quantifying genetic ancestry is necessary to fully understand the genetic mechanisms of racial disparities of monoclonal gammopathies. We describe our study of 881 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from the bone marrow samples was genotyped on the Precision Medicine Research Array. Biogeographical ancestry was assessed using the Geographic Population Structure Origins tool, which provides the ancestral breakdown of 36 admixture components for each individual representing different geographic regions and outputs admixture proportions corresponding to those ancestries (Elhaik, Nat Commun, 2014). We evaluated whether an increase in the percentage of African Ancestry altered the odds of any primary cytogenetic abnormality. We demonstrated that a 10% increase in the percentage of African ancestry was associated with a 6% increase in the odds of detecting either an t(11;14), t(14;16) or t(14;20) (odds ratio = 1.06, 95% CI: 1.02 - 1.11; p-value = 0.05). Although we observed an increase in the odds of each of the individual translocations with 10% increase in African ancestry, l t(11;14) (OR=1.03) t(14;16) (OR=1.11) and t(14;20) (OR=1.10), these three abnormalities were combined to achieve greater statistical power. The differences in cytogenetic abnormalities were most striking among individuals with ≥80.0% African (n=120) compared to individuals with <0.1% African ancestry (n=235). Using these cutoff values comparing the ≥80.0% and <0.1% African ancestry cohorts, there was a higher prevalence of t(11;14), t(14;16) and t(14;20) (51% vs. 33%, respectively, p-value=0.008) and a lower prevalence of trisomies (with or without IgH translocations) (48.3% vs. 61.3%, respectively, p-value=0.066). In addition, the ≥80% African ancestry cohort also displayed a slightly lower prevalence of monosomy 13/13q deletion compared to the cohort with <0.1% African ancestry (34.2% vs. 38.7%, respectively, p-value=0.021). Further, we observed a significantly higher prevalence in the proportion of females with monoclonal gammopathies among the ≥80% African ancestry cohort compared to the cohort with <0.1% African ancestry (56.7% vs. 42.1%, respectively, p-value=0.028). We complement our past studies that relied on self-reported race and characterized patients' demographic and uniformly collected cytogenetic data in relation to genetically defined African ancestry. We demonstrate the prevalence of having one of three specific subtypes (i.e., t(11;14), t(14;16), or t(14;20)) was significantly higher in those with highest (≥80%) compared to lowest (<0.1%) African ancestry demonstrating African ancestry is associated with a subtype distribution of MM that differs from non-African individuals. These differences were only revealed after analysis of individuals with the highest and lowest percentage of African ancestry. Since 38.3% of the individuals with ≥80% African ancestry have the favorable t(11;14) translocation (in comparison to 26.8% of <0.1% African individuals), this finding may explain improved survival with equal access to care. Understanding the genetic mechanisms of this disparity is a fundamental step to understanding differences in incidence and outcomes of patients with monoclonal gammopathies. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Stewart:Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy; Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Novartis: Research Funding; KITE: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: 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; 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; Merck: 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; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding.

