Flowers, Christopher R.

Abstract Great heterogeneity in survival exists for patients newly diagnosed with diffuse large B-cell lymphoma (DLBCL). Three scoring systems incorporating simple clinical parameters (age, lactate dehydrogenase, number/sites of involvement, stage, performance status) are widely used: the International Prognostic Index (IPI), revised IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI). We evaluated 2124 DLBCL patients treated from 1998 to 2009 with frontline rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; or variant) across 7 multicenter randomized clinical trials to determine which scoring system best discriminates overall survival (OS). Median age was 63 years, and 56% of patients were male. Five-year OS estimates ranged from 54% to 88%, from 61% to 93%, and from 49% to 92% using the IPI, R-IPI, and NCCN-IPI, respectively. The NCCN-IPI had the greatest absolute difference in OS estimates between the highest- and lowest-risk groups and best discriminated OS (concordance index = 0.632 vs 0.626 [IPI] vs 0.590 [R-IPI]). For each given IPI risk category, NCCN-IPI risk categories were significantly associated with OS (P ≤ .01); the reverse was not true, and the IPI did not provide additional significant prognostic information within all NCCN-IPI risk categories. Collectively, the NCCN-IPI outperformed the IPI and R-IPI. Patients with low-risk NCCN-IPI had favorable survival outcomes with little room for further improvement. In the rituximab era, none of the clinical risk scores identified a patient subgroup with long-term survival clearly <50%. Integrating molecular features of the tumor and microenvironment into the NCCN-IPI or IPI might better characterize a high-risk group for which novel treatment approaches are most needed.

7544 Background: Great heterogeneity in survival exists for patients (pts) newly diagnosed with aggressive BCL. Three scoring systems based on simple clinical parameters (age, lactate dehydrogenase, number/sites of involvement, stage, performance status) are widely used: the international prognostic index (IPI), revised-IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI). We studied BCL pts treated with R-CHOP to determine which scoring systems best identifies subgroups with poor outcomes that might benefit from new approaches. Methods: Individual pt data from 7 multicenter trials (1998-2009) of pts with BCL (86% DLBCL) treated front-line with R-CHOP (or variant) were analyzed to determine whether IPI, R-IPI, or NCCN-IPI best discriminated overall survival (OS). The concordance index (c-index) from a proportional hazards model, stratifying on trial and induction therapy, quantified predictive accuracy of each scoring system. Results: 2561 pts (median age 63 yrs, 56% male) were classified into IPI, R-IPI, and NCCN-IPI risk groups (Table). With a median follow-up of 5 yrs, NCCN-IPI had the greatest absolute difference in OS estimates between the highest and lowest risk groups at 1, 3, and 5 yrs, and best discriminated OS (c-index = 0.631, Table). Conclusions: In an independent and large cohort of pts, NCCN-IPI performs best in risk-stratifying pts with aggressive BCL, readily distinguishing pts at high and low risk for treatment failure using clinical parameters (5-yr OS between 48 and 92%). Improvement over the simpler IPI appears incremental, and IPI may remain a valuable alternative. Work integrating molecular features of the tumor into the (NCCN-) IPI is in progress to define high risk groups where targeted novel approaches are needed most. [Table: see text]

