Thompson, Carrie A.

Introduction: With the expanding use of CAR-T cell therapy, which is associated with serious adverse effects (AEs), there is a need to characterize the patient's experience over time to guide patient/provider education, and help optimize symptom management. This study reports on longitudinal evaluation of patient-reported quality of life (QOL) and symptom burden of CAR-T cell therapy compared with established forms of cellular therapy i.e autologous stem cell transplant (autoSCT) and allogeneic SCT (alloSCT). Methods: Patients with hematologic malignancies were prospectively recruited in three cohorts: CAR-T, autoSCT and alloSCT. The primary endpoint was change in QOL from baseline, using the FACT-G questionnaire. Secondary endpoints were patient-reported AEs (PRO-AEs) using 7 items from the PRO-CTCAE and assessment of cognition/memory using the NeuroQOLv2 questionnaire. PRO-CTCAE data was graded using a composite score (combining frequency, severity, and interference) and rates, using a method adjusting for pre-existing baseline symptoms, were compared using Fisher's exact test. We also evaluated the time profile of PRO-AEs using the Toxicity over Time (ToxT) approach, a longitudinal approach to AE analysis (Thanarajasingam Lancet Onc 2016). Patients completed questionnaires at baseline, week 2 and monthly thereafter. Results: From 07/2018 to 06/2019, 93 patients were recruited (CAR-T: 20; autoSCT: 37; alloSCT: 36). At data cut-off, week 2 and months 1, 2 and 3 data were available in 74, 62, 46 and 35 patients, respectively. There was no difference in patient age across the 3 groups (median age 63, range 23-77; p=0.26). Baseline QOL by FACT-G total score (mean=83.4, SD=14.7; p=0.77), side effect bother by FACT-G GP5 (66/93 [71%] a little bit or less; p=0.72), activities and function (70/93 [75%] fairly normal activities or no limitations; p=0.68) and cognition by NeuroQOL t-score (mean=52.2, SD=8.13; p=0.39) were similar across 3 groups at baseline. The CAR-T group experienced significantly less worsening in QOL (FACT-G) than both autoSCT and alloSCT groups (Fig. 1a). Worsening in overall QOL nadired at week 2, after which QOL gradually returned to baseline in all groups. When comparing changes from baseline in overall QOL, statistically significant differences between groups were evident at week 2 (CAR-T vs autoSCT p<0.001; vs alloSCT p<0.001), month 1 (CAR-T vs autoSCT p=0.02; vs alloSCT p=0.003), month 2 (CAR-T vs autoSCT p=0.02; vs alloSCT p=0.001) and month 3 (CAR-T vs autoSCT p=NS; vs alloSCT p=0.03). Results for physical (Fig. 1b) and functional well-being (WB) were similar, with significantly less QOL worsening at week 2 in CAR-T vs autoSCT and alloSCT groups. The most common PRO-AEs (Table 1) in the CAR-T group were decreased appetite (59%), diarrhea (53%) and fatigue (44%). There was no statistically significant difference in the PRO-AEs between CAR-T and autoSCT groups. However, patients undergoing alloSCT had significantly higher proportion of PRO-AEs vs the CAR-T group, except for neuropathy and sad feelings. AutoSCT and alloSCT groups had significant worsening of FACT-G side effect bother (GP5) at week 2, which was significantly different from that of the CAR-T group, following which the side effect bother gradually returned to baseline. (Fig. 1c) This was the first application of ToxT to PRO-CTCAE data (graphs not shown). Bar charts of maximum grade frequency and stream plots of mean grade over time demonstrate that mouth sores, fatigue, diarrhea and decreased appetite peak at week 2 and improve by month 3, and are of lesser severity in the CAR-T vs autoSCT and alloSCT groups. The trajectory of maximum grade across all PRO-AEs was similar by group as overall side effect burden by FACT-G GP5. Heatmap visualization demonstrated significant intra-patient variability and allowed inspection of data completeness. No difference in cognition and memory was observed across the three groups over the first three months. (Fig. 1d) Conclusion: This study is the first to our knowledge to provide comprehensive PRO data comparing QOL, patient-reported AEs and cognition in CAR-T cell therapy vs. auto- and alloSCT, and the first application of ToxT to PRO-CTCAE data. Short-term QOL, including physical and functional WB domains was better in the CAR-T group vs. SCT groups. These data can serve as a guide for patient education and symptom management in CAR-T cell therapy. Co-senior authors: SKK & ACD Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Gertz:Johnson and Johnson: Speakers Bureau; Celgene: Consultancy; Appellis: Consultancy; Amyloidosis Foundation: Research Funding; DAVA oncology: Speakers Bureau; Physicians Education Resource: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Medscape: Consultancy, Speakers Bureau; Prothena Biosciences Inc: Consultancy; Teva: Speakers Bureau; Research to Practice: Consultancy; Annexon: Consultancy; Alnylam: Consultancy; International Waldenstrom Foundation: Research Funding; Ionis/Akcea: Consultancy; Spectrum: Consultancy, Research Funding; i3Health: Other: Development of educational programs and materials; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Ansell:Trillium: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding. Bennani:Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

