Thompson, Carrie A.

Abstract Background: Modern clinical practice places time restrictions on the amount of information and processing that can be done in a typical office visit. Meeting patient needs within the healthcare system is challenging, contributing to patient (pt) and clinician dissatisfaction and potential suboptimal outcomes. We developed an electronic point-of-care case management system for cancer patients. The system's goal is to obtain patient-reported quality of life (PROQOL) outcomes and provide clinicians with information regarding QOL issues to address. We tested this system in a prospective randomized study against usual care (UC). The primary objective was to determine if PROQOL system would improve mean QOL over time. Methods: Eligible pts were adults with multiple myeloma (MM) or light amyloidosis (AL), seen at Mayo Clinic. Pts were randomized 2:1 to PROQOL system or usual care. PROQOL system was offered prior to every visit. Pts select from various categories about their single biggest concern, and receive a printed list of actionable resources based on selected concern. Clinicians also receive the PROQOL results to review with pts. Providers and pts randomized to PROQOL completed a "was it worth it survey" (WIWI). An 8 item Linear Analogue Self-Assessment was used to assess QOL, the primary endpoint. Secondary endpoints included distribution of choices of most important concern and their relationship to demographic and clinical variables. The study was powered to detect a 0.5 standard deviation difference in QOL between groups. Results: There were 233 patients enrolled (171 MM, 62 AL) in this study between July 1, 2016 and April 17, 2018. Median age was 65 years (range 31-87), and 59% were male. Median time from diagnosis to study consent was 37 months (PROQOL 36.3 versus UC 39.6). Median follow up for all patients was 15 months (range: 0.7-22 months), and median follow up for PROQOL pts was 16 months and 14 months for UC pts (p=0.027). There were 25 pts that have died since enrollment and 208 alive. No statistical significant difference in QOL between PROQOL and UC (median QOL 7 vs 7, p=0.75). The most prevalent main concerns selected were Physical Health (37%) and Cancer and Diagnosis (27%), the pattern remained even when pts were given the option to pick a second major concern 26% and 20% respectively. No other concern was listed more than 8% at baseline. All category selections and percentages are shown in the Figure. The average time to complete the instrument was 6 minutes. Pts found the PROQOL system was worth their time (75%), they would choose it again (87%) and recommend it to others (82%). Eighty-two percent of clinicians did not think the PROQOL tool negatively impacted their practice and 75% believed they may see improvement in pt wellbeing. Discussion: The PROQOL system was well received by pts and clinicians but did not improve QOL relative to UC in this study. The PROQOL demonstrates that majority of patients strongly desire more information regarding ongoing therapy, clinical trials, prognosis and toxicity. Further investigation is needed to understand whether communication is limited by time or mechanism of information delivery, such as verbal or written, or whether it should be undertaken in a separate allied health care visit. Figure. Figure. Disclosures Gertz: spectrum: Consultancy, Honoraria; Abbvie: Consultancy; Research to Practice: Consultancy; annexon: Consultancy; Medscape: Consultancy; Physicians Education Resource: Consultancy; Amgen: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; Apellis: Consultancy; Ionis: Honoraria; celgene: Consultancy; Prothena: Honoraria. Kumar:Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Lacy:Celgene: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.

Abstract Introduction Primary testicular lymphoma (PTL) and testicular involvement of systemic lymphoma (secondary testicular lymphoma, STL) are uncommon. Most testicular lymphomas are aggressive B-cell lymphomas and portend a high risk of extranodal relapse, including CNS relapse. As such, when testicular lymphoma is identified, the management strategy is often changed to include CNS prophylaxis. It is, therefore, essential to evaluate testicular involvement in diagnosis and staging. F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a standard imaging modality to stage aggressive lymphoma. Physiologic testicular FDG activity can be challenging to distinguish from involvement by lymphoma given a wide range of normal. An imaging threshold for testicular PET uptake would be clinically relevant and would help guide management. We compare testicular FDG avidity in testicular lymphoma with physiologic testicular FDG avidity. Methods Records of patients (pts) diagnosed with PTL or STL from 2002-2018 enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource were reviewed, yielding 36 pts with testicular lymphoma. Physiologic testicular avidity by FDG PET/CT was determined from analysis of 70 randomly selected patients who received FDG PET/CT from 2013-2018 prior to treatment for non-lymphoma indications including pulmonary nodule, lung carcinoma, head & neck squamous cell carcinoma, gastrointestinal carcinomas, and metastatic melanoma, without known hematologic malignancy, testicular pathology, or history of testicular infection/surgery by medical chart review. Imaging parameters compared in this analysis were SUVmax and SUVmean. All FDG PET/CT exams were reviewed centrally by a nuclear medicine radiologist and a radiology resident. Results Of the 36 pts with testicular lymphoma, 19 had an orchiectomy prior to FDG PET/CT and 7 pts did not have a staging FDG PET/CT scan. Of the 10 pts with intact testes at the time of staging FDG PET/CT, 1 pt with low-grade lymphoma was excluded. 