Citations

Abstract Introduction: Rituximab (R) improves survival in CLL when added to intensive chemotherapy (such as fludarabine and cyclophosphamide [FCR]), and has been shown to improve progression-free survival when given as maintenance therapy following first- or second-line chemoimmunotherapy. However, early phase 2 studies demonstrated only modest activity of R monotherapy (R mono), particularly at standard dose, and its use as a single agent for pts with CLL has remained controversial. Here we describe the characteristics and outcomes of pts with CLL receiving R mono in a real-world setting. Methods: The Connect CLL registry (NCT01081015), a multicenter, prospective observational cohort study, enrolled 1,494 pts with CLL between 2010-2014 from 179 community, 17 academic, and 3 government sites throughout the USA. Pts were ≥ 18 years and were enrolled ≤ 2 months after initiating any line of therapy (LOT). Pts were treated according to individual physician/patient decision making, and adherence to IWCLL standards cannot be determined. Bone marrow biopsy and CT scans were at the physician's discretion; responses were physician assessed and not centrally reviewed. For this analysis, pts were stratified by first (LOT1) or subsequent LOT (LOT≥2), and by treatment with R mono or other treatments (Tx). Kaplan-Meier methods were used to estimate event-free survival (EFS; event defined as death, progression/relapse, or transformation). A log-rank test was used to evaluate differences in EFS. A multivariate analysis of EFS in LOT1 pts was performed using the Cox regression model. Results: Of the pts enrolled, 105 (11.8%) pts in LOT1 and 81 (13.4%) pts in LOT≥2 received R mono. Of the pts receiving other Tx, 620/784 pts (79%) in LOT1 and 333/524 pts (64%) in LOT≥2 received R in combination. In LOT1, pts receiving R mono were older than pts receiving other Tx (74 vs 67 years; P < 0.0001); in LOT≥2 median ages were comparable (71 vs 70 years). Pts receiving R mono were more likely to have Rai stage 0-1 CLL than pts receiving other Tx, both in LOT1 (63.9% vs 51.8%; P = 0.05) and LOT≥2 (70.4% vs 49.9%; P = 0.005). The proportion of pts receiving R mono vs other Tx with a CCI score of ≥ 4 was similar in LOT1 (25.7% vs 22.8%) and LOT≥2 (32.1% vs 28.4%). Median absolute lymphocyte count at enrollment was lower in pts on R mono vs other Tx in LOT1 (27.7 vs 46.1 x 109/L) but similar in LOT≥2 (35.6 vs 30.2 x 109/L). A lower proportion of pts receiving R mono had FISH/cytogenetics performed at enrollment than pts receiving other Tx in LOT1 (48.6% vs 67.0%; P = 0.0002) and LOT≥2 (30.9% vs 49.6%; P = 0.002). The most common Tx regimens in LOT1 were FCR (25.9%), bendamustine plus R ([BR] 21.0%), R mono (11.8%), fludarabine plus R ([FR] 6.3%), and chlorambucil (4.6%); and in LOT≥2, BR (27.6%), R mono (13.4%), FCR (8.6%), bendamustine (7.4%), FR (4.1%), and ofatumumab (4.0%). The most common reason to initiate treatment in any LOT was bone marrow failure (LOT1, 44.8% vs 39.3%; LOT≥2, 37.0% vs 31.5%; R mono vs other Tx). Duration of therapy was shorter for pts receiving R mono vs other Tx in LOT1 (1.4 vs 4.1 months) and LOT≥2 (1.6 vs 3.3 months). In LOT1, responses were lower in pts on R mono vs other Tx: overall response rate (ORR) was 38.1% vs 64.2% (complete response [CR], 16.2% vs 41.2%; P < 0.0001). In LOT≥2, ORR was 25.9% vs 24.6% for pts on R mono vs other Tx (CR, 9.9% vs 11.5%; P = 0.68). Partial response did not differ significantly between pts receiving R mono vs other Tx in LOT1 and LOT≥2. Pts receiving R mono in LOT1 had inferior median EFS vs other Tx (34 vs 50 months; log-rank P = 0.04); however, after adjusting for factors such as ECOG status and del(17p) status, there was no difference in EFS (HR 0.932; P = 0.79). In pts in LOT≥2, median EFS was similar for R mono and other Tx (15 months for both groups; P = 0.93). Conclusions: Pts receiving R mono as frontline CLL therapy were older than pts receiving other Tx. They also had lower stage disease and lower lymphocyte counts, a shorter duration of treatment, and inferior response rates. Pts receiving R mono in relapsed CLL, more closely approximated pts receiving other Tx in terms of age. Despite the shorter duration of therapy for pts receiving R mono in LOT1 and LOT≥2, both groups had similar ORR and EFS in LOT≥2. These data reinforce the idea that R mono is inadequate as frontline therapy in progressive CLL, and demonstrate the opportunity for improvement in relapsed/refractory disease with novel agents relative to traditional chemoimmunotherapy approaches. Disclosures Sharman: Celgene: Research Funding; Gilead: Research Funding; Pharmacyclics: Research Funding; Acerta: Research Funding; TG Therapeutics: Research Funding; Seattle Genetics: Research Funding. Davids:Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: 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; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie, Janssen, Gilead: Consultancy; Celgene Corporation: Consultancy. Farber:Seattle Genetics: Research Funding. Grinblatt:Celgene Corporation: Consultancy, Speakers Bureau. Lamanna:Gilead: Research Funding; AbbVie: Research Funding; Genentech: Research Funding; Infinity: Research Funding; Pronai: Research Funding; Celgene Corporation: Research Funding. Mato:TG Therapeutics: Consultancy; Abbvie: Research Funding; Acerta Pharma: Research Funding; Gilead Sciences: Research Funding; ProNAi: Research Funding; TG Therapeutics: Research Funding; Theradex: Research Funding; Pharmacyclics: Consultancy; Gilead Sciences: Consultancy; Abbvie: Consultancy. Nabhan:Astellas: Research Funding; Seattle Genetics: Research Funding; Cardinal Health: Consultancy; Infinity: Consultancy; Abbvie: Consultancy; Genentech: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Employment, Equity Ownership. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:Seattle Genetics: Research Funding; Optum Rx, Seattle Genetics, Genentech/Roche: Consultancy; Celgene Corporation: Consultancy, Honoraria; Spectrum, Janssen, Infinity, AbbVie, Acerta, Pharmacyclics, TG Therapeutics: Research Funding; Millennium: Consultancy, Research Funding; Gilead: Consultancy, Research Funding.

