ALBERT

Description: Introduction: Smoking is a potential risk factor for the development of non-Hodgkin lymphoma (NHL), and prior studies have reported inferior survival in tobacco users with certain subtypes of the disease (Taborelli et al, BMC Cancer, 2017; Ollberding et al, Br J Haematol, 2013). For instance, tobacco smokers with NHL had an inferior overall survival (OS) compared to non-smokers in a series of 471 patients who were managed up front with either chemotherapy (68%), radiation (27%), or observation, and this appeared to be most pronounced in patients with follicular lymphoma and in those with a 20+ pack year smoking history (Geyer et al, Cancer, 2010). The impact of tobacco use on survival specifically in patients with mantle cell lymphoma (MCL) has not been well studied. We conducted a multicenter study in MCL and evaluated the prognostic impact of tobacco use. Methods: We included patients with MCL from 12 sites who were ≥18 years old and for whom smoking status was known at the time of diagnosis. Cases were evaluated for reported smoking status at the time of diagnosis (active smoker, prior smoker, or never smoker) and standard baseline clinical prognostic data were obtained for each patient. Descriptive statistics were generated for these characteristics and were then compared across smoking status using chi-squared tests, Fisher's exact tests, or ANOVA, where appropriate. Overall survival (OS) and progression free survival (PFS) were estimated using the Kaplan-Meier method, and were compared using log-rank tests. Results: Of 946 included patients, 456 (48.2%) reported never using tobacco, 360 (38.7%) reported prior tobacco use, and 130 (13.7%) reported active tobacco use at the time of diagnosis. Median age was 59 in the active smoker group, 65 in prior smokers, and 61 in never smokers (p 〈 0.001). Any major medical comorbidity (defined as the presence of CAD, CHF, diabetes, CKD, ESRD, COPD, DVT, prior malignancy, or cirrhosis) was present in 59 (45.4%) of the active smokers, 143 (39.7%) of the prior smokers, and 140 (30.7%) of the never smokers (p = 0.002). Intensive induction regimens were used in 58.2% of active smokers, 47.2% of prior smokers, and 58.4% of never smokers (p=0.007). There were no significant differences between groups in regards to sex, race, ECOG performance status, Ann Arbor stage, time to first treatment, and use of auto transplant in first remission. Patients with no prior history of tobacco use were less likely to have a high risk MIPI score at diagnosis (26% high risk) compared to prior smokers (39.5%) and active smokers (32.5%, p=0.019). With a median follow up of 3.5 years after diagnosis, there was no significant difference between the 3 groups with regards to PFS or OS (Figure 1). Five-year OS in the never smoker group was 79.8% (95% CI: 74.8%, 83.9%) vs 75.1% (64.5%, 82.9%) in the active smoker group, and 80.6% (74.6%, 85.3%) in the prior smoker group (log rank p = 0.4079). Five- year progression free survival was 50.4% (44.6%, 56.0%) in the never smoker group, 42.5% (32.2%, 52.5%) in the active smoker group, and 50.2% (43.5%, 56.6%) in the prior smoker group (log rank p= 0.3595). Conclusions: Our data suggest that active or prior smoking does not significantly impact OS or PFS in patients with MCL. This study is limited by the fact that amount of current or former tobacco use was not available and it is not known how many current tobacco users ultimately stopped smoking during the course of their treatment. Future studies should incorporate more specific information regarding smoking history including pack-years and time between discontinuation of tobacco use and date of diagnosis. While tobacco use and other modifiable cardiovascular risk factors should be addressed as appropriate for all patients with MCL, current and former tobacco users can still achieve prolonged PFS and OS and may be candidates for intensive treatments after consideration of their other comorbidities and disease-specific risk factors. Disclosures Calzada: Seattle Genetics: Research Funding. Kolla:Amgen: Equity Ownership. Bachanova:Gamida Cell: Research Funding; GT Biopharma: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Celgene: Research Funding; Novartis: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees. Gerson:Seattle Genetics: Consultancy; Abbvie: Consultancy; Pharmacyclics: Consultancy. Barta:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Mundipharma: Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Takeda: Research Funding; Merck: Research Funding; Mundipharma: Honoraria; Bayer: Consultancy, Research Funding; Seattle Genetics: Honoraria, Research Funding. Danilov:Celgene: Consultancy; Abbvie: Consultancy; TG Therapeutics: Consultancy; Bayer Oncology: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Janssen: Consultancy; AstraZeneca: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Aptose Biosciences: Research Funding; Bristol-Meyers Squibb: Research Funding; MEI: Research Funding; Pharmacyclics: Consultancy; Verastem Oncology: Consultancy, Other: Travel Reimbursement , Research Funding; Curis: Consultancy; Takeda Oncology: Research Funding; Seattle Genetics: Consultancy. Grover:Seattle Genetics: Consultancy. Karmali:Astrazeneca: Speakers Bureau; Takeda, BMS: Other: Research Funding to Institution; Gilead/Kite; Juno/Celgene: Consultancy, Speakers Bureau. Hill:Seattle Genetics: Consultancy, Honoraria; Takeda: Research Funding; Amgen: Research Funding; TG therapeutics: Research Funding; AstraZeneca: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celegene: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Research Funding; Kite: Consultancy, Honoraria; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Ghosh:Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Speakers Bureau; Genentech: Research Funding; Celgene: Consultancy, Research Funding, Speakers Bureau; Forty Seven Inc: Research Funding; Gilead/Kite: Consultancy, Speakers Bureau; Spectrum: Consultancy, Speakers Bureau; AbbVie: Consultancy, Speakers Bureau; T G Therapeutics: Consultancy, Research Funding; Astra Zeneca: Speakers Bureau. Park:BMS: Consultancy, Research Funding; Rafael Pharma: Membership on an entity's Board of Directors or advisory committees; G1 Therapeutics: Consultancy; Teva: Consultancy, Research Funding; Gilead: Speakers Bureau; Seattle Genetics: Research Funding, Speakers Bureau. Epperla:Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Hamadani:Pharmacyclics: Consultancy; ADC Therapeutics: Consultancy, Research Funding; Merck: Research Funding; Celgene: Consultancy; Janssen: Consultancy; Medimmune: Consultancy, Research Funding; Sanofi Genzyme: Research Funding, Speakers Bureau; Otsuka: Research Funding; Takeda: Research Funding. Kahl:TG Therapeutics: Consultancy; BeiGene: Consultancy; Seattle Genetics: Consultancy; ADC Therapeutics: Consultancy, Research Funding. Martin:Janssen: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Teneobio: Consultancy; Celgene: Consultancy; Karyopharm: Consultancy. Flowers:Karyopharm: Consultancy; Denovo Biopharma: Consultancy; Burroughs Wellcome Fund: Research Funding; AbbVie: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Spectrum: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Bayer: Consultancy; Acerta: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Optimum Rx: Consultancy; Millenium/Takeda: Research Funding; Eastern Cooperative Oncology Group: Research Funding; National Cancer Institute: Research Funding; V Foundation: Research Funding; BeiGene: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; TG Therapeutics: Research Funding. Cohen:Genentech, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Takeda Pharmaceuticals North America, Inc.: Research Funding; Gilead/Kite: Consultancy; LAM Therapeutics: Research Funding; UNUM: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Bristol-Meyers Squibb Company: Research Funding.

Description: Introduction: Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma in the world and has a median age at diagnosis in the seventh decade. FL in young adults (YA; 40 years old or younger) is extremely rare. Currently, there are no standard approaches guiding treatment of YA patients with FL, and very little is known about disease characteristics and outcomes of YA patients with FL given limited research conducted in this vulnerable population. To gain further insight into FL in YA, we analyzed the National LymphoCare Study (NLCS) to describe disease and patient characteristics, as well as features of treatment in YA patients with FL. We previously reported that 2-year progression-free survival (PFS) is an important survival endpoint in patients with FL undergoing chemo-immunotherapy. Hence, we also sought to characterize 2-year PFS in this age group and compare it to older cohorts. Methods: Evaluablepatients were identified in the NLCS, and those between 18–40 years of age with newly diagnosed FL at any stage were classified as YA patients. Patients with mixed histology or transformed disease were excluded, as were patients with progression of disease prior to beginning first-line treatment. Survival probability was estimated by the Kaplan-Meier method. We estimated the association of age group with PFS using hazard ratios (HR) and 95% confidence intervals (CI) from multivariable Cox models. Results: A total of164 YA patients with FL were analyzed, representing 6.2% of the NLCS population, similar to the observed frequency in the Surveillance, Epidemiology, and End Results (SEER) Program data (4.8% of all FL). Sixty nine percent of YA patients had advanced stage disease. The majority of patients (80%) had low-grade histology, and 50% had good risk disease according to the Follicular Lymphoma International Prognostic Index (FLIPI). Nineteen percent of patients (31/164) underwent watchful waiting, 12% received rituximab monotherapy, and 47% received chemo-immunotherapy (61% of whom received R-CHOP [rituximab, doxorubicin, vincristine, prednisone]). There was no significant difference in FLIPI score or other baseline disease characteristics compared to adult patients aged 41–60 years. Eleven deaths occurred among YA with FL; only 5 of these were lymphoma related. Overall survival (OS) at 2 years was 97.4% (95% CI 93.3%, 99.0%), and at 5 years, 93.7% (88.3%, 96.7%), which was similar to patients aged 41–60 (97.2% [96.0%, 98.0%] at 2 years, and 92.0% [90.1%, 93.5%] at 5 years). After a median follow-up of 7.1 years, OS in YA FL was 92%. Through follow-up, there were 64 PFS events. The estimated 2-year PFS (95% CI) for YA and adults 41–60 was 75.9% (67.1%, 82.6%) and 80.9% (78.1%, 83.4%), respectively. After adjusting for FLIPI score, there was no difference in PFS for YA with FL requiring first-line treatment (excluding watchful waiting) compared to adults aged 41–60 years (HR=0.93; 95% CI 0.69, 1.25), and no difference in OS compared to adults aged 41–60 years (HR=1.19; 95% CI 0.64, 2.23). Conclusions: In the largest cohort of YA patients with FL to date, we found few differences in outcomes compared to patients aged 41–60. FLIPI and other disease characteristics were similar to adults aged 41–60 years. There were no differences between YA FL and adults aged 41–60 in PFS for all treated patients. OS in the YA group of patients with FL was outstanding. YA patients with FL have reassuringly similar outcomes to patients aged 41–60. Fertility preservation and survivorship issues should be taken into consideration when defining management strategies, but otherwise these data support that YA patients with FL should not be approached differently from older adults with the same disease. Disclosures Byrtek: Genentech, Inc.: Employment, Equity Ownership. Dawson:Genentech, Inc.: Employment, Equity Ownership. Zhou:RTI-HS: Employee of RTI-HS, which has research contracts with Genentech Other. Flowers:Seattle Genetics: Consultancy; Spectrum: Consultancy, Research Funding; Sanofi: Research Funding; Abbott: Research Funding; Novartis: Research Funding; OptumRx: Consultancy; Millennium/Takeda: Research Funding; Janssen: Research Funding; Celgene: Research Funding; Allos: Consultancy. Farber:Gilead: Speakers Bureau; Janssen/Pharmacyclics: Speakers Bureau; Seattle Genetics: Speakers Bureau; Leukemia Lymphoma Society NJ Chapter: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion: Speakers Bureau, Stock ownership Other. Cerhan:Genentech, Inc.: LymphoCare Scientific Advisory Board Other. Link:Genentech, Inc.: Consultancy, Scientific Advisory Board for Genentech Other.

Description: Abstract 1689 Poster Board I-715 Introduction The use of the proteasome inhibitor bortezomib has demonstrated activity in multiple myeloma and lymphomas. The HDAC inhibitor romidepsin is being evaluated in CTCL and PTCL, though its activity in B-cell lymphomas is less clear. We hypothesized that the combination of bortezomib and romidepsin would result in synergistic apoptosis in different B-cell NHL cell lines based upon the observed activity of this combination in more mature B-cell malignancies such as myeloma. Experimental Design Daudi, HT, Ramos and SUDHL-4 cell lines were exposed to different concentrations of bortezomib and romidepsin, separately, concurrently, and sequentially. Cell viability was assessed using MTT-assay, induced apoptosis was evaluated using Annexin V and PI staining from 24-48 hours. Apoptosis was also evaluated using western blot analysis of caspases and PARP cleavage. LC3 and HDAC6 level expressions were performed to determine if the effect of the combination was a result of the aggresome or autophagy pathway. Cell cycle studies were also performed to study if there were any changes after treating cells with the combination. Results The combination of bortezomib and romidepsin resulted in synergistic B-cell apoptosis as measured by MTT-assay with combination indices of 〈 0.5. This was associated with increased caspases and PARP cleavage as early as 24 hours after exposure. Order of addition experiments demonstrated definite sequence specificity. When romidepsin was added first, and 6 hours later followed by bortezomib, apoptosis was enhanced, compared to both agents being given concurrently or when bortezomib was administered first. Cell cycle analysis studies demonstrated that pretreatment of cells with romidepsin for 6 hours followed by the addition of bortezomib arrested the cells in G2M phase. HDAC6 expression was significantly reduced following combination therapy, and LC3-I was cleaved to LC3-II in treated cells suggesting that the combination affected aggresome formation and autophagy. Conclusion The combination of romidepsin and bortezomib at low nanomolar concentrations suggests that this may be an important clinical combination to test in patients with relapsed or refractory B-cell malignancies. Sequence of administration data is currently being tested to determine if the effect is a result of autophagy inhibition as is seen in myeloma cell lines. Additional mechanistic studies will be presented with the goals of identifying predictors of response that can then be validated in prospective clinical trials. Disclosures Lechowicz: Gloucester: Consultancy. Kaufman:Millennium: Consultancy; Genzyme: Consultancy; Celgene: Consultancy; Merck: Research Funding; Celgene: Research Funding. Lonial:Gloucester: Research Funding; Novartis: Consultancy; BMS: Consultancy; Millennium: Consultancy, Research Funding; Celgene: Consultancy. Flowers:Millennium: Research Funding.

Description: 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