9 pts with aggressive lymphomas were included in this analysis: 7 pts with diffuse large B cell lymphoma (DLBCL), and 1 pt each with Burkitt lymphoma and peripheral T-cell lymphoma, not otherwise specified. Testicular lymphoma was diagnosed by orchiectomy in 3 pts, and by percutaneous biopsy in 3 pts. 3 pts did not have tissue sampling of testes or scrotal contents, but had an overtly FDG-avid testicle highly suggestive of lymphomatous involvement. These 3 pts had biopsy-proven lymphoma in parotid, bone marrow, and stomach, respectively, with mean testicular SUVmax 6.8 among the 3 pts. The median age in this analysis cohort was 62 years (range 35-88). Of the 7 analyzed DLBCL pts, 6 were non-germinal center B-cell, and 1 was germinal center B-cell. The 70 control pts had a median age of 55 years (range 18-90). In known testicular lymphoma cases and control pts, SUVmax and SUVmean were assessed from the most FDG-avid testicle. Median SUVmax for the testicular lymphoma cases was 13.3 (range 5.5-29.9), compared to a median of 3.8 (range 2.1-5.5) for controls (Wilcoxon p <0.0001). Median SUVmean for testicular lymphoma was 9.2 (range 3.3-18.6), compared to a median of 3.1 (range 1.8-4.8) for controls (Wilcoxon p <0.0001) (Figure). Based on this dataset, an SUVmax cutoff of 5.0 has a sensitivity of 100% (95% CI 66.3-100) and a specificity of 98.6% (98% CI 92.3-99.9) for the detection of testicular involvement by an aggressive lymphoma. Conclusions FDG PET/CT shows significantly greater FDG avidity for testicular aggressive lymphomas as compared with physiologic testicular FDG uptake in this single institution study with centralized radiology review of cases and controls. Testicular FDG avidity greater than an SUVmax 5.0 in staging aggressive lymphoma is highly suggestive for testicular involvement, warranting follow-up clinical exam and ultrasound, at a minimum. In addition, testicles with SUVmax <5.0 in a patient with known lymphoma but no symptoms or signs of testicular involvement should be considered negative for the purposes of staging and treatment planning. A pragmatic limitation of this study is that most patients with testicular involvement of lymphoma underwent orchiectomy prior to FDG PET/CT scan, which decreased our sample size. Larger, pooled studies would be helpful to validate these clinically relevant findings. Figure. Figure. Disclosures 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.

Abstract Background: To address the current and long-term unmet health needs of the growing population of patients diagnosed with non-Hodgkin lymphoma (NHL) and NHL survivors, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/) with recruitment at eight centers (Cornell University, Emory University, Mayo Clinic, MD Anderson, University of Iowa, University of Miami, University of Rochester, and Washington University). Here we report on the first two years of enrollment. Methods: All patients ≥18 years with NHL (exclusive of chronic lymphocytic leukemia) newly diagnosed within 183 days of enrollment are eligible. LEO reviews all pathology diagnoses; annotates and harmonizes all cases with clinical, epidemiologic, pathology and treatment data; builds a NHL tumor bank that includes an H&E slide, a formalin-fixed, paraffin-embedded tissue sample and extracted tumor DNA and RNA; collects peripheral blood and banks DNA, serum, plasma and buffy coat in a central biorepository; and prospectively follows patients regularly to ascertain disease progression/relapse, retreatment, transformation, second cancers, survival, updated exposures, and patient-reported outcomes. We examined the demographics, clinical characteristics, and NHL subtypes for patients enrolled in the first two years (2015-2017) in LEO Cohort and compared these characteristics to population-based registry data from the United States Surveillance, Epidemiology, and End Results (SEER) Program for 2011-2015. We also examined baseline body mass index (BMI), comorbidities and quality of life (QOL) measured with the Functional Assessment of Cancer Therapy - General (FACT-G) scores (0-100 scale) overall and by NHL subtype that were collected in LEO but not available in SEER or any other large, prospective US NHL cohort. Results: From 2015-2017, LEO enrolled 3244 NHL patients across 47 states (Figure) with a median age at diagnosis of 62 years (range, 18-94 years) and 55.9% male. Based on self-identified race/ethnicity, 85% of the participants were White, 7.4% were Black/African American, 2.6% were Asian, and 3.5% other or more than one race; 9.9% were Hispanic (regardless of race). The most common subtypes were diffuse large B-cell (DLBCL, 33.9%), follicular (FL, 22.2%), mantle cell (MCL, 9.3%), marginal zone (MZL, 8.3%), T-cell (TCL, 10.9%), and all other NHL subtypes (other NHL, 15.3%). These distributions were all within 5% of SEER data, except that 6.8% of participant in LEO were in the age 80+ years group compared to 18.5% in SEER (Table). The overall prevalence of self-reported comorbidities at enrollment was 13.5% for heart disease (range, 12.1% for FL to 16.6% for MCL), 8.8% for diabetes (range, 7.1% for FL to 9.8% for DLBCL), 4.6% for osteoporosis (range, 3.0% for MCL to 6.3% for MZL), and 5.9% for autoimmune disease (range, 2.0% for MCL to 12.6% for MZL). The median BMI was 27.9 kg/m2 (range, 26.7 kg/m2 for TCL to 28.3 kg/m2 for MZL). A total of 2151 participants completed the FACT-G with a median score of 78.7 (range, 75.9 for TCL to 83.3 for MZL). Complete IPI data were available on 2296 participants: 40.4% IPI 0-1, 32.7% IPI 2, 18.3% IPI 3, 8.7% IPI 4-5. Overall, 73.7% of participants were initially treated with systemic therapy (range, 40.0% for MZL and 91.8% for DLBCL); 14.5% were initially managed with observation (range, 1.4% DLBCL and 31.8% for FL); and 2.0% were initially manage with radiotherapy (range, 0.2% for MCL and 3.8% for DLBCL). Outcomes are being prospectively collected. Conclusions: LEO is a large and diverse cohort of newly diagnosed NHL patients and the first two years of enrollment reflects the broader United States NHL population. Enrollment is ongoing. This unique resource will enable examination of the interactions among a broad array of clinical and molecular factors and their impact on multiple outcomes, to improve prognostication, identify new approaches to improve outcomes and survivorship, and facilitate clinical trials using this infrastructure. Disclosures Flowers: Acerta: Research Funding; Pharmacyclics/ Janssen: Consultancy; OptumRx: Consultancy; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; Burroughs Wellcome Fund: Research Funding; Spectrum: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Abbvie: Consultancy, Research Funding; Bayer: Consultancy; Millennium/Takeda: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; Gilead: Consultancy; BeiGene: Research Funding; Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Genentech/Roche: Consultancy; Gilead: Research Funding; Genentech/Roche: Research Funding; National Cancer Institute: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Research Funding. Nastoupil:TG Therappeutics: Research Funding; Merck: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Research Funding; Spectrum: Honoraria; Gilead: Honoraria; Genentech: Honoraria, Research Funding; Karus: Research Funding; Novartis: Honoraria; Juno: Honoraria. Kahl:Acerta: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Juno: Consultancy; CTI: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Abbvie: Consultancy. Casulo:Celgene: Research Funding; Gilead: Honoraria. Friedberg:Bayer: Honoraria. Lossos:Affimed: Research Funding. Martin:Seattle Genetics: Consultancy; Kite: Consultancy; Janssen: Consultancy; Gilead: Consultancy; Bayer: Consultancy; AstraZeneca: Consultancy. Leonard:Bayer: Consultancy; Celgene: Consultancy; Biotest: Consultancy; United Therapeutics: Consultancy; BMS: Consultancy; MEI Pharma: Consultancy; Juno: Consultancy; Sutro: Consultancy; Karyopharm: Consultancy; ADC Therapeutics: Consultancy; Pfizer: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy; Genentech/Roche: Consultancy; Novartis: Consultancy. Bernal-Mizrachi:Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership; Takeda Pharmaceutical Company: Research Funding. Maurer:Morphosys: Research Funding; Nanostring: Research Funding; Celgene: Research Funding. Cerhan:Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Background: Limited data exist regarding histological transformation in patients with Waldenström macroglobulinemia (WM). In this study, we present the disease characteristics, outcomes and risk factors for histological transformation in WM. Methods: Patients with WM seen at Mayo Clinic, Rochester, MN between January 1996 and December 2017 were included. Patients with aggressive non-Hodgkin lymphoma in the setting of WM were considered to have transformation. Univariate analysis for comparing baseline characteristics of transformed versus non-transformed WM was performed using Wilcoxon, Fisher's exact and Chi square tests, as appropriate. Multivariate analysis was performed using logistic regression analysis. Cox proportional hazard method was used to assess the impact on time-to-transformation. All time-to-event analyses were calculated by the Kaplan-Meier method. Results: Of 1014 patients with WM, 42 patients (4.1%) developed histological transformation. The median follow-up for the entire cohort was 9.5 years (95% CI: 8.8-10.5 years). The cumulative probability of transformation was 2.3% at 5 years, 5.3% at 10 years and 8.5% at 15 years from diagnosis of WM, respectively. There was no difference in the 5-year cumulative probability of transformation in patients with WM diagnosed between 1996-2000 (2.1%), 2001-2005 (2.9%), 2006-2010 (2.7%) and 2011-2015 (3.8%), p=0.61.The disease characteristics, laboratory parameters and histology at transformation are outlined in Table 1. In patients with DLBCL histology (n=39), Revised-International Prognostic Index was calculable in 28 patients, with 50% (n=14), 46% (n=13) and 4% (n=1) of DLBCL belonging to poor, good and very good risk groups, respectively. For DLBCL histology, 18 patients were classifiable using Hans algorithm into germinal center B-cell (GCB) type (n=3, 17%) and non-GCB type histology (n=15, 83%). The median lines of therapy received prior to transformation for the entire cohort was 2 (range 0-9), with 5 (11%) patients not having received any therapy for WM before transformation. Number of lines of alkylator-based therapy used prior to transformation [median 1 