Cerhan, James R.

9057 Background: The incidence of CAM use among patients with cancer is higher when compared to the general population. However, there are few studies examining CAM use in NHL survivors, and limited data are available regarding beliefs in CAM. This study was conducted to examine the prevalence of CAM use in NHL, define CAM beliefs among NHL survivors, and explore differences between patients with indolent and aggressive lymphoma. Methods: Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource from 2002-2008. NHL patients who completed the 3-year post diagnosis questionnaire, which includes questions regarding CAM use and beliefs, were included in this study. Chi-squared tests and Wilcoxon rank-sum tests were used to assess the association of CAM use with prognostic and demographic factors. Results: 719 patients were included with a median age of 63 years (range 22-92). 53% were male. Overall, 636 (89%) reported ever using CAM. 78% of patients used vitamins and 54% alternative therapies (chiropractic (36%) and massage therapy (24%)). Among CAM users, 141 (22%) believe CAM can assist the body to heal, 123 (19%) believe CAM can relieve cancer symptoms, 115 (18%) believe CAM use gives a feeling of control, 106 (17%) believe CAM can boost immunity, 24 (4%) believe CAM can cure cancer, and 35 (6%) believe CAM can prevent the spread of cancer. Female gender was associated with increased overall CAM use (p<0.0001) as well as use of vitamins (p<0.0001), herbal supplements (p=0.006) and alternative therapy (p=0.0002) specifically for cancer. Older age was also associated with increased vitamin use (p=0.005) and decreased herbal supplements use (p=0.008). There was no significant difference in overall CAM use between those with follicular lymphoma grades I-II (n=195, 91%) and non-relapsed diffuse large B-cell lymphoma (n=151, 87%), although massage therapy was utilized more often by FL survivors (29% versus 18%, p=0.005). Conclusions: CAM modalities are used by the majority of NHL survivors (89%). The assessment of CAM use and education regarding potential harms is imperative for the NHL survivor.

8061 Background: RCHOP is the standard first line therapy for DLBCL but elderly patients often receive suboptimal doses. Relative dose intensity (RDI) less than 70-90% has previously been associated with inferior survival in CHOP treated patients. We now investigate the clinical determinants of immunochemotherapy dose delivery and comparative effectiveness of dose intensity on lymphoma-specific outcomes in persons over the age of 60. Methods: SPORE MER (Molecular Epidemiology Resource) is a prospectively accrued observational study in which University of Iowa and Mayo Clinic patients with a diagnosis of Non Hodgkins lymphoma have been enrolled since 2003. We reviewed data from the Iowa patients along with medical records from DLBCL patients over the age of 60 treated with anthracycline based chemo immunotherapy consecutively enrolled up through to December 2009. Statistical tests of associations between RDI and clinicopathologic factors, as well as survival outcomes, were performed. Results: We identified 92 patients over the age of 60. The median age was 70. 47 subjects experienced at least 1 dose reduction, 26 subjects had dose delays, and 20 subjects were unable to complete therapy. RDI ranged from 0.30-1.70 (median 0.92) for doxorubicin and from 0.29-1.70 (median 0.97) for cyclophosphamide. 24 and 19% of subjects received < 80% of RDI for dox and cy respectively. Age (p< 0.001 for both dox and cy) and performance status (p=0.04 for dox) but not number of medications, ACE27 co-morbidity score, body surface area, nor need for acute care visits were associated with decreased RDI. Adjusted for IPI, RDI did NOT have a significant effect on event-free or lymphoma-specific survival. Conclusions: Among clinical variables, age is the dominant variable associated with delivery dose intensity for chemotherapy in elderly patients treated for DLBCL; and among elderly patients receiving chemo immunotherapy, we did not find an association between dose intensity and lymphoma-specific outcomes. [Table: see text]

