Larson, Melissa C.

Introduction Due to the higher metabolic demand, malignant cells have an increased dependency on the nucleocytoplasmic trafficking of proteins, as compared to normal cells. Chromosome region maintenance protein 1 (CRM1), encoded by the XPO1 gene, is the main protein receptor which facilitates export of molecules, including tumor suppressor proteins, from the nucleus to the cytoplasm thereby making them inactive. Expression of CRM1 in tumor tissue has been shown to be an independent prognostic marker in several solid tumors and in acute myeloid leukemia; high CRM1 expression by immunohistochemistry (IHC) was associated with more aggressive disease and shorter survival. Importantly, selinexor, a first in class small molecule inhibitor of CRM1, was recently approved for the treatment of relapsed diffuse large B-cell lymphoma (DLBCL). The expression of CRM1 on tumor cells and the assessment of its prognostic impact have not been studied in patients (pts) with DLBCL or primary mediastinal B-cell lymphomas (PMBCL). Methods Paraffin embedded tumor tissue from pts with DLBCL or PMBCL treated with immunochemotherapy was assessed for CRM1 expression through IHC on tissue microarray (TMA). CRM1 anti-rabbit monoclonal antibody (Cell Signaling, catalog-no: 46249) was used at 1:100 dilution. Tumor cell grading was based on CRM1 staining in tumor cells compared to background non-malignant lymphocytes and non-malignant lymphocytes in spleen and tonsillar tissue controls. Renal cell carcinoma (RCC) was used as a positive control [known high levels of CRM1 staining (Inoue et al, J Urol, 2012)]. Two expert hematopathologists (RLK & AJW) independently scored CRM1 nuclear staining and assigned a grade of 0-3; 0 (no definitive nuclear staining, equal to background lymphocytes), 1 (dim nuclear staining), 2 (consistent nuclear staining, nuclear detail still visible behind the stain) and 3 (strong nuclear staining obscuring most nuclear detail, staining equivalent to RCC control). The average CRM1 score per case across all available cores on the TMA was calculated. Low CRM1 expression for a case was arbitrarily defined as a score of 0-2.0; high CRM1 expression was score 2.1-3.0. Scoring reliability between reviewers and between cores was assessed using intra-class correlation coefficient; score 0.75-0.90 was considered as a good scoring reliability. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. The association of CRM1 expression and risk of failing to achieve EFS at 24 months after diagnosis (EFS24) was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, while the association of CRM1 expression with continuous EFS and overall survival (OS) was estimated using Kaplan-Meier curves and hazard ratios (HR) and 95% CI from Cox regression models. Results Tumor tissue from 282 pts was studied for CRM1 staining, including 275 pts with DLBCL and 7 pts with PMBCL. Median age of the study population was 61 years (range: 18-93) and 59% were male. The median follow-up for the entire cohort was 88.6 months. The first-line treatment regimens and baseline patient characteristics at diagnosis are outlined in Figure 1A. Of the 282 pts, 200 (71%) had high level of CRM1 expression and 82 (29%) had low CRM1 expression [only 4 (1.4%) had no or 0 staining]. Intra-class correlation coefficient to measure scoring reliability was 0.8. There was no difference in International Prognostic Index (IPI), ECOG performance score, lactate dehydrogenase or age at diagnosis among the groups with high CRM1 expression compared to low CRM1 expression (Figure 1A). The EFS24 failure was 29% for pts with low CRM1 expression while 26% in pts who had high CRM1 expression, OR=1.16, 95% CI 0.63-2.07; p=0.63. Null associations were also observed for EFS (HR=1.21, 95% CI 0.80-1.83; p=0.38) and OS (HR=1.02, 95% CI 0.61-1.69; p=0.95), (Figure 1B, C). Results were similar when adjusted for gender and IPI. Conclusion CRM1 expression by IHC on paraffin embedded tumor tissue is feasible in DLBCL and PMBCL. These data demonstrate that the CRM1 protein, the target for selinexor, is indeed expressed in the vast majority of these tumors; only 1.4% had no staining. However, CRM1 expression by IHC is not a prognostic marker for EFS24, EFS or OS. Whether CRM1 staining predicts selinexor response has not been studied but should be included in any new studies using CRM1 inhibitors. Disclosures Maurer: Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Witzig:Acerta: Research Funding; Immune Design: Research Funding; Incyte: Consultancy; MorphSys: Consultancy; Celgene: Consultancy, Research Funding; Spectrum: Consultancy; Karyopharm Therapeutics: Research Funding; AbbVie: Consultancy.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