line (range 0-4)] was comparable to that used in patients in whom WM did not occur [median 1, (range 0-6); p=0.78]. The median time-to-transformation from diagnosis of WM was 4.8 years (95% CI: 2.6- 6.8 years). Overall survival (OS) from transformed disease was 3.2 years (95% CI 1.1-3.9 years). The 10-year OS of patients with WM who transformed was lower (46%) compared to patients without transformation (60%) with a trend towards statistical significance (p=0.08), Figure 1. The MYD88L265P mutation status was available in 333 patients in the entire cohort (22/42 pts in the transformed cohort and 311/972 in the non-transformed cohort. Of the 22 patients in the transformed WM cohort, 13 (59%) exhibited MYD88WT genotype and 9 (41%) exhibited MYD88L265P genotype. The risk of transformation was higher in patients with MYD88WT status [Odds ratio 6.3 (95% CI: 2.6-15.5); p<0.0001]. Additionally, serum lactate dehydrogenase (LDH) was higher at diagnosis of WM in patients who transformed, Table 2. On multivariate analysis, MYD88WT status remained an independent predictor of disease transformation [p=0.003; odds ratio 10 (95% CI: 2.1-48)]. On univariate Cox proportional hazard analysis for identifying predictors of time-to-transformation, only MYD88WT mutational status was associated with shorter time-to-transformation with a 5-year transformation rate of 15.5 % for MYD88WT versus 2.6 % with MYDL265P mutated patients [risk ratio 6.2 (95% CI: 2.5-16.4); p<0.0001]. Treatment-related details in the transformed cases were available for 37 (90%) patients; R-CHOP was the most common (41%) frontline therapy for transformation; 33% patients underwent autologous stem cell transplantation for transformed disease during their disease course. Conclusion: Histological transformation is an uncommon event in WM but confers poorer survival. MYD88WT genotype is independently associated with histological transformation a shorter time-to- transformation in patients with WM. Disclosures Ansell: Trillium: Research Funding; Pfizer: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Takeda: Research Funding; Celldex: Research Funding; Regeneron: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding. Gertz:Apellis: Consultancy; Alnylam: Honoraria; spectrum: Consultancy, Honoraria; janssen: Consultancy; annexon: Consultancy; Prothena: Honoraria; Abbvie: Consultancy; Amgen: Consultancy; celgene: Consultancy; Teva: Consultancy; Medscape: Consultancy; Ionis: Honoraria; Research to Practice: Consultancy; Physicians Education Resource: Consultancy. Dingli:Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding.

Abstract Background: Lymphoma survivors are at risk for long term and late effects which may negatively impact quality of life (QOL). Few studies have assessed the impact of change in health behaviors (physical activity (PA), alcohol and smoking) on QOL, specifically in lymphoma survivors. Methods: Patients were prospectively enrolled <9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource cohort. At baseline and 3-year follow-up (FU3), patients self-reported QOL, smoking, alcohol use and PA. At FU3, survivors reported change in health behaviors since diagnosis. The Godin Leisure Score Index (LSI) was calculated as a measure of PA (Pophali et al. ASH 2017); modeled by tertile. QOL was measured via FACT-G; the total and sub-scales [emotional (EWB), functional (FWB), physical (PWB), and social/family well-being (SFWB)] scores were transformed to a 0-100 scale. Patients who completed <80% of QOL questions were excluded. The association of QOL with health behaviors was evaluated using Kruskal-Wallis test (categorical) and Spearman's rank correlation (continuous). Results: At baseline, 2805 participants were evaluable for QOL of which 2025 were evaluable at FU3. The median age at diagnosis was 62 (IQR 52-69) years. Majority were males (N=1148, 56.8%), with advanced stage III-IV disease (N=941, 62%) and ECOG performance status <2 (N=1935, 96%). At baseline, smoking status was current (N=237), former (N=802), never (N=1310) or not available (NA, N= 456). Current smokers had a significantly lower median QOL scores compared to former and never smokers for EWB (75 vs 79 vs 79, p<0.01), FWB (71 vs 79 vs 79, p<0.01), PWB (86 vs 89 vs 89, p=0.02), SFWB (86 vs 90 vs 89, p=0.02) and total FACT-G (67 vs 82 vs 82, p<0.01). The median number of pack years smoked among current &former smokers was 15.6 (range 0.5-126). There was a significant negative correlation of pack years smoked with FWB (p=0.05), and total FACT-G (p=0.04). At FU3, current smokers had significantly lower median scores compared to former and never smokers for FWB (80 vs 86 vs 86, p=0.01), PWB (89 vs 93 vs 93, p<0.01) and total FACT-G (82 vs 86 vs 87, p<0.01). EWB (83 vs 88 vs 88, p=0.12) and SFWB (83 vs 86 vs 86, p=0.12) were also lower than never and former smokers but not statistically significant. Smoking status was decreased (N=30), increased (N=0) and unchanged (N=1439) at FU3. There was no association of change in smoking with change in QOL. At baseline, alcohol use status was current (N=1189), former (N=506), never (N=518) and NA (N=592). Current alcohol users had the highest FWB (79 vs 75 vs 75, p<0.01) vs former and never users. Former users vs current and never users had the lowest PWB (86 vs 89 vs 89, p<0.01), SFWB (88 vs 89 vs 92, p<0.01) and total FACT-G (80 vs 83 vs 82, p<0.01). EWB was similar in all 3 groups. Among current & former alcohol