Citations

Abstract Overweight [body mass index (BMI) 25.0-29.9 kg/m2] and obesity (BMI ≥30 kg/m2) are risk factors for several malignancies. The Iowa Women's Health Study was examined to determine whether increased BMI was associated with leukemia development. Over 40,000 Iowa women (ages 55-69 years) completed a self-administered lifestyle and health questionnaire in 1986 that included current height and weight. Two hundred women developed leukemia during the period 1986 to 2001 including 74 acute myelogenous leukemia (AML) and 88 chronic lymphocytic leukemia. The risk of AML was increased among women who reported being overweight or obese (relative risk, 1.9; 95% confidence interval, 1.0-3.4; relative risk, 2.4; 95% confidence interval, 1.3-4.5; Ptrend = 0.006) compared with women of normal weight. There was little evidence of a positive association for chronic lymphocytic leukemia (Ptrend = 0.6). Given the prevalence of overweight and obesity in the United States, the population attributable risk of AML due to obesity could approach 30%.

Abstract In 1997, the American Institute for Cancer Research (AICR) published 14 recommendations related to diet for individuals to reduce cancer incidence on a global basis; smoking was also discouraged. We operationalized these into nine recommendations that are particularly relevant to western populations in a cohort of 29,564 women ages 55 to 69 years at baseline in 1986 who had no history of cancer or heart disease. The cohort was followed through 1998 for cancer incidence (n = 4,379), cancer mortality (n = 1,434), cardiovascular disease (CVD) mortality (n = 1,124), and total mortality (n = 3,398). The median number (range) of recommendations followed was 4 (0-8), and 33% of the cohort had ever smoked. Women who followed no or one recommendation compared with six to nine recommendations were at an increased risk of cancer incidence [relative risk (RR) 1.35, 95% confidence interval (CI) 1.15-1.58] and cancer mortality (RR 1.43, 95% CI 1.11-1.85), but there was no association with CVD mortality (RR 1.06, 95% CI 0.78-1.43). We calculated the population attributable risk (PAR) to estimate the proportion of cancer incidence, cancer mortality, and CVD mortality that theoretically would have been avoidable if the entire cohort had never smoked, had followed six to nine recommendations, or had done both. The PARs for smoking were 11% (95% CI 10-13) for cancer incidence, 21% (95% CI 17-24) for cancer mortality, and 20% (95% CI 16-23) for CVD mortality. The PARs for not following six to nine recommendations were 22% (95% CI 12-30) for cancer incidence, 11% (95% CI −5 to 24) for cancer mortality, and 4% (95% CI −20 to 19) for CVD mortality. When smoking and the operationalized AICR recommendations were combined together, the PARs were 31% (95% CI 19-37) for cancer incidence, 30% (95% CI 15-40) for cancer mortality, and 22% (95% CI 4-36) for CVD mortality. These data suggest that the adherence to the AICR recommendations, independently and in conjunction with not smoking, is likely to have a substantial public health impact on reducing cancer incidence and, to a lesser degree, cancer mortality at the population level.