Abstract Background Peripheral T-cell lymphomas (PTCLs) are aggressive non-Hodgkin lymphomas with poor overall survival rates. The genetics of PTCLs are poorly understood. We have described t(6;14)(p25.3;q11.2) translocations in PTCLs involving the T-cell receptor-alpha (TRA@) and interferon regulatory factor-4 (IRF4) genes and overexpressing IRF4 protein (also called MUM1). IRF4/MUM1 is a lymphoid transcription factor involved in differentiation and growth, the expression of which is tightly regulated in normal T cells. However, nearly half of PTCLs constitutively express IRF4/MUM1 and this expression is associated with adverse overall survival in several PTCL subtypes. Because IRF4/TRA@ translocations are rare among PTCLs (<1%), we investigated the presence of other genetic abnormalities of IRF4 in PTCLs and examined their association with IRF4/MUM1 protein expression. Methods Studies were performed on paraffin tissue sections of PTCL specimens diagnosed by standard WHO criteria. IRF4/MUM1 immunohistochemical staining (IHC) was performed using the MUM1p antibody and considered positive if nuclear staining was present in >30% of tumor cell nuclei. Fluorescence in situ hybridization (FISH) was performed using breakapart probes constructed from bacterial artificial chromosome DNA that hybridized to loci flanking the IRF4 locus on 6p25.3. Cases with 3 or more fusion signals were considered to have extra copies of the IRF4 gene. In a subset of cases, PCR and Sanger sequencing was performed on DNA extracted from frozen PTCL tumor tissue with primer sets that amplified exons 1 to 10 of the IRF4 gene. Data were analyzed using the chi-square test or t test. Results PTCLs from 277 patients (175 M:102 F; median age, 61 y) were examined by both IHC and FISH. The subtype distribution was: PTCL, not otherwise specified (NOS), 93; anaplastic large cell lymphoma (ALCL), 89; angioimmunoblastic T-cell lymphoma (AITL), 27; cutaneous T-cell lymphoma (CTCL), 43; and cytotoxic PTCL, 25. IRF4/MUM1 was positive in 116 cases (42%). Extra copies of IRF4 were identified in 18 PTCLs (7%), 83% of which were IRF4/MUM1 protein-positive. In contrast, 39% of cases without extra copies of IRF4 were IRF4/MUM1 protein-positive (p=0.0002). The mean percentage of tumor cells with IRF4/MUM1 staining was 69% in cases with extra copies of IRF4, compared to 30% in cases without extra copies (p=0.0001). Extra copies of IRF4 were seen in 13% of ALCLs, 4% each of PTCLs, NOS and cytotoxic PTCLs, 2% of CTCLs, and 0% of AITLs (p=0.02). Two IRF4/MUM1-positive cases had an IRF4/TRA@ translocation (1%). We selected 30 IRF4/MUM1-positive PTCLs for Sanger sequencing. In this subset, we identified a previously undescribed non-synonymous variant in exon 5 (S309R) in 1 case (3%) without an extra copy of IRF4. Conclusions Extra copies of IRF4 are seen in ∼7% of PTCLs, are significantly associated with IRF4/MUM1 expression, and are preferentially observed in ALCLs. Both IRF4/TRA@ translocations and IRF4 mutations are rare in PTCLs. These data suggest that genetic abnormalities of the IRF4 gene – specifically extra copies of IRF4 – may contribute to IRF4/MUM1 expression in some PTCLs. Since IRF4/MUM1 expression is seen in ∼42% of PTCLs, these data also imply the existence of other regulatory mechanisms. Characterizing these mechanisms may be important clinically, since IRF4/MUM1 has been proposed as a therapeutic target in human cancers but drugs that directly inhibit its function are currently not available. Disclosures: No relevant conflicts of interest to declare.