users, the average number of drinks/week was 3.4. There was no association of the number of drinks/week with QOL except positive correlation with PWB (p=0.01). At FU3, alcohol use was reported as increased (N=40), decreased (N=312) and no change (N=875). There was no association between change in alcohol use and change in QOL. At baseline, participants with PA in the high vs low LSI tertile had significantly better median scores for FWB (79 vs 75, p=0.01), SFWB (93 vs 89, p<0.01) and total FACT-G (83 vs 82, p<0.01). At FU3, participants in the high vs low LSI tertile had significantly better scores on all subscales and total FACT-G with a significant positive correlation between the continuous LSI and QOL (FACT-G total and subscales). PA was reported as increased (N=107), decreased (N=394) and no change (N=495). There was a significant association of increase in PA with change in PWB (p<0.01), EWB (p=0.01), FWB (p<0.01) and total FACT-G (p<0.01) from baseline to FU3, but not with change in SFWB (p=0.06). In a sensitivity analysis, QOL was modeled for health behaviors in the subset of patients with QOL assessed prior to treatment; results were consistent with the QOL in all patients. Conclusions: Health behaviors in lymphoma survivors are significantly associated with QOL: better QOL is associated with not smoking, more PA, and moderate alcohol use. Current alcohol consumption, as opposed to former alcohol use, may be related to overall health status. An increase in PA after lymphoma diagnosis is associated with an increase in the QOL. Thus, modification of health behaviors has the potential to impact QOL in lymphoma survivors. Figure. Figure. Disclosures Cerhan: Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background An association has been identified between the diagnosis-to-treatment interval (DTI) and prognostic clinical factors and outcomes in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) (Maurer et al, JCO 2018). This association can result in inadvertent selection bias in clinical trials to exclude patients with aggressive disease due to the inability to delay treatment long enough to fulfill enrollment criteria, compromising the validity of clinical trial study results to the general population. A similar concern exists in the relapsed/refractory (r/r) DLBCL setting regarding patient selection bias against patients with aggressive disease requiring immediate treatment following relapse or progression. Here we examine the time from progression after immunochemotherapy (IC) to the initiation of first salvage chemotherapy and its association with outcome. Patients and Methods: Newly diagnosed patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER), now a subcohort of the Lymphoma Epidemiology of Outcomes (LEO) Cohort Study, and followed for progression/relapse, retreatment, and death. This analysis includes patients with their first r/r DLBCL following frontline IC who initiated aggressive salvage chemotherapy as identified and included in a previous publication (Farooq et al, BJH 2017). The progression-to-treatment interval (PTI) was defined as the time in days from date of progression from IC to initiation of salvage therapy. The date of progression was defined as either a) the date biopsy was obtained for patients who had a biopsy to confirm progressive disease or b) the date of the scan or clinical examination indicating progression in patients who did not have a biopsy performed. Event-free survival (EFS) was defined as time from initiation of first salvage therapy to progression or relapse, initiation of new anti-lymphoma therapy, or death due to any cause; overall survival (OS) was defined as time from initiation of salvage therapy until death due to any cause. Results: 162 patients with r/r DLBCL enrolled in the MER from 2002-2012 who initiated aggressive salvage chemotherapy with intent to transplant and had confirmed dates for both progression after IC and start of salvage therapy were evaluated. Median age at first progression for these patients was 64 years (range 36-76) and 104 (64%) were male. Median time from diagnosis to first progression on IC was 6.7 months (IQR: 4.6-12.7). Initial salvage therapy was R-ICE (80%), R-DHAP (8%), rituximab, oxaliplatin, cytosine arabinoside, and dexamethasone (ROAD) (6%), and other (7%). At a median follow-up of 49 months from initiation of salvage therapy (IQR: 33-74), 116 patients had died (72%). Median PTI was 6 days (IQR: 2-13). 110 patients (68%) had biopsy confirmation of disease prior to initiating salvage therapy; median PTI was 2 days (IQR: 1-7) for patients who had biopsy confirmation vs. 7 days (IQR: 3-16) in patients without biopsy confirmation, Wilcoxon p=<0.0001. There was no difference in OS from initial salvage therapy between patients who did not have biopsy confirmation (HR=1.09, 95% CI: 0.74-1.60, p=0.68) compared to patients with biopsy confirmation. Patients with short PTI (0-6 days) had significantly worse overall survival from initiation of salvage therapy (median OS = 7.6 months, HR=2.10 (95% CI: 1.43-3.08) compared to patients who initiated therapy 7 or more days from progression (median OS = 29.7 months), logrank p<0.0001. This association remained consistent (HR=2.20, 95% CI: 1.48-3.26, p<0.0001) after adjusting for age and biopsy confirmation of progression. Short PTI was also associated with inferior response rate to initial salvage therapy (51% vs. 65%, p=0.058), lower rate of proceeding to transplant after initial salvage therapy (33% vs. 55%, p=0.0063), lower rate of being event-free 24 months from initial salvage therapy (16% vs 33%, p=0.011) and lower rate of ever proceeding to transplant (44% vs. 59%, p=0.057). Conclusions: A short progression-to-treatment interval is strongly associated with inability to proceed to transplant and inferior overall survival in r/r DLBCL. These results have implications for the design and interpretation of clinical trials in the relapsed/refractory setting. Figure. Figure. Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Ansell:Trillium: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Pfizer: Research Funding; Celldex: Research Funding; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Nanostring: Research Funding; Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background Age has long been recognized as a poor prognostic factor in diffuse large B-cell lymphoma (DLBCL) and more recently, a lower baseline quality of life (QOL) in patients with aggressive lymphomas has been associated with an inferior overall survival (OS) (Thompson et al, 2018). Older patients with DLBCL are a heterogeneous population and commonly have comorbid conditions. A concerted effort to identify prognostic factors specific to this population is needed, including QOL. Event free survival at 24 months (EFS24) is a well validated endpoint in DLBCL (Maurer et al, 2014) and is clinically relevant in older patients. We hypothesized that baseline clinical factors and QOL would be associated with EFS24 this patient group. Methods Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis into the University of Iowa/Mayo Clinic Lymphoma SPORE Molecular Epidemiology Resource (MER). Patients ≥70 years of age with DLBCL were utilized in this study. All pathology was reviewed by a lymphoma hematopathologist. Clinical variables were abstracted from the medical record. QOL was assessed at enrollment using a single item Linear Analogue Self-Assessment (LASA) and the 27-item Functional Assessment of Cancer Treatment-General questionnaire (FACT-G), which includes physical, social, emotional, and functional well-being domains. QOL scores were converted to a 0-100 scale to facilitate modeling. QOL was analyzed in patients who completed 80% of the FACT-G questions. Characteristics of the study population were compared using the Wilcoxon rank sum test. Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the associations of clinical and continuous QOL variables with failure to achieve EFS24 using logistic regression models, with a stepwise approach to identify parsimonious multivariate models. Results A total of 389 patients ≥70 years were identified with DLBCL in the MER from 2002-2015; patient characteristics are described in the Table. In the entire study population, univariate predictors of EFS24 failure included age (OR per 10 years=1.85, 95% CI: 1.26-2.71), ECOG PS ≥2 (OR=4.78, 95% CI: 2.92-7.83), elevated LDH (OR=3.59, 95% CI: 2.27-5.65), stage III-IV (OR=1.94, 1.25-3.02), bulky disease (OR=2.86, 95% CI: 1.43-5.72), international prognostic index (OR=8.71 for IPI 5 vs IPI 0, 95% CI: 3.04-24.9), and receipt of an anthracycline (OR=0.25, 95% CI: 0.12-0.51), while none of the other variables in the Table, including the QOL variables, were statistically significant. In a stepwise multivariate analysis, only ECOG PS ≥2 (OR= 3.56, 95% CI: 2.09-6.06) and elevated LDH (OR=2.86, 95% CI: 1.78-4.60) remained significant predictors of EFS24 failure. QOL data were available on 220 patients, with 120 completing QOL questionnaires prior to receipt of chemotherapy. Compared to participants who completed their FACT-G before the initiation of chemotherapy, the median FACT-G physical (85.7 v. 71.4, p=0.005) and functional (71.4 vs 57.1, p=0.008) scores were lower in participants completing FACT-G after initiation of therapy, were higher on FACT-G emotional (79.2 vs 83.3, p=0.03), and showed no differences on FACT-G social (89.3 vs 91.7, p=0.60) or FACT-G total score (78.2 vs 74.1, p=0.17). Given these differences, we restricted the QOL analysis to those with pre-treatment data. In univariate analysis, the LASA score (OR= 0.98, 95% CI: 0.84-1.14), the FACT-G total (OR=0.99, 95% CI: 0.96-1.01), physical (OR=0.98, 95% CI: 0.97-1.00), social (OR=1.00, 95% CI: 0.98-1.03), emotional (OR= 1.00, 95% CI: 0.98-1.02), and functional (OR=0.99, 95% CI: 0.98-1.01) scores were not associated with EFS24 failure. In a stepwise multivariate analysis of clinical variables in patients with pretreatment QOL scores, only ECOG PS ≥2 (OR=3.56, 95% CI: 1.30-9.72) and female sex (OR=0.37, 95% CI: 0.16-0.85) were associated with failure to achieve EFS24. Conclusions In patients aged 70 years and older, ECOG PS and LDH were the strongest independent predictors of failure to achieve EFS24. As expected, physical and functional scores were lower in patients who completed their QOL after initiation of treatment. Restricting the analysis to patients with pre-treatment data, QOL was not associated with failure to achieve EFS24. Additional studies are needed to identify the most robust clinical predictors of EFS24, while QOL does not appear to be an important predictor of EFS24. Disclosures Reagan: Seattle Genetics: Research Funding. Maurer:Morphosys: Research Funding; Celgene: Research Funding; Nanostring: Research Funding. Cerhan:Celgene: Research Funding; Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board.