7573 Background: While most patients (pts) with follicular lymphoma (FL) usually have favorable outcomes, those with refractory disease after first-line anti-CD20 based immunochemotherapy (IC), or progression within 24 months of diagnosis (POD24) have higher risk of premature death. There are no standard approaches for treating this vulnerable group and studies testing novel agents are ongoing in this setting. We sought to investigate clinical practice treatment choices and efficacy for pts with POD24 that align with eligibility criteria for the randomized SWOG1608 which compares IC with novel agents in this population. Methods: This was a multicenter observational cohort study from the LEO Consortium. Eligible pts had grade 1-3a FL diagnosed between 1/1/2002 and 2/1/2019, and initiated therapy after POD24 to first-line bendamustine or CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone) based IC. Observation, radiotherapy, or rituximab monotherapy were permitted prior to IC and pts with transformation prior to the subsequent therapy after IC were excluded as per S1608. Outcomes of interest were overall and complete response rate (ORR/CR), progression-free survival (PFS), and overall survival (OS). Results: We identified 196 eligible pts with early progression to IC (39% antiCD20 Benda; 61% antiCD20 CHOP) who received subsequent therapy. Median age at post IC treatment was 57 years, 78% grade 1-2 FL. Treatments for pts with POD24 included CHOP- or Benda-based in 31%, salvage/hematopoietic stem cell transplant (HSCT) in 27%, novel therapies in 10% (including phosphatidylinositol 3-kinase inhibitors), antiCD20 monotherapy in 9%, and lenalidomide-based treatment in 8% (table); 21% of pts were treated on clinical trials. Across all treatments, ORR (CR) was 63% (37%) (95% CI: 55-70). At a median follow up of 6.2 years, 2 year PFS was 22% (95% CI: 17%-29%) and 5 year OS was 71% (95% CI: 65-79). Outcomes by regimen are shown in the table. Conclusions: Pts with FL experiencing POD24 following first-line IC are treated heterogeneously, with many pts still receiving IC as subsequent therapy. Despite modest CR rates and low 2-year PFS, 5-year OS appear to be improving compared to historical outcomes. This supports the ongoing need to investigate novel treatments in this population. [Table: see text]

7562 Background: Lymphoma is the 6th most common cancer and a top 10 cause of cancer deaths. To date, there are no blood test approaches to population screening, and accurate surveillance markers are limited. Methylated DNA markers (MDMs) are broadly informative for early detection of cancer but have not been extensively studied for lymphomas. We sought to discover and validate MDMs in DNA extracted from lymphoma tissue, then test these MDMs in archival blood plasma specimens. Methods: Reduced representation bisulfite sequencing (RRBS) was performed on DNA extracted from a discovery set of frozen tissues of classic Hodgkin lymphoma (CHL, n=24), non-Hodgkin lymphoma (NHL, n=78: T-cell-TCL (8); diffuse large B-cell-DLBCL (18); follicular-FL (12); mantle cell-MCL (20); marginal zone-MZL (15)), and lymphoma cell line samples (27); controls included benign lymph node (n=11) and healthy donor buffy coat (n=30). 30 MDMs were ranked by fold-change and AUC and used to design methylation-specific PCR assays for biological validation on independent DNA samples extracted from FFPE tissue from 13 normal lymph node and 63 lymphoma samples and 36 buffy coats from healthy patients. Target enrichment long-probe quantitative-amplified signal assays were developed for 16 MDMs and then assayed in plasma-extracted, bisulfite-converted DNA samples from 390 independent treatment-naïve lymphoma patients (100 CHL and 290 NHL:100 DLBCL, 73 FL, 41 MCL, 41 MZL, 35 TCL), and 210 controls without cancer. Lymphoma plasma samples and 159 controls were provided by the Lymphoma SPORE (CA97274); 51 controls came from a 7-county population archive. Classification of lymphoma cases vs controls was modeled with random forest method and cross-validated across 500 bootstrap samples of each dataset. Results: For MDMs tested in DNA from independent biological validation samples, cross-validated random forest models identified 60/63 cases (95% sensitivity) and 12/13 normal controls (92% specificity). In plasmas, a panel of 16 MDMs ( ZNF503, VWA5B1, HOXA9_5195, GABRG3, ITGA5, MAX_chr17_793, BNC1_2407, CDK20, MAX_chr4_184, TPBG, DNAH14, SYT2, CACNG8, FAM110B, and NRN1) detected 78% (95% CI, 74-82%) of lymphoma cases at 90% specificity. Excluding MZL and TCL, sensitivity increased to 84% (80-88%) including 26/49 (53% (38-67%)) of stage I and 59/71 (83% (71-90%)) of stage II cases. Conclusions: MDMs show promise to detect lymphoma. These markers could be evaluated as part of multicancer early detection testing and could also be evaluated as response markers to treatment and subsequent surveillance.[Table: see text]

