Farooq, Umar

3639 Background: The first phase of the multi-center prospectively specified retrospective study Validating Indicators To Associate Recurrence (VITAR), assessing the relationship between GCC gene expression in formalin fixed (FFPE) LNs and time to recurrence (TTR) in stage II CC pts not treated with adjuvant chemotherapy (Sargent, Annals Surg Onc 2011), showed promising initial results. Here we report a validation set of 463 new stage II CC pts. Methods: GCC mRNA was quantified by RT-qPCR using FFPE LNs from untreated T3N0 CC pts diagnosed from 1999-2008 with at least 12 LNs examined , blinded to clinical outcomes. Patients were classified by GCC LN ratio (LNR) (high risk: LNR > 0.1; low risk: LNR ≤ 0.1), with LNR defined as ratio of GCC positive to GCC informative LNs. Cox regression models tested the relationship between GCC and the primary endpoint of TTR, adjusted for age, tumor grade, number of LN examined pathologically, and lymphovascular invasion. Mismatch repair (MMR) status was also assessed. All primary analyses and cut-points were pre-specified. Results: 46pts (10%) recurred (rec), median follow-up was 65 months, median LNs examined was 20, and 42% (195/463) were classified high risk. Overall, TTR was not significantly associated with binary GCC LNR risk class (HR=1.47, p=.208) or DFS (HR= 1.39, p=.097). One site’s (n=97) tissue grossing method precluded appropriate LN assessment with existing GCC qualification methods. Excluding this site resulted in a TTR HR=1.91, p=0.051 (multivariate). In a post-hocanalysis excluding this site and using a 3-level GCC risk group of high (LNR > 0.20), intermediate (0.10 < LNR < 0.20) and low (LNR < 0.10), high risk group pts had a 5-yr rec risk of 22% versus 8% in low risk (HR 2.72, p=0.006). MMR status was not significantly associated with TTR (multivariate p=0.30). Conclusions: GCC status is a promising prognostic factor in appropriately staged stage II CC pts not treated with adjuvant therapy independent of traditional histopathology risk factors, but GCC determination must be performed with methodology adapted to the tissue procurement and fixation technique. Outcome associations were strengthened when considering a 3-level GCC categorization.

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&lt;0.0001) with OS24 ranging from 18% in patients with TTP &lt;6 months compared to 69% in patients with TTP &gt; 36 months (Figure A). Additionally, age at PD (spline p-value&lt;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 &lt;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 &lt;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 &lt;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: In the SCHOLAR-1 study, the largest published retrospective analysis of outcomes in patients with refractory large B-cell lymphoma (LBCL), the objective response rate (ORR) was 26% and complete response (CR) rate was 7% with available salvage therapies in the pre-chimeric antigen receptor (CAR) T cell therapy era (Crump et al. Blood. 2017). These results served as a benchmark for assessing novel therapies. ZUMA-1 (NCT02348216) is the pivotal, multicenter, single-arm Phase 1/2 study evaluating axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 CAR T cell therapy, in patients with refractory LBCL that supported the approval of axi-cel in the United States and Europe. At a median follow-up of 27.1 months, the ORR in ZUMA-1 was 83% and the CR rate was 58% (Locke et al. Lancet Oncol. 2019). Here, we describe comparative analyses of outcomes in ZUMA-1 and SCHOLAR-1 after adjusting for potential imbalances in refractory status between the 2 studies. Methods: Patients in both studies had refractory LBCL defined as stable disease of ≤ 6 months with ≥ 4 cycles of frontline or ≥ 2 cycles of later-line therapy, progressive disease as best response, or relapse ≤ 12 months post autologous stem cell transplant (SCT). To address potential imbalances between studies in refractory status which could affect response and survival outcomes, standardized analyses were performed which equally weighted the proportions of patients by refractory categorization (primary refractory, refractory to ≥ second-line therapy, or relapse after SCT) and presence of post-refractory SCT in each study. Stratified Cochran-Mantel-Haenszel (CMH) tests and Cox models were used to compare the odds ratio for response and hazard ratio (HR) for survival between ZUMA-1 and SCHOLAR-1. P values were descriptive and were not adjusted for multiplicity. Results: Axi-cel was administered to 101 patients in the pivotal Phase 2 portion of ZUMA-1. In the SCHOLAR-1 analysis, 508 patients were evaluable for response and 497 were evaluable for survival. The median follow-up in ZUMA-1 was 2.3 years, and