7536 Background: While most patients (pts) with follicular lymphoma (FL) achieve an initial response to treatment, the majority relapse. Recently, Goldman et al. reported that surveillance imaging (SI) in FL is not associated with improved post-relapse outcomes and is frequently associated with false positive scans (ASH 2017). The goal of this study was to validate these findings. Methods: Pts were enrolled in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER), a prospective cohort of newly diagnosed lymphoma pts. All pts were followed for events including relapse, re-treatment, and death, with events verified by medical records. Pts eligible for this study had biopsy proven FL grade 1, 2, or 3a who had achieved a response after induction therapy and later relapsed. Initial and post-treatment management was per treating physician. Medical records were re-reviewed in pts with events for clinical details at relapse in relationship to planned follow-up visits and SI. Relapse detection was categorized either by clinical suspicion (CS) via history, exam, and/or labs; or by SI in an asymptomatic pt. Univariate survival analysis was conducted for each variable. The hazard ratio with 95% confidence interval based on Cox Proportional Hazard models was presented along with the log rank test p-value. Kaplan-Meier plots were produced to evaluate the difference in overall survival (OS) between groups. Results: 1121 FL pts were enrolled in MER from 2002-2015, of which 117 were eligible. Median age at diagnosis was 60 years (range 35-83), and 61% were men. Median time to relapse of the 117 pts was 26 months (range 9-125). At a median follow-up from relapse of 69 months (range 0.23-179), 26 pts died. Pts completed a median of 3 imaging studies (range 0-15) during post-induction surveillance. 63 relapses (56%) were detected based on CS; 50 (44%) were detected by SI; and 4 were unknown. There was no difference in OS from relapse for those with relapse detected via CS vs. SI (HR = 0.98 [0.45,2.12], p = 0.96). Conclusions: Routine SI does not appear to improve survival outcomes in FL after achieving first remission, and its common practice should be questioned. Further investigation of prognostic markers to identify high-risk FL pts who may benefit from SI is needed.

7086 Background: The HRQOL of patients (pts) with chronic lymphocytic leukemia (CLL) has not been adequately delineated across patient, disease and treatment characteristics. We evaluated HRQOL of CLL pts undergoing treatment in the United States (US) by age, gender and line of therapy. Methods: Data were collected in Connect CLL, a prospective observational US registry. Physicians provided data on demographics, clinical characteristics and line of therapy at enrollment. HRQOL was self-reported by pts at enrollment using the Functional Assessment of Cancer Therapy-Leukemia, an instrument that yields a leukemia-specific total HRQOL score (FACT-Leu) and a cancer-specific total HRQOL score (FACT-G). Mean total scores were analyzed by line of therapy, age and gender. Statistical significance was ascertained by ANOVA using SAS 9.2. Multivariate analyses were conducted to assess the relative association of line of therapy, age and gender with HRQOL. Results: Among 1,252 pts enrolled from 161 geographically diverse centers (90% community, 8% academic, 2% veterans/military), pts were predominantly male (63%), white (89%) with mean age 69 yrs. Pts were categorized by line of therapy at enrollment: First 61%, Second 18%, Third 11%, Higher 9%; and by age group: <65 33%, 65-74 35%, 75+ 32%. Univariate analyses suggested that the total FACT-Leu score was significantly better in men than women (P=0.004); in pts aged 65-74 vs younger or older pts (P=0.033); and in pts initiating first-line treatment vs pts receiving subsequent treatments (P=0.0002). Similar results were found with the FACT-G score except that gender differences were not statistically significant. Multivariate analysis confirmed that line of therapy (P=0.007), gender (P<0.0001), and age group (P=0.039) were each associated with significant differences in the FACT-Leu total score. Conclusions: Results from the Connect CLL Registry indicate that HRQOL is better among pts initiating first-line therapy compared to pts initiating subsequent treatments, and that this remains true when age and gender are considered. Future analyses should determine how HRQOL may change over time relative to treatment and treatment response.

