Larson, Melissa C.

e24030 Background: Physical activity (PA) in cancer survivors improves quality of life (QOL), functioning, fatigue, and reduces the risk of treatment complications, cancer recurrence and death. However, the optimal intervention for increasing PA is not established. Most prospective studies have shown a 6-12-week program to be an effective intervention but this is often not feasible. Therefore, we piloted a one-time individualized exercise prescription in our cardiac rehabilitation center to improve PA in cancer survivors. Methods: We prospectively enrolled cancer survivors who had completed curative intent treatment, with no evidence of active disease in this pilot study. Survivors who consented underwent a consultation with an exercise physiologist for needs assessment followed by a supervised exercise session with a tailored exercise prescription. Survivors also filled out surveys assessing their PA and QOL at baseline (bl), 3, 6 and 12 months after intervention. Clinical information was collected via chart review. We estimated longitudinal PA score and change in PA using mixed models incorporating scores from all available time points using SAS (v 9.4). Results: Between May 2018 and January 2020, 50 participants (26 lymphoma and 24 solid tumor survivors) completed the intervention. 20% participants were on maintenance therapy during the study. Clinical characteristics of 42 evaluable participants are summarized in Table. The survey response rate was 82%, 58%, 58%, 46% at bl, 3, 6 and 12 months respectively. The level of PA improved with time [mean (SE) PA score: 58.5 (4.3) bl, 63.9 (4.8) at 3, 57.6 (4.8) at 6, 62.6 (5.3) at 12 months]. The change in PA from baseline to follow-up time-points [bl vs 3m p=0.41; bl vs 6m p=0.88; bl vs 12m p=0.55] or between the lymphoma and solid tumor survivors was not statistically significant and limited by sample size. No significant trend in QOL was seen. Conclusions: Individualized exercise prescription using the cardiac rehabilitation program may be a feasible, widely applicable tool to implement a PA intervention among cancer survivors. The trend towards improvement in PA in this novel one-time intervention provides intriguing evidence and deserves future study in larger sample sizes to understand if it can improve and create sustainable PA change comparable to longer term exercise interventions.[Table: see text]

AbstractThe development of cardiovascular disease (CVD) in long‐term survivors of lymphoma is of increasing importance. Here, we characterize the cumulative incidence and risk factors for CVD in lymphoma patients diagnosed in the current treatment era. From 2002–2015, newly diagnosed lymphoma patients (>18 years) were enrollment into a prospective cohort study that captured incident CVD, consisting of congestive heart failure (CHF), acute coronary syndrome (ACS), valvular heart disease (VHD), and arrhythmia. The cumulative incidence of CVD was calculated with death modeled as a competing risk. We estimated the association of treatment with anthracyclines or radiotherapy and traditional CVD risk factors with incidence of CVD using hazard ratios (HR) and 95% confidence intervals (CI) estimated from Cox regression. After excluding prevalent CVD at lymphoma diagnosis, the study consisted of 3063 patients with a median age of 59 years (range 18–95). The cumulative incidence of CVD at 10‐years was 10.7% (95% CI, 9.5%–12.1%). In multivariable analysis, increasing age (HR = 1.05 per year, p < 0.001), male sex (HR = 1.36, p = 0.02), current smoker (HR = 2.10, p < 0.001), BMI > 30 kg/m2 (HR = 1.45, p = 0.01), and any anthracycline treatment (HR = 1.57, p < 0.001) were all significantly associated with risk of CVD. Anthracyclines were associated with increased risk of CHF (HR = 2.71, p < 0.001) and arrhythmia (HR = 1.61, p < 0.01), but not VHD (HR = 0.84, p = 0.58) or ACS (HR = 1.32, p = 0.24) after adjustment for CVD risk factors. Even in the modern treatment era, CVD remains common in lymphoma survivors and preventive efforts are required that address both treatment and CVD risk factors.

