Citations

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Purpose The development of cardiovascular disease (CVD) is a relatively rare but a clinically important adverse event in the treatment of lymphoma, particularly in individuals receiving anthracyclines. There are few studies assessing the role of germline genetic susceptibility as a predictor of CVD in this setting. We evaluated the association of 24 single nucleotide polymorphisms (SNPs) from candidate genes involved in anthracycline-induced cardiotoxicity, CVD, and venous thromboembolism with new-onset CVD in a prospective cohort of lymphoma patients treated in the modern era. Methods All patients were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma SPORE. Enrollment from 2002-2015 was offered to patients with newly diagnosed lymphoma who were age ≥18 years. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants provided a peripheral blood sample, from which DNA was extracted. All patients were prospectively contacted every 6 months for the first 3 years from diagnosis and then annually thereafter to assess disease status, re-treatment and development of new morbidities, including CVD. Reported CVD events included congestive heart failure (CHF), coronary heart disease (CHD), arrhythmia, valvular heart disease (VHD), and other CVD. These events were identified during follow-up and validated against medical records. Genotyping was conducted using a custom Illumina iSelect platform with rigorous quality controls. For each SNP, Cox regression was used to estimate Hazard Ratios (HRs) and 95% confidence intervals (CI) with time to first CVD, using death without CVD as a competing risk. HRs were also obtained for time to CHF, using death without CVD as a competing risk. We also modeled these events for all patients and for patients receiving frontline anthracyclines. Each SNP was modeled as having a log-additive (per minor allele) effect in the regression model. An ordinal test was used to assess the trend, with a nominal P<0.05 considered statistically significant. Results There were a total of 3,063 newly diagnosed lymphoma patients (excluding chronic lymphocytic leukemia) with no history of CVD at time of lymphoma diagnosis, of which 1280 had genotype data available for analysis. The median age at diagnosis was 59 years (range, 18-95) and 56% were male. The most common subtypes were follicular (26.3%), diffuse large B-cell (23.2%), Hodgkin (11.3%), marginal zone (11.2%), mantle cell (6.3%) and T-cell (5.4%) lymphoma. Anthracycline-based chemotherapy as initial therapy was used in 52% of individuals. At a median follow-up of 6.9 years (range, 0.1-17.1), 363 (30.7%) patients died, and 173 (13.5%) had new-onset CVD after lymphoma diagnosis. There were 234 incident CVD events in the 173 patients: 49 CHD, 50 CHF, 27 VHD, 103 arrhythmias, and 5 other CVD. Results are shown in the Table. When assessing all CVD, F5 (rs4524) (HR=1.28, 95% CI=1.03-1.58) and F11 (rs4253399) (HR=1.30, 95% CI=1.06-1.61) were associated with CVD at P<0.05; both associations attenuated slightly when restricted to anthracycline-treated patients (F5 HR=1.18, 95% CI=0.89-1.57; F11 HR=1.23, 95% CI=0.92-1.65). F11 was more strongly associated with CHF (HR=1.55, 95% CI=1.03-2.34) and CHF in anthracycline-treated patients (HR=1.64, 95% CI=0.98-2.76). NCF4 (rs1883112) was marginally associated with lower risk of CVD overall (HR=0.81, 95% CI=0.66-1.00), and this association strengthened when restricted to anthracycline-treated patients (HR=0.67, 95% CI=0.49-0.91) and showed similar trends in HRs for CHF (HR=0.77, 95% CI=0.51-1.16) and CHF in anthracycline-treated patients (HR=0.72, 95% CI=0.41-1.28), although neither estimate was statistically significant at P<0.05. Finally, while XDH (rs2236168) was not associated with all CVD (HR=1.00, 95% CI=0.81-1.23), it was associated with CHF (HR=0.66, 95% CI=0.47-0.93) overall, but attenuated in anthracycline-treated patients (HR=0.81, 95% CI=0.55-1.18). Conclusions In this exploratory study of candidate SNPs from the literature, we found limited evidence for a role of germline genetic variability in predicting risk of CVD or CHF in a cohort of lymphoma patients, especially after accounting for multiple testing. To fully address this hypothesis, future studies will need larger sample sizes and more comprehensive genetic assessment. Disclosures Maurer: Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Nowakowski:NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