Abstract Introduction: Given the availability of several active treatment (tx) options for CLL, providers frequently make tx recommendations based on their interpretation of objective data from consensus criteria and clinical trials. There are limited data describing patient (pt) experience, values, and insights when encountering multiple tx choices. The CLL Society, a pt-driven, physician-curated nonprofit organization focused on the unmet needs of the CLL community, sought to explore how CLL pts make tx decisions. Methods: A 64 question survey was distributed online and in paper to CLL pts from Oct-Dec 2017. The survey was developed by CLL experts. The research was IRB-approved. Chi square was used for statistical comparison; all other analyses are descriptive in nature. Results: 1147 pts from 48 states completed the survey. Median age was 65 (range 28-86), 46% male, 96% Caucasian. Who Influences Tx: Of the 79% of pts who have seen a CLL expert, defined as a provider focused on CLL at an academic/research center, 95% rated their provider as very/extremely influential. 83% of pts rated their own opinions as influential, and 59% rated the opinion of a general hematologist/oncologist (gen heme/onc), a provider who sees a variety of cancer types, as influential. Only 27% let their provider unilaterally decide on tx. Of these pts, 19% trusted their provider to select the best tx and 5% didn't have enough understanding to be involved in the decision. 64% were influenced by outside sources of information, such as family members, live CLL support groups, or online sources including pt experts and forums. Factors in Deciding Tx: Of the 67% pts surveyed who have been treated, 73% were offered > 1 tx choice by their provider. In deciding on a specific tx, 94% of respondents stated that it was very/extremely important to know there were tx options beyond their current regimen. The most important factors in selecting tx were response rate (91%), overall survival (88%), progression-free survival (86%), long-term side effects (82%), ability to achieve undetectable MRD (80%), cost/insurance coverage (66%), and location of tx (50%) with significant differences between subgroups (Table 1). Tx was impacted by cost or insurance issues for 16% (n=104). Of these 104 pts, cost/insurance issues resulted in tx delay for 34 and change in tx plan for 24 pts. 16 pts reported that an appeal led to coverage and 16 pts received financial assistance. Pts' willingness to accept potentially curable but high-risk tx was heavily influenced by availability of alternate options. For example, 18% of pts would be willing to consider cytotoxic chemotherapy if alternate options were available vs. 72% if no options were available. Similar patterns were seen with CAR-T tx (28% vs. 79%) and bone marrow transplantation (BMT) (15% vs. 66%) with significant differences between subgroups (Table 2). 82% were willing to take a life-long tx for long-term control without potential for cure. Willingness to accept long-term tx was significantly higher in treated vs. untreated pts (84% vs. 77%, p=.006) and men vs. women (84% vs. 78%, p=.008). Clinical Trial Education: Of those treated by a gen heme/onc, 31% received no education on clinical trials. Only 15% reported good understanding of trial opportunities vs. 52% treated by a CLL expert (p<.001). 9% of pts treated by gen heme/onc were offered clinical trial participation vs. 47% treated by a CLL expert (p<0.001). Pts who declined or would have declined participation cited reasons including preference for "proven" tx (38%), distance from trial site (29%), fear (20%), frequent imaging (20%), overall lack of understanding about trials (15%), and lack of information (14%). Summary: Most respondents want to be and are involved in tx decisions. While providers are influential in decision making, CLL pts also rely on outside sources. Gender, age, and tx status influence which factors drive decision making. Pts select tx based on response rate, survival, side effects, and ability to achieve deep response and want a plan in place for sequencing tx. The majority are willing to consider long-term tx without cure. Few want chemotherapy, CAR-T, or BMT unless they have no other options. A significant opportunity for improvement in education on clinical trial opportunities was identified. This description of pt experience, values, and preferences enriches the informed consent process and may lead to more tailored and patient-centered care. Disclosures Kennard: AbbVie, Gilead, Verastem: Consultancy. Furman:TG Therapeutics: Consultancy; Loxo Oncology: Consultancy; Sunesis: Consultancy; Verastem: Consultancy; Incyte: Consultancy, Other: DSMB; Genentech: Consultancy; Acerta: Consultancy, Research Funding; Gilead: Consultancy; Janssen: Consultancy; Pharmacyclics LLC, an AbbVie Company: Consultancy; AbbVie: Consultancy. Pagel:Pharmacyclics, an AbbVie Company: Consultancy; Gilead: Consultancy. Davids:Sunesis: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Consultancy, Research Funding; Surface Oncology: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Merck: Consultancy; Merck: Consultancy; MEI Pharma: Consultancy, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; MEI Pharma: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Sunesis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Verastem: Consultancy, Research Funding; Surface Oncology: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Roche: Consultancy; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Verastem: Consultancy, Research Funding; BMS: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Surface Oncology: Research Funding; Verastem: Consultancy, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sunesis: Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy; Roche: Consultancy. Nabhan:Cardinal Health: Employment, Equity Ownership. Kay:Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees. Siddiqi:Juno Therapeutics: Other: Steering committee. Brander:Genentech: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Teva: Consultancy, Honoraria; Novartis: Consultancy, Other: DSMB; TG Therapeutics: Consultancy, Honoraria, Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; BeiGene: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; Acerta: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding; DTRM: Other: Institutional research funding for non investigator initiated clinical trial, Research Funding. Wierda:AbbVie, Inc: Research Funding; Genentech: Research Funding. Hill:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees. Cheson:AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Choi:Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Gilead: Speakers Bureau; Rigel: Consultancy; Genentech: Speakers Bureau; AbbVie, Inc: Consultancy, Speakers Bureau. Mato:AstraZeneca: Consultancy; Prime Oncology: Honoraria; Portola: Research Funding; Johnson & Johnson: Consultancy; Medscape: Honoraria; Celgene: Consultancy; TG Therapeutics: Consultancy, Research Funding; Acerta: Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Regeneron: Research Funding.