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

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value<0.0001) with OS24 ranging from 18% in patients with TTP <6 months compared to 69% in patients with TTP > 36 months (Figure A). Additionally, age at PD (spline p-value<0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles:Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Introduction. The VES-13 is a simple, self-reported, function based tool originally developed to screen community-dwelling populations to identify persons age 65 years and older at risk of death and functional decline, including death in the next 12 months. The VES-13 items include patient's age, self-rated overall health status, functional limitations in physical activity, and functional disabilities in more complex activities of daily living. As part of the Lymphoma Epidemiology of Outcomes (LEO) cohort study, we collected self-reported VES-13 data at study enrollment on all participants regardless of age, and here we report on the prevalence of vulnerable status defined by the VES-13 and its association with 1-year mortality, overall, stratified at age 65 years, and in the subset of diffuse large B-cell lymphoma (DLBCL) treated with immunochemotherapy. Methods. From 7/2015 to 6/2017, 3253 participants with NHL were enrolled within 6 months of their diagnosis into the LEO cohort. 2004 were evaluable on VES-13, and 1183 (59%) completed it before the initiation of therapy. VES-13 scores range from a minimum of 0 (low risk for decline) to a maximum of 10 (greatest risk for decline), and a score ≥3 was classified as vulnerable. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants were contacted every 6 months for the first three years and then annually thereafter to identify outcomes. Medical records were reviewed by LEO clinicians to classify cause of death according to a standard protocol. Therapy was determined by the treating physician, and this was independent of knowledge of the VES-13 score. The association of VES-13 with 1-year mortality from date of diagnosis was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression models, which also provided model c-statistics. Results. The median age of the 2004 participants in this analysis was 62 years (range 18-94); 57% were male; 54% were ≥65 years; and 28% had a normal body mass index (BMI), 1% were underweight, 35% were overweight, and 36% were obese. Clinically, 59% of participants had an aggressive subtype, 65% were stage III-IV, 24% had B-symptoms, and 11% had a performance status (PS) of ≥2. Overall, 28% of participants were classified as vulnerable (95% CI 26%-30%), with a higher prevalence among those completing the survey after initiation of therapy (38%, 95% 34%-41%) versus before initiation of therapy (22%, 95% CI 20%-24%), and a higher prevalence for those ≥65 years (32%, 95% CI 29%-34%) versus <65 (25%, 95% CI 22%-28%). During the first year of follow-up, there were 147 deaths, with 61% due to lymphoma, 14% therapy-related, 12% due to another malignancy, 3% other causes, and 10% unknown. Significant univariate predictors of 1-year mortality included vulnerable status, older age, PS 2+, stage III-IV, aggressive subtype, lower quality of life score, lower BMI, and 1 or more comorbidities, and were included the backward stepwise modeling. Vulnerable status (OR=3.26, 95% CI 2.15-4.96) was retained in the final model that also included age, PS, stage, and NHL subtype (Table), with a model c-statistic of 0.81. As shown in the Table, these associations were similar whether VES-13 was assessed before (c-statistic 0.77) or after (c-statistic 0.81) initiation of therapy. After adjustment for age, PS, stage and NHL subtype, vulnerable status predicted 1-year mortality in participants ≥65 years (OR=1.96, 95% CI 1.15-3.36; model c-statistic 0.80) as well as <65 years (OR=6.38, 95% CI 3.07-13.3; model c-statistic 0.83). Finally, in the DLBCL patients treated with immunochemotherapy (N=521), vulnerable status (38% of patients) also predicted 1-year mortality (OR=2.11, 95% CI 1.05-4.26) after adjusting for age, PS and stage (model c-statistic 0.78). Conclusion. Based on a self-administered VES-13, 28% of newly diagnosed NHL patients in the LEO cohort were classified as vulnerable, including 25% of those <65 years, and vulnerable status was a strong predictor of 1-year-mortality, even in the subset of DLBCL patients treated with presumed curative intent. These results highlight the potential value of screening for vulnerable status overall and across a wider age spectrum in order to optimize patient management and reduce early mortality. Disclosures Martin: Karyopharm: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Casulo:Gilead: Honoraria, Other: Travel, accommodation, expenses; Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses. Cohen:Takeda Pharmaceuticals North America, Inc.: Research Funding; Bristol-Meyers