INTRODUCTION: EMZL is a heterogeneous disease with variable risk for relapse and progression. Based on age ≥70 years, stage III-IV and elevated LDH, Thieblemont et al (Blood. 2017) developed the MALT-IPI to identify high-risk patients. In this index, disease characteristics (stage and LDH) account for 66% while a disease nonspecific characteristic (age) for 33% of the index score. We reported (Am J Hematol. 2019) that EMZL with multiple mucosal sites (MMS) at diagnosis is characterized by shorter survival and increased incidence of higher grade transformation. To better recognize disease-attributable high-risk patients, we developed a new EMZL prognosis score chiefly based on patient's disease characteristics. METHODS: The revised (R)-MALT-IPI was developed using a retrospective data set of 405 EMZL patients treated at the University of Miami (UM) from 1995 to 2017. Cox proportional hazards regression analysis was conducted to evaluate the effect of the potential prognostic variables on progression-free survival (PFS) and overall survival (OS) and to develop the new index R-MALTI-IPI based on PFS. Model validation was performed in two independent cohorts of EMZL patients from the University of Iowa/Mayo Clinic Molecular Epidemiology Resource (MER) database (n=297) and the IELSG-19 study (n=400) used for the development of MALT-IPI. Performance of various prognostic indices was compared using AIC statistics, and concordance c-statistics by Harrell (CH) and by Gonen and Heller (CGH). RESULTS: Among the candidate variables tested in univariable analysis, the following were statistically significant predictors of shorter PFS: age >60, age ≥70, anemia (Hb<12g/dL), stage III-IV, ECOG PS ≥2, elevated serum LDH, number of extranodal sites >1, number of nodal sites >4, and presence of MMS at diagnosis, defined as EMZL with ≥2 different extranodal sites excluding spleen and bone marrow. A stepwise Cox regression analysis yielded a multivariable model with four independent predictors of shorter PFS: age >60 (HR=1.53, p=0.010), elevated LDH (HR=1.73, p=0.004), stage III-IV (HR=2.03, p=0.0003) and presence of MMS (HR=2.78, p<0.0001). Based on this, a new index R-MALT-IPI was developed with scores ranging from 0 to 5, calculated as a sum of 1 point for age >60, elevated LDH, stage III-IV, and 2 points for MMS. The R-MALT-IPI defined 4 risk groups: low-risk (score 0 (35%), reference group), low-medium risk (score 1 (39%), HR=1.91, p=0.005), medium-high risk (score 2 (13%), HR=3.77, p<0.0001), and high-risk (score 3+ (13%), HR=8.54, p<0.0001). When compared with MALT-IPI, R-MALT-IPI better stratifies and separates high risk patients (26%) into medium-high risk and high-risk patients with a median PFS of 5.8 years (2.9-9.1) and 1.8 years (1.3-2.6) respectively, compared to 2.6 years (1.8-4.7) in the high-risk MALT-IPI patients (16.8%). The R-MALT-IPI index also distinguished patients with different OS. For validation, we analyzed R-MALT-IPI index performance in independent Iowa/Mayo Clinic MER and IELSG-19 cohorts. Both R-MALT-IPI and MALT-IPI were useful in distinguishing PFS and OS in all the cohorts. In the UM training cohort, the concordance c-statistics' values for the two indices were similar: for PFS, CH=0.6893 and CGH=0.6611 for R-MALT-IPI, and CH=0.6551 and CGH=0.6367 for MALT-IPI; for OS, CH=0.7017 and CGH=0.6813 for R-MALT-IPI, and CH=0.7029 and CGH=0.67715 for MALT-IPI. In the validation cohorts, the concordance c-statistics' values for the two indices were also similar, but slightly lower than in the UM cohort for PFS. When comparing medium-high to high-risk R-MALT-IPI groups, there was a reduction of 4 years in median PFS in the UM cohort, and reduction in median EFS of 5.6 years in the MER cohort, an important difference between these risk groups identified by the R-MALT-IPI index. CONCLUSIONS: R-MALT-IPI is a new index for EMZL centered principally on disease characteristics. Overall, there is a similar prediction of PFS (EFS) by R-MALT-IPI and MALT-IPI indexes; however, R-MALT-IPI better recognizes a high-risk group accounting for 13% of EMZL patients with short median PFS and thus obviates the waiting period needed to recognize patients with shorter EFS24. Collaborative studies addressing best treatment approach for these high-risk EMZL patients are eagerly needed. Disclosures Alderuccio: Agios: Other: Immediate family member; Foundation Medicine: Other: Immediate family member; OncLive: Consultancy; Targeted Oncology: Honoraria; Puma Biotechnology: Other: Immediate family member; Inovio Pharmaceuticals: Other: Immediate family member. Thieblemont:Cellectis: Membership on an entity's Board of Directors or advisory committees; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Zucca:Kite, A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; AstraZenaca: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Merck: Research Funding; Celltrion Helathcare: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other: Travel Grant. Lossos:NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen Scientific: Membership on an entity's Board of Directors or advisory committees.