Abstract Background Disease characteristics, treatment patterns, and outcomes of follicular lymphoma (FL) patients (pts) aged >80 years (yrs) are infrequently reported. Further, the original Follicular Lymphoma International Prognostic Index (FLIPI) has not been formally studied in pts aged >80 yrs. Also, whether FLIPI is associated with overall survival (OS) in pts aged >80 yrs is unknown. Patients/Methods We used the National LymphoCare Study—a Genentech-sponsored, prospective, multicenter registry of FL pts across the United States without study-specific treatment—to comprehensively analyze FL pts aged >80 yrs. Using Pearson chi-squared tests, associations of age groups with disease characteristics and overall response rate (ORR) were examined. Median progression-free survival (PFS) and OS by treatment regimen were estimated using the Kaplan-Meier method. Cox proportional hazards regression that were adjusted for baseline disease factors were used to assess treatment differences in PFS, lymphoma-related mortality (LRM, which includes both disease and treatment-related deaths), and OS, as well as the significance of age by treatment interactions. FLIPI and a prognostic model that was constructed based on the findings were used to predict outcomes. Results Of 2649 evaluable pts, 209 (8%) were aged >80 yrs. Compared with pts aged ≤60, pts aged >80 yrs were more likely to be Caucasian, to have worse performance status, to have lower hemoglobin (Hgb) value (<12 g/dL), to have less advanced stage, and were less likely to be treated at academic institutions. When treated, pts aged >80 yrs had lower ORR compared with younger pts and were less likely to receive rituximab + chemotherapy (P <.001) or anthracyclines (P <.001). Use of maintenance rituximab was not significantly different between pt groups when rituximab induction was given (61% for pts aged ≤60, 38% for pts aged >80, P=.11), but it was more frequent in pts aged >80 yrs when rituximab + chemotherapy induction was administered (44% for pts aged ≤60, 59% for pts aged >80, P=.04). After adjusting for maintenance use, sex, and baseline factors, age significantly differentiated PFS impact of observation, rituximab monotherapy, and rituximab + chemotherapy (P=.01). No treatment regimen provided superior PFS or OS in pts aged >80 yrs. With a median follow-up of 6.5 yrs for all pts (4.3 yrs for pts aged >80 yrs), 5-year OS was 59% and 92%for patients aged >80 and ≤60 yrs, respectively. OS in pts aged >80 yrs varied based on FLIPI score (log-rank test, P=.01; Figure 1). Interestingly, the percentage of deaths that were disease-related was 40% in pts aged >80 yrs, which was comparable to the percentage in pts aged ≤60 yrs of 48%. Cox modeling showed that lower Hgb, B symptoms, and male sex predicted worse OS (P<.01) but not PFS in pts aged >80 yrs. Recognizing that males have inferior OS compared with females in the general population, we constructed a prognostic model composed only of B symptoms and Hgb level <12 (low risk=no factors; high risk=1 or more factors; P<.0001; Figure 2). FLIPI and the proposed prognostic model significantly differentiated both OS and LRM (Table 1). Conclusions In the largest, prospectively published study of FL pts aged >80 yrs, OS, PFS, ORR, and treatment selection varied compared with younger pts. Surprisingly, the proportion of deaths attributed to lymphoma in these older FL pts was 40%. FLIPI was associated with outcome in pts aged >80 yrs and so was a proposed prognostic model comprising lower Hgb and B symptoms. Independent validation of this model is required, and additional prospective studies that are designed for the oldest old are warranted. Disclosures: Nabhan: Genentech Inc.: Advisory Board Other, Honoraria. Byrtek:Genentech, a member of the Roche group: Employment, Equity Ownership; Roche: Equity Ownership, stock options, stock options Other. Dawson:Genentech: Employment; Roche: Equity Ownership. Link:Genentech: Consultancy; Millenium: Consultancy; Pharmacyclics: Consultancy; Spectrum: Consultancy; Genentech: Research Funding; Millenium: Research Funding; Pharacyclics: Research Funding. Zelenetz:GSK: Consultancy; Celgene: Consultancy; Cephalon: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Sanofi-Aventis: Consultancy; Genentech: Research Funding; GSK: Research Funding; Roche: Research Funding; Cancer Genetics: Scientific Advisor Other. Cerhan:Genentech: Scientific Advisory Board of the National LymphoCare Study Other. Flowers:Abbott, Celgene, Millennium/Takeda, Sanofi, Spectrum, Janssen: Research Funding; Celgene, Genentech Bio-oncology: Consultancy.