PURPOSE: This study assessed the long-term quality of life (QOL) of patients with aggressive lymphoma subtypes treated with autologous hematopoietic cell transplant (autoHCT) compared with those without history of transplant. METHODS: Patient-reported QOL measures were prospectively gathered from patients enrolled in the Iowa/Mayo Specialized Program of Research Excellence Molecular Epidemiology Resource cohort with aggressive lymphoma subtypes. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G), Functional Assessment of Chronic Illness Therapy–Fatigue Scale, State-Trait Anxiety Inventory (STAI), and Profile of Mood States instruments and with a numeric rating scale for overall QOL and spiritual QOL. The autoHCT group and no HCT groups were compared at 3 years (FU3) and 6 years (FU6) after lymphoma diagnosis. RESULTS: In total, 980 patients with lymphoma (106 autoHCT and 874 no HCT) diagnosed between 2002 and 2013 were included for analysis. The mean FACT-G total score was similar in the autoHCT and no HCT groups at FU3 (89.9 v 90.1, P = .64) and also at FU6 (91.5 v 89.6, P = .44). No differences between the autoHCT and no HCT groups were identified in the FACT subscales. The STAI identified lower anxiety in the autoHCT group by mean STAI1 (state) at FU3 (30.1 v 33.4, P < .01) and by mean STAI2 (trait) at FU6 (30.1 v 33.5, P = .02). No other clinically meaningful differences were identified between the two groups using the other QOL instruments. CONCLUSION: Patients remaining in remission at 3 and 6 years after diagnosis had a high level of QOL with no significant differences associated with history of treatment with autoHCT.

PURPOSE: Patients' concerns regarding clinical trial (CT) participation include apprehension about side effects, quality of life (QoL), financial burden, and quality of care. METHODS: We prospectively evaluated the experience of patients with multiple myeloma or lymphoma who were treated on CTs (CT group, n = 35) versus patients treated with standard approaches (non-CT group, n = 88) focusing on QoL, financial burden of care, and patients' perception of quality of care over a 1-year period. RESULTS: There were no significant differences in any of the patient-reported outcomes in CT versus non-CT groups. We observed an initial decline in overall QoL in the first 3 months across both groups, driven primarily by physical and functional well-being. QoL gradually improved and was above baseline by month 12. Patients reported highest improvement in the functional well-being subdomain. Patients in both groups reported high satisfaction with the quality of care received, and there were no differences in overall satisfaction, communication with team, or access to care. At baseline, 16%-19% of patients reported financial burden, which increased to a peak of 33% in the CT group and to 49% in the non-CT group over the course of 1 year. There was no significant difference in financial burden in the two groups overall. Most of the patients reported getting all the care that was deemed medically necessary in both groups. However, a significant proportion of patients reported having to make other kinds of financial sacrifices because of their cancer (CT group: 33% of patients at baseline and 21%-40% over 1 year; non-CT group: 19% at baseline and 25%-36% over 1 year). CONCLUSION: Patients treated on CTs reported comparable QoL and quality of care with the non-CT group. A high proportion of patients reported financial burden over time in both groups. Our findings can serve as a guide to educate patients regarding CT participation and highlight the need to address the significant financial burden experienced by patients with cancer.

Abstract Background: A previous International Lymphoma Epidemiology (InterLymph) Consortium evaluation of joint associations between five immune gene variants and autoimmune conditions reported interactions between B-cell response-mediated autoimmune conditions and the rs1800629 genotype on risk of B-cell non–Hodgkin lymphoma (NHL) subtypes. Here, we extend that evaluation using NHL subtype-specific polygenic risk scores (PRS) constructed from loci identified in genome-wide association studies of three common B-cell NHL subtypes. Methods: In a pooled analysis of NHL cases and controls of Caucasian descent from 14 participating InterLymph studies, we evaluated joint associations between B-cell–mediated autoimmune conditions and tertile (T) of PRS for risk of diffuse large B-cell lymphoma (DLBCL; n = 1,914), follicular lymphoma (n = 1,733), and marginal zone lymphoma (MZL; n = 407), using unconditional logistic regression. Results: We demonstrated a positive association of DLBCL PRS with DLBCL risk [T2 vs. T1: OR = 1.24; 95% confidence interval (CI), 1.08–1.43; T3 vs. T1: OR = 1.81; 95% CI, 1.59–2.07; P-trend (Ptrend) &lt; 0.0001]. DLBCL risk also increased with increasing PRS tertile among those with an autoimmune condition, being highest for those with a B-cell–mediated autoimmune condition and a T3 PRS [OR = 6.46 vs. no autoimmune condition and a T1 PRS, Ptrend &lt; 0.0001, P-interaction (Pinteraction) = 0.49]. Follicular lymphoma and MZL risk demonstrated no evidence of joint associations or significant Pinteraction. Conclusions: Our results suggest that PRS constructed from currently known subtype-specific loci may not necessarily capture biological pathways shared with autoimmune conditions. Impact: Targeted genetic (PRS) screening among population subsets with autoimmune conditions may offer opportunities for identifying those at highest risk for (and early detection from) DLBCL.