INTRODUCTION Complete staging via bone marrow assessment is standard of practice for most newly diagnosed non-Hodgkin lymphoma (NHL) patients, though the methods and patterns of bone marrow assessment vary in clinical practice. The 2014 Lugano Classification recommends that in diffuse large B cell lymphoma (DLBCL), positron emission tomography-computed tomography (PET/CT) alone can designated advanced-stage disease in many, but not all cases. For most other NHL subtypes, obtaining bone marrow biopsy (BMB) remains recommended. We aimed to assess clinical practice patterns of marrow assessment and the performance of PET/CT in the clinical setting. METHODS The Lymphoma Epidemiology of Outcomes (LEO) cohort is a prospective cohort from 8 academic medical centers. All patients were enrolled in the cohort within 6 months of diagnosis, and prospectively followed. Patients with lymphoma enrolled in the cohort between July 2015 and June 2017 were included for study. Baseline clinical characteristics at diagnosis, whether BMB and/or PET/CT were performed, and the result of these assessments were extracted from the database. Bone marrow involvement on PET/CT was abstracted via review of radiology reports. No central review of images was utilized. RESULTS In total, 3,251 patients were included, consisting of the following subtypes: DLBCL (n=1,098), follicular lymphoma (FL n=730), mantle cell lymphoma (MCL n=302), T-cell lymphoma (TCL n=278), marginal zone lymphoma (MZL n=269), other NHL (n=505) and composite lymphoma (n=69). Other clinical characteristics are shown in table 1. Staging BMB was obtained in 78% of patients, PET/CT was obtained in 56%, both BMB and PET/CT were obtained in 46% while neither were obtained in 9% (table 1). There were significant differences in lymphoma subtype between patients having any bone marrow assessment vs no bone marrow assessment (p<0.0001). MCL (96%) and DLBCL (94%) most frequently had any bone marrow assessment, while MZL was least frequently assessed (84%). Patients undergoing any bone marrow assessment had higher median lactate dehydrogenase (247 U/L vs 206 U/L, p=0.0002). Among the 8 enrolling medical centers, the frequency of bone marrow biopsy at diagnosis ranged from 53% to 86%, and the frequency of PET/CT at diagnosis ranged from 34% to 94% (p<0.0001, table 1). Among patients with DLBCL who had positive bone marrow by either PET/CT or BMB (N=107), 41% (44/107) were positive by BMB only, 19% (20/107) were positive by PET/CT only, and 40% (43/107) were positive by both. Figure 1 shows results of similar analyses for FL, MCL, MZL, and TCL. Using BMB as a gold standard for marrow involvement, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of PET/CT for marrow assessment in DLBCL is 49%, 90%, 68% and 91%, respectively. Data for other subtypes are shown in table 2. Patients undergoing both PET/CT and BMB had lower median LDH (231 U/L vs 254 U/L, p=0.0032) than those undergoing one or neither assessments. CONCLUSION Overall, 91% of all patients with newly diagnosed NHL had bone marrow assessments via either PET/CT, BMB or both. The significant variation in the frequency of BMB and PET/CT use among the 8 centers reflects a lack of clear consensus on staging practices regarding bone marrow assessment. The sensitivity of PET/CT for detection of marrow involvement was less than 50% across all subtypes. It is possible the sensitivity of PET/CT could be improved via a centralized review process of the images, as is performed in some clinical trials, however practical approaches for implementing such reviews in clinical practice have not been developed. Between 92-100% of all positive bone marrows were detected by BMB, except in cases with DLBCL in which 19% of positive bone marrows were missed by BMB but detected by PET/CT. Thus, in our analysis, BMB more reliably captured marrow involvement that PET/CT in all subtypes in which both assessments were performed. However, neither PET/CT nor BMB alone were able to identify all cases of bone marrow involvement and use of both modalities may be necessary in cases where marrow involvement would have clinically important implications. Disclosures Rutherford: Juno Therapeutics Inc: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria. Kahl:ADC Therapeutics: Consultancy, Research Funding; Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; BeiGene: 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. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Casulo:Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses; Gilead: Honoraria, Other: Travel, accommodation, expenses. Cohen:Astra Zeneca: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Hutchison: Research Funding; Gilead/Kite: Consultancy; Bristol-Meyers Squibb Company: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Genentech, Inc.: Consultancy, Research Funding. Nastoupil:TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Spectrum: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Bayer: Honoraria. Flowers:Denovo Biopharma: Consultancy; Spectrum: Consultancy; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; Acerta: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; TG Therapeutics: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; Millenium/Takeda: Research Funding; Celgene: Consultancy, Research Funding. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Martin:Sandoz: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; I-MAB: Consultancy; Janssen: Consultancy.