Abstract Abstract 4607 Background: Extramedullary Chronic Lymphocytic Leukemia (EM-CLL) is reported in the literature as case reports and series. The prognosis, patient characteristics, and treatment approaches to EM-CLL have not been fully analyzed in a comprehensive fashion. We therefore conducted a systematic review to better understand EM-CLL and how it might affect patients' clinical course and overall survival. Methods: A comprehensive PubMed search for English-language articles published between 1975 and 2010 in peer-reviewed journals was performed. EM-CLL was defined as CLL identified pathologically in these reports outside the bone marrow. Search terms included “EM-CLL, CLL outside the bone marrow, and CLL with each system [central nervous system (CNS), genitourinary (GU/GYN), gastrointestinal (GI), cardiac, pulmonary, head and neck (H+N), skin, and vascular]. Data regarding location, sex, age at diagnosis, systemic CLL disease setting (relapsed vs. initial diagnosis), laboratory studies/prognostic factors (when available), treatment for CLL (yes or no), use of chemotherapy (yes or no), use of radiation (yes or no), use of rituximab (R), use of purine analogues, outcome (alive or deceased), and follow-up were collected. A z-test was used for assessing proportion differences for gender, stage (Rai 0, 1, 2 vs. Rai 3, 4), and chemotherapy (yes or no). Univariate analyses were performed via the Chi-square test. A backward stepwise logistic regression analysis was performed to identify whether disease location, disease setting, age, and sex influence overall survival (OS). No other multivariate analyses were performed due to large percent of missing data for different variables of interest. A two-tailed P level of 0.05 was considered statistically significant in all analyses; which were performed using SPSS for windows, version 19.0 (SPSS Inc.) and StarStat (DataStar, Inc.). Results: A total of 192 cases were identified [63 (33%) skin, 51 (27%) CNS, 26 (14%) GI, 20 (10%) GU/GYN, 10 (5%) lung, 10 (5%) ocular, 6 (3%) heart, 5 (3%) H+N, and 1(0.5%) vascular]. Median age at detection of EM-CLL was 62 (range, 25–89); significantly more men were diagnosed with EM-CLL versus women (61% vs. 35%; z = 3.51, P <.001); more patients were at early stage (Rai 0, 1, 2) versus advanced (Rai 3 and 4) at time of EM-CLL but this difference was not statistically significant (22% vs. 18%; z = .43, P =.665). More patients with EM-CLL were treated with chemotherapy for their underlying systemic CLL than not (65% vs. 15%, z = 6.25, P <.001); very few patients received radiotherapy which was delivered for their EM-CLL (n=35; 18%) but treatment information was missing from approximately 20% of cases. Our analysis showed that there was no significant difference with regard to number of patients treated with chemotherapy before and after the contemporary chemoimmunotherapy era (defined here as before and after the year 2000) (87% vs. 78%, p =.171). As far as systemic therapy for CLL, 12% of pts received R and 14% received purine analogues. Tissue sites such as CNS (45%), ocular (33%), H+N (25%), and skin (19%) were significantly more likely to receive XRT (p <.001) but no significant differences between the sites were reported for chemotherapy (p =.106), R (p =.237), and purine analogues (p =.090) usage. OS data was missing in 56 cases (29%), 79 were alive (41%), and 54 (28%) were dead at the time of case reporting. Backward stepwise logistic regression analysis, which included data for 102 (53%) patients showed that EM-CLL detected in patients with systemic relapse had worse OS compared to those with EM-CLL at time of initial CLL diagnosis (p =.027), while no significant effect on OS was found for tissue site (p =.085), age (above or below 70, p =.796), and sex (p =.791). All other variables were not significant. Conclusions: To our knowledge, this analysis represents the largest collection of EM-CLL series examined comprehensively. EM-CLL is not an uncommon presentation in the spectrum of CLL/SLL with skin and CNS being the most commonly affected organs (33% and 27% respectively). Patients who develop EM-CLL in the relapsed setting have significantly worse OS compared to those who manifest EM-CLL at the time of initial diagnosis. We propose that this subset of CLL needs a prospective and robust accumulation of prognostic and clinical data as it will be critical in defining how extramedullary presentation affects the biology and natural course of CLL. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 680 Cytogenetics play an essential role in determining the prognosis of patients with AML. However, there is still no validated cytogenetics grouping scheme that specifically applies to patients undergoing allogeneic stem cell transplantation, which hampers accurate prognostication and risk stratification. We studied 821 adult patients (median age 41, range 18–74) who underwent SCT between 1999 and 2004 for AML (excluding APL) in CR1 or CR2 and who were reported to the CIBMTR from centers with >20 patients meeting study criteria. 75% of patients received a myeloablative conditioning. 496 patients had a normal karyotype. The primary cytogenetics reports were manually reviewed for 92% of the patients with an abnormal karyotype. We compared the ability of the 6 existing grouping schemes (MRC, CALGB, EORTC/GIMEMA, SWOG/ECOG, DFCI, and Monosomal Karyotype (MK) classifications) to stratify patients, using both the Akaike Information Criterion in multivariable models and the C-statistic. Among all existing schemes, the DFCI system provided a marginally superior stratification for overall and leukemia-free survival. We also built a new classification using individual cytogenetic abnormalities in a Cox model that included other significant covariates (performance status, therapy-related disease, conditioning intensity, graft source, donor match, duration of CR1, and gender match). This CIBMTR scheme (see Table), which is similar to the DFCI scheme except for the inclusion of patients with t(8;21) in the intermediate group, could stratify patients into 3 groups with similar treatment-related mortality but significantly different overall survival, leukemia-free survival, and incidence of relapse. This scheme appeared to apply to both patients in CR1 and in CR2 (see FiguresF2). This transplant-specific scheme could be adopted for prognostication purposes and to stratify patients with karyotypic abnormalities entering transplantation clinical trials. Future studies may clarify the relative outcome of patients with t(8;21) and refine this scheme with the inclusion of molecular abnormalities. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2877 Background: Racial disparity has been well documented in a number of cancers but the impact of race on CLL in the contemporary era is unclear. While preliminary evidence suggests that Black (B) patients (pts) have worse survival than their White (W) counterparts (Shenoy et al, Clin Lymph Myleoma Leuk, 12/2011), the importance of sex, age, socioeconomic status (SES), and whether the wide use of single use or combined therapy with R in CLL over the last decade plus has affected overall survival (OS) have not been fully explored. Further, outcome of Hispanic (H) and Asian/Pacific Islanders (A/PI) CLL pts has not been fully studied. Methods: We examined population based survival data from SEER 13 (1993–2008) for CLL within and across various races. We also investigated the impact of sex, age, and socioeconomic status (SES) on their clinical outcome. Outcomes were examined over two consecutive 8-year (yr) periods: Era-1 (1993–2000) and Era-2 (2001–2008) with the assumption of R therapy in CLL patients being more frequent after 2001 (market research data not shown). Results: We identified 24,964 pts [W =21,363 (85.5%), H =1,197 (4.7%), B =1,709 (6.8%), and A/PI =695 (2.7%)]. Differences were notable for a greater male predominance among A/PI [62% vs. 57% (B), 56% (H), 58% (W); P=0.03]; a higher proportion of pts >80 among W [22% vs. 17% (H), 15% (B), 16% (A/PI); P<0.001], and higher SES among A/PI and W pts compared to B and H (P<0.001). OS for all patients was significantly better in Era-2 vs. Era-1 at 5-yrs (65% vs. 60.4%, P<0.0001). This improvement was statistically significant in all races except A/PI pts (P=0.71) (Table). Improved survival across eras was also noted in all age groups (<50 (P<0.00001), 50–59 (P=0.007), 60–69 (P<0.0001), 70–79 (P<0.0001), >80 yrs (P<0.0001)). Further, improved OS was noted in the two SES classes evaluated (0–15% and 15.1–30% below poverty line respectively). While there were no statistical differences between males and females within either era, improvement in OS was noted in both sexes in Era-2 versus Era-1 (P<0.0001). We subsequently compared OS within and across races (Table). Despite the fact that OS improved in all races, W pts continued to have better OS in Era-2. In Era-1, while W pts had better OS than B and H pts, the OS is similar between W and A/PI. Although OS improved in all SES classes, patients with higher SES continued to have better OS in Era-2 (P<0.0001 for both). Conclusions: The OS of CLL pts has improved in the contemporary era for both sexes, all age groups, and all races except A/PI individuals. The improvement in outcome in Era-2 might be partially explained by increased use of R and other novel agents that became available after 2001. Despite the broad nature of these improvements, racial and SES differences in the survival persist and deserve further validation and pursuit of the causes. Disclosures: Shanafelt: Genentech: Research Funding; GlaxoSmith Klein: Research Funding; Teva/Cephalon: Research Funding; Celgene: Research Funding. Kay:Genentech: Research Funding; Glaxosmith Klein: Research Funding.

Abstract Background: Contemporary approaches to non-Hodgkin lymphoma (NHL) and chronic lymphocytic leukemia (CLL) incorporate chemo-immunotherapy, biologic combinations, and immune modulating agents; toxicities in elderly pts (³65 years) receiving these therapies are not well studied. Further, clinical and biologic factors predicting these toxicities in pts receiving biologic therapy remain undefined. Methods: We reviewed data on NHL and/or CLL pts treated prospectively on 14 studies by the Alliance for Clinical Trials in Oncology from 2004-2014 (Table 1). Toxicity was assessed per the NCI CTCAE at the time of trial enrollment, and the probabilities of experiencing grade 3 and grade 4 hematologic (hem) and non-hematologic (non-hem) toxicities were modeled as a function of age (³65 years vs. < 65), time on study, treatment (biologics only vs. biologic + chemotherapy), gender, race, LDH, performance status (PS), stage, and an age-by-treatment interaction using logistic regression. Results: A total of 1199 pts (409 age ³ 65; 790 age < 65; 736=CLL and 463=NHL) were included. Among these patients, 493 received only biologic therapy (166 age ³ 65; 327 age < 65; 104 CLL; 389 NHL), and 706 received biologic + chemotherapy (243 age ³ 65; 463 age < 65; 632 CLL; 74 NHL). Among CLL pts (259 pts ³ 65), the effect of age on the probability of experiencing a grade 3 heme toxicity differed by treatment type (age-by-treatment interaction p = 0.047). Specifically, the adjusted odds ratio (OR) (age ³ 65 vs. < 65) for pts receiving only biologic therapy was 3.075 (95% CI: 1,15-8.25), and that for pts receiving biologic + chemotherapy was 1.044 (95% CI: 0.69 - 1.57). Similar results were seen in CLL pts for grade 4 heme toxicities (age-by-treatment interaction p = 0.033; biologic OR 6.937, 95% CI: 1.76-27.35; biologic + chemo OR 1.484, 95% CI: 1.04-2.13). No such interactions were found in CLL pts for grade 3 and 4 non-hem toxicities; however, older pts had significantly higher odds of experiencing a grade 3 non-hem toxicity than younger pts (adjusted OR 1.40; p = 0.047; 95% CI: 1.004-1.96). No age group differences were found in CLL pts for grade 4 non-hem toxicity. Similar analyses were performed for NHL pts (150 pts ³ 65), but no significant age group differences were found. Among CLL pts, women had significantly higher odds of experiencing a grade 3 heme toxicity than men (OR 2.01; p = 0.0007); no difference in grade 3 non-hem or any grade 4 toxicity was noted. Non-Caucasian CLL pts had higher odds of experiencing a grade 4 non-hem toxicity than Caucasians (OR 2.892; p = 0.0029), but no other toxicity differences were found. Worse PS was associated with increased toxicities (OR 1.871; p = 0.0009: grade 3 heme; OR 1.647; p = 0.0025: grade 3 non-hem; OR 1.410; p = 0.0448: grade 4 heme, and OR 1.931; p = 0.0252: grade 4 non-hem). CLL pts with advanced stage disease had higher odds of experiencing a heme toxicity (grade 3: OR 1.95; p = 0.0007, grade 4: OR 1.451; p = 0.0329), but no stage associations were found for non-hem toxicities. Among NHL pts, men had significantly higher odds of experiencing a grade 4 hematologic toxicity than women (OR 4.351; p = 0.0169), but no other toxicity differences were found. An increase in LDH was associated with significantly higher odds of experiencing grade 3 non-hem and grade 4 heme toxicities (grade 3 non-heme: OR 1.633; p = 0.021, grade 4 heme: OR 2.039, p = 0.0182), but no such effect was found for grade 3 heme or grade 4 non-hem. Worse PS was associated with higher odds of experiencing grade 3 toxicities (heme: OR 2.025; p = 0.0344, non-hem: OR 2.458; p = 0.0013), but no such differences were found for grade 4 toxicities. No significant stage or race effects were found in NHL patients. Conclusion: In pts ³65 years who have CLL or NHL, we identified several clinical and disease-related factors as potential predictors of developing grade 3 and/or 4 heme and non-hem toxicities (Table 2). A prognostic model is being constructed to predict toxicities in these under-studied pts to guide management and monitoring. Further, the impact of these toxicities on outcomes is being analyzed and will be presented at the meeting. Disclosures Hurria: Celgene: Other: Research; Optum Health Care SOlutions: Consultancy, Other: Conference panel, research; Boehringer Ingelheim Pharmaceuticals: Consultancy; Sanofi: Consultancy; Carevive: Consultancy; Novartis: Other: Research; GTx, Inc: Consultancy. Bartlett:Gilead: Consultancy. Cheson:Gilead: Research Funding; Acerta: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding. Nabhan:Infinity: Consultancy; Abbvie: Consultancy; Cardinal Health: Consultancy; Seattle Genetics: Research Funding; Genentech: Consultancy, Research Funding; Astellas: Research Funding; Celgene Corporation: Consultancy, Research Funding.