6571 Background: Chronic lymphocytic leukemia (CLL) is associated with an increased risk of developing second cancers. However, it is unknown whether CLL alters the natural history of these cancers once they occur. Methods: All patients with breast (n=583,838), colon (n=412,932), prostate (n=632,922), lung (n=489,290), kidney (n=95,902), pancreas (n=82,121) and ovarian (n=61,958) cancer reported to the Surveillance, Epidemiology, and End Results (SEER) Program from 1990 to 2007 were identified. Overall survival (OS; death due to any cause) and cancer-specific survival (death due to cancer site of interest) were examined, comparing patients with or without pre-existing CLL. Age- and sex-adjusted hazard ratios (HRs) were calculated. Results: OS for patients with pre-existing CLL was inferior for patients with breast (HR=1.61; p<0.001), colon (HR=1.65; p<0.001), kidney (HR=1.41; p<0.001), prostate (HR=1.75; p<0.001), and lung (HR=1.22; p<0.001) cancer after adjusting for age and sex. After excluding CLL-related deaths, OS remained shorter among patients with breast (p<0.001), colon (p<0.001), kidney (p=0.03), prostate (p<0.001), and lung (p<0.001) cancer. Cancer-specific survival was inferior for patients with breast (HR=1.29; p=0.03), colon (HR=1.75; p<0.001), and lung cancer (HR=1.17; p<0.001) who had pre-existing CLL after adjusting for age and sex. Conclusions: Several common cancers, including breast, colon, and lung, have inferior overall and cancer-specific survival when there is coexistent CLL. [Table: see text]

17511 Background: There is no published data regarding BMI changes in survivors of adult lymphoma. Methods: In October of 2004, we mailed a 23-page survey to 95 randomly selected patients from a population of 2,485 of patients from the Mayo Tumor Registry who had survived lymphoma from 5 to 20 years. 56 completed the survey for a 68% participation rate. Body mass index (BMI) was calculated using clinical height and weight in the medical record at the time of diagnosis and self-reported weight in the survey response. Physical activity was self-reported. Based on their pattern of response, patients were categorized into a stage of change of current exercise (precontemplation, contemplation, preparation, action, or maintenance). Mood disturbance (depression, anxiety, and distress) and quality of life (QOL) were self-reported using the CES-D, POMS, STAI, FACT-G, and LASA questionnaires. Scores were transformed to a scale of 0–100 (higher score meaning higher functioning) to allow comparisons with a difference of 10 points considered significant. Results: The median age at completion of the questionnaire was 62 years (range: 25–85). The median time since diagnosis was 11 years (range: 6–20). Regular fitness, defined as a stage of change for exercise of action or maintenance, was reported by 48% of the respondents. The median BMI at diagnosis was 25.6 and the median at time of questionnaire was 27.0, which is a statistically significant change. Change in BMI, BMI at diagnosis, and BMI at time of questionnaire had no correlation with level of physical activity. Obese patients had a mean score of 72 points on the LASA, which was significantly lower than normal and underweight patients (mean 86) and overweight patients (mean 84). Conclusions: A small, but significant increase in body mass index was noted in long-term lymphoma survivors from the time of diagnosis to long-term follow-up. Patients who were obese at time of diagnosis may have lower quality of life down-the-road, as measured by the LASA years following treatment. This data represents the first information about body mass index in survivors of adult lymphoma. Further larger studies are needed to confirm these preliminary data and to evaluate other aspects of health behavior in long-term survivors of lymphoma. No significant financial relationships to disclose.