Purpose The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case–control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies. Participants A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents. Findings to date Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case–control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution. Future plans The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case–control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.

Abstract Background Anthracyclines are one of the most effective chemotherapeutic agents but can lead to a decline in cardiac function. Two-dimensional (2D) speckle tracking echocardiography derived strain can predict cancer therapeutics-related cardiac dysfunction (CTRCD). However, little is known about the role of three-dimensional (3D) strain imaging in this setting. Purpose To evaluate different methods of Left Ventricular Ejection Fraction (LVEF) and strain, and to identify the best strain parameter for the prediction of CTRCD in this group of patients during treatment with anthracycline based chemotherapy. Methods Patients with Hodgkin's or non-Hodgkin's lymphoma receiving anthracycline based chemotherapy were enrolled. 2D-and 3D echocardiography was performed at 3 time points: baseline, at the completion of chemotherapy and at 1 year. 2D- and 3D LVEF (2D Modified Quinones, M-mode, 2D Biplane, 3D Philips, 3D GE and 3D TomTec), peak systolic left and right ventricular longitudinal, radial and circumferential strain and strain rate were measured. CTRCD was defined as a decrease in LVEF >10% to an absolute value of <53%. Lin's Concordance Correlation Coefficient (CCC) was calculated to compare LVEF methods. ANOVA, uni and multivariate analysis was performed. Results A total of 130 patients (79 males, mean age 57±16 years) were enrolled; 104 non-Hodgkin.3D Philips and GE (CCC 0.88 [0.84, 0.92]), 3D Philips and biplane (CCC 0.84 [0.79, 0.89]), 3D Philips and Modified Quinones (CCC 0.86 [0.82, 091]) as well as 3D Philips and M-mode (CCC 0.77 [0.70, 0.85]) showed strong correlation for LVEF. 15 patients (12%) developed CTRCD. Mean cumulative anthracycline dose was 263±65 mg/m2. 2D and 3D global longitudinal and circumferential peak systolic strain, 2D global longitudinal systolic and early diastolic strain rate, 2D global early diastolic strain rate, 2D right ventricular longitudinal peak systolic strain and systolic strain rate, 2D global radial peak systolic strain and systolic strain rate measured at the completion of chemotherapy were independent predictors of the development of CTRCD at 1 year post-chemotherapy. The strongest predictors of CTRCD were 3D global longitudinal and circumferential strain either individually (area under the curve, 0.90 and 0.95 respectively) or combined (area under the curve, 0.95) at the completion of chemotherapy; a cutoff value of <-18% in 3D global longitudinal strain had a sensitivity of 82% and specificity of 99%, while for 3D global circumferential peak systolic strain of <-24% had a sensitivity of 82% and specificity of 95%. Conclusions To our knowledge this is the first prospective trial that shows that 3D EF correlates well with other EF methods and 3D strain predicts cardiac toxicity in this group of patients with a very good sensitivity and specificity. Acknowledgement/Funding Department of Cardiovascular Medicine-Mayo Clinic Rochester MN