Abstract Introduction The natural history of patients (pts) with CLL varies considerably. Some pts may have asymptomatic, indolent disease, while others may require therapy soon after diagnosis. Clinical staging systems cannot identify early-stage pts at risk for inferior survival. Genetic and molecular markers can provide additional information regarding prognosis and response prediction, however their utilization is limited in routine practice (Mato, ASCO 2015) and results can be conflicting. There is a need to identify characteristics of early-stage CLL pts at highest risk of early progression and/or death. Such characteristics, if readily available and reproducible, may identify such pts as candidates for early-intervention studies; particularly in the era of kinase inhibitor therapy. The Connect CLL registry is the largest prospective study describing real-world management of a diverse cohort of 1494 CLL pts. In this analysis, we evaluate key factors that are associated with early CLL-associated mortality in clinical practice. Methods Connect CLL is a multicenter, observational cohort study aimed at understanding patterns of CLL management without a study-specific intervention. Eligible pts were adults with a clinical diagnosis of CLL, enrolled between 2010-2014 at 179 community (n=1311), 17 academic (n=155), and 3 government (n=28) sites within ≤ 2 months of initiating either a first line of therapy (LOT1), or a second-line or subsequent LOT (LOT≥2). The goal of this analysis was to assess potential predictors of death occurring within 18 months following initiation of frontline therapy (LOT1). Because CLL usually presents as an indolent disease associated with a prolonged clinical course, we chose to define early mortality as any death occurring ≤ 18 months following enrollment. Univariate logistic regression was performed against potential predictor variables including several clinical characteristics and comorbid conditions, race, treatment (Tx) site, insurance type, geographic region, presence of prior malignancies, genetic prognostic factors (FISH and cytogenetics) at enrollment, cell surface markers (Zap70, CD38), reasons for Tx initiation and Tx choice for LOT1 (BR or FCR). IgVH mutation status and genetic mutations (P53, Notch1, etc.) were not included as these tests were not readily performed in community practice. Predictors that were found to be significant at the 0.15 significance level were included in the multivariable logistic regression using a stepwise variable selection process, to identify factors associated with early mortality. Results 1344 pts completed 18 months of follow-up (n=1111) or died (n=233) within the first 18 months of study enrollment. Of these, 801 pts were enrolled in LOT1 (median time from diagnosis to LOT1 was 1.3 yrs; IQR, 0.1-3.7 yrs). 82 of 801 pts (10.2%) died within 18 months of LOT1 initiation; 41% of pts with available clinical information who died, were defined as early-stage (Rai stage 0-1). Of 26 potential predictors evaluated, 12 were associated with early mortality among LOT1 pts at the 0.15 significance level in univariate analyses including site, race, age>75, CD-38, del(17p), ECOG PS, CCI group, RAI stage, creatinine clearance, insurance status, reasons for Tx initiation, anemia and lymphocytosis. In multivariable logistic regression analyses we identified 4 independent predictors of mortality within 18 months of LOT1 initiation (Table): pt age, CD38 expression, baseline decreased hemoglobin (as a reason for Tx initiation), and race. Conclusions In CLL pts with Rai stage 0-1, we have identified 4 factors which are associated with increased risk of death within 18 months of initiation of frontline therapy. Interestingly, neither choice of FCR vs BR chemoimmunotherapy nor the presence of del(17p)/del(11q) were predictors of early mortality. Using these results, we are currently designing a model to predict early mortality in early-stage pts. Once validated, these results may provide a framework for trials targeting the "early stage, symptomatic" CLL pts for novel interventions. Table. Multivariable Analysis: Predictors of Early Mortality Odds ratio estimate 95% CI P -value LOT1, n=781 Age ≥75 years, yes vs no 2.2 1.4-3.5 0.0012 CD38 expression, positive vs negative/not specified 1.8 1.1-2.9 0.0182 Anemia as reason for treatment, yes vs no 2.1 1.3-3.4 0.0040 Race, other vs Caucasian 1.9 1.0-3.8 0.0501 Disclosures Farber: Gilead: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Speakers Bureau. Mato:TG Therapeutics: Research Funding; Genentech: Consultancy; AbbVie: Consultancy, Research Funding; Pronai Pharmaceuticals: Research Funding; Gilead: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Janssen: Consultancy. Nabhan:Celgene Corporation: Honoraria, Research Funding. Kipps:Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor. Flowers:Spectrum: Research Funding; Pharmacyclics: Research Funding; Infinity Pharmaceuticals: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Spectrum: Research Funding; Genentech: Research Funding; Onyx Pharmaceuticals: Research Funding; Acerta: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; AbbVie: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy; Onyx Pharmaceuticals: Research Funding; Genentech: Research Funding; Millennium/Takeda: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Infinity Pharmaceuticals: Research Funding; Acerta: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Millennium/Takeda: Research Funding. Kay:Pharmacyclics: Research Funding; Hospira: Research Funding; Genentech: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharma: Research Funding; Celgene Corporation: 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 Corporation: 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. Grinblatt:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy, Speakers Bureau; Genentech: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; AbbVie: Consultancy. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Consultancy. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene Corporation: Employment, Equity Ownership. Sharman:TG Therapeutics, Inc.: Research Funding; Celgene Corporation: Consultancy, Research Funding; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Research Funding; Calistoga: Honoraria; Roche: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding.

