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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 .

Abstract Introduction: Recent results from two independent patient series have shown that chromosomal rearrangements of DUSP22 (DUSP22r+) and TP63 (TP63r+) can predict outcome in ALK-negative anaplastic large cell lymphoma (ALK-ALCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) with morphologic features resembling ALK-ALCL (Parilla-Castellar E, Blood 2014; Pedersen MB, Blood 2017). While DUSP22r+ is predictive for excellent survival similar to that of ALK+ALCL after CHOP/CHOP-like therapy, the rarely occurring TP63r+ is associated with an aggressive clinical behavior and poor outcome. The largest subgroup, i.e. patients with neither ALK nor DUSP22 nor TP63 rearrangements (triple negative), show a 5 year (yr) overall survival (OS) intermediate between that of ALK-/DUSP22r+ and ALK-/TP63r+ patients. The aim of the present study was to assess the impact of upfront high-dose therapy with autologous stem cell transplant (HDT/ASCT) on outcome in adult ALCL and PTCL-NOS patients according to their ALK, DUSP22 and TP63 status. The survival results from the two published series were pooled with those of a Nordic Lymphoma Group trial, the NLG-T-01 (d'Amore et al, JCO 2012), where patients were treated with 1st line CHOEP/CHOP followed, in chemosensitive cases, by upfront HDT/ASCT. Methods: Fluorescence in situ hybridization was performed on sections of previously constructed tissue microarrays using break-apart probes for the DUSP22-IRF4 and TP63 loci and a dual-fusion probe for TBL1XR1/TP63 fusion [inv(3)(q26q28)]. Evaluation of DUSP22 and TP63 rearrangements was performed in a blinded fashion without knowledge of PTCL subtype, clinical course, or outcome. Three independent patient cohorts were included: (i) one from Mayo Clinic consisting of 31 DUSP22r-, ALK-ALCL and PTCL-NOS (triple negative: 25; TP63r+: 6); (ii) one from Denmark consisting of 93 DUSP22r-, ALK-ALCL and PTCL-NOS (triple negative: 90; TP63r+: 3); and one from the NLG-T-01 trial consisting of 46 ALK-ALCL and PTCL-NOS (triple negative: 37; TP63r+: 1; DUSP22r+: 8), leading to a total study population of 170 patients. ALK+ ALCL was not included in the analysis, since no patients with this histology entered the NLG-T-01 trial. Association of genetic subtype with OS was assessed using Kaplan-Meier curves and Cox proportional models for hazard rate ratios (HR). Significant differences were defined as P &lt;0.05. Results: The eight DUSP22r+ patients (7 ALK-ALCL and 1 PTCL-NOS) from NLG-T-01 had a 5-yr OS of 83%, (95%CI 27-97), similar to that reported for DUSP22r+ in the Mayo and Danish cohorts (90% and 80%, respectively). No lymphoma-related events were observed in this subset. The only event was a septic death due to HDT-induced cytopenias in a patient who was in complete remission (CR). Among the 162 patients with DUSP22r-, ALK-ALCL and PTCL-NOS, those consolidated with HDT/ASCT (n=47) had a significantly better outcome (5-yr OS: 45%) than those treated with induction chemotherapy alone (5-yr OS: 30%) (n=115) (P=0.01). The patients in the HDT/ASCT group were younger (P&lt;0.001) and had more advanced stage disease (P=0.002). The improvement in OS persisted when the analysis was limited to transplant eligible patients and adjusted for age and stage (HR 0.52 95%CI 29-91, P=0.023) or International Prognostic Index (IPI) groups (HR 0.49, 95%CI 20-82, P=0.006). Of the 10 TP63r+ patients found within the pooled cohort, 6 were transplant eligible. Of these, 5 were not transplanted (intention-to-treat) while one was (Nordic trial). The latter patient achieved a still ongoing long-term CR, while all others died of lymphoma progression. Conclusion: In ALK-ALCL and PTCL-NOS patients from the NLG-T-01 trial, DUSP22r+ was associated with a very good outcome, similar to that seen in DUSP22r+ patients who had not undergone upfront autologous transplant. This observation supports the impression that upfront HDT/ASCT may not be of benefit in these patients. TP63r+ predicted poor outcome in non-transplanted patients. The impact of HDT/ASCT in the TP63r+ setting could not be adequately evaluated, since only one patient from the NLG-T-01 trial cohort was found to be TP63r+. Notably, this patient was the only survivor of the TP63r+ subset. For DUSP22r-, ALK-ALCL and PTCL-NOS patients taken as one group, those who received upfront HDT/ASCT had a superior survival compared to their age- and IPI-matched non-transplanted counterparts. Disclosures Ellin: ROCHE: Consultancy, Research Funding; CTI: Consultancy. Mannisto: Roche: Honoraria, Other: Travel expence; Takeda: Honoraria, Other: Travel expence; Amgen: Other: Travel expence; Novartis: Other: Travel expence; Celgene: Other: Travel expence; Gilead: Other: Travel expence; Pfizer: Honoraria; SOBI: Honoraria. Cerhan: Janssen: Other: Scientific Advisory Board (REMICADELYM4001); Janssen: Other: Multiple Myeloma Registry Steering . Toldbod: Takeda Pharma: Honoraria.

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.