Abstract Background: Most indolent non-Hodgkin lymphomas (NHL) are considered incurable and are characterized by relapse and remission. Quality of life (QOL) can be affected by disease burden, side effects of treatment, and psychosocial effects of living with an incurable cancer. However, little is known about QOL in this population. We sought to describe QOL at diagnosis, change in QOL over time, and determinants of QOL in patients with indolent NHL. Methods: Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource. Eligibility criteria included age ≥18 years; exclusion criteria included HIV-positive, non-US residency, and inability to complete surveys in English. All pathology was reviewed by a hematopathologist. Patients with indolent B-cell NHL were included in this analysis. QOL was measured at baseline (BL) and 3 year follow-up (FU3) with the Functional Assessment of Cancer Therapy-General scale (FACT-G), which measures 4 QOL domains: physical, social/family, emotional, and functional well-being (WB), and a single item Linear Analogue Self-Assessment (LASA) for measuring overall QOL. Patients who did not complete ≥80% of FACT-G questions were excluded. Clinical data were abstracted according to a standard protocol and patients were systematically followed for events (progression, re-treatment, and death). CLL patients were excluded for event analysis due to differing definitions of outcome (progression/retreatment vs. time to first treatment). Treatment was defined as any systemic therapy (including chemotherapy, targeted agents, or antibody therapy) or radiation. QOL scores were transformed to a 0-100 scale with 100 representing the best WB or QOL for analysis. Change in QOL was assessed using the raw difference in QOL scores (i.e., FU3-BL) on the 0-100 scale with positive change scores representing improvements; minimally important difference was defined as 7 points on the transformed FACT-G subscales per previously established cutoffs (Yost and Eton, Eval & Health Prof. 2005 Jun;28:172-91). Changes in QOL from BL to FU3 were assessed using one-sample t-test of change scores vs zero. Results: 1050 patients with indolent NHL enrolled between 9/02-12/2012 and with both pre-treatment and 3 year QOL were included. QOL was collected after initiation of therapy in 215 patients (20%) and prior to therapy in 835 (80%). Subtype was 41% CLL/SLL, 32% grade 1-2 FL, 14% extranodal marginal zone, and 13% other (lymphoplasmacytic lymphoma and other). Median age was 62 years and 55% were male. At the FU3 assessment, 577 patients (55%) had received treatment, 42 (4%) transformed, and 53 (25%) had an event. Emotional WB significantly improved from BL to FU3 (mean +5.9, SD 13.3, p&lt;0.001) while social/family WB significantly decreased (mean -4.8, SD 20.5, p&lt;0.001). Functional WB, physical WB, overall FACT-G, and overall QOL LASA were not significantly different at FU3. These findings were similar in those who were treated vs. those who were observed. We also assessed the results stratified by non-CLL patients who had progression or retreatment between BL and FU3. In patients with an event in the first 3 years, emotional WB had a significant improvement from BL to FU3 (mean +3.6, SD 13.6, p=0.002) and overall QOL LASA decreased (mean -4.0, SD 20.0, p=0.01). In patients without an event, improvements were reported in functional WB (mean +2.1, SD 18.2, p=0.01), physical WB (mean +2.5, SD 13.9, p=0.001), emotional WB (mean +7.0, SD 15.0, p&lt;0.001), and overall QOL LASA (mean +1.1, SD 11.4, p=0.03), while social/family WB decreased (mean -6.2, SD 20.3, p&lt;0.001). Sensitivity analysis showed that results were consistent in the subset of patients who had BL QOL collected prior to initiating therapy. While many changes were statistically significant, they were small in magnitude; only the improvement in emotional WB in the entire cohort approached clinical significance and met that criterion in those without an event. Conclusions: In this large sample of patients with indolent lymphoma, QOL generally remained stable from diagnosis to FU3 while emotional WB showed improvement, suggesting psychosocial adaptation by the patient. Mean score changes in functional WB, physical WB, emotional WB, social/family WB, and overall QOL were more pronounced in patients without an event in the first 3 years since diagnosis. Disclosures Shanafelt: Hospira: Research Funding; AbbVie: Research Funding; Celgene: Research Funding; GlaxoSmithKline: Research Funding; Genentech: Research Funding; Jannsen: Research Funding; Pharmacyclics: Research Funding.