Squibb Company: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Gilead/Kite: Consultancy; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Kahl:Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Maurer:Nanostring: Research Funding; Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Nastoupil:Spectrum: Honoraria; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Janssen: Honoraria, Research Funding; Novartis: Honoraria; TG Therapeutics: Honoraria, Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding. Rutherford:Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees. Flowers:National Cancer Institute: Research Funding; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Bayer: Consultancy; Spectrum: Consultancy; Karyopharm: Consultancy; Acerta: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; AstraZeneca: Consultancy; Optimum Rx: Consultancy; V Foundation: Research Funding; Celgene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Denovo Biopharma: Consultancy; Gilead: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding.

95 Background: The incidence of immune-related adverse events (irAEs) in patients (pts) with Hodgkin lymphoma (HL) treated with PD-1 antibodies (ICI) is reported at 28 - 36%. However, clinically relevant information on irAE onset, duration, or impact on ability to continue therapy in HL is lacking. Additionally, these incidence rates reflect CTCAE definition and grading, and not ASCO Clinical Practice Guidelines irAE-specific criteria (Brahmer et al, JCO 2018). The objectives of this study were to evaluate the rate and severity of irAEs in HL pts using ASCO criteria, to characterize irAE time profile and to assess their impact on continued therapy. Methods: HL pts who received a PD-1 inhibitor for the treatment of HL at Mayo Clinic Rochester between January 1, 2011 and March 1, 2018 were identified. We conducted a detailed, longitudinal retrospective chart review with definition and grading of irAEs in accordance with ASCO guidelines. Results: Fifty-one pts were identified (35% women, median age at ICI initiation 35 years [range: 19-87]). Median duration of follow up from ICI start was 30 months (range: 1-70). Thirty-one pts (61%) had any irAE. Most common irAEs were inflammatory/bullous dermatoses (23.5%/5.9% any grade/grade3-4; median onset/duration of 81/57 days[d]), followed by thyroiditis (22%/0%; 86/84 d), colitis (14%/8%; 82/106 d), pneumonitis (14%/10%; 241/182 d), and hepatitis (14%/4%; 71/35 d). Fifteen pts (29%) required treatment with steroids. Among the total cohort, ICI was held for an irAE 28 times (50% of 56 total irAEs). Median treatment cycles held was 2.5 and ICI was permanently discontinued due to irAE in 18%. The most common irAE leading to treatment discontinuation was pneumonitis, followed by neuritis (5.9%/3.9%; 179/374 d), colitis, and rash. Conclusions: The rate of irAEs of 61% in this cohort as defined by ASCO consensus criteria was higher than reported in trials. Most irAEs were characterized by delayed onset of several months and prolonged duration, particularly pneumonitis and neuritis. Treatment discontinuation due to irAEs was infrequent overall. Further studies are needed to determine whether the development or duration of irAEs is predictive of treatment outcomes in HL.

Abstract Background Medical education encompasses many activities (e.g., teaching, supervision, mentorship, and administration). Little research has explored what the term "medical education" means to trainees or assessed the importance postgraduate medical trainees place on education as part of their career plans. Methods We conducted a survey of fellows in all subspecialty training programs at a three-site academic institution. We asked multiple choice and Likert scale questions to characterize fellows’ perception of, interest and training in medical education. Results One hundred sixty-nine of 530 (31.9%) fellows responded. Fellows were training in subspecialties of internal medicine (49.7%) and surgery (13.0%), among others. Most fellows planned careers in academic medicine (38.5% clinician-educator, 22.5% clinician-investigator, 17.2% academic clinician). Fellows reported that their conception of medical education involved supervising trainees in a clinical capacity (93.5%), teaching in the classroom (89.3%), and providing mentorship for trainees (87.6%). Respondents identified “being an educator” as extremely (43.8%) or moderately (43.2%) important for their future careers. Only 30.2% had received formal training in medical education, but 61.5% felt that formal training should be required for those pursuing careers with strong educational components. Conclusions Most subspecialty fellows surveyed planned careers in academics and felt that medical education was important to their professional future. While less than a third received formal training in education, almost two thirds felt that such training should be required for a career as an educator. This study provides evidence for the creation and promotion of educational training programs for trainees interested in careers involving medical education.