Abstract Introduction: The addition of Rituximab to chemotherapy has significantly improved the outcome of patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL). Patients treated with immunochemotherapy for DLBCL who achieve EFS24 (event-free for 2 years after diagnosis) have an overall survival equivalent to that of the age- and sex-matched general population. Relapses after achieving EFS24 have been considered to be unusual but have been understudied. We sought to define the rate, clinical characteristics, treatment pattern, and outcomes of such relapses. Methods: 1448 patients with newly diagnosed DLBCL from March 2002 to June 2015 were included. Patients were enrolled in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma SPORE, treated per physician choice (predominantly R-CHOP immunochemotherapy) and followed prospectively. An event was defined as progression or relapse, unplanned re-treatment after initial therapy, or death from any cause. Cumulative incidence of relapse and non-relapse mortality after achieving EFS24 were analyzed as competing events using Gray's test in the EZR software. Post-relapse survival was defined as time from relapse to death from any cause and analyzed using Kaplan-Meier method in SPSS (V22). Results: Among the 1448 patients, 1260 (87%) had DLBCL alone at diagnosis, and 188 (13%) had concurrent indolent lymphoma (follicular lymphoma 115, marginal zone lymphoma 18, chronic lymphocytic leukemia 14, lymphoplasmacytic lymphoma 4, unspecified 37) at diagnosis. After a median follow-up of 83.9 months, 896 patients achieved EFS24. For all 896 patients who achieved EFS24, the cumulative incidence of relapse (CIR) was 5.7%, 9.3% and 13.2%, respectively, at 2, 5 and 10 years after achieving EFS24. Patients with concurrent indolent lymphoma at diagnosis had a higher CIR compared to those with DLBCL alone at diagnosis (10.2 vs 4.8% at 2 years, 15.7 vs 8.0% at 5 years, 28.8 vs 9.7% at 10 years, P<0.001; Figure 1). There were a total of 84 patients who relapsed after achieving EFS24. The median age at initial diagnosis was 66 years (range 35-92), and 48 (57%) were male. At diagnosis, 11 (13%) had ECOG PS >1, 37 (50%) had LDH elevation, 62 (74%) were stage III-IV, 14 (17%) had more than 1 extranodal site, and 26 (31%) were poor risk by R-IPI score. There were 58 patients with DLBCL alone at diagnosis who relapsed after achieving EFS24, and 38 (75%) relapsed with DLBCL, 13 (25%) relapsed with indolent lymphoma (predominantly follicular lymphoma), and pathology was unknown in 7 patients. In contrast, there were 26 patients with concurrent indolent lymphoma at diagnosis who relapsed after achieving EFS24, and 9 (41%) relapsed with DLBCL, 13 (59%) relapsed with indolent lymphoma, and pathology was unknown in 4 patients. In the 47 patients who relapsed with DLBCL after achieving EFS24, 45% received intensive salvage chemotherapy, 19% received regular intensity chemotherapy, 9% received CNS directed chemotherapy, and 36% went on to receive autologous stem cell transplant (ASCT). In the 26 patients who relapsed with indolent lymphoma after achieving EFS24, 27% were initially observed, 54% received regular intensity chemotherapy, 4% received intensive salvage chemotherapy, and 19% received ASCT after subsequent progression. The median post-relapse survival (PRS) for all patients with a relapse after achieving EFS24 was 38.0 months (95% CI 27.5-48.5). The median PRS for patients who relapsed with DLBCL and indolent lymphoma after achieving EFS24 were 29.9 (19.9-39.9) and 89.9 (NR-NR) months, respectively (P=0.002; Figure 2). Conclusions: Relapses after achieving EFS24 in patients with DLBCL were uncommon in the rituximab era. Patient with DLBCL alone at diagnosis can relapse with either DLBCL or indolent lymphoma (3:1 ratio). Patients with concurrent DLBCL and indolent lymphoma at diagnosis had a significantly higher CIR, and relapses with DLBCL and indolent lymphoma were similar (2:3 ratio). Even with high intensity salvage chemotherapy and consolidative ASCT, patients who relapsed with DLBCL had a significantly worse survival compared to those who relapsed with indolent lymphoma. Late relapses with DLBCL remain clinically challenging, with a median survival of 2.5 years after relapse. Figure 1. Figure 1. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Ansell:Takeda: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Celldex: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.