Abstract Background Disease characteristics, treatment patterns, and outcomes of follicular lymphoma (FL) patients (pts) aged >80 years (yrs) are infrequently reported. Further, the original Follicular Lymphoma International Prognostic Index (FLIPI) has not been formally studied in pts aged >80 yrs. Also, whether FLIPI is associated with overall survival (OS) in pts aged >80 yrs is unknown. Patients/Methods We used the National LymphoCare Study—a Genentech-sponsored, prospective, multicenter registry of FL pts across the United States without study-specific treatment—to comprehensively analyze FL pts aged >80 yrs. Using Pearson chi-squared tests, associations of age groups with disease characteristics and overall response rate (ORR) were examined. Median progression-free survival (PFS) and OS by treatment regimen were estimated using the Kaplan-Meier method. Cox proportional hazards regression that were adjusted for baseline disease factors were used to assess treatment differences in PFS, lymphoma-related mortality (LRM, which includes both disease and treatment-related deaths), and OS, as well as the significance of age by treatment interactions. FLIPI and a prognostic model that was constructed based on the findings were used to predict outcomes. Results Of 2649 evaluable pts, 209 (8%) were aged >80 yrs. Compared with pts aged ≤60, pts aged >80 yrs were more likely to be Caucasian, to have worse performance status, to have lower hemoglobin (Hgb) value (<12 g/dL), to have less advanced stage, and were less likely to be treated at academic institutions. When treated, pts aged >80 yrs had lower ORR compared with younger pts and were less likely to receive rituximab + chemotherapy (P <.001) or anthracyclines (P <.001). Use of maintenance rituximab was not significantly different between pt groups when rituximab induction was given (61% for pts aged ≤60, 38% for pts aged >80, P=.11), but it was more frequent in pts aged >80 yrs when rituximab + chemotherapy induction was administered (44% for pts aged ≤60, 59% for pts aged >80, P=.04). After adjusting for maintenance use, sex, and baseline factors, age significantly differentiated PFS impact of observation, rituximab monotherapy, and rituximab + chemotherapy (P=.01). No treatment regimen provided superior PFS or OS in pts aged >80 yrs. With a median follow-up of 6.5 yrs for all pts (4.3 yrs for pts aged >80 yrs), 5-year OS was 59% and 92%for patients aged >80 and ≤60 yrs, respectively. OS in pts aged >80 yrs varied based on FLIPI score (log-rank test, P=.01; Figure 1). Interestingly, the percentage of deaths that were disease-related was 40% in pts aged >80 yrs, which was comparable to the percentage in pts aged ≤60 yrs of 48%. Cox modeling showed that lower Hgb, B symptoms, and male sex predicted worse OS (P<.01) but not PFS in pts aged >80 yrs. Recognizing that males have inferior OS compared with females in the general population, we constructed a prognostic model composed only of B symptoms and Hgb level <12 (low risk=no factors; high risk=1 or more factors; P<.0001; Figure 2). FLIPI and the proposed prognostic model significantly differentiated both OS and LRM (Table 1). Conclusions In the largest, prospectively published study of FL pts aged >80 yrs, OS, PFS, ORR, and treatment selection varied compared with younger pts. Surprisingly, the proportion of deaths attributed to lymphoma in these older FL pts was 40%. FLIPI was associated with outcome in pts aged >80 yrs and so was a proposed prognostic model comprising lower Hgb and B symptoms. Independent validation of this model is required, and additional prospective studies that are designed for the oldest old are warranted. Disclosures: Nabhan: Genentech Inc.: Advisory Board Other, Honoraria. Byrtek:Genentech, a member of the Roche group: Employment, Equity Ownership; Roche: Equity Ownership, stock options, stock options Other. Dawson:Genentech: Employment; Roche: Equity Ownership. Link:Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy; Genentech: Research Funding; Millenium: Research Funding; Pharacyclics: Research Funding. Zelenetz:GSK: Consultancy; Celgene: Consultancy; Cephalon: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Sanofi-Aventis: Consultancy; Genentech: Research Funding; GSK: Research Funding; Roche: Research Funding; Cancer Genetics: Scientific Advisor Other. Cerhan:Genentech: Scientific Advisory Board of the National LymphoCare Study Other. Flowers:Abbott, Celgene, Millennium/Takeda, Sanofi, Spectrum, Janssen: Research Funding; Celgene, Genentech Bio-oncology: Consultancy.