1002 Background: Physical activity is a modifiable breast cancer risk factor, perhaps mediating risk reduction through regulation of estrogen metabolism. Evidence regarding effect of physical activity is conflicting partly because breast cancer is a heterogenous constellation of different tumor subtypes with differing etiologies. No prospective study has examined the relationship between physical activity and breast cancer incidence based on ER/PR status or histological subtype. Objective: Examine effect of physical activity on breast cancer incidence based on ER/PR status and histological subtypes of breast cancer. Methods: The Iowa Women’s Health Study is a prospective cohort study of postmenopausal women (N=41,837). Physical activity was self-reported on baseline questionnaire, and three levels (high, medium and low) were defined. Breast cancer incidence, histologic subtype and ER/PR status, through 18 years of follow-up, were ascertained by linkage with the Iowa SEER Cancer Registry. Cox proportional hazards models were used to estimate multivariate relative risks (RRs) and 95% confidence intervals (CIs) of breast cancer, adjusting for other breast cancer risk factors. Results: During 554,819 person-years of follow-up, 2548 incident cases of breast cancer were observed. High physical activity was associated with decreased risk for breast cancer (RR 0.91, 95 % CI 0.81–1.01) compared to low activity. The protective effect was most marked in ER+/PR− (RR 0.66, CI 0.46–0.94), intermediate in ER−/PR− (RR 0.80, CI 0.56–1.15), weakest in ER+/PR+ (RR 0.94, CI 0.81–1.08), and elevated in ER-/PR+ (RR 1.42, CI 0.67–3.01) tumors. Higher physical activity was also associated with a decreased risk of invasive ductal/lobular carcinoma (RR 0.90, CI 0.80–1.02), but not with invasive breast cancer with a favorable histology (RR 1.19, CI 0.78–1.81). Conclusions: Higher physical activity was associated with a 10% decreased risk of breast cancer. Unexpectedly, risk reduction was most marked in PR- tumors, particularly ER+/PR-, and the more aggressive histologic forms. Further studies are needed to confirm these findings, and also evaluate other risk factors based on ER/PR status and histological subtypes. No significant financial relationships to disclose.

Abstract Background: Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis. Methods: Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated (P &lt; 5 × 10−8) with high-density lipoprotein (HDL, n = 164), low-density lipoprotein (LDL, n = 137), total cholesterol (TC, n = 161), and triglycerides (TG, n = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance–weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI). Results: HDL was positively associated with DLBCL (OR = 1.14; 95% CI, 1.00–1.30) and MZL (OR = 1.09; 95% CI, 1.01–1.18), while TG was inversely associated with MZL risk (OR = 0.90; 95% CI, 0.83–0.99), all at nominal significance (P &lt; 0.05). A positive trend was observed for HDL with FL risk (OR = 1.08; 95% CI, 0.99–1.19; P = 0.087). No associations were noteworthy after adjusting for multiple testing. Conclusions: We did not find evidence of a clear or strong association of these lipid traits with the most common NHL subtypes. While these IVs have been previously linked to other cancers, our findings do not support any causal associations with these NHL subtypes. Impact: Our results suggest that prior reported inverse associations of lipid traits are not likely to be causal and could represent reverse causality or confounding.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p&lt;0.01) and D8 (p&lt;0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p&lt;0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI&gt;2, compared to 33% of non-D4 with IPI&gt;2 (p&lt;0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p&lt;0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p&lt;0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. 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. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Background The CLL international prognostic index (CLL-IPI) is a validated staging system for newly diagnosed CLL patients that combines age, clinical stage, serum Beta-2 microglobulin, TP53, and IGHV mutation status into a single score and stratifies CLL patients into four prognostic risk groups with a c-statistic=0.72. Next-generation sequencing identified ~60 putative driver genes recurrently mutated in CLL, and genome-wide association studies (GWAS) identified 41 single nucleotide polymorphisms (SNPs) associated with CLL risk. We previously showed that a polygenic risk score (PRS) of the weighted average of the number of risk alleles among these 41 SNPs is associated with risk of both CLL and MBL. We and others have also shown that the tumor mutational load (TML), i.e., the total number of somatically mutated driver genes, is associated with time to first treatment (TTT). Herein, we examine whether TML and PRS add prognostic value beyond CLL-IPI for disease progression in a cohort of CLL and high-count MBLs. Methods We used the Mayo Clinic CLL resource to identify newly diagnosed (&lt;2 years) previously untreated CLL and high-count MBL patients based on the 2008 International Workshop CLL (IWCLL) criteria . We extracted DNA from CD5+/CD19+ clonal B-cells and sequenced the coding regions of 59 recurrently mutated CLL driver genes using a custom SureSelect panel and Illumina HiSeq 4000. The median coverage depth per sample was 2737X. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies &gt;0.01%, and by those identified in 1000 Genomes Project, ExAC and/or ESP6500 databases, unless present in known mutation hotspots or COSMIC. After filtering high/moderate impact mutations, we calculated the TML. Peripheral blood DNA was genotyped on Illumina Omni Express array; after extensive quality control metrics, we calculated the germline PRS as the weighted average of the number of risk alleles among the CLL susceptibility SNPs. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) to test for associations with TTT. We analyzed the low/intermediate risk groups and the high/very high risk groups in stratified analyses and tested for interactions between CLL-IPI and TML or PRS. Survival curves are displayed using the Kaplan-Meier method using P-values from the log-rank test; the PRS is displayed by tertile categories. Results Of 166 patients (120 CLL, 46 MBL), 67% were male and the median age at diagnosis was 62 years (range: 28-85). The median follow-up was 5.3 years, and 73 were treated for progressive disease as defined by the 2018 IWCLL criteria. Low risk CLL-IPI comprised 51%, intermediate risk 27%, high risk 18%, and very-high risk 4%. The most commonly somatically mutated genes were TP53 (8%), CHD2 (8%), BIRC3 (8%), SF3B1 (7%), and NOTCH1 (7%). We found that 42%, 32%, and 26% of patients had 0, 1, or 2+ genes with somatic mutations, respectively. The median PRS was 8.3 (range: 6.1-11.3) which is consistent with our prior PRS study on CLL/MBL and higher than 7.53 found in 7983 controls from our prior work. When we modeled TML, PRS, sex, CLL/MBL status and CLL-IPI together with the joint effects of CLL-IPI with TML and PRS, both interactions were significant in the model (P=0.008 for TML*CLL-IPI and P=0.014 for PRS*CLL-IPI), with c-statistic=0.81 (CI:0.77-0.85). When we stratified by CLL-IPI, TML was associated with shorter TTT in the low/intermediate risk (N=129, continuous HR=2.36 per TML category, CI: 1.61-3.47, P=1.3x10-5, Figure 1.A), but there was no evidence of an association in the high/very-high risk (N=37, continuous HR=1.12 per TML category, CI: 0.70-1. 78, P=0.64, Figure 1.B), after adjusting for PRS, sex, and CLL/MBL status. However, the PRS was associated with a shorter TTT in the high/very-high risk (continuous HR=1.77 per SD, CI: 1.00-3.12, P=0.048, Figure 1.D), with no evidence of an association in the low/intermediate risk (continuous HR=0.95 per SD, CI: 0.95-1.29, P=0.74, Figure 1.C), after adjusting for TML, sex, and CLL/MBL status. Conclusions In our study, TML was prognostic among the low/intermediate CLL-IPI risk groups while the PRS was prognostic among the high/very-high CLL-IPI risk groups. Sequencing CLL driver genes and genotyping relevant germline SNPs at time of diagnosis may provide incremental value over the CLL-IPI in predicting TTT among patients with CLL/MBL. Disclosures Parikh: MorphoSys: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Janssen: Research Funding; Ascentage Pharma: Research Funding; Acerta Pharma: Research Funding. Ding:Merck: Research Funding; DTRM Biopharma: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Kay:MorphoSys: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Agios: Other: DSMB.