Abstract Introduction: Although increased physical activity (PA) is associated with improved quality of life in cancer survivors, knowledge about the impact of PA on survival in lymphoma patients is limited. We studied the impact of usual adult PA and PA at 3 years after diagnosis on overall (OS) and lymphoma-specific (LSS) survival in lymphoma patients. Methods: Lymphoma patients were prospectively enrolled within 9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource (MER) cohort. At baseline, patients reported their usual level of mild, moderate and strenuous PA during most of their adult life and (for survivors) again at the 3 year follow-up (FU3), which we used to calculate the Godin Leisure Score Index (LSI). At FU3, patients also self-reported change in PA since diagnosis as increase, decrease or no change. LSI was modeled as a continuous score (per 10-point change) and by tertile. Survival was measured as time from diagnosis (for baseline PA) and time from FU3 (for FU3 PA) until death due to any cause and due to lymphoma. We evaluated the association of PA with outcome using Kaplan-Meier curves as well as hazard ratios (HRs) and 95% confidence intervals (CI) from Cox models stratified by lymphoma subtype and adjusted for age and sex (and baseline PA for change models). Results: Of 4087 patients enrolled in the MER at the Mayo Clinic from 2002-2012, 3129 had baseline PA data (3060 evaluable for LSI), and 1845 (1395 evaluable for LSI) had FU3 PA data. In the baseline cohort, 57.6% of the patients were male. The median age at diagnosis was 62 years (range 18-92 years). The majority had ECOG performance status &lt;2 (95%), no B-symptoms (85.8%) and advanced disease (63% stage III-IV) at diagnosis. The subtypes included were CLL/small lymphocytic (25.9%), follicular (18.1%), diffuse large B-cell (16.8%), marginal zone (8.1%), Hodgkin (6.9%), mantle cell (4.8%), T-cell (5%) and other (14.3%) lymphomas. The median LSI was 28 (range 0-57) at baseline and 25 (range 0-67) at FU3 with a median change in LSI of -2 (range -57 to 61). At a median follow-up of 84 months from diagnosis (IQR 59-119 months) in the cohort with baseline PA, there were 806 total deaths (271 in pts with FU3 PA) and 423 lymphoma deaths (104 in pts with FU3 PA). When analyzed as a continuous variable, baseline LSI was associated with OS (HR 0.95, 95% CI 0.91-0.99) and LSS (HR 0.95, 95% CI 0.90-1.01). LSI by tertiles was associated with OS (p&lt;0.0001, Figure 1a), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.81, 95% CI 0.68-0.97) and LSS (HR 0.81, 95% CI 0.63-1.04). For prognosis after 3 years, continuous LSI at FU3 was also associated with superior OS (HR 0.84, 95% CI 0.77-0.92) and LSS (HR 0.76, 95% CI 0.65-0.89). LSI at FU3 by tertiles was associated with OS (p&lt;0.0001, Figure 1b), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.51, 95% CI 0.34-0.77) and LSS (HR 0.33, 95% CI 0.16-0.67). A change in LSI from baseline to FU3 PA was associated with superior OS (HR 0.84, 95% CI 0.76-0.92) and LSS (HR 0.74, 95% CI 0.64-0.87) after FU3. Change in LSI by tertiles was associated with OS (p=0.0007, Figure 1c). Compared to patients with stable LSI (change of LSI between -10 and 5 points from baseline, n=429), patients who increased LSI by &gt; 5 (n=464) had improved OS (HR 0.67, 95% CI 0.45-1.00) and LSS (HR 0.47, 95% CI 0.23, 0.94), while patients who decreased LSI by &gt;10 (n=477) had no association with OS or LSS. Self-reported change in PA from baseline to FU3 was associated with OS (p&lt;0.0001, Figure 1d); compared to no change, an increase in PA was not associated with OS (HR 0.79, 95% CI 0.42-1.48) or LSS (OR 0.73, 0.26-2.07), while a reduction in PA was associated with inferior OS (HR 1.94, 95% CI 1.49-2.53) and LSS (HR 2.51, 95% CI 1.62-3.88). After excluding patients with any events through FU3, the associations for the PA change and OS remained significant for change in LSI (HR 0.91, 95%CI 0.83-1.00) and self-reported decrease in PA (HR 1.83, 95%CI 1.32-2.53). Conclusions: Patients with a higher level of usual PA during adult life have significantly better OS and LSS after lymphoma diagnosis compared to those who are less physically active. Higher PA in 3-year survivors is also associated with improved survival, while a reduction in physical activity is associated with worse outcomes. These data support an important role for PA in lymphoma survivorship and also provide a strong rationale for a PA intervention trial in lymphoma patients. Figure 1 Figure 1. Disclosures Cohen: LAM Therapeutics, Inc: Research Funding; Takada: Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bioinvent: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees. Flowers: Spectrum: Consultancy; OptumRx: Consultancy; Acerta: Research Funding; Onyx: Research Funding; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; National Cancer Institute: Research Funding; Burroughs Welcome Fund: Research Funding; Genentech/Roche: Consultancy, Research Funding; Gilead: Consultancy; Research to Practice: Research Funding; Millennium/Takeda: Research Funding; National Institutes Of Health: Research Funding; Prime Oncology: Research Funding; Educational Concepts: Research Funding; Abbvie: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Seattle Genetics: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Celgene: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Clinical Care Options: Research Funding. Cerhan: Janssen: Other: Scientific Advisory Board (REMICADELYM4001); Janssen: Other: Multiple Myeloma Registry Steering .