Abstract Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

Abstract Copy number alterations and loss of heterozygosity (LOH) are common in ovarian tumors, including those on the X chromosome. In females, one of the two copies of the X chromosome is transcriptionally silenced during embryonic development in the epigenetic process of X chromosome inactivation. Specifically, the long non-coding RNA XIST is expressed from the inactive X (Xi) and triggers chromosome-wide DNA methylation. In breast and ovarian cancer cell line studies, Xi deletion with concomitant duplication of the active X (Xa) has been observed. We sought to examine which copy of the X chromosome, Xi or Xa, was gained or lost in a set of primary ovarian tumors, using levels of XIST expression as a marker of X chromosome inactivation. To determine which copy of the X chromosome harbors the alterations, we compared local copy number and LOH in Xq13.2 to the levels of XIST expression in a set of N=134 primary tumors from Mayo Clinic ovarian cancer patients. Associations with XIST expression were examined using ANOVA, with copy number of two and absence of LOH as the reference level. Associations between expression of XIST and expression of other genes were examined with a mixed effects model, to account for the repeated observations across patients, after adjusting for local copy number and LOH. Associations with XIST expression and overall survival were performed with Cox regression. Additional analyses were performed in a subset of 24 patient tumors with X chromosome-wide alterations to simplify interpretation, including a visual examination of chromosome-wide DNA methylation patterns. Analyses were replicated in N=106 ovarian tumors from TCGA with X chromosome-wide alterations. Across N=134 ovarian tumors from Mayo Clinic, abnormal copy number (not equal to two) was associated with reduced XIST expression (p=0.0016). Levels of XIST expression were similar for tumors with one copy and tumors with three or more copies. Similarly, levels of XIST expression were also lower for tumors with LOH (p=5.2E-5). Reduced XIST expression was also associated with poorer survival, after adjusting for age (p=0.02). Results were consistent in the subset of tumors with whole chromosome alterations (pCN=0.0013, pLOH=0.0019). For these tumors, when LOH was present, XIST expression was low and the DNA methylation distribution is bi-modal, suggesting that the inactive X was lost and the active X was gained. Furthermore, after adjusting for copy number and LOH, XIST expression was weakly positively correlated with the median expression of other genes (R2=0.04, p=0.026). In N=106 TCGA tumors with whole X chromosome alterations, XIST expression was also reduced in tumors with abnormal copy number (p=0.006) and with LOH (p=1.5E-10). Taken together, because XIST is expressed from Xi and not Xa, associations of reduced XIST expression with abnormal copy number and presence of LOH suggest that Xi is frequently lost and Xa is gained in ovarian tumors, consistent with cell line studies. These results should be further validated in larger sample sets and examined for association with clinical outcome. Citation Format: Yilin Song, Bryan M. McCauley, Melissa C. Larson, Sebastian M. Armasu, Kate Lawrenson, Ellen L. Goode, Nicholas B. Larson, Stacey J. Winham. Evidence of decreased X chromosome inactivation in primary ovarian tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2452.

Abstract Background Factors associated with chronic immune stimulation such as transplantation, autoimmune disorders and chronic infections have been linked to the development of lymphoma, while atopic conditions have been associated with a lower risk of lymphoma. Vaccinations, in part due to their ability to induce immune responses, have been hypothesized to play a role in lymphoma etiology, but there are few studies with mixed results and limited data on lymphoma subtypes. Methods We used a clinic-based study of 2461 newly diagnosed lymphoma cases and 2253 frequency-matched controls enrolled from 2002-2015. Participants self-reported lymphoma risk factors and history of vaccinations for hepatitis A, hepatitis B, chicken pox, yellow fever and influenza. Pathology was centrally reviewed, and included 644 chronic lymphocytic leukemia, 528 follicular (FL), 414 diffuse large B-cell (DLBCL), 170 marginal zone, 127 mantle cell, 119 T-cell, 181 Hodgkin, and 278 other lymphoma subtypes. We used polytomous logistic regression to estimate odds ratio (OR) and 95% confidence intervals (CI) of vaccinations with lymphoma risk overall and for subtypes, adjusting for age, sex, socioeconomic status, body mass, alcohol use, smoking history, recreational sun exposure, and family history of hematological malignancies. Results The median age at diagnosis for lymphoma cases was 64 years (range: 18-93) and 58% were male; the median age at enrollment for controls was 62 years (range: 18-91) and 53% were male. The most common vaccinations among controls were influenza (90%) followed by hepatitis B (41%), hepatitis A (30%), chicken pox (20%), and yellow fever (11%). Vaccination for influenza was inversely associated with lymphoma risk overall (OR=0.75, CI:0.61-0.93, P=0.01), with an inverse trend for increasing number of vaccinations (P=0.0001). In subtype analyses, Influenza vaccination was inversely associated with risk of FL (OR=0.59, CI:0.43-0.80, P=0.001), DLBCL (OR=0.64, CI:0.44-0.92, P=0.02) and other lymphoma subtypes (OR=0.66, CI:0.43-1.00, P=0.05). Vaccination for hepatitis A was inversely associated with lymphoma risk (OR=0.79, CI:0.65-0.96, P=0.02), with no significant subtype-specific associations. No evidence of association with vaccination for hepatitis B (OR=0.95, CI:0.79-1.14) and chicken pox (OR=0.83, CI:0.68-1.02) for lymphoma risk overall or for any subtypes. Although no evidence of an association with vaccination for yellow fever (OR=0.81, CI:0.62-1.06) for lymphoma risk, it was inversely associated with DLBCL risk (OR=0.42, CI:0.22-0.80). Conclusion Vaccination for influenza and hepatitis A were inversely associated with overall lymphoma risk. DLBCL was associated with vaccination for influenza and yellow fever. FL was associated with vaccination for influenza. Understanding the underlying mechanisms for a putative protective effect of certain vaccines could provide new clues to the etiology and prevention of lymphoma. Citation Format: Geffen Kleinstern, Dennis P. Robinson, Carrie A. Thompson, Melissa C. Larson, Cristine Allmer, Grzegorz S. Nowakowski, Timothy G. Call, Stephen M. Ansell, Andrew L. Feldman, Neil E. Kay, Thomas M. Habermann, Susan L. Slager, James R. Cerhan. Vaccination history and risk of lymphoma and its major subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1054.