Abstract Introduction: Results of several large clinical trials have demonstrated inferior outcomes when CLL patients (pts) with del(17p), or to a lesser extent del(11q), are treated with chemoimmunotherapy. Despite recent advances in targeted therapies, management of unfavorable-risk CLL pts remains challenging. The Connect CLL registry is the largest prospective study describing real-world management of a diverse cohort of CLL pts. Here we describe the management of genetically unfavorable-risk CLL pts enrolled in the Connect CLL Registry, and test associations between del(17p)/del(11q) mutations and treatment choice, timing of therapy, and outcomes. Methods: Connect CLL is a longitudinal, multicenter, observational cohort study of 1,494 CLL pts enrolled between 2010-2014 at 179 community (n=1,311), 17 academic (n=155), and 3 government (n=28) sites within ≤ 2 months of initiating any line of therapy (LOT). For this study, pts were categorized into 3 risk-groups based on FISH or cytogenetic results at enrollment: unfavorable risk [either del(17p)/del(11q) positive]; favorable risk [absence of del(17p)/del(11q)]; and unknown risk (testing not performed). Analyses were stratified by LOT at enrollment (LOT1 vs LOT≥2). The risk of an EFS defining event was compared across risk-groups stratified by LOT, including only pts who had been followed for ≥ 2 years (yr) or those who had an EFS defining event (death, progression/relapse, transformation) within 2 yr, using multivariable (MV) logistic regression. Logistic regression analyses of 18 potential predictors were performed, and significant predictors (0.10 significance level) were then included in a MV logistic regression model to identify factors associated with inferior 2 yr EFS. Results: Of 1,494 pts enrolled, 225 had unfavorable risk [del(17p), n = 94; del(11q), n = 131], 621 favorable risk, and 648 unknown risk. For pts enrolled at LOT1 (n = 889), 141 had unfavorable risk, 425 favorable risk, and 323 unknown risk. Baseline characteristics for unfavorable-risk pts in LOT1 included: median age 69 yr (62% age ≥ 65 yr); 91% White; 53% Rai stage ≥ 2; 86% community sites; 0.9 yr median time from dx to LOT1; 8% family history CLL. With the exception of age, unfavorable-risk pts treated at LOT1 with FCR (median age 63) and BR (median age 74) had similar demographic characteristics. Median time from dx to LOT1 was 1.0, 1.3, and 2.0 yr for unfavorable risk, favorable, and unknown-risk pts, respectively (P < 0.001 for comparison of the 3 risk-subgroups). Reasons for initiating treatment in unfavorable-risk pts were lymphadenopathy (42%), bone marrow failure (36%), and lymphocytosis (33%) (similar across the 3 risk-subgroups). The three most common therapies for unfavorable-risk pts in LOT1 were FCR (36%), BR (21%), and FR (6%). Favorable-risk (FCR 26%, BR 24%, and R 10%) and unknown-risk pts (FCR 22%, BR 18%, and R 17%) were treated similarly in LOT1. Median time from dx to LOT≥2 was 3.5 yr in unfavorable-risk pts (vs 4.1 yr in favorable and 4.6 yr in unknown risk pts; P = 0.04 for comparison of the 3 risk-groups). For unfavorable-risk pts in LOT≥2, BR (26%), R (11%), and FCR (10%) were most commonly prescribed. At any LOT, only 10% of unfavorable-risk pts participated in a clinical trial and 4% underwent allogeneic SCT. Overall, 33% (LOT1) and 68% (LOT≥2) of pts experienced an EFS defining event within 2 yrs. Pts with unfavorable risk (vs favorable risk) had significantly worse outcomes, with a higher-risk of death/progression/relapse/transformation within 2 yr (LOT1: 42% vs 29%, respectively, P = 0.008; and LOT≥2: 80% vs 61%, P = 0.004). Unfavorable-risk pts treated with either FCR or BR at LOT1 had similar risk of an EFS defining event (odds ratio [OR] 0.9, CI 0.6-1.5). Results from MV analyses of LOT1 pts identified unfavorable risk (OR 1.9; 95% CI 1.1-3.1; P = 0.0168) and age ≥ 75 years (OR 2.6; 95% CI 1.5-4.3; P = 0.007) as independent predictors of inferior EFS at 2 yr. Conclusions: Although similar treatment approaches were used among the 3 risk-subgroups, pts with unfavorable-risk CLL had inferior outcomes at LOT1 and LOT≥2. These results, from a real-world setting, validate the findings for unfavorable risk pts from large clinical trials. These data underscore the importance of performing genetic risk stratification on all CLL pts, and support consideration of clinical trials and/or B cell receptor targeted agents for unfavorable risk CLL pts when treatment is considered. Disclosures Mato: Janssen: Consultancy; AbbVie: Consultancy, Research Funding; Genentech: Consultancy; Pronai Pharmaceuticals: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; TG Therapeutics: Research Funding. Nabhan:Celgene Corporation: Honoraria, Research Funding. Flowers:Celegene: Other: Unpaid consultant, Research Funding; Celegene: Other: Unpaid consultant, Research Funding; Spectrum: Research Funding; Infinity Pharmaceuticals: Research Funding; OptumRx: Consultancy; Onyx Pharmaceuticals: Research Funding; Janssen: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Onyx Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Gilead Sciences: Research Funding; Genentech: Research Funding; Acerta: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Millennium/Takeda: Research Funding; Infinity Pharmaceuticals: Research Funding; Spectrum: Research Funding; Millennium/Takeda: Research Funding; Pharmacyclics: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Acerta: Research Funding. Kay:Pharmacyclics: Research Funding; Genentech: Research Funding; Tolero Pharma: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding. Kipps:Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor; Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor. Grinblatt:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau. 