AbstractNon‐steroidal anti‐inflammatory drugs (NSAIDs) and statin drugs may protect against the development of non‐Hodgkin lymphoma (NHL), but data are limited, particularly for NHL subtypes. Furthermore, some in vitro, animal and epidemiologic data suggest there may be a synergistic effect of these two agents, but there has been no test of this hypothesis in NHL. We evaluated the self‐reported use of NSAIDs and statins in a clinic‐based study of 1703 NHL patients and 2199 frequency‐matched controls. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusted for potential confounding variables. We observed an inverse association of regular use of low‐dose aspirin with risk of NHL (OR = 0.82; 95% CI 0.70‐0.96) that was stronger with longer duration of use (P < .01). There were no associations for use of regular or extra‐strength aspirin, ibuprofen, other NSAIDs, statins or other cholesterol‐lowering drugs with NHL risk, while an inverse association with COX‐2 inhibitors was equivocal. There was also no interaction of low‐dose aspirin and statins on NHL risk. Inverse associations of similar magnitude to all NHL were observed for regular use of low‐dose aspirin with diffuse large B‐cell, follicular, marginal zone and all other lymphomas, although not all associations were statistically significant. In conclusion, low‐dose aspirin but not regular/extra strength aspirin, other NSAIDs or statin use was associated with lower risk of NHL. Beyond the potential for the primary prevention of NHL, these data also point to a role of anti‐platelet or other effects of low‐dose aspirin in lymphomagenesis that warrant follow‐up.

Background: Obesity is increasing worldwide, with the highest prevalence in the United States. High or low body mass index (BMI) is a well-established risk factor for increased all-cause mortality and also has been associated with cancer-specific mortality. However, the impact of BMI on survival following diagnosis with lymphoma currently remains controversial. We leveraged a prospective cohort of lymphoma patients to assess the relationship of BMI two years prior to diagnosis (BMI-2), at diagnosis (BMI-dx), and three-years post-diagnosis (BMI+3) with lymphoma-specific survival (LSS) as the primary endpoint and with event-free survival (EFS) and overall survival (OS) as secondary endpoints. Patient and Method: Patients were prospectively enrolled at lymphoma diagnosis to the SPORE Molecular Epidemiology Resource (MER) cohort at Mayo Clinic and University of Iowa from 2002-2015. BMI-2 and BMI+3 were self-reported in patient questionnaires, while BMI-dx was extracted from the medical chart. Patients with extreme BMI (BMI &lt;14 and BMI ≥50) were excluded from the analysis. BMI change from BMI-2 to BMI-dx and from BMI-dx to BMI+3 was categorized as no change (-5% to 5%), decrease (&gt;-5%), and increase (&gt;+5%). Person-time at risk was assessed from lymphoma diagnosis until death or last follow-up, except for analyses of BMI change from BMI-dx to BMI+3, which started person-time at risk when the 3-year (+/- 6 months) follow-up questionnaire was returned. Cause of death was assigned by a study clinician. For all lymphoma patients combined and in the most common subtypes, we evaluated the association of BMI at each time point and change in BMI with EFS, LSS, and OS using hazard ratios (HRs) and 95% confidence intervals (CI) from multivariable adjusted Cox models. Results: A total of 4,009 lymphoma patients (including 670 diffuse large B-cell lymphoma [DLBCL], 689 follicular lymphoma [FL] and 1018 chronic lymphocytic leukemia/small lymphocytic lymphoma [CLL/SLL] and 1,632 others) with data on BMI-dx were included. Among them, 2,955 patients had BMI-2 and 2,004 had BMI+3 and were evaluable for change in BMI. The median age of all patients at diagnosis was 61 years (range 18-92 years), and 94% of patients had ECOG performance status &lt;2. At the time of diagnosis, 28% were normal weight (BMI 18.5-25), 1% were underweight (BMI &lt;18.5), 39% were overweight (BMI 25-30) and 32% were obese (BMI ≥30). With a median follow-up of 108 months from diagnosis (IQR 83-143 months), 1320 deaths were observed, 48% of which were due to lymphoma. Patients with FL who were obese at BMI-2 had significantly shorter LSS (HR: 3.02, 95%CI: 1.43-6.41, p=0.004). Associations between obesity at BMI-2 and LSS were not evident for DLBCL (HR: 1.04, 95%CI: 0.62-1.76, p=0.879) or CLL/SLL (HR: 1.10, 95%CI: 0.71-1.70, p=0.668) (Table). BMI-dx was not associated with LSS in any lymphoma patients, except that DLBCL