Abstract Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

8052 Background: A significant proportion of patients with FL experience an early relapse and a subsequent poor outcome. While several prognostic indices have been developed, none were designed to predict early failure. Recently, we established that lack of intrafollicular CD4+ T-cell expression predicted risk of early failure, and integrating this microenvironment biomarker with the Follicular Lymphoma International Prognostic Index, termed BioFLIPI, further improved identification of FL patients at risk of early failure ( Blood 2019;134(suppl1):121). However, the microenvironment may be influenced by the genetic composition of tumor. We investigated whether the CD4 biomarker and BioFLIPI were impacted by genetic features of the tumor as assessed by a 23-gene expression prognostic score ( Lancet Oncol 2018;19:549-61). Methods: Of the 186 cases with FL grade 1-3A treated with immunochemotherapy (IC) in our prior study, 152 had digital expression quantification of 23 selected genes (23-GEP score), which used RNA from formalin-fixed, paraffin-embedded samples. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS at 24 months. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals from logistic regression models. We also used Cox regression to assess associations with continuous EFS and overall survival (OS). Results: 28% of patients failed to achieve EFS24. Lack of CD4+ intrafollicular expression (38% of patients, OR = 2.33, p = 0.024) and high risk 23-GEP score (26% of patients, OR = 3.52, p = 0.001) each predicted early failure, and in a multivariable model that included FLIPI, both CD4+ (OR = 2.26, p = 0.046) and 23-GEP score (OR = 2.26, p = 0.0.057) remained predictors. Similarly, BioFLIPI modeled as a continuous score (1-4, OR per one point increase = 2.31, p < 0.001) predicted early failure, and the association remained (OR = 2.14, p < 0.001) when the high risk 23-GEP score (OR = 2.79, p = 0.013) was included in the model. When stratified on 23-GEP score, BioFLIPI was a stronger predictor of early failure in low risk (74%, OR = 2.51, p = 0.002) relative to high risk (26%, OR = 1.55, p = 0.27) patients. Similar patterns were observed for EFS and OS. Conclusions: CD4+ T-cell infiltrate and tumor gene expression appear to be independently predictive of early failure in newly diagnosed FL patients treated with IC. Future studies should integrate and validate these measures.

e19112 Background: We sought to study the longitudinal QOL, financial burden and perception of care (PoC) in patients with multiple myeloma (MM) or lymphoma (LYM) enrolled on CTs vs. those receiving standard care (Non-CT). Methods: QOL was evaluated using the FACT-G questionnaire (baseline, 1, 2, 3, 6 & 12 months). Financial burden was evaluated using 10 questions adapted from the MEPS survey and PoC was evaluated using 6 questions adapted from the CAHPS survey (baseline, 3, 6 & 12 months). Results: 35 (28%) of 123 enrolled patients were treated on CTs. Demographics, QOL and financial burden are shown in the Table. Baseline QOL scores were similar in CT vs non-CT groups. Age, gender and self-reported performance status were significantly associated with baseline QOL, while lines of therapy & cancer type were not. There were no significant differences in change in QOL vs. baseline in patients treated on CTs vs. non-CTs at 1, 2, 3, 6 and 12 months from baseline, including the total FACT-G score and the subdomains of functional, physical, emotional and social well-being. Within each group, there was a decline in QOL over the first 3 months, specifically physical WB, which gradually returned to baseline. Baseline answers to financial burden questions were similar, except patients on CTs reported less need for taking extended time-off from work (22% vs. 46%, p = 0.02). The only statistically significant difference in individual Qs over 1 year was ‘worry about having to pay large medical bills’ at the 6-month timepoint (CT: 0%, non-CT: 52%, p = 0.01). Using a composite score from 4 MEPS questions, financial burden over time was lower in the CT group, though the differences were not statistically significant. There were no significant differences in PoC at baseline, 3, 6 and 12 months in the two groups based on answers to each of the six questions adapted from the CAHPS survey. Conclusions: Over 1 year, patients on CTs experienced a similar QOL and perceived their care to be similar as non-CT patients. Financial burden was lower in the CT group, though differences were not statistically significant. [Table: see text]

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.