Abstract Background An association has been identified between the diagnosis-to-treatment interval (DTI) and prognostic clinical factors and outcomes in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) (Maurer et al, JCO 2018). This association can result in inadvertent selection bias in clinical trials to exclude patients with aggressive disease due to the inability to delay treatment long enough to fulfill enrollment criteria, compromising the validity of clinical trial study results to the general population. A similar concern exists in the relapsed/refractory (r/r) DLBCL setting regarding patient selection bias against patients with aggressive disease requiring immediate treatment following relapse or progression. Here we examine the time from progression after immunochemotherapy (IC) to the initiation of first salvage chemotherapy and its association with outcome. Patients and Methods: Newly diagnosed patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER), now a subcohort of the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study, and followed for progression/relapse, retreatment, and death. This analysis includes patients with their first r/r DLBCL following frontline IC who initiated aggressive salvage chemotherapy as identified and included in a previous publication (Farooq et al, BJH 2017). The progression-to-treatment interval (PTI) was defined as the time in days from date of progression from IC to initiation of salvage therapy. The date of progression was defined as either a) the date biopsy was obtained for patients who had a biopsy to confirm progressive disease or b) the date of the scan or clinical examination indicating progression in patients who did not have a biopsy performed. Event-free survival (EFS) was defined as time from initiation of first salvage therapy to progression or relapse, initiation of new anti-lymphoma therapy, or death due to any cause; overall survival (OS) was defined as time from initiation of salvage therapy until death due to any cause. Results: 162 patients with r/r DLBCL enrolled in the MER from 2002-2012 who initiated aggressive salvage chemotherapy with intent to transplant and had confirmed dates for both progression after IC and start of salvage therapy were evaluated. Median age at first progression for these patients was 64 years (range 36-76) and 104 (64%) were male. Median time from diagnosis to first progression on IC was 6.7 months (IQR: 4.6-12.7). Initial salvage therapy was R-ICE (80%), R-DHAP (8%), rituximab, oxaliplatin, cytosine arabinoside, and dexamethasone (ROAD) (6%), and other (7%). At a median follow-up of 49 months from initiation of salvage therapy (IQR: 33-74), 116 patients had died (72%). Median PTI was 6 days (IQR: 2-13). 110 patients (68%) had biopsy confirmation of disease prior to initiating salvage therapy; median PTI was 2 days (IQR: 1-7) for patients who had biopsy confirmation vs. 7 days (IQR: 3-16) in patients without biopsy confirmation, Wilcoxon p=<0.0001. There was no difference in OS from initial salvage therapy between patients who did not have biopsy confirmation (HR=1.09, 95% CI: 0.74-1.60, p=0.68) compared to patients with biopsy confirmation. Patients with short PTI (0-6 days) had significantly worse overall survival from initiation of salvage therapy (median OS = 7.6 months, HR=2.10 (95% CI: 1.43-3.08) compared to patients who initiated therapy 7 or more days from progression (median OS = 29.7 months), logrank p<0.0001. This association remained consistent (HR=2.20, 95% CI: 1.48-3.26, p<0.0001) after adjusting for age and biopsy confirmation of progression. Short PTI was also associated with inferior response rate to initial salvage therapy (51% vs. 65%, p=0.058), lower rate of proceeding to transplant after initial salvage therapy (33% vs. 55%, p=0.0063), lower rate of being event-free 24 months from initial salvage therapy (16% vs 33%, p=0.011) and lower rate of ever proceeding to transplant (44% vs. 59%, p=0.057). Conclusions: A short progression-to-treatment interval is strongly associated with inability to proceed to transplant and inferior overall survival in r/r DLBCL. These results have implications for the design and interpretation of clinical trials in the relapsed/refractory setting. Figure. Figure. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Ansell:Trillium: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Pfizer: Research Funding; Celldex: Research Funding; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Age has long been recognized as a poor prognostic factor in diffuse large B-cell lymphoma (DLBCL) and more recently, a lower baseline quality of life (QOL) in patients with aggressive lymphomas has been associated with an inferior overall survival (OS) (Thompson et al, 2018). Older patients with DLBCL are a heterogeneous population and commonly have comorbid conditions. A concerted effort to identify prognostic factors specific to this population is needed, including QOL. Event free survival at 24 months (EFS24) is a well validated endpoint in DLBCL (Maurer et al, 2014) and is clinically relevant in older patients. We hypothesized that baseline clinical factors and QOL would be associated with EFS24 this patient group. Methods Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis into the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER). Patients ≥70 years of age with DLBCL were utilized in this study. All pathology was reviewed by a lymphoma hematopathologist. Clinical variables were abstracted from the medical record. QOL was assessed at enrollment using a single item Linear Analogue Self-Assessment (LASA) and the 27-item Functional Assessment of Cancer Treatment-General questionnaire (FACT-G), which includes physical, social, emotional, and functional well-being domains. QOL scores were converted to a 0-100 scale to facilitate modeling. QOL was analyzed in patients who completed 80% of the FACT-G questions. Characteristics of the study population were compared using the Wilcoxon rank sum test. Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the associations of clinical and continuous QOL variables with failure to achieve EFS24 using logistic regression models, with a stepwise approach to identify parsimonious multivariate models. Results A total of 389 patients ≥70 years were identified with DLBCL in the MER from 2002-2015; patient characteristics are described in the Table. In the entire study population, univariate predictors of EFS24 failure included age (OR per 10 years=1.85, 95% CI: 1.26-2.71), ECOG PS ≥2 (OR=4.78, 95% CI: 2.92-7.83), elevated LDH (OR=3.59, 95% CI: 2.27-5.65), stage III-IV (OR=1.94, 1.25-3.02), bulky disease (OR=2.86, 95% CI: 1.43-5.72), international prognostic index (OR=8.71 for IPI 5 vs IPI 0, 95% CI: 3.04-24.9), and receipt of an anthracycline (OR=0.25, 95% CI: 0.12-0.51), while none of the other variables in the Table, including the QOL variables, were statistically significant. In a stepwise multivariate analysis, only ECOG PS ≥2 (OR= 3.56, 95% CI: 2.09-6.06) and elevated LDH (OR=2.86, 95% CI: 1.78-4.60) remained significant predictors of EFS24 failure. QOL data were available on 220 patients, with 120 completing QOL questionnaires prior to receipt of chemotherapy. Compared to participants who completed their FACT-G before the initiation of chemotherapy, the median FACT-G physical (85.7 v. 71.4, p=0.005) and functional (71.4 vs 57.1, p=0.008) scores were lower in participants completing FACT-G after initiation of therapy, were higher on FACT-G emotional (79.2 vs 83.3, p=0.03), and showed no differences on FACT-G social (89.3 vs 91.7, p=0.60) or FACT-G total score (78.2 vs 74.1, p=0.17). Given these differences, we restricted the QOL analysis to those with pre-treatment data. In univariate analysis, the LASA score (OR= 0.98, 95% CI: 0.84-1.14), the FACT-G total (OR=0.99, 95% CI: 0.96-1.01), physical (OR=0.98, 95% CI: 0.97-1.00), social (OR=1.00, 95% CI: 0.98-1.03), emotional (OR= 1.00, 95% CI: 0.98-1.02), and functional (OR=0.99, 95% CI: 0.98-1.01) scores were not associated with EFS24 failure. In a stepwise multivariate analysis of clinical variables in patients with pretreatment QOL scores, only ECOG PS ≥2 (OR=3.56, 95% CI: 1.30-9.72) and female sex (OR=0.37, 95% CI: 0.16-0.85) were associated with failure to achieve EFS24. Conclusions In patients aged 70 years and older, ECOG PS and LDH were the strongest independent predictors of failure to achieve EFS24. As expected, physical and functional scores were lower in patients who completed their QOL after initiation of treatment. Restricting the analysis to patients with pre-treatment data, QOL was not associated with failure to achieve EFS24. Additional studies are needed to identify the most robust clinical predictors of EFS24, while QOL does not appear to be an important predictor of EFS24. Disclosures Reagan: Seattle Genetics: Research Funding. Maurer:Morphosys: Research Funding; Celgene: Research Funding; Nanostring: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.