the median follow-up from the overall SCHOLAR-1 study ranged from 7.6 to 14.8 years across different cohorts. In general, ZUMA-1 patients were more heavily pretreated with more patients receiving ≥ 3 lines of therapy as compared with SCHOLAR-1 (69% vs 23%). ZUMA-1 also had more patients who were refractory to second- or later-line therapy compared with SCHOLAR-1 (76% vs 62%). Fewer patients in ZUMA-1, however, were classified as primary refractory vs those in SCHOLAR-1 (26% vs 45%), and a similar proportion of patients between studies relapsed within 1 year of SCT (21% vs 18%). After standardization, the ORR and CR rate in ZUMA-1 vs SCHOLAR-1 were 72% and 54% vs 22% and 7%, respectively. The odds ratios for ORR and CR rate were 7.2-fold and 11.5-fold higher, respectively in ZUMA-1 vs SCHOLAR-1 (CMH test; P < .0001 for both ORR and CR rate). The 2-year survival rate after standardization was 50% (95% CI, 40% - 59%) in ZUMA-1 and 12% (95% CI, 9% - 15%) in SCHOLAR-1, which translated to a 73% reduction in the risk of death in ZUMA-1 relative to SCHOLAR-1 (HR, 0.27; P < .0001). Conclusions: This standardized analysis of the ZUMA-1 and SCHOLAR-1 studies indicates that treatment with axi-cel in this selected population results in 11.5-fold higher odds of CR and a 73% reduction in the risk of death compared with standard salvage regimens in an unselected population. Although limited by retrospective evaluation and cross-study comparisons, these results support the previous literature indicating that axi-cel is a highly effective treatment option for patients with refractory LBCL. Disclosures Neelapu: Pfizer: Consultancy; BMS: Research Funding; Cellectis: Research Funding; Precision Biosciences: Consultancy; Allogene: Consultancy; Acerta: Research Funding; Cell Medica: Consultancy; Celgene: Consultancy, Research Funding; Karus: Research Funding; Novartis: Consultancy; Poseida: Research Funding; Unum Therapeutics: Consultancy, Research Funding; Incyte: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Merck: Consultancy, Research Funding. Locke:Novartis: Other: Scientific Advisor; Kite: Other: Scientific Advisor; Cellular BioMedicine Group Inc.: Consultancy. Bartlett:Genentech, Inc.: Research Funding; Gilead: Research Funding; Autolus: Research Funding; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Affimed: Research Funding; Immune Design: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Kite Pharma: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding. Reagan:Kite, A Gilead Company: Consultancy; Curis: Consultancy; Seattle Genetics: Research Funding. Miklos:Celgene-Juno: Consultancy; Novartis: Consultancy; Adaptive Biotechnologies: Consultancy, Research Funding; Janssen: Consultancy; Miltenyi: Consultancy, Research Funding; AlloGene: Consultancy; Kite, A Gilead Company: Consultancy, Research Funding; Pharmacyclics: Consultancy, Patents & Royalties, Research Funding; Becton Dickinson: Consultancy; Precision Bioscience: Consultancy. Jacobson:Pfizer: Research Funding; Celgene: Consultancy, Other: travel support; Humanigen: Consultancy, Other: travel support; Precision Biosciences: Consultancy, Other: travel support; Bayer: Consultancy, Other: travel support; Novartis: Consultancy, Honoraria, Other: travel support; Kite, a Gilead Company: Consultancy, Honoraria, Other: travel support. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. Siddiqi:BeiGene: Research Funding; Celgene: Research Funding; TG Therapeutics: Research Funding; Kite, A Gilead Company: Research Funding; Seattle Genetics: Speakers Bureau; Janssen: Speakers Bureau; Juno Therapeutics: Consultancy, Other: travel support, Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Research Funding, Speakers Bureau. Crump:Kite/Gilead: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Servier: Consultancy. Kuruvilla:Seattle Genetics: Honoraria; Roche: Honoraria; Novartis: Honoraria; Merck: Honoraria; Karyopharm: Honoraria; Janssen: Honoraria; Gilead: Honoraria; Celgene: Honoraria; BMS: Honoraria; Astra Zeneca: Honoraria; Amgen: Honoraria; Seattle Genetics: Consultancy; Roche: Consultancy; Merck: Consultancy; Karyopharm: Consultancy; Gilead: Consultancy; Abbvie: Consultancy; BMS: Consultancy; Janssen: Research Funding; Roche: Research Funding. Van Den Neste:Gilead: Other: travel support. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Navale:Kite, A Gilead Company: Employment, Equity Ownership; Gilead: Equity Ownership; Allogene: Equity Ownership; Cellectis: Equity Ownership; Bluebird Bio: Equity Ownership; Amgen: Equity Ownership; Organesis: Equity Ownership; Jounce: Equity Ownership; Editas: Equity Ownership; Intellia: Equity Ownership. DePuy:Kite, A Gilead Company: Consultancy, Other: travel support. Kim:Kite, A Gilead Company: Employment.