Introduction: Anthracyclines are a first-line chemotherapeutic agent used in the treatment of lymphoma. Myocardial injury can occur with cumulative doses of 250 to 350 mg/m 2 . Echocardiography is the imaging modality for identifying cardiac dysfunction; however, identifying diastolic changes in patients who received anthracyclines is not well represented in the literature. Variables used to identify diastolic dysfunction include lateral e’, medial e’, mitral E velocity, mitral A velocity, E/A, E/e’, and left atrial volume index (LAVI). This study aims to identify if diastolic abnormalities develop in a prospective cohort of patients with HL or NHL who received anthracycline-based chemotherapy. Methods: Lymphoma patients treated with anthracyclines from 2013 to 2020 were prospectively enrolled. Echocardiography was obtained at 3-time points: before the start of chemotherapy (T1), at 3-6 months (T2), and 9-12 months (T3). Diastolic variables were measured and analyzed based on the total cumulative dose of Doxorubicin per body surface area. Those that received greater than 250 mg/m 2 and greater than 300 mg/m 2 of Doxorubicin were compared to those that did not. Statistical analysis was done using one-way ANOVA, adjusting for age and gender. Results: 151 total patients (90 males, mean age 61 + 16 years); 21 (14%) HL, 130 (85%) NHL. 114 and 66 patients received a total cumulative dose of Doxorubicin greater than 250 mg/m 2 and 300 mg/m 2 , respectively. There was no significant association with 250 mg/m2. Lateral e’ at T3 vs. T1, was lower in those that received greater than 300 mg/ m 2 compared to those received less (0.09 vs 0.10, p < 0.02) (Table 1). Conclusions: Lateral e’ decreased at 1-year follow-up in those that received higher doses of Doxorubicin. These changes in the lateral e’ could reflect early relaxation abnormalities related to anthracycline-based chemotherapy and suggest that changes in lateral e’ should be considered by itself since E/e’ was normal.