6122 Background: Clinicians and investigators commonly use ECOG PS and clinician-reported patient (pt) fatigue as surrogates for HRQOL, a multi-faceted construct that comprehensively looks at the pt perspective on disease and well-being. Because limited data exist on the relationships between PS, fatigue, and HRQOL for CLL pts, we examined the associations between these measures, and 3 validated HRQOL instruments: the Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu), EQ-5D, and Brief Fatigue Inventory (BFI). Methods: Data were collected in CONNECT CLL, a prospective US observational registry initiated in 2010. Patient demographics and clinical characteristics were provided by clinicians. Patient HRQOL was self-reported at enrollment using the FACT-Leu, EQ-5D, and BFI. Scores were analyzed by ECOG PS (0, 1, 2-4) and clinician-reported fatigue (yes, no). Differences in HRQOL scores were assessed by ANOVA. Results: HRQOL data were reported by 899 pts from 148 community, 10 academic, and 3 government centers. ECOG PS was available on 711 pts. Overall HRQOL, measured by mean FACT-Leu, FACT-G and EQ-5D Visual Analogue Scale (VAS), worsened with ECOG PS severity and was worse in pts with fatigue (all p<0.0001). All FACT-Leu domains except social/family were worse in pts with fatigue and those with higher ECOG PS. Mean EQ-5D pain/discomfort, mobility, self care and usual activities domain scores worsened in severity as ECOG worsened and for pts with fatigue (all p<0.011). BFI data indicated that global fatigue, fatigue severity and fatigue-related interference worsened by ECOG severity and were associated with clinician-reported fatigue (all p<0.0001). Conclusions: Initial CONNECT CLL results confirm that HRQOL worsens with worsening ECOG PS and was worse among pts with fatigue, especially in physical/functioning domains, pain/discomfort, and mobility. These results indicate that baseline ECOG PS and physician-rated fatigue are rapid assessments that predict robust measures of HRQOL. Future analyses are planned to examine how HRQOL, ECOG PS and fatigue change over time with changes in treatment and CLL disease status.

Introduction MCL is a rare cancer, and current clinical practice guidelines list multiple reasonable treatment options for MCL. It can be challenging to select treatment (Tx) that optimizes outcomes for patients with a mixture of presenting features. In 2014, we developed an online MCL decision support tool designed to provide expert guidance on optimal treatment for defined patient scenarios. An analysis of the 2014 tool showed that Tx recommendations of community healthcare providers (HCPs) did not consistently align with experts for many patients with MCL (Gopalsamy, et al. Leuk Lymphoma. 2019 Mar 8:1-9). Here we report data from an updated version (2018) of this tool that captures evolving practice patterns among experts and community HCPs and the differences between these 2 groups. Methods In September 2018, a panel of 5 experts provided Tx recommendations for 240 patient scenarios in newly diagnosed or relapsed/refractory MCL. These distinct scenarios were defined by patient and disease characteristics that the experts considered important when making treatment choices, including disease setting, patient age and fitness, cytogenetic information, LDH and Ki67 levels, and information on any previous lines of therapy. To use the tool, HCPs entered specific patient/disease characteristics using expert-defined options along with the Tx planned for each case. The users were then shown the 5 MCL expert recommendations and were asked to indicate the impact of the expert recommendations on their planned Tx approach. Results A total of 387 HCPs entered 654 patient scenarios in the 2018 online tool. For HCPs who answered an optional question on how many patients with MCL they treat per year (n = 145), 81% indicated that they treated 10 or fewer patients with MCL per year, highlighting the rarity of this disease and lack of experience among many