AbstractBackgroundEpidemiological studies have demonstrated a positive association between chronic lymphocytic leukaemia (CLL) and non-melanoma skin cancer (NMSC). We hypothesized that shared genetic risk factors between CLL and NMSC could contribute to the association observed between these diseases.MethodsWe examined the association between (i) established NMSC susceptibility loci and CLL risk in a meta-analysis including 3100 CLL cases and 7667 controls and (ii) established CLL loci and NMSC risk in a study of 4242 basal cell carcinoma (BCC) cases, 825 squamous cell carcinoma (SCC) cases and 12802 controls. Polygenic risk scores (PRS) for CLL, BCC and SCC were constructed using established loci. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs).ResultsHigher CLL-PRS was associated with increased BCC risk (OR4th-quartile-vs-1st-quartile = 1.13, 95% CI: 1.02–1.24, Ptrend = 0.009), even after removing the shared 6p25.3 locus. No association was observed with BCC-PRS and CLL risk (Ptrend = 0.68). These findings support a contributory role for CLL in BCC risk, but not for BCC in CLL risk. Increased CLL risk was observed with higher SCC-PRS (OR4th-quartile-vs-1st-quartile = 1.22, 95% CI: 1.08–1.38, Ptrend = 1.36 × 10–5), which was driven by shared genetic susceptibility at the 6p25.3 locus.ConclusionThese findings highlight the role of pleiotropy regarding the pathogenesis of CLL and NMSC and shows that a single pleiotropic locus, 6p25.3, drives the observed association between genetic susceptibility to SCC and increased CLL risk. The study also provides evidence that genetic susceptibility for CLL increases BCC risk.

Background MBL is a precursor to chronic lymphocytic leukemia (CLL) and is subclassified into low-count (LC) MBL (absolute B-cell count&lt;0.5x109/L) and high-count (HC) MBL (absolute B-cell count between 0.5 and 5x109/L). We previously reported that a polygenic risk score (PRS) based on a weighted average of 41 CLL-susceptibility variants was associated with risk of both MBL and CLL among a cohort of individuals from CLL families. Here we evaluate this PRS in an independent cohort of MBL and CLL individuals of European ancestry (EA), all of whom were ascertained agnostic to CLL family-history status. We also evaluate the PRS by MBL subtype (LC/HC), and in African American (AA) CLL cases and controls. Methods We genotyped 535 EA MBLs (139 HC-MBL, 396 LC-MBLs), 735 CLLs (640 EA, 95 AA), and 2,866 controls (2,631 EA, 235 AA) from the Mayo Clinic CLL Resource, Duke University, and Weill Cornell Medical College. We computed the CLL-PRS for each individual and used logistic regression to estimate odds ratios (OR) and 95% confidence intervals, adjusting for age and sex. To assess discriminatory accuracy, we computed the c-statistic. Among EA individuals, we calculated a trend test among LC-MBL, HC-MBL, and CLL risk using the P-value for heterogeneity from a polytomous logistic regression analysis. Moreover, we plotted a boxplot for the PRS among controls, LC-MBL, HC-MBL, and EA CLL, as well as for AA CLL cases and controls, and tested the statistical difference using the Kruskal Wallis test and Mann-Whitney test, respectively. Results We found a significant association of PRS with overall MBL risk (OR=1.87, P=1.1x10-28) with good discrimination (c-statistic=0.72). Significant associations were also found for LC-MBL (OR=1.75, P=7.5x10-19, c-statistic=0.72), HC-MBL (OR=2.22, P=1.4x10-17, c-statistic=0.74), and CLL of EA (OR=2.60, P=1.2x10-62, c-statistic=0.78), with a significant difference among these cohorts (Figure 1.A) and a significant positive trend across these cohorts (Pheterogeneity=8.4x10-6). Although we observed a 33% increased risk of CLL in AA (c-statistic=0.57), the PRS was borderline significant (P=0.07, Figure 1.B). Conclusion The CLL-PRS is a strong prediction-tool for risk of CLL and MBL among individuals of EA. Future studies are needed to improve the PRS for AAs including performing GWAS of AA in order to identify CLL-susceptibility SNPs that are more representative within known CLL loci and to discover novel CLL loci that are unique for AAs. Disclosures Parikh: GlaxoSmithKline: Honoraria; Janssen: Honoraria, Research Funding; Ascentage Pharma: Research Funding; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; Genentech: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Verastem Oncology: Honoraria. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Brander:Genentech: Consultancy, Honoraria, Other, Research Funding; Juno/Celgene/BMS: Other, Research Funding; MEI Pharma: Other, Research Funding; Ascentage: Other, Research Funding; ArQule: Consultancy, Other, Research Funding; NCCN: Other; Teva: Consultancy, Honoraria; Tolero: Research Funding; AstraZeneca: Consultancy, Honoraria, Other, Research Funding; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Other, Research Funding; Pfizer: Consultancy, Other; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Novartis: Consultancy, Other; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Tolero: Research Funding; Teva: Consultancy, Honoraria; DTRM: Other, Research Funding; BeiGene: Other, Research Funding; Novartis: Consultancy, Other; NCCN: Other; Verastem: Consultancy, Honoraria, Other, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Research Funding; Abbvie: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees. Furman:Acerta: Consultancy; AstraZeneca: Consultancy, Research Funding; Beigene: Consultancy; Abbvie: Consultancy; Pharmacyclics: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy, Research Funding; Verastem: Consultancy; Incyte: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Speakers Bureau; Loxo Oncology: Consultancy; Oncotarget: Consultancy. Shanafelt:Mayo Clinic: Patents & Royalties: and other intellectual property; Genentech, Pharmacyclics LLC, an AbbVie Company, AbbVie, GlaxoSmithKline, and Merck: Research Funding.

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.