patients who were underweight at BMI-dx (n=10) experienced shorter LSS (HR: 3.52, 95%CI: 1.22-10.1, p=0.020). This correlated significantly with presence of B symptoms (p=0.004) and may signify aggressive disease. Across all subtypes, &gt;5% decrease in BMI from BMI-2 to BMI-dx was associated with significantly shorter LSS in patients with (HR: 2.02, 95%CI: 1.65-2.48, p&lt;0.001). However, only for FL patients, &gt;5% increase in BMI from BMI-dx to BMI+3 also was associated with significantly shorter LSS in subsequent years (HR: 3.74, 95%CI: 1.30-10.8, p=0.014). The associations reported for LSS generally were similar for EFS and OS. Conclusions: FL patients with obesity prior to diagnosis or who experienced increasing BMI after the diagnosis had significantly shorter LSS. The impact of weight control after the diagnosis of FL patient outcomes warrants investigation. Figure Disclosures Maurer: Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Kite: Research Funding; Bayer: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

Introduction: Lymphoma is the most common cancer among adolescents and young adults (AYAs). We examined changes in health-related quality of life (HRQoL) and its predictors in AYA patients (pts). Patients and Methods: We identified AYA pts (aged 18-39) enrolled 2002-2015 in a prospective cohort of pts with newly diagnosed lymphoma from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, part of the Lymphoma Epidemiology of Outcomes cohort. Enrollment could occur prior to or after initiation of treatment. We measured HRQoL using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at baseline, 12, and 24 months. FACT-G yields five HRQoL domain scores: emotional well-being (EWB), functional WB (FWB), physical WB (PWB), social/family WB (SFWB), and total FACT-G score (a sum of the domains). Pts completing &lt;80% of the FACT-G questions were excluded. Linear mixed models with random subject intercepts estimated changes in FACT-G scores from baseline. The covariates in multivariate analysis were lymphoma subtype, stage, and treatment. Interaction effects between treatment (chemotherapy and/or radiation) and subtype were added to the model. We calculated effect sizes (ES) for the magnitude of mean change scores: 0.2, 0.5, and 0.8 were considered small, medium, and large ESs, respectively. Only ESs for mean score differences with p&lt;0.05 are reported. Results: We identified 467 pts; median age at diagnosis was 30 years, median follow up was 5.9 years. 53% of pts completed the baseline FACT-G assessment pre-treatment, 47% completed after treatment began. Pts assessed after treatment initiation had lower baseline total FACT-G (ES -0.25), FWB (ES -0.27), and PWB scores (-0.46); but baseline EWB was higher in pts assessed prior to treatment (ES 0.20). There was no association between HRQoL scores at baseline or over time and lymphoma subtype, stage, or treatment type, or interactions. Total FACT-G scores modestly improved over time, ES 0.32 at 1 year and ES 0.45 at 2 years after enrollment. EWB, FWB, and PWB also improved over time (ES 0.36, 0.44, 0.30 at 1 year; and 0.49, 0.56, 0.38 at 2 years, respectively). SFWB scores slightly worsened over time (ES -0.24 at 1 year and -0.12 at 2 years). Conclusions: AYA pts with lymphoma had higher baseline total FACT-G scores, FWB and PWB prior to therapy initiation compared to after initiation. HRQoL improved from diagnosis through the first 2 years after diagnosis, except for SFWB. Neither stage, lymphoma subtype, nor treatment type affected change in HRQoL. The lack of improvement in SFWB suggests social interventions and future studies should examine factors impacting SFWB in AYA pts. Disclosures Cohen: Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding; Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Bayer: Consultancy. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Background: Aggressive lymphomas are potentially curable; however, long-term effects of treatment and the disease may adversely affect patients' quality of life (QoL). We investigated QoL at baseline, and up to nine years after diagnosis in survivors of aggressive lymphomas. Methods: Patients newly diagnosed with lymphoma were prospectively enrolled within nine months of diagnosis in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) and systematically followed. Patients were enrolled from September 1, 2002 until June 30, 2015. Eligibility criteria included being a United States resident, English-speaking, no history of HIV, and aged 18 years and