Background: While there is evidence in the literature of increased prevalence of hypogammaglobulinemia in chronic lymphocytic leukemia (CLL), there are no studies evaluating the prevalence of hypogammaglobulinemia in newly diagnosed diffuse large B cell lymphoma (DLBCL) or the relationship between hypogammaglobulinemia and presentation or outcomes. The objective of this study was to examine the prevalence of hypogammaglobulinemia in newly diagnosed DLBCL patients and to test the hypothesis that DLBCL patients with baseline hypogammaglobulinemia have a distinct clinical profile and outcome. Methods: We obtained banked frozen sera from 200 newly diagnosed, treatment-naïve, DLBCL patients from the Lymphoma SPORE Molecular Epidemiology Resource (MER), a prospective cohort study conducted at the Mayo Clinic and the University of Iowa. IgG/A/M levels were measured using immunoturbidimetric assay whereas IgE level was measured using electrochemiluminescence immunoassay; deficiency was defined using standard reference ranges. IgE levels were considered deficient if &lt;2 UI/ml. The associations between Ig deficiencies and clinical factors were evaluated with Wilcoxon rank sum and chi-squared (Fisher's exact, where appropriate) tests. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death, and EFS24 was defined as EFS at 24 months after diagnosis (achieve or failure to achieve EFS24). The association of Ig levels with EFS24 was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression, and with EFS, overall survival (OS), and lymphoma-specific survival (LSS) was estimated using Kaplan-Meier curves and hazard ratios (HR) and 95% CI from Cox regression. Results: The mean age (SD) of the cohort was 65.6 (13.4) years, 54% were males and 98% of the patients were white. Over a median follow-up of five years, there were 59 (29.5%) deaths. The prevalence of hypogammaglobulinemia, defined as any deficiency, in newly diagnosed, treatment-naïve DLBCL was 22.1% (44/199) in our cohort, and the most common Ig deficiency was for IgG (&lt;700 mg/dL, 13.5%), followed by IgM (&lt;40 mg/dL, 9.0%), IgE (&lt;2 UI/ml, 7.5%) and IgA (&lt;70 mg/dL, 4.0%) (Table1). There were no statistically significant differences between Ig deficient and non-deficient patients in terms of age at diagnosis, gender, stage, cell of origin, or MYC double hit status. However, median LDH levels were higher in Ig deficient patients (228 vs 194, p&lt;0.01). Any immunoglobulin deficiency was associated with inferior EFS (HR 1.94, 95% CI 1.16-3.24) (Figure 1) and OS (HR 2.02, 95% CI 1.17-3.49), and these associations were not attenuated after adjustment for the international prognostic index (IPI). Any Ig deficiency was also associated with failure to achieve EFS24 (OR=2.13, 95% CI 1.00-4.60) after adjusting for IPI. Conclusions: To the best of our knowledge, this is the first study to report the prevalence of hypogammaglobulinemia in treatment naïve DLBCL. We found that any Ig deficiency was not uncommon in our cohort and it was associated with an inferior EFS and OS in DLBCL patients. The prevalence of hypogammaglobulinemia in DLBCL patients seems to be lower than has been described in CLL patients. While the underlying relationship between these two immunologic disorders deserves further study, our findings highlight the interaction between global immune dysfunction and emergence of a clonal B cell process. Disclosures 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. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Introduction: Front-line immunochemotherapy (IC) with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is expected to cure 60-70% of newly diagnosed DLBCL. Up to one third of newly diagnosed DLBCL patients will have relapsed or refractory (r/r) disease.1 Current standard of care for these patients is salvage chemotherapy and, if chemosensitive, to be followed by high dose chemotherapy with hematopoietic cell rescue (autoHCT).2,3 Event-free survival at 24 months (EFS24) after frontline IC is associated with excellent long-term outcomes with overall survival (OS) similar to that of age- and sex-matched controls.4 In comparison, those achieving EFS24 following autoHCT for relapsed or refractory disease may have increased risk for late-mortality compared to the general population with advanced age and intensive salvage therapy contributing to the risk of late complications.5 We sought to better characterize EFS24 after autoHCT to determine the utility of this end-point for informed clinical decisions, patient management, and future clinical trials. Methods: Patients were prospectively enrolled onto the Molecular Epidemiology Resource (MER) of the University of Iowa/ Mayo Clinic Specialized Program of Research Excellence (SPORE). Patients were followed for relapse, retreatment, and death; all events were validated by medical record review. For this analysis patients were included if they were consented within 9 months of initial diagnosis of DLBCL between 2002-2012, had received anthracycline-based IC (R-CHOP, or similar), and eventually had undergone autoHCT for r/r DLBCL. Patients with primary central nervous system (CNS) lymphoma or post-transplant lymphoproliferative disorders (PTLD) were excluded. Overall survival (OS) was defined as time from autoHCT until death due to any cause. Event-free survival (EFS) was defined as time from autoHCT until progression, relapse, retreatment, or death due to any cause. OS from achieving EFS24 after autoHCT was compared to age- and sex-matched general US population. Results: 108 patients underwent autoHCT for relapsed DLBCL between 2002 and 2017. Median age at autoHCT of the patients was 60 years (range 20-78) and 72 (67%) were male. The most common transplant conditioning regimen was BEAM (82%). At a median follow-up after autoHCT of 85 months (range 1-171), 72 patients (67%) had an event and 64 patients (59%) had died. Kaplan-Meier estimates for EFS and OS at 24 months from the time of autoHCT were 49% (95% CI: 40-59) and 61% (95% CI: 52-71), respectively. 52 patients had a progression within 24 months of autoHCT; OS from progression was poor (median OS=2.8 months, 95% CI: 1.8-6.0; 5 year OS = 9%, 95% CI: 2-22). 48 patients achieved EFS24 after autoHCT; median OS from achieving EFS24 was 136 months (95% CI: 92-NE) and 5 year OS was 79% (95% CI: 68-93). This was inferior to the background population (Figure 1, SMR=3.64, 95% CI: 2.11-6.27, p<0.0001). Eight patients had a progression after achieving EFS24 from autoHCT. OS from progression in these 8 patients (median OS=27.3 months, 95% CI: 14.4-NE; 5 year OS = 16%, 95% CI: 3-93) was improved compared to progression within 24 months of autoHCT (p=0.072, figure B). Cause of death (COD) in EFS24 achievers was progression of lymphoma (n=6), infection (n=1), secondary malignancy related to therapy (n=3), heart disease (n=1), and unknown (n=1). Conclusions: Patients achieving EFS24 after salvage chemotherapy and autoHCT have a favorable long-term prognosis; however, overall survival remained inferior to the general population. Most common COD after achieving EFS24 was progression of lymphoma. In spite of this, EFS24 remains a valuable end-point for informed clinical decisions, patient management, and future clinical trials. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding.