AbstractHistological transformation in Waldenström macroglobulinemia (WM) is an uncommon complication, with limited data, particularly regarding the impact of MYD88 L265P mutation on transformation. We examined risk factors and outcomes associated with transformation in WM, highlighting the role of MYD88 L265P mutation. Patients with WM seen at Mayo Clinic, Rochester, USA and University Hospital of Reims, France, between 01/01/1996 and December 31, 2017 were included; 50 (4.3%) of 1147 patients transformed to a high‐grade lymphoma, with median time‐to‐transformation of 4.5 (range 0‐21) years in the transformed cohort. The MYD88 L265P mutation status was known in 435/1147 (38%) patients (406 with non‐transformed WM and 29 patients in transformed cohort). On multivariate analysis, MYD88 WT status alone was an independent predictor of transformation (odds ratio, 7[95%CI: 2.1‐23]; P = .003). Additionally, the MYD88 WT status was independently associated with shorter time‐to‐transformation (HR 7.9 [95%CI: 2.3‐27; P = .001]), with a 5‐year transformation rate of 16% for MYD88 WT vs 2.8% with MYD88 L265P mutated patients. Patients with transformation demonstrated a significant increase in risk of death compared to patients who did not transform (HR 5.075; 95%CI: 3.8‐6.8; P < .001). In conclusion, the MYD88 WT status is an independent predictor of transformation and associated with a shorter time‐to‐transformation. Additionally, transformation conferred an inferior overall survival in patients with WM.

Abstract Background: EBV+ DLBCL not otherwise specified (EBV+ DLBCL NOS) is defined by the 2016 WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues as EBV+ DLBCL in patients without a history of known immunosuppression. Among Asian patients, adverse outcomes have been associated with EBV+ vs EBV- DLBCL, and circulating EBV has been found in the majority of patients. Outcome studies of unselected North American patients are few and non-confirmatory. No formal assessment of immunosuppression status has been incorporated into these analyses, leading to the variable inclusion of patients with a history of immunocompromised states. We sought to determine the prevalence, presenting clinical and pathology characteristics, and outcomes of DLBCL by EBV and immunosuppression status in an unselected prospective cohort study of North American patients. Methods: From 2002-2012, we enrolled consecutive, newly diagnosed patients with lymphoma who were ≥ 18 years and HIV negative into the Molecular Epidemiology Resource (MER). Clinical, laboratory, and treatment data were abstracted from medical records. All DLBCL patients with available tissue microarrays (TMAs), excluding primary mediastinal DLBCL and post-transplant lymphoproliferative disorders, were included. Medical records were reviewed using a standard protocol to classify patients as immunocompromised based on a documented history of primary immunodeficiency or iatrogenic immunosuppression. DLBCL cell of origin was classified as germinal center (GCB) vs non-GCB using the Hans algorithm. CD30 was determined using immunohistochemistry; positivity cutoff was 20% of neoplastic cells. EBV testing was performed by in-situ hybridization for EBV encoded small RNAs (EBERs), and scored by an expert hematopathologist. Event free survival (EFS) was defined as time from diagnosis until recurrence, retreatment, or death due to any cause. Associations with EFS and Overall Survival (OS) were obtained using the methods of Kaplan-Meier (KM). Hazard Ratios (HRs) and 95% confidence intervals (CIs) were estimated from Cox regression models. Results: 1,100 patients with DLBCL were enrolled into the MER; 362 had TMAs available for this analysis. Median age was 63 years (range 20-89) and 59% were male. 89.5% of patients were treated with immunochemotherapy. 