HCPs. Furthermore, 74% of these HCPs indicated that they refer &lt; 50% of their patients with MCL for clinical trials or to academic institutions for treatment. Among experts, the primary consensus for initial Tx recommendations for younger, fit patients with MCL were bendamustine/rituximab (BR) and R-HyperCVAD in both 2014 and 2018. In comparison, HCPs chose these 2 regimens for a minority of these patients with newly diagnosed MCL in both editions of the tool. For older or unfit patients, experts predominantly recommended BR (85% in 2014 and 75% in 2018), whereas only 35% to 40% of HCPs would choose this regimen in this setting (Table). Of note, nearly 20% of the HCPs indicated that they would recommend a very aggressive chemotherapy regimen for older or unfit patients. For patients with disease progression after chemoimmunotherapy, the recommendation of ibrutinib-based therapy increased among experts from 2014 to 2018 from 66% to 100%. By contrast, the percentage of HCPs using ibrutinib-based therapy remained low at 29% or 37% in the 2014 and 2018 tools, respectively. Another 14% of HCPs selected acalabrutinib, another approved BTK inhibitor, in 2018. In addition, approximately 20% of HCPs in both 2014 and 2018 were unsure of the optimal use of additional therapy (eg, ASCT or maintenance therapy) for their patients. Among users who answered an optional question on the tool's clinical impact, 47% indicated the expert recommendations changed their planned Tx and 38% indicated that expert recommendations confirmed their approach. Conclusions The analysis of data from this online tool found several areas of expert consensus regarding Tx for patients with MCL, particularly for patients who are progressing after initial chemoimmunotherapy. However, practice patterns of community HCPs did not align with expert recommendations for the majority of cases entered into the tool. The consistent Tx gap between practice patterns of community HCPs and expert recommendations over this time period suggests a continued need for online decision tools like this and other innovative approaches to involve experts in the care of patients with MCL. A detailed comparison of expert and user data from the online tool will be presented. Disclosures Ansell: LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding. Leonard:Nordic Nanovector: Consultancy; Karyopharm Therapeutics: Consultancy; BeiGene: Consultancy; Sutro Biopharma: Consultancy; Gilead: Consultancy; MorphoSys: Consultancy; Merck: Consultancy; Nordic Nanovector: Consultancy; Akcea Therapeutics: Consultancy; MorphoSys: Consultancy; Miltenyi: Consultancy; Epizyme, Inc: Consultancy; AstraZeneca: Consultancy; Bayer Corporation: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; ADC Therapeutics: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Gilead: Consultancy; Bayer Corporation: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Celgene: Consultancy; Karyopharm Therapeutics: Consultancy; Sutro Biopharma: Consultancy; Epizyme, Inc: Consultancy; Merck: Consultancy; Sandoz: Consultancy; Akcea Therapeutics: Consultancy; Miltenyi: Consultancy; Sandoz: Consultancy; ADC Therapeutics: Consultancy; BeiGene: Consultancy. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Wang:BioInvent: Consultancy, Research Funding; Dava Oncology: Honoraria; Aviara: Research Funding; Juno Therapeutics: Research Funding; Celgene: Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; VelosBio: Research Funding; Acerta Pharma: Consultancy, Research Funding; Kite Pharma: Consultancy, Research Funding; Guidepoint Global: Consultancy; MoreHealth: Consultancy, Equity Ownership; Loxo Oncology: Research Funding. Flowers:BeiGene: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Millenium/Takeda: Research Funding; V Foundation: Research Funding; Bayer: Consultancy; Acerta: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Eastern Cooperative Oncology Group: Research Funding; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Karyopharm: Consultancy.