Abstract Background: We previously demonstrated that putative clinical epidemiologic exposures associated with leukemia risk are prevalent among AML patients, and some are associated with unique cytogenetic risk group and with clinical phenotype (Finn, Cancer Epidemiol, 2015). Herein, we studied genome-wide DNA methylation in a cohort of AML patients to evaluate the association of individual hyper- and hypo-methylated CpG sites with epidemiologic exposures and overall survival. Methods: The Mayo Clinic AML Epidemiology Cohort is a highly annotated retrospective case series of 295 consecutive patients (pts) with AML diagnosed and treated at Mayo Clinic Florida and Arizona, with central cytogenetics performed in all cases. The prevalence of clinical epidemiologic exposures, past medical and family history as well as medication use and lifestyle was systematically obtained. After IRB approval, we interrogated the cytogenetic database and successfully obtained leukemia DNA from available remnant diagnostic cytogenetic cell pellets in a cohort of 148 AML patients in the Mayo epidemiology case series and performed an assessment of genome-wide DNA methylation using the Infinium HumanMethylation450K BeadChip. Samples were processed using the R Bioconductor package 'minfi' using Subset Within Array Quantile Normalization (PMID: 22703947). Individual CpGs that did not reach a detection p-value of <0.05 were filtered out. An internal control was included on each array to control for significant batch effects. To determine differential methylation status of 473,864 individual CpG sites (after exclusion of 11,648 that did not pass detection p-value QC), CpG sites with low interpatient variability, a standard deviation <0.05 of methylation values, and an interquartile range <0.05 of methylation values and X/Y chromosome probes were excluded from analysis. Epidemiologic and important clinical variables occurring in at least 10 pts or previously-identified as of unique interest but in fewer than 10 pts were evaluated, including sex, BMI, performance status and comorbidities, tobacco and alcohol use, family history of hematologic malignancy, medications of interest, history of toxin exposure, and history of immunosuppression and/or solid organ transplant or of secondary and therapy-related AML (t-AML). Associations of differential hypo or hypermethylation at 281,259 CpG sites with epidemiologic exposures/clinical variables were evaluated using Spearman's test of correlation (continuous or ordinal exposures), a Wilcoxon rank sum test (dichotomous exposures), or a Kruskal-Wallis rank sum test (multi-category exposures). A Bonferroni correction was applied for multiple testing, after which p <1.8 x 10-7 was considered as significant. To reduce the likelihood of false-negative findings, we additionally considered p <5 x 10-6 as indicating suggestive evidence of an association. CpG sites and associated gene and function was determined using Illumina manifest file & www.ncbi.nlm.nih.gov/gene. Results: Statistically significant associations (p <1.8 x 10-7) with individual epidemiologic and clinical exposures were identified for 109 unique CpG sites, corresponding to differential methylation in the genes listed in Table. Specifically, obesity (predominantly hypomethylation), specific cytogenetic lesions and risk group, and gender were highly significantly associated with unique CpG methylation status, but not smoking, toxin exposure, family history, comorbidity or performance status, secondary or t-AML, immunosuppression, medication use, of family history in the analysis. A further 353 unique CpG sites had defined suggestive associations (ongoing analysis). We also identified 8 additional CpG sites where differential methylation was associated with overall survival (p <5 x 10-6, Table). Conclusion: Obesity, cytogenetic lesions, and sex are associated with differential methylation of unique CpG sites at AML diagnosis, using stringent univariate statistical criteria. Significant CpG sites were identified in genes previously linked to AML biology and prognosis (DOCK6, HOXB3, MIR10A, FOXN3/CHES1, GPX1, MYST2/KAT7, PTPRD), but some represent novel findings in AML. These preliminary results suggest an association of some AML risk factors and clinical variables with unique gene methylation and will undergo validation in a prospective AML epidemiology dataset. Disclosures Cerhan: Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Foran:Agios: Research Funding; Xencor, Inc.: Research Funding.