Background With lymphoma survival rates increasing and a growing population of long-term survivors, the development of cardiovascular disease (CVD) in this patient population is of increasing importance. Anthracyclines are critical in the management of many lymphoma subtypes. However, there is a risk of developing anthracycline-induced CVD. Here, we estimate the cumulative incidence of CVD in adult lymphoma survivors and investigate risk factors associated with post diagnosis CVD. Methods Participants were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002-2015, the MER offered enrollment to all patients with newly diagnosed lymphoma who are US residents and age &gt;18 years. Participants completed a risk factor questionnaire, and clinical and treatment data were abstracted from medical records. Patients were contacted every 6 months for the first 3 years after diagnosis and annually thereafter to assess disease status, re-treatment and new onset morbidity including CVD. CVD events, including congestive heart failure (CHF), coronary artery disease (CAD), valvular heart disease (VHD), and arrhythmia were identified and validated against medical records. CHF was validated with the Cardiovascular Health Study Criteria and/or the Framingham Criteria. CAD, arrhythmia, and VHD were validated using clinical definitions. We calculated the cumulative incidence of CVD, with death modeled as a competing risk. The association of risk factors and treatments with risk of CVD was estimated using hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression with a competing risk of death. Risk factors included age, sex, diabetes, smoking, body mass index (BMI), and treatment with anthracyclines or radiation therapy. Results The study consisted of 3,063 lymphoma patients after excluding those with chronic lymphocytic leukemia and CVD prior to lymphoma diagnosis. The median age at diagnosis was 59 years (range 18-95), and 56% were males. At a median follow-up was 6.9 years (range 0.8-17.1), 640 patients (21%) had died without CVD and 485 patients self-reported CVD post lymphoma diagnosis, of which 280 (57.7%) were validated. Cardiovascular events included 86 CHF, 78 CAD, 40 VHD, and 164 arrhythmias. The cumulative incidence of CVD (Figure 1) at 5 and 10 years was 6.0% (95% 5.2%-7.0%) and 10.7% (95% CI 9.5%-12.1%), respectively. In multivariable analysis, increasing age (HR=3.93 per 5 years, p&lt;0.001), male sex (HR=1.33, p=0.03), former smoker (HR=1.04, p=0.77), current smoker (HR=1.96, p&lt;0.001), BMI&gt;30 kg/m2 (HR=1.50, p=0.01), and anthracycline treatment (HR=1.49. p&lt;0.001) were all significantly associated with risk of overall CVD, while there was no association with diabetes (HR=0.92, p=0.70) or radiation therapy (HR=1.05, p=0.78) in the multivariable model. Anthracycline use was significantly associated with increased risk of CHF (HR=2.64, p&lt;0.001) and arrhythmia (HR=1.51, p&lt;0.01), but not VHD (HR=0.83, p=0.56) or CAD (HR=1.23, p=0.31) after adjustment for the cardiac risk factors. The number of anthracycline cycles ranged from 0 to 12. 63.2% of individuals that received anthracyclines received 6 cycles. The 5-year cumulative incidence of CVD for 0, 1-5, 6, and &gt;6 anthracycline cycles was 5%, 6.9%, 7.4%, and 7.7%, respectively. Adjusting for cardiac risk factors, the number of anthracycline cycles was significantly associated with increased risk of CVD (1-5 cycles HR=1.34, p=0.11; 6 cycles HR=1.51, p&lt;0.01; &gt;6 cycles HR=2.04, p=0.03). Furthermore, the number of anthracycline cycles was associated with CHF (1-5 cycles HR=2.82, p&lt;0.001; 6 cycles HR=2.46, p&lt;0.001; &gt;6 cycles HR=5.33, p&lt;0.001) and arrhythmia (1-5 cycles HR=1.31, p=0.27; 6 cycles HR=1.60, p&lt;0.01; &gt;6 cycles HR=1.65, p=0.27). Conclusions In the modern treatment era, the risk of new onset CVD in patients with lymphoma without a history of CVD is approximately 1% per year after diagnosis. Arrhythmia and CHF were the most commonly occurring CVD events in this cohort. Both traditional CVD risk factors and treatment with anthracyclines was associated with an increased risk of developing CVD, and anthracyclines were a risk factor for arrhythmia and CHF in particular. Prevention of CVD in lymphoma patients will need to address both treatment and traditional lifestyle factors. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

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.