Introduction: Allogeneic hematopoietic stem cell transplant is curative in several benign and malignant hematological disorders with predominant complications of graft versus host disease (GVHD) and infections. Antithymocyte globulin (ATG) has been shown in a number of prospective trials to be effective in decreasing acute and chronic GVHD. Recently, it has been suggested that recipient peripheral blood absolute lymphocyte count (ALC) on the day of ATG administration interacts with the dose of ATG administered and predicts transplantation outcomes. This interaction may depend on the preparative regimen and the dose of ATG used. We sought to determine if ALC at time of administration of ATG could impact clinical outcomes in a population treated with busulfan fludarabine myeloablative regimen and a fixed dose of 4mg/kg of ATG. Methods: Seventy consecutive pts who underwent a matched unrelated donor transplant with ATG at the University of Iowa were retrospectively analyzed. All pts received rabbit ATG (Thymoglobulin®) at 0.5 mg/kg on day -3, 1.5 mg/kg on day -2 and 2 mg/kg on day -3 in addition to tacrolimus and methotrexate. All received a myeloablative preparative regimen with busulfan (130 mg/m2 days -6 to -3) and fludarabine (40 mg/m2 daily for 4 days). All but one pt received peripheral blood as graft source. The effect of patient, disease, and transplant characteristics on 2-year outcomes was evaluated using Cox and Fine and Gray regression models. For relapse, GVHD, and infections, death was considered a competing risk. Estimated effects of predictors are reported as hazard ratios (HR) along with 95% confidence intervals. Results: Median age was 59 years (range 20-68). Underlying diagnoses were AML (n=47), MDS (n=20) and CML (n=3). Disease Risk index showed 25 high risk, 24 intermediate risk and 21 low risk patients in this study. The median peripheral blood ALC on day -3 was 100 × 102/µL (range 0-1144 × 102/µL). Univariate analysis showed no significant impact of ALC (100+ vs <100) on 2-year risk of developing GVHD (HR 1.49, 95%CI: 0.77 - 2.86), 2-year overall infection risk (HR 0.86, 95%CI: 0.51-1.46), 2-year bacterial (HR 0.78, 95%CI: 0.43-1.42), viral (HR 0.81, 95%CI: 0.41-1.60) and fungal (HR 0.63, 95% CI: 0.17-2.33) infections. Univariate analysis also showed no significant influence on 2-year relapse-free survival (HR 0.67, 95%CI: 0.21-2.14) and overall survival (HR 0.84, 95%CI: 0.34-2.06). Significantly worse survival is seen in MDS in comparison to leukemic pts, and high disease risk in comparison to low disease risk. Conclusion: Our single center experience using busulfan and fludarabine myeloablative regimen and rabbit ATG dosed at 4 mg/kg showed no interaction between the recipient peripheral blood ALC and ATG to predict GVHD, infections, overall and relapse free survival. Disclosures Farooq: Celgene: Honoraria; Kite Pharma: Research Funding.

Abstract Backgroud. The treatment landscape of mantle cell lymphoma (MCL) has significantly changed in last decades. Improvement in diagnosis and understanding of disease biology has been coupled with emergence of new therapeutic options, including targeted agents. While MCL outcome data comes primarily from clinical trials (CT), the impact of therapeutic advances on pattern of care (POC) and outcome in MCL in the general population is not well characterized. This study sought to characterize changes in pattern of care and outcomes of patients with MCL in a prospective observational series in the rituximab era. Methods. The study included consenting adult patients with newly diagnosed MCL that were prospectively enrolled into the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) Molecular Epidemiology Resource, from 09/01/2002 to 06/30/2015. Demographic, clinical and prognostic factors were abstracted, and all patients were actively followed for re-treatment, relapse and death. Since bendamustine-rituximab (BR) regimen starting to be used in 2010, we defined patients enrolled from 09/01/2002 to 12/31/2009 as era 1 and those enrolled 01/01/2010 to 06/30/2015 as era 2. Baseline characteristics and outcomes, evaluated in terms of event free survival (EFS), overall survival (OS) and cause of death, were analyzed and compared between the two eras identified. Results. 