Abstract Background: Bendamustine and rituximab (BR) is a commonly chosen frontline treatment for grade 1-2 (G 1-2) follicular lymphoma (FL). This regimen resulted in significantly longer progression-free survival (PFS) and less toxicity compared to R-CHOP in the STiL trial [Rummel et al., 2013] and similar results were observed when comparing BR vs. R-CHOP/R-CVP in the confirmatory BRIGHT trial [Flinn et al., 2014, updated ASCO 2017]. Importantly, patients (pts) with grade 3A (G 3A) FL were excluded from these studies, yet the conclusions are often extrapolated to the treatment of G 3A pts. Notably, G 3A pts were included in the GALLIUM trial which used both bendamustine as well as CHOP backbones. Several retrospective studies of FL pts treated with frontline R-CHOP have demonstrated equivalent or improved overall survival (OS) for G 3A relative to G 1-2 FL (Koch et al., 2016, Mustafa et al., 2017, Yuan et al., 2017, Maeshima et al., 2013). Early disease progression after diagnosis and treatment with frontline R-CHOP has been identified as a strong predictor of poor OS in FL pts [Casulo et al. 2015], but has not been assessed in pts treated with frontline BR. We retrospectively evaluated outcomes of a large cohort of FL pts treated with frontline BR and stratified the results based on histologic grade and early vs. late progression. Methods: We reviewed medical records of adult (age >18) pts with FL treated with frontline BR at 18 US cancer centers. There was no central pathology review; each academic institution confirmed the diagnosis and grade of FL. Pts with unknown grade, grade 3B, or cutaneous FL were excluded. Baseline characteristics between grades were evaluated with the Chi-Square test for categorical variables and the Mann-Whitney U for continuous variables. Outcomes were calculated from time of initiation of BR. Patients were grouped according to whether or not they had progression of disease within 24 months of BR initiation (POD24). OS and PFS were estimated using the Kaplan-Meier method and compared using the log-rank test. Results: 687 pts were included (616 G 1-2, 71 G 3A). Median age at diagnosis was 60 years (range 21-94) with 53% men. The median time from diagnosis to treatment with BR was 1 month (range 0-139). The median duration of follow up for the entire cohort was 39 months. Characteristics including age, gender, ethnicity, ECOG, stage, LDH, and FLIPI are shown in the Table, and did not significantly differ between G 1-2 and G 3A pts. Rates of complete response (CR) and partial response (PR) to BR were similar between G 1-2 and G 3A (72%/22% vs. 66%/21%, respectively, p=0.114) but progressive disease (PD) was higher in G 3A pts (11.9%) relative to G 1-2 (4.8%, p=0.025). As shown in the Figure (A and B), 3-year PFS and OS was significantly longer for G 1-2 vs. G 3A pts (75% vs. 65%, log rank p= 0.035 and 90% vs. 86%, log rank p=0.007, respectively). Seventy-six (12.3%) of the 616 G 1-2 pts and 13 (18.3%) of the 71 G 3A pts experienced POD24 (p=0.19). For pts with both G 1-2 and G 3A FL, POD24 was associated with inferior OS (3-year OS 95% vs. 57% for G 1-2 pts, log rank p<0.0001 and 3-year OS 93% vs. 37% for G 3A pts, log rank p<0.0001) compared to patients without POD24. Conclusions: G 3A FL pts have an inferior OS and are significantly more likely to have PD to frontline BR compared to G 1-2, which differs from the published experience with R-CHOP. Similar to outcomes after frontline treatment with R-CHOP, POD24 after frontline BR for FL is associated with very poor OS. Future studies are needed to address optimal frontline management of pts with G3A as well as for pts with any grade FL with POD24 after frontline BR. Disclosures Nastoupil: Novartis: Honoraria; Genentech: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; TG Therappeutics: Research Funding; Karus: Research Funding; Merck: Honoraria, Research Funding; Spectrum: Honoraria; Janssen: Research Funding; Juno: Honoraria. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Maurer:Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Smith:Genentech: Research Funding; Incyte Corporation: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Merck Sharp and Dohme Corp.: Consultancy, Research Funding; Acerta Pharma BV: Research Funding; Seattle Genetics: Research Funding. Lossos:Affimed: Research Funding. Portell:TG therapeutics: Research Funding; Amgen: Consultancy; Kite: Research Funding; AbbVie: Research Funding; BeiGene: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Research Funding; Infinity: Research Funding. Calzada:Seattle Genetics: Research Funding. Cohen:Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Seattle Genetics: 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: Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; BioInvent: Consultancy. Ghosh:Spectrum: Consultancy; SGN: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics, an Abbvie Company: Consultancy, Research Funding, Speakers Bureau; TG Therapeutics: Honoraria, Research Funding; Celgene: Consultancy; Juno: Consultancy, Research