Jaye, David L.

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 Abstract 2686 Background: DLBCL is the most commonly occurring form of non-Hodgkin lymphoma and is a highly curable disease, but one that is universally fatal if untreated or improperly treated. In a series of studies, we have reported racial disparities in the clinical presentation and the treatment outcomes for patients (pts) with DLBCL in the United States (Shenoy Cancer 2010; Flowers CEBP 2012). These studies showed that black pts with DLBCL are diagnosed at an age a decade younger than whites, are more likely to have advanced stage disease, and are less likely to survive 5 years. One explanation is that black patients in the US less often receive standard of care therapy (Flowers CEBP 2012). However, in a cohort study of 533 white and 144 black patients with DLBCL managed at Emory and University of Alabama-Birmingham (UAB) black race predicted worse overall survival (OS) even when black and white pts received the same therapy (CHOP; Hazard ratio [HR] 1.8, p<0.001) suggesting a biological basis for some disparities(Flowers Leuk Lymph 2012). To address these findings, we examined whether known prognostic biological variants within DLBCL [germinal center B-cell-like (GCB) and activated B-cell like (ABC)] occurred differentially between black and white pts. Methods: DLBCL pts within our Emory-UAB cohort study provided formalin fixed material available for construction of tissue microarrays (TMAs). All pts were diagnosed with DLBCL from 1990–2010 and met the following criteria: age ≥18 years at diagnosis and available information on demographics, race, gender, initial clinical features at presentation, treatment, and treatment outcomes. H&E slides from each block for each case were reviewed to identify the appropriate zone(s) for cores. Sections placed in TMAs underwent immunohistochemistry (IHC) staining with BCL6, GCET1, CD10, FOXP1, and LMO2 antibodies. The percentages of positive cells were scored in 10% increments for each antibody, and recorded for each case by 3 hematopathologists blinded to the clinical and demographic data. The Choi algorithm was used to classify all patients into ABC and GCB subsets; other IHC algorithms were used as a backup. Descriptive statistics for the baseline characteristics of black and white pts were compared using Chi-square tests. Kaplan–Meier estimation was used to evaluate OS for the two groups and compared with the log-rank test. To evaluate prognostic factors and the effects of treatment on OS, Cox proportional hazards models were used controlling for age, sex, stage, LDH, performance status, presence of B-symptoms, race, treatment (R-CHOP vs. other), and ABC subtype. Results: Tissues for 26 black pts and 63 white pts meeting all eligibility criteria were evaluated in the TMA. There were significant differences in baseline characteristics between the two groups; 23% of black pts vs. 46% of whites were > 60 years of age (p=0.04), 73% had stage III/IV disease vs. 56% (p=0.03), and 77% had an LDH>ULN vs. 51% (p=0.04). There were no significant differences between the two racial groups in terms of sex, ECOG PS, presence of B-symptoms (38% vs. 29%p=0.46), extranodal sites (50% vs. 78% ≤ 1, p=0.27), IPI risk, or treatment received (RCHOP 46% vs. 40% p=0.93). By the Hans, Natkunam, Tally, and Choi algorithms black patients more commonly presented with the poor-risk ABC/non-GCB subtype (by Choi black 64% ABC vs. white 37%; p= 0.01, Table). After controlling for clinical confounders including age, sex, stage, LDH, performance status, presence of B-symptoms, race, treatment (RCHOP vs. other), and ABC subtype, being >60 years of age [HR 3.1 95% CI 1.3–7.2], being black (HR 3.5 95% CI 1.5–8.2), and receiving treatment other than RCHOP (HR 12.8, 95% CI 3.2–50.6) were associated with inferior OS. Conclusions: The rate of ABC DLBCL is significantly higher in black pts compared to white pts in this university-based cohort from the Southern United States. Additional studies confirming these findings in larger populations and examining the mutations associated with these differences are underway to address biological differences intrinsic to DLBCL that may in part explain comparatively adverse features and outcomes for black pts with DLBCL. Disclosures: Flowers: Celgene, Spectrum, Millennium, Gilead, Janssen: Research Funding; Genentech/Roche (unpaid), Millennium (unpaid), Celgene: Consultancy. Bernal-Mizrachi:Empire Genomics (not related to current work): Patents & Royalties. Sinha:Celgene: Research Funding. Jaye:Millenium Pharmaceuticals (For single lecture on immunohistochemical subtyping of large B cell lymphomas): Honoraria.