16 cases (4.4%) were EBV+, and 39 cases (10.8%) were considered immunocompromised. EBV positivity was more common in immunocompromised vs immunocompetent patients, with a trend towards significance (10.3% vs 3.7%; P = 0.08). The non-GCB subtype was more frequent in EBV+ vs EBV- DLBCL (62.5% vs 34.1%; P < 0.01), but similar between immunocompromised vs immunocompetent patients (48.7% vs 33.7%; P = 0.10). CD30 positivity was also more frequent in EBV+ vs. EBV- DLBCL (25.0% vs 8.1%; P < 0.01), but similar between immunocompromised vs immunocompetent patients (12.8% vs 8.4%, respectively; P = 0.45). With a median follow-up of 59 months (range 0.7-155.2) there were 151 events and 119 deaths. KM curves by EBV and immunosuppression status are shown in the figure. There was no association of EBV positivity with EFS (HR = 0.76; 95% CI 0.34-1.72) or OS (HR = 0.99; 95% CI 0.43-2.25). A history of immunosuppression was not associated with EFS (HR = 1.17; 95% CI 0.72-1.89) or OS (HR = 1.02; 95% CI 0.58-1.78). In the subset of immunocompetent patients, there was no association of EBV positivity with EFS (HR = 0.54; 95% CI 0.17-1.70) or OS (HR = 0.71; 95% CI 0.23-2.25). Similar results were obtained after adjustment for the International Prognostic Index. Circulating viral DNA was detected in 2/5 EBV+ and 4/49 EBV- DLBCL patients. Conclusions: The prevalence of EBV+ DLBCL NOS is <5% among patients in a large North American cohort, similar to other western populations. There was no evidence of an association of EBV or immunosuppression status with EFS or OS, with low power for detecting associations with EBV. Circulating viral DNA was infrequent among either EBV+ or EBV- DLBCL patients. While immunosuppression has been previously shown to be a risk factor for DLBCL, it was not found to be associated with EFS or OS. Overall, these results suggest divergent outcomes among EBV+ DLBCL patients between western and non-western populations, underscoring differences in host factors or circulating viral strains. Disclosures Ansell: BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding.

Abstract Background: Multiple Myeloma (MM) patients have a substantially reduced Health related Quality of Life (HQOL) at diagnosis (dx) due to disease related symptoms like bone pain (58%) and fatigue (32%). HQOL monitoring is becoming increasingly important, owing to improved survival and the impact of treatment-related toxicity. As a result HQOL assessments are increasingly being used in clinical trials, but the literature regarding the relationship between HQOL and outcomes in MM is sparse. We used the Mayo Clinic “Hematology Patient Reported Symptom Screen” (HPRSS) to assess the impact of HQOL on outcomes in newly dx patients with MM. Methods:We retrospectively reviewed charts of 453 patients with newly dx MM seen at Mayo Clinic, Rochester from 2009 to 2014. All patients who visit Mayo clinic hematology clinic complete a 3-point questionnaire (HPRSS) on pain, fatigue and quality of life (QOL) (scored on a scale of 0-10; with 0 being the least fatigue or pain and 10 being the best QOL). Pain, fatigue and QOL scores documented at the time of dx and at 6 months were collected. JMP version 10 was used for the data analysis. Results: The median age at diagnosis was 67 years (range, 33-95); 60% were male. The estimated median OS for the cohort was 21 m (95% CI19, 23); 387 (85%) of the patients were alive at last follow up and the median OS for the population was not reached. The median (IQR) scores for pain, fatigue and QOL were 4 (2, 6), 3 (1, 5), and 7 (5, 9), respectively. First, we examined the relationship between each of the scores (dichotomized at the median) and the baseline characteristics. Higher fatigue scores were associated with lower Hb and serum albumin and higher beta 2 microglobulin, LDH and marrow plasmacytosis. Higher pain scores were seen in female patients, and associated with higher B2M, serum calcium and lytic bone lesions. Lower QOL scores were associated with lower absolute lymphocyte count and serum albumin levels. Next, we examined the relationship between the scores and the OS from diagnosis. The overall survival was inferior for patients with higher pain scores, fatigue scores and lower QOL scores (Figure). In a multivariable analysis, fatigue scores were most strongly associated with survival outcome. Patients with adverse scores in two or more of pain, fatigue and QOL had significantly inferior outcomes (Figure). In univariate analysis, age, B2M, LDH, FISH high risk and the presence of two or more adverse scores were all significantly associated with poorer OS. In a multivariable analysis, age, LDH and the presence >=2 adverse scores were all associated with shorter OS. Of the 453 patients included in the study, 222 patients had pain, fatigue and QOL scores at 6 month from