Abstract Background: The frequency of preventive health practices (PHP), including age-appropriate screening for malignancies and immunizations, and compliance with PHP guidelines in lymphoma survivors is not known. We hypothesized that lymphoma survivors are more compliant with PHP than the general population but PHP rates may differ based on patient, disease and treatment characteristics. Methods: We identified eligible lymphoma patients from the SPORE Molecular Epidemiology Resource (MER) cohort, in which they were prospectively enrolled within <9 months of diagnosis. Survivors self-reported their PHP (colon, breast, and prostate cancer screening and influenza immunization) in the 3-year follow-up questionnaire (FU3). Per US Preventative Task Force guidelines, survivors were considered eligible for colon cancer screening if they were >50 years and compliant if they had fecal occult blood test <1 year or sigmoidoscopy/colonoscopy <10 years of FU3. Eligible female survivors age 50-75 were considered compliant if they had a mammogram after lymphoma diagnosis. Since USPTF recommended against PSA for prostate cancer screening in 2012, males aged 55-69 prior to 2012 were eligible and considered compliant if PSA test was <1 years of FU3. Survivors who received influenza vaccine <1 year of FU3 were considered compliant. Chi-square test was used to compare characteristics of compliant vs non-compliant patients; p value <0.05 was considered statistically significant. Results: Of 4248 lymphoma patients enrolled in the MER from 2002 to 2012, 2038 participants completed FU3. 1105 (54%) were male. The median age at diagnosis was 61 (IQR 51-69) years. There were equal numbers of indolent (N=1002) and aggressive (N=995) histologies with follicular (N=515, 25%) and diffuse large B-cell (N=427, 21%) being the most common. Majority (N=1221, 60%) had advanced stage (Ann Arbor III-IV) disease. 469 (23%) had events prior to FU3 and majority 1777 (88%) had received lymphoma treatment prior to FU3. Of 2038 survivors who completed FU3, 1576 (79%) reported having colon cancer screening in their lifetime. 1328 (91%) of 1570 eligible survivors were compliant, 124 (9%) were not and 118 had missing information. Survivors of age >60 years (93% vs 88%, p<0.001), those with indolent lymphomas (93% vs 90%, p=0.046), or diagnosed between 2010-2012 (94% vs 90% 2002-2009, p=0.007) were more likely to be compliant. There was no difference in colon cancer screening by sex, events prior to FU3 or treatment prior to FU3. 872 (96%) of 933 female survivors reported having a breast exam and 836 (91%) had a mammogram at least once in their lifetime. 495 (96%) of 561 eligible female survivors were compliant, 23 (4%) were not and 43 had missing information. There was no difference in mammographic screening based on age (<=60 vs >60), histology (indolent vs aggressive), events prior to FU3, treatment prior to FU3 or year of diagnosis (2010-2012 vs 2002-2009). 737 (78%) of 1105 male survivors had a PSA test at least once in their lifetime. 115 (71%) of 216 eligible men were compliant, 47 (29%) were not and 54 had missing information. There was no difference in PSA screening by age (<=60 vs >60), histology (indolent vs aggressive), events prior to FU3, and treatment prior to FU3. 1818/2038 (90%) survivors had received an influenza vaccine in their lifetime. 1567 (81%) were compliant, 373 (19%) were not and 98 had missing information. Survivors >60 years of age (87% vs 75%, p<0.001) and those with indolent lymphoma (83% vs 78%, p=0.015) were more likely to be compliant. There was no difference in influenza vaccination by sex, events prior to FU3, treatment prior to FU3, and year of diagnosis (2010-2012 vs 2002-2009). As a comparison in the US general population [Figure], between 2002-2015 the highest rates published by the Center for Disease Control are 62% for colon cancer screening (2015), 75% for mammography (insured, 2003) and influenza vaccination 42% (2015). Per the American Cancer Society, the highest rate of PSA screening was 54% (2010). Conclusion: Lymphoma survivors have high compliance with PHP recommendations compared to the general population. Although older individuals and those with indolent histologies were more likely to receive influenza vaccination and colon cancer screening, this difference was not seen in breast and prostate cancer screening practices. Lymphoma treatment and events did not seem to affect the frequency of PHP among survivors. Figure. Figure. Disclosures Cerhan: Nanostring: Research Funding; Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding.

Abstract Abstract 2656 Background Treatment of non-Hodgkin lymphoma (NHL) can lead to development of cardiovascular disease (CVD). We sought to describe the cumulative incidence of CVD in adult NHL survivors diagnosed in the recent treatment era (since 2002) and identify clinical and treatment predictors for its development. Methods All patients 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). The MER offers enrollment to all consecutive patients with newly diagnosed NHL who are US residents and age >18 years. Clinical data from the time of diagnosis and treatment data are abstracted from medical records using a standard protocol. Patients are prospectively contacted via telephone or in person per protocol every 6 months for the first 3 years from diagnosis and yearly afterwards to assess disease status and development of comorbid conditions. CVD events, including heart failure (HF), myocardial infarction (MI), arrhythmia, pericarditis, and valvular heart disease, occurring after diagnosis were identified during follow-up and validated against medical records. HF was validated with the Cardiovascular Health Study Criteria and/or the Framingham Criteria. MI was validated using case definition standards of coronary heart disease, while arrhythmia, pericarditis, and valvular heart disease were validated using clinical definitions. The prevalence of CVD and associations between CVD and clinical characteristics (sex, age) and treatment (radiation, anthracyclines) were performed using Cox models with a competing risk approach. Results 1164 patients with NHL were enrolled into the MER at Mayo Clinic between 9/1/2002–2/28/2008. 