348 patients with newly diagnosed MCL were enrolled. Five patients had no available data for front-line treatment and were excluded. The analysis was thus conducted on 343 patients: 169 patients were diagnosed in era 1 with a median follow-up of 131.2 months vs. 174 in era 2 with a median follow-up of 58.9 months. Baseline clinical characteristics and MIPI score were similar across the two eras ( Table 1). Frontline induction treatment was significantly different in the two eras. BR use was 0 vs 49 (28.2% ), R-CHOP/CHOP like regimen in 89 (52.7%) vs 45 (25.9%), high-dose cytarabine (HiDAC)-based therapy in 1 (0.6%) vs 28 (16.1%), intensified regimens (HyperCVAD) in 16 (9.5%) vs 8 (4.5%), other regimens (including R-cladribine, R-fludarabine-mitoxantrone, rituximab monotherapy) in 35 (20.7%) vs 13 (7.5%) patients in era 1 vs era 2 respectively. Non-systemic treatment (observation, surgery or radiation only) was performed in 25 (14.8%) vs 31 (17.8%), while 9 (8%) vs 12 (7.7%) patients were enrolled in clinical trials, in era 1 vs era 2 respectively. Autologous stem cell transplantation (ASCT) as consolidation of first line treatment or use of Rituximab maintenance was not different between Era 1 and Era 2. Among the entire cohort of 343 MCL patients, 3y-EFS and 3y-OS were 51.9% (95% IC 46.7-57.6) and 73.5 (95% CI 68.8-78.4), respectively (Figure 1). 3y-EFS was 45.9% (95% CI 39.0-54.1%) vs 58.4 (95% CI, 51.2-66.6%) in Era 1 vs Era 2 (HR 0.69 (0.51-0.92), p=0.006), 3y-OS was 70.9% (95% CI, 63.4-78.1%) vs 76.1% (95% CI, 69.7-83.1%) in Era 1 vs Era 2 (HR 0.87 (0.61-1.24), p=0.26), respectively (Figure 1). In a univariate analysis, high risk simplified MIPI was prognostic of lower EFS and OS in both the era groups. Conclusion. The BR regimen entered front line therapy of MCL resulting in decline of R-CHOP use in the MER. While rates of ASCT remain similar over the two eras, high dose cytarabine usage in induction therapy has increased. While POC in MCL continue to evolve, the introduction of bendamustine and high-dose citarabine based regimens resulted in an improvement in EFS but not OS in this observational cohort-based analysis. Findings in this study are important for design and planning of future clinical trials incorporating novel agents in induction therapy of MCL. Figure 1. Figure 1. Disclosures Cohen: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. 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: Axi-cel is a US FDA-approved, autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy for the treatment of patients with relapsed or refractory large B cell lymphoma with ≥ 2 prior systemic therapies. This report focuses on high-risk patient populations as well as long-term durability of response, and B cell recovery. Methods: In ZUMA-1, eligible patients with refractory large B cell lymphoma (diffuse large B cell lymphoma, primary mediastinal B cell lymphoma, transformed follicular lymphoma) underwent leukapheresis and received low-dose conditioning followed by a target dose of 2 × 106 anti-CD19 CAR T cells/kg (Neelapu and Locke et al. NEJM. 2017; NCT02348216). Outcomes in patients with double-expressor B cell lymphoma (MYC [≥ 40%] and BCL-2 [≥ 50%] protein expression by immunohistochemistry [IHC]) or high-grade B cell lymphoma (HGBCL), defined as double- or triple-hit (MYC+ and BCL2+ and/or BCL6+ by fluorescence in situ hybridization) or not otherwise specified (MYC- and > 70% Ki67 by IHC) were examined by independent pathology review. Exploratory analyses, including normal B cell levels in peripheral blood over time and frequency of use of safety interventions of interest, were also performed. A long-term follow-up analysis will be conducted with a data cutoff of August 11, 2018 for all 108 patients, including the HGBCL subgroup. Results: As of August 11, 2017, all 108 patients had at least 1 year of follow-up, with a median follow-up of 15.4 months. The objective response rate (ORR) was 82% with a complete response (CR) rate or 58%. The CR rate was 53% (29/55) in patients with disease refractory to ≥ 2 consecutive prior lines of therapy and 72% (18/25) in patients who had relapsed within 12 months after autologous stem cell transplantation. High-risk genetics were assessed in the 47 evaluable pre-treatment tumor samples: 37 patients (79%) had HGBCL or double-expressor B cell lymphoma and had an ORR of 89% (33/37) including a CR rate of 68% (25/37). Forty-two percent of patients overall had ongoing responses with a median follow-up of 15.4 months including 49% (18/37) of patients with high-risk genetics. To investigate the relationship between B cell recovery and ongoing response, B cell levels were assessed over time. Overall, of the 87 evaluable