Abstract Background: Differences in outcomes between clinical trial and real-world populations in follicular lymphoma (FL) may be partially explained by differences in characteristics between clinical trial and real-world patients. We hypothesized that certain eligibility criteria may be responsible for populations with distinct demographic/clinical characteristics. In this study, we compiled a list of eligibility criteria from front-line FL trials and evaluated their impact on the potentially eligible FL patients derived from two national cohort studies. We also evaluated whether criteria were duplicative and whether liberalizing criteria might enlarge the pool of eligible patients. Methods: Eligibility criteria were abstracted from 21 first-line clinical trials in FL in the FLASH database. We identified FL patients in two prospective cohort studies: Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO). Descriptive statistics were used to characterize patients included and excluded by various eligibility criteria. Chi-square tests and two-sample t-tests were used to compare categorical and continuous variables, respectively. To study the relative impact of individual criteria, we used a step-wise approach to quantify the number of additional patients excluded with each individual criterion after applying other criteria. Results: Eligibility criteria included in at least one-third of studies included stage (present in all 21 trials), renal function (18), HIV/AIDS status (17), performance status (16), history of other malignancies (16), hepatic function (16), and cardiac function (15), other serious health conditions (13), pregnancy status (10), neuro/psych function (10), age (10), metabolic disease status (9), CNS involvement (9), birth control (8), pulmonary function (8), WBC count (8), HBV status (8), platelet count (7), measurable disease (7), HCV status (7), active infection status (7), and CD20 status (7). We included 738 patients with newly diagnosed FL undergoing first-line therapy from MER and 703 patients from LEO. In MER, the median age was 60 years; 87% were White vs 2% non-White, 80% were non-Hispanic vs 2% Hispanic, and 68% were stage III/IV. In LEO, the median age was 60 years; 88% were White vs 11% non-White, 87% were non-Hispanic vs 11% Hispanic, and 67% were stage III/IV. Exclusion criteria impacting more than 10% of patients included stage (31 in LEO vs 29% in MER), self-reported serious health conditions (17 vs 31%), prior cancer diagnosis (14 vs 11%), performance status (10%), and platelet count (10%). Patients excluded due to the following criteria tended to be older: renal function (median 70 vs 60 years in MER), prior malignancy (67 vs 58 years), and self-reported serious health conditions (66 vs 58 years). This pattern was consistent in LEO (Table). No eligibility criteria significantly impacted race/ethnicity. Given the potential for multiple eligibility criteria to exclude the same patients, we used a step-wise approach to evaluate the impact of individual criteria on patients remaining after exclusion by the larger group of criteria, and whether liberalizing criteria impacted patient numbers and/or demographics. We found that only self-reported serious health conditions significantly impacted numbers (excluding 43% of patients), whereas hematologic parameters and performance status excluded only 5 and 1% of patients, respectively, with no impact on age or race/ethnicity. We also found that liberalizing stage requirement from III-IV to II-IV could increase enrollment by 14% in MER (12% in LEO), and liberalizing platelet requirement from ≥150,000 to ≥100,000 could increase enrollment by 11% (15% in LEO). Moreover, liberalizing eligibility criteria had no impact on age or race/ethnicity of the patient pool or on EFS (Figure). Conclusions: We found that excluding patients with prior malignancies, poor renal function, and self-reported serious health conditions appears to inadvertently exclude older patients. Other serious health conditions appeared to exclude a significant number of patients but did not impact patient demographics. Liberalizing certain criteria, including stage and platelet requirement, could potentially increase trial accrual, but would not likely correct the older patient deficit. These findings may serve in developing consensus eligibility criteria for future first-line FL trials. Figure 1 Figure 1. Disclosures Flowers: Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Allogene: Research Funding; Biopharma: Consultancy; Cellectis: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Epizyme, Inc.: Consultancy; Genmab: Consultancy; Iovance: Research Funding; Guardant: Research Funding; Burroughs Wellcome Fund: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Spectrum: Consultancy; Gilead: Consultancy, Research Funding; Amgen: Research Funding; Xencor: Research Funding; Nektar: Research Funding; SeaGen: Consultancy; Ziopharm: Research Funding; TG Therapeutics: Research Funding; 4D: Research Funding; Acerta: Research Funding; Denovo: Consultancy; Novartis: Research Funding; Pfizer: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; BeiGene: Consultancy; AbbVie: Consultancy, Research Funding; Kite: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Pharmacyclics: Research Funding. Link: MEI: Consultancy; Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding. Friedberg: Acerta: Other: DSMC ; Bayer: Other: DSMC ; Novartis: Other: DSMC . Cohen: Genentech, Takeda, BMS/Celgene, BioInvent, LAM, Astra Zeneca, Novartis, Loxo/Lilly: Research Funding; Janssen, Adaptive, Aptitude Health, BeiGene, Cellectar, Adicet, Loxo/Lilly, AStra ZenecaKite/Gilead: Consultancy. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Lossos: Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy; NCI: Research Funding; Verastem: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; NIH grants: Research Funding; University of Miami: Current Employment. Nastoupil: Bayer: Honoraria; Gilead/Kite: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; ADC Therapeutics: Honoraria; IGM Biosciences: Research Funding; Takeda: Honoraria, Other: DSMC, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Caribou Biosciences: Research Funding; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; MorphoSys: Honoraria. Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; BMS: Research Funding. Cerhan: NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Martin: ADCT: Consultancy.