diagnosis. At 6 months, the median (IQR) scores for pain, fatigue and QOL were 2 (1, 5), 4 (2, 5), and 7 (5, 9), respectively; suggesting improved pain, worsened fatigue and static QOL scores. Improvement in the scores by at least one point was seen in 50%, 49% and 38% for the fatigue, pain and QOL scores. While the pain and fatigue scores at 6 months correlated inversely with OS, improvements in the scores in any of the three were not associated with any improved outcomes. Conclusion: A simple, patient reported scoring system for pain, fatigue, and overall perceived QOL at the time of diagnosis is a powerful predictor of survival outcomes in patients with newly diagnosed MM and should be considered routinely in clinical practice. The results of these patient reported measures can be utilized to develop risk-adapted trials in patients with MM. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Vitamin D insufficiency (VDI) has been associated with inferior outcomes in patients with follicular lymphoma (FL) treated with chemotherapy and an anti-CD20 monoclonal antibody (immunochemotherapy; IC), but whether this association holds for other FL treatment strategies is not known. Furthermore, the subset of FL patients who experience clinical failure (progression, treatment failure) within 12 months of diagnosis have an inferior outcome compared to those achieving event-free status at 12 months (EFS12). We tested the hypothesis that VDI is predictive of early clinical failure (failure to achieve EFS12), overall survival (OS), and lymphoma-specific survival (LSS) among FL patients overall and in subgroups who were initially observed, treated with IC, rituximab monotherapy, or other therapies. Methods: We evaluated this hypothesis in a prospective cohort study of 642 newly diagnosed patients with grade 1-3a FL enrolled from 2002-2012 in the Molecular Epidemiology Resource of the Iowa/Mayo Lymphoma SPORE. Serum 25-hydroxyvitamin D [25(OH)D] was measured by liquid chromatography-tandem mass spectroscopy, and VDI was defined as serum total 25(OH)D <20 ng/mL (per Institute of Medicine criteria) determined within 120 days of diagnosis. We used logistic regression to estimate Odds Ratios (ORs) and 95% confidence intervals (CIs) and Cox regression to estimate hazard ratios (HRs) and 95% CIs. We adjusted for FLIPI, body mass index, and month of blood draw; for the full cohort, we additionally adjusted for treatment. Results: The mean age at diagnosis was 60 years (range 23-93); 19% of patients had VDI. The median follow-up period was 59 months (range 1-144), with 297 events and 78 deaths (42 due to lymphoma). Treatment included IC (N=252), observation (N=218), rituximab monotherapy (N=76), or other (N=96). OS and EFS curves are shown in figures 1 and 2. In the full cohort, VDI was associated with inferior EFS12 (OR=2.05; 95% CI 1.18-3.54), OS (HR=2.35; 95% CI 1.37-4.02), and LSS (HR=2.97; 95% CI 1.52-5.80). In the IC subset, VDI was associated with inferior EFS12 (OR=3.00; 95% CI 1.26-7.13), OS (HR=2.86; 95% CI 1.39-5.85), and LSS (HR=2.96; 95% CI 1.29-6.79). VDI was associated with inferior OS in patients who were observed (HR=2.25; 95% CI 1.20-6.76) or received other therapies (HR=3.06; 95% CI 1.01-9.24). VDI was positively but not statistically significantly associated with inferior EFS12 in the patients who were observed (OR=1.71; 95% CI 0.70-4.17) or received other therapies (OR=2.31; 95% CI 0.41-13.07), with low power for these associations; LSS could not be estimated due to lack of deaths. There were too few events (N=10) and deaths (N=5) in the R-monotherapy group for analysis. Conclusions: We confirm previous findings that vitamin D insufficiency is associated with adverse long-term prognosis among patients with FL treated with IC, and extend these findings to patients who are initially observed or treated with other therapies. For the first time, we observed an association of vitamin D insufficiency with early clinical failure, suggesting a potentially modifiable factor to address in this subset of patients with poor outcomes. Whether treating VDI improves outcomes in FL warrants assessment. Disclosures Ansell: BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding. Nowakowski:Celgene: Research Funding; Morphosys: Research Funding; Bayer: Consultancy, Research Funding.