older. All pathology was reviewed by a lymphoma hematopathologist. Aggressive lymphoma subtypes included in this analysis were diffuse large B-cell lymphoma (DLBCL), Follicular lymphoma grade III, Hodgkin lymphoma, T-cell lymphoma, other non-Hodgkin lymphoma not otherwise specified (NOS), other B-cell lymphoma NOS, and composite lymphomas. We assessed QoL using the Functional Assessment of Cancer Therapy-General (FACT-G) scale at baseline enrollment, and 1, 2, 3, 6, and 9 years post-diagnosis. FACT-G measures well-being (WB) in four domains-physical (PWB), social/family (SFWB), emotional (EWB), and functional (FWB), which are added to create a total (TOT) score. Those who completed &lt;80% of FACT-G questions at any given time point were excluded. "Survivor" was defined as alive at 1, 2, 3, 6, and 9 year follow-up with no active disease or treatment within six months of a given time point. Because patients could be enrolled at any time within the first 9 months from diagnosis, the timing of initial QoL assessment was variable with respect to the start of lymphoma treatment. We estimated longitudinal change in QoL using mixed models incorporating scores from all available time points. The minimally important difference (MID) of 5 points for TOT and 2 points for each of the domains was used to determine clinical significance. Scores were compared to United States (US) general population normative data. All statistical analyses were performed using SAS (v 9.4). Results: From September 1, 2002 to June 30, 2015, 3,028 patients with aggressive lymphomas were prospectively enrolled in the MER, of which 2,018 completed the FACT-G baseline assessment (47% prior to therapy initiation). Of these patients, 1,144 at 1 year, 1,002 at 2 years, 943 at 3 years, 562 at 6 years, and 289 at 9 years post-diagnosis completed the FACT-G at both baseline and each of the respective time points after diagnosis, and met the definition of survivor. The median age at diagnosis of patients analyzed was 59 years (range 18-93). The cohort included 844 female patients (42%). DLBCL comprised 44% of cases, Hodgkin lymphoma 19%, T-cell lymphoma 11%, composite lymphomas 8%, other B-cell NOS 6%, other non-Hodgkin lymphoma NOS 7%, and Follicular lymphoma grade III 5%. QoL increased from baseline enrollment; the largest increase was seen from baseline to 1 year after diagnosis with a statistically and clinically significant mean TOT score difference of 6.7 points. This mean increase in TOT scores sustained over time with an increase of 7.8 points above baseline at 9 years after diagnosis. At baseline, PWB, EWB, and FWB scores were significantly lower in patients with aggressive lymphomas compared to the US general population, and SFWB scores were significantly higher (all p &lt;0.01); however, only SFWB and EWB score differences were clinically significant as defined by MID. SFWB showed only a mild decline from baseline over the course of 9 years. Despite this, all SFWB scores were statistically and clinically significantly higher than the US general population at 1, 2, 3, 6, and 9 years after diagnosis (all p &lt;0.01 and MID &gt;2). Patients with aggressive lymphomas TOT scores at baseline did not differ from the US general population, while TOT scores at 1, 2, 3, 6, and 9 years post-diagnosis were statistically and clinically significantly higher. Overall, the results in the pre-treatment QoL subset were consistent with the cohort as a whole. Conclusions: In survivors of aggressive lymphomas, QoL improved primarily in the first year after diagnosis, and sustained over the course of nine years. QoL in survivors of aggressive lymphomas is higher than the United States general population at 1, 2, 3, 6, and 9 years after diagnosis. Figure Disclosures Maurer: Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees. Farooq:Kite, a Gilead Company: Honoraria. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE were used for the study. DNA was isolated from whole tumor cell suspensions and RNA was isolated from both whole tumor and B cell enriched cell suspensions. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p&lt;0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p&lt;0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p&lt;0.01), and C2 for an INT phenotype related to a LZ signature (p&lt;0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (&gt;5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p&lt;0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p&lt;0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.