Funding; Abbvie: Consultancy, Speakers Bureau; Genentech: Research Funding; F. Hoffman-La Roche Ltd: Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Forty seven Inc: Research Funding. Barta:Janssen: Membership on an entity's Board of Directors or advisory committees; Merck, Takeda, Celgene, Seattle Genetics, Bayer: Research Funding. Caimi:Kite Pharma: Other: Advisory Board Participation; Genentech: Other: Advisory Board PArticipation, Research Funding; Celgene: Speakers Bureau; Kite Pharma: Other: Advisory Board Participation. Danilov:Gilead Sciences: Consultancy, Research Funding; Aptose Biosciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Astra Zeneca: Consultancy; Genentech: Consultancy, Research Funding; Takeda Oncology: Research Funding; TG Therapeutics: Consultancy; Verastem: Consultancy, Research Funding. Martin:AstraZeneca: Consultancy; Seattle Genetics: Consultancy; Janssen: Consultancy; Kite: Consultancy; Gilead: Consultancy; Bayer: Consultancy. Kamdar:Seattle Genetics: Speakers Bureau; Genentech: Consultancy. Kahl:AstraZeneca: Consultancy; Acerta: Consultancy; Juno: Consultancy; CTI: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Abbvie: Consultancy; Genentech: Consultancy; ADC Therapeutics: Consultancy. Hill:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract Backgroud. The treatment landscape of mantle cell lymphoma (MCL) has significantly changed in last decades. Improvement in diagnosis and understanding of disease biology has been coupled with emergence of new therapeutic options, including targeted agents. While MCL outcome data comes primarily from clinical trials (CT), the impact of therapeutic advances on pattern of care (POC) and outcome in MCL in the general population is not well characterized. This study sought to characterize changes in pattern of care and outcomes of patients with MCL in a prospective observational series in the rituximab era. Methods. The study included consenting adult patients with newly diagnosed MCL that were prospectively enrolled into the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) Molecular Epidemiology Resource, from 09/01/2002 to 06/30/2015. Demographic, clinical and prognostic factors were abstracted, and all patients were actively followed for re-treatment, relapse and death. Since bendamustine-rituximab (BR) regimen starting to be used in 2010, we defined patients enrolled from 09/01/2002 to 12/31/2009 as era 1 and those enrolled 01/01/2010 to 06/30/2015 as era 2. Baseline characteristics and outcomes, evaluated in terms of event free survival (EFS), overall survival (OS) and cause of death, were analyzed and compared between the two eras identified. Results. 348 patients with newly diagnosed MCL were enrolled. Five patients had no available data for front-line treatment and were excluded. The analysis was thus conducted on 343 patients: 169 patients were diagnosed in era 1 with a median follow-up of 131.2 months vs. 174 in era 2 with a median follow-up of 58.9 months. Baseline clinical characteristics and MIPI score were similar across the two eras ( Table 1). Frontline induction treatment was significantly different in the two eras. BR use was 0 vs 49 (28.2% ), R-CHOP/CHOP like regimen in 89 (52.7%) vs 45 (25.9%), high-dose cytarabine (HiDAC)-based therapy in 1 (0.6%) vs 28 (16.1%), intensified regimens (HyperCVAD) in 16 (9.5%) vs 8 (4.5%), other regimens (including R-cladribine, R-fludarabine-mitoxantrone, rituximab monotherapy) in 35 (20.7%) vs 13 (7.5%) patients in era 1 vs era 2 respectively. Non-systemic treatment (observation, surgery or radiation only) was performed in 25 (14.8%) vs 31 (17.8%), while 9 (8%) vs 12 (7.7%) patients were enrolled in clinical trials, in era 1 vs era 2 respectively. Autologous stem cell transplantation (ASCT) as consolidation of first line treatment or use of Rituximab maintenance was not different between Era 1 and Era 2. Among the entire cohort of 343 MCL patients, 3y-EFS and 3y-OS were 51.9% (95% IC 46.7-57.6) and 73.5 (95% CI 68.8-78.4), respectively (Figure 1). 3y-EFS was 45.9% (95% CI 39.0-54.1%) vs 58.4 (95% CI, 51.2-66.6%) in Era 1 vs Era 2 (HR 0.69 (0.51-0.92), p=0.006), 3y-OS was 70.9% (95% CI, 63.4-78.1%) vs 76.1% (95% CI, 69.7-83.1%) in Era 1 vs Era 2 (HR 0.87 (0.61-1.24), p=0.26), respectively (Figure 1). In a univariate analysis, high risk simplified MIPI was prognostic of lower EFS and OS in both the era groups. Conclusion. The BR regimen entered front line therapy of MCL resulting in decline of R-CHOP use in the MER. While rates of ASCT remain similar over the two eras, high dose cytarabine usage in induction therapy has increased. While POC in MCL continue to evolve, the introduction of bendamustine and high-dose citarabine based regimens resulted in an improvement in EFS but not OS in this observational cohort-based analysis. Findings in this study are important for design and planning of future clinical trials incorporating novel agents in induction therapy of MCL. Figure 1. Figure 1. Disclosures Cohen: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.