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&lt;0.0001) with OS24 ranging from 18% in patients with TTP &lt;6 months compared to 69% in patients with TTP &gt; 36 months (Figure A). Additionally, age at PD (spline p-value&lt;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 &lt;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 &lt;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 &lt;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.

Abstract Introduction: CLL pts who qualify for participation in clinical trials are often thought of as a highly selected population. Therefore whether safety and efficacy data from these trials can be generalized to all CLL pts treated in the community setting is unknown. Prospective, observational studies can provide significant insight into pt and disease characteristics as well as practice patterns in different clinical settings. Here we report on baseline demographics and clinical characteristics for 1495 pts who participated in the Connect CLL observational study and were followed prospectively. Methods: Connect®CLL is a non study-specific intervention treatment registry which collects data on the management and outcomes of CLL pts in clinical, academic and community practices in the US. Clinical information was prospectively collected for pts treated in community (n=1312), academic (n=155), or government (n=28) centers in the US between March 2010 – Jan 2014. Pts eligible for treatment or on treatment not more than 2 months were eligible for enrollment. Here we report on the baseline demographics and clinical characteristics of a large CLL cohort stratified by AG, LOT and type of treatment center. Results: In total,1,495 pts were accrued with a median age at enrollment of 69 (range 22–99). Pts were Caucasian (92.2%), African American (6.9%), and male (63.7%). Median duration from diagnosis until initial therapy was 3.7 years (range 0-32). Age of enrolled pts was as follows; 527 pts age <65 years (AG1; 35%); 511 age 65–75 (AG2; 34%); and 457 age ≥75 (AG3; 31%) at the time of enrollment. Eight hundred and ninety-four pts (60%) were treated with front-line therapy (LOT1); 259 pts (17%) treated with second-line therapy (LOT2); and 342 pts (23%) treated beyond second-line therapy (LOT≥3). Patient age: Pt age was associated with significant variation in several clinical variables: Charlson comorbidity index (CCI) score ≥3 (AG 1-3 35/46/52%; p<0.0001), ECOG PS = 0 (AG1-3 60, 48, 33%, p<0.0001) and mean creatinine clearance (CrCl) decreased (AG 1-3 101/74/53 mL/min; p<0.0001). Line of therapy: Differences in baseline characteristics were observed between pts enrolled at LOT1 (previously untreated) and LOT2/≥3 (relapsed). Median age of pts accrued at LOT1 was 68 vs. 70 years for pts enrolled at LOT2/≥3 (p=0.007). ECOG PS = 0 was more frequent at LOT1 (50%) vs. LOT2/≥3 (42%) (p=0.0057). CCI ≥3 at LOT1 was 41% vs 48% at LOT2/≥3 (p=0.0072). When adjusted for differences in age, logistic regression also demonstrated significant differences in performance status (PS=0; p=0.02) and CCI (p=0.03) stratified by LOT. Treating institution: Baseline characteristics also differed by type of treating institution. Median age was 63 vs 70 at academic vs non-academic (p<0.0001). Pts treated at academic sites also had lower CCI (CCI≤2; 72.9% vs 54.2%, academic vs non-academic) and higher median CrCl (86.4 vs 71.4) (p=0.0017). In this registry, only 16% of patients under age 75 had CCI <= 2, CrCl >= 70 mL/min, ECOG 0-1; 18% of patients on LOT1 and 12% of pts on LOT=2 would satisfy these criteria. They represent 36% of eligible pts from academic treating institutions and 13% from community/government centers. Only 20% of patients on LOT1 would satisfy enrollment criteria for CLL8 (FCR vs. FC; Hallek; Lancet 2010) CLL 10 (BR vs FCR; NCT00769522). which did not have enrollment restrictions for age. Conclusions:Baseline characteristics of pts with CLL vary by age, LOT, and type of treatment center. In this large prospective observational study, our data confirms that older pts typically have a worse ECOG PS, more medical comorbidities, more advanced disease when initiating therapy and a lower probability of being treated at an academic medical center. Based on inclusion criteria used for entry in either CLL10 or CLL8, fewer than 20% of pts satisfy age, renal clearance, and fitness profile to be considered suitable for aggressive chemoimmunotherapy in the front line setting. This observation supports the notion that current clinical trial data might not always be applicable to the general CLL population seen in the community and argues for designing clinical trials that are more generalizable to the broader pt population. Disclosures Sharman: Gilead: Research Funding; Calistoga: Honoraria; Phamacyclics: Honoraria, Research Funding; Celgene: Consultancy, Research Funding; TG therapeutics: Research Funding; Roche: Research Funding. Mato:Millennium: Speakers Bureau; Celgene: Speakers Bureau; Genentech: Speakers Bureau. Kay:Celgene: Research Funding. Kipps:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Genentech-Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weiss:Celgene: Consultancy; Phamacyclics: Consultancy; Gilead: Consultancy. Nabhan:Celgene: Honoraria, Research Funding. Flinn:Celgene: Research Funding. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy. Farber:Alexion: Equity Ownership, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Speakers Bureau; Janssen/Pharmacyclics: Speakers Bureau; Seattle Genetics: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:TG Therapeutics: Research Funding; Phamacyclics: Research Funding; Acerta: Research Funding; Abbvie: Research Funding; Infinity: Research Funding; Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Spectrum: Research Funding; Gilead: Research Funding; Genentech: Unpaid consultancy Other; Millennium: Research Funding, Unpaid consultancy, Unpaid consultancy Other; Seattle Genetics: Consultancy; Prescription Solutions: Consultancy; Celgene: Unpaid consultancy Other.

Abstract 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.