646 were male (56%) and median age at diagnosis was 62 years (range 20–93). Median follow-up of all cases was 59 months (range 1–105). 131 patients reported CVD prior to the diagnosis of NHL and were excluded from analyses. An additional 76 patients did not have follow-up and were excluded. Of the 957 remaining patients, 75 (7.8%) self-reported a new diagnosis of CVD. Of these, 71 cases had available medical records. 57 of the 71 reviewed cases (80%) were validated (18 HF, 9 MI, 21 arrhythmia, 2 pericarditis, and 10 valvular heart disease). Cumulative incidence of CVD at 1, 3, 5, and 7 years was 1.3%, 3.7%, 5.2%, and 7.4%, respectively. Median time from NHL diagnosis to CVD was 26.5 months (range 1–84). Older age was associated with increased risk of overall CVD (p-value<0.001). Gender (p=0.59), radiation therapy (p=0.61), and anthracycline treatment (p=0.25) were not associated with the incidence of overall CVD. Among types of CVD, anthracycline use was associated with development of HF (HR=5.30; p-value=0.008) and arrhythmia (HR=2.68; p-value=0.04). Radiation was associated with development of arrhythmia (HR=2.73; p-value=0.03), while older age was associated with development of HF (HR=1.36 per 5 year increment; p-value=0.003) and arrhythmia (HR=1.25 per 5 year increment; p-value=0.02). Conclusions The risk of CVD in patients with NHL is approximately 1% per year after the initial diagnosis of lymphoma. The most commonly occurring CVDs in this cohort of NHL survivors were arrhythmia and HF. Treatment with anthracyclines and radiation are associated with increased risk of developing some types of CVD. 80% of self-reported CVD events in NHL survivors were validated using epidemiologic criteria. Future studies will include building models incorporating comorbid health conditions and lifestyle factors to determine risk of CVD as well as the impact of CVD on quality of life. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 3642 Purpose: The reactive pathologic background in nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) consists of lymphocytes and histocytes. Lymphopenia is a negative prognostic factor in NLPHL as it is also in Classical Hodgkin Lymphoma (cHL). In cHL, tumor-associated macrophages recruited from circulating monocytes are a negative prognostic factor for survival. Thus, we studied if the peripheral blood absolute lymphocyte count/absolute monocyte count ratio at diagnosis (ALC/AMC-DX), as a surrogate biomarker of the host response against the neoplastic lymphocytic and/or histiocytic (L&H) cells, affects survival in NLPHL. Patients and Methods: We performed a retrospective analysis of the association between ALC/AMC-DX and survival in 103 consecutive NLPHL patients that were followed at Mayo Clinic from 1974 to 2010. Receiver operating characteristic (ROC) and area under the curve (AUC) were used for ALC/AMC-DX cut-off value analysis and proportional-hazards models were used to compare survival based on the ALC/AMC-DX ratio. Results: The cohort included 67 (65%) males and 36 (35%) females. Eighty-three percent of patients presented with stage I/II and 17% with stage III/IV. Sixty-five (63%) patients were treated with radiation alone; 20 (20%) patients with chemotherapy alone; and 18 (17%) patients with chemotherapy and radiation. The median follow-up period was 8.9 years (range: 0.3–31 years). An ALC/AMC-DX ≥ 2.1 was the best cut-off value for survival with an empirical AUC of 0.82 (95% confidence interval [CI], 0.78 to 0.88), a sensitivity of 70% (95% CI, 61% to 76%) and specificity of 84% (95%CI, 59% to 82%). The cut-off value for ALC/AMC-DX ≥ 2.1 was validated by the k-fold cross validation method, showing a cross validation ROC with an AUC of 0.82 (95% CI, 0.72 to 0.93) for an ALC/AMC-DX ≥ 2.1. Using Kaplan-Meier analysis, we studied overall survival (OS), lymphoma-specific survival (LSS), progression-free survival (PFS), and time to progression (TTP) based on ALC/AMC-DX ≥ 2.1. Patients with an ALC/AMC-DX ≥ 2.1 experienced an superior OS, LSS, PFS, and TTP compared with patients with an ALC/AMC-DX < 2.1: [OS: median was 22.1 years vs 6.3 years, 10-years OS rates of 89% (95%CI, 80% to 98%) vs 44% (95%CI, 27% to 65%), p < 0.0001, respectively; LSS: median was not reached vs 7.3 years, 10-years LSS rates of 99% (95%CI, 90% to 100%) vs 45% (95%CI, 29% to 67%), p < 0.0001, respectively; PFS: median was 20.6 years vs 4.6 years, 10-years PFS rates of 84% (95%CI, 80% to 97%) vs 26% (95%CI, 18% to 55%), p < 0.0001, respectively; and TTP: median was not reached vs 5.5 years, 10-years TTP rates of 93% (95%CI, 85% to 99%) vs 29% (95%CI, 19% to 57%), p < 0.0001, respectively]. After adjusting for the International Prognostic Factors and the International Prognostic Score, ALC/AMC-DX remained an independent prognostic factor for OS {hazard ratio (HR), 0.42, 95%CI, 0.23 to 0.64, p< 0.004]; LSS [HR, 0.14; 95%CI, 0.04 to 0.23, p <0.0004]; PFS [HR, 0.14; 95%CI, 0.03 to 0.34, p < 0.001], and TTP [HR, 0.16, 95%CI, 0.04–0.34, p <0.002]. Conclusion: ALC/AMC-DX is an independent prognostic factor for survival and provides a single biomarker to predict clinical outcomes in NLPHL. Disclosures: No relevant conflicts of interest to declare.