patients, 47% had no detectable B cells at baseline, and the remainder had levels close to or below the lower level of quantification of the assay. In patients with ongoing responses at 12 months post axi-cel infusion, 19 of the 35 (54%) patients with evaluable samples had detectable B cells at 12 months. This suggests B cell recovery in some patients with ongoing response as only 6 of 40 (15%) patients with evaluable samples had detectable B cells at 3 months after axi-cel infusion. Grade ≥ 3 cytokine release syndrome (CRS) and neurologic events (NE) were generally reversible and reported for 12% and 31% of patients, respectively. Tocilizumab (45%) and/or steroids (28%) were used for the management of CRS and NE. The use of vasopressors, dialysis, and intubation were minimal, used in only 17%, 3%, and 3% of patients, respectively. Thirty-four percent of patients (37/108) had either no CRS/NE (6%) or only Grade 1 CRS with (12%) or without (16%) Grade 1 NE. For these 37 patients, the median onset of CRS and NE was 2 and 7 days after infusion, respectively. Patients with Grade 0 - 1 CRS/NE had similar efficacy (ORR, 86%; CR, 65%) but lower peak/AUC CAR T cell levels vs the overall population. Updated safety and efficacy results will be presented with a minimum follow-up of 2 years and a median follow-up of 27.1 months. Conclusion: High rates of durable response were observed in patients with HGBCL and double-expressor B cell lymphoma, with approximately half of these patients (18/37) maintaining CR at ≥ 1 year. Efficacy in these high-risk populations was comparable to the overall patient population in ZUMA-1. At 12 months, B-cell recovery was observed in over half the patients with ongoing remission. About one-third of patients had only Grades 0 - 1 CRS/NE yet showed comparable efficacy with the overall patient population. High-grade CRS/NE were largely managed with tocilizumab and steroids with very low use of intensive or invasive interventions. Updated outcomes with a minimum of 2 years of follow-up will be presented. Disclosures Neelapu: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; Poseida: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Karus: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Miklos:Adaptive Biotechnologies: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pharmacyclics - Abbot: Consultancy, Research Funding; Kite - Gilead: Consultancy, Research Funding; Genentech: Research Funding; Janssen: Consultancy, Research Funding. Hill:Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Deol:Novartis: Consultancy; Kite Pharmaceuticals: Consultancy. Reagan:Seattle Genetics: Research Funding. Flinn:Agios: Research Funding; Forty Seven: Research Funding; Celgene: Research Funding; Calithera: Research Funding; Portola: Research Funding; Kite: Research Funding; Forma: Research Funding; Genentech: Research Funding; Verastem: Research Funding; BeiGene: Research Funding; Incyte: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Janssen: Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; ArQule: Research Funding; Constellation: Research Funding; Infinity: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Curis: Research Funding; Pharmacyclics: Research Funding; Verastem: Consultancy, Research Funding; Takeda: Research Funding; Pfizer: Research Funding. McSweeney:Kite, a Gilead Company: Honoraria, Membership on an entity's Board of Directors or advisory committees. Munoz:Alexion: Consultancy; Genentech: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria; Janssen: Consultancy; Kite: Consultancy, Honoraria, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Gilead: Speakers Bureau; Pfizer: Consultancy; Bristol-Myers Squibb: Consultancy. Siddiqi:Juno Therapeutics: Other: Steering committee. Herrera:Bristol-Myers Squibb: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Seattle Genetics: Research Funding; Pharmacyclics: Consultancy, Research Funding; AstraZeneca: Research Funding; Gilead Sciences: Research Funding; Merck, Inc.: Consultancy, Research Funding; KiTE Pharma: Consultancy, Research Funding; Immune Design: Research Funding. Xue:Kite, a Gilead Company: Employment. Jiang:Kite, a Gilead Company: Employment. Bot:KITE: Employment. Rossi:KITE: Employment. Kim:Kite, a Gilead Company: Employment. Go:KITE: Employment. Locke:Cellular BioMedicine Group Inc.: Consultancy; Novartis Pharmaceuticals: Other: Scientific Advisor; Kite Pharma: Other: Scientific Advisor.

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.