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; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Kozloff:Genentech: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; AbbVie: Consultancy. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Consultancy. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene Corporation: Employment, Equity Ownership. Sharman:Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Calistoga: Honoraria; Roche: Research Funding; TG Therapeutics, Inc.: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Abstract Introduction: The median age at diagnosis for CLL pts in the US is 72 years. As clinical trials in CLL have largely enrolled younger pt populations, data on disease and pts' characteristics, patterns of care, prognosis, and molecular features in aged CLL pts are limited. With this in mind, we conducted a prospective study to update critical demographic data and patterns of care for aged CLL pt. Patients and Methods: Connect®-CLL is a US-based prospective, longitudinal, multi-center, observational registry that is aimed at understanding patterns of CLL management without a study-specific intervention. We enrolled pts treated at an academic (n=155) or community (n=1340) setting between 2010 and 2014. Eligible pts were adults with a clinical diagnosis of CLL who required therapy 2 or less months after enrollment. Data on demographics, baseline characteristics, and treatment selection are presented here using descriptive statistics. Continuous variables are reported using appropriate measures of dispersion and central tendency (means, medians, ranges and standard deviations) while categorical variables are summarized as number and percentage of the analysis population. Results: Of 1495 enrolled pts, 457 (30.5%) were ≥75 years (57.3% males, 94.2% white). Rai stage III/IV was noted in 33% and B symptoms (predominantly fatigue (58.4%)) were observed in 68.1%. These and other clinical baseline characteristics appeared similar to pts younger than 75 years except that older pts had more prior malignancies (33.5% vs. 19.8%) and co-morbidities (69.3% vs. 53.6%). Cardiac, neurologic, and renal disorders were the most common morbidities in pts ≥75 years (11.5%, 8.8%, and 4.6% respectively). Imaging studies were performed in 157 (60.2%) older pts and in 416 (65.7%) pts less than 75 years prior to initial therapy. Percentage of pts with bulky nodes (> 5 cm) by imaging was similar in the two groups, 19.3% overall. Prognostic biomarker data were available on 247 pts (178 (72%) <75 years; 69 (28%) ≥75 years). While a higher proportion of older pts had CD38+ CLL (55.1% vs. 40.5%, P=0.038), the proportions of patients with ZAP-70+ CLL were similar between the two groups. In total, 137 (9%) older and 378 (25%) younger pts had 17p and 11q analysis by FISH at enrollment prior to first-line therapy. Of these, 27.0% of pts ≥75 years and 20.6% of pts <75 years had a deletion of either 17p or 11q (P=0.125). Out of all pts enrolled in the registry, 894 (60%) received first-line treatment (261 (29%) pts ≥75 years and 633 (71%) <75 years) as their indication for study entry. Amongst these treated pts, interim analysis shows (data cutoff date: 25 June 2014) progressive marrow failure was more commonly used as the indication for therapy in older pts compared to younger pts (52.1% vs. 38.5%; P<0.001), while splenomegaly was a more common cause for therapy in younger pts (16% vs. 9%; P<0.01). Rai stage III/IV at time of first therapy was 46% and 49% for younger and older pts, respectively. Progressive lymphocytosis was used as the indication for therapy in one third of pts regardless of age. Seventy-four percent of older CLL pts received first-line therapies containing rituximab (R) vs. 85% in pts <75 years (P<0.0001). R-bendamustine was the most common first-line regimen for CLL pts ≥75 (23.4%) while FCR was more commonly given to pts <75 years (32.5%). R-monotherapy was used in 18.8% of older pts versus 9.5% in pts <75 years (P<0.0001). Of note, approximately 25% of CLL pts ≥75 years did not receive R-based regimens for initial therapy. Conclusions: Connect®-CLL is the largest prospective, multicenter CLL registry in the United States. CLL Pts ≥75 years more frequently overexpress CD38 and may more commonly demonstrate high risk cytogenetics by FISH, although the difference did not reach statistical significance. Pts ≥75 years also more commonly had co-morbid diseases, and surprisingly 25% did not receive first-line R-based therapy. CLL pts are rarely included in front-line clinical trials (<3%). Given that novel therapies are increasingly available for CLL patients a continued analysis is warranted to determine their use in elderly vs younger patients as well. A longer follow up is needed to evaluate the impact of these findings on outcomes. Disclosures Nabhan: Celgene: Honoraria, Research Funding. Off Label Use: Lenalidomide in CLL. Kay:Celgene: Research Funding. Mato:Genentech, Celgene, Millenium: Speakers Bureau. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kipps:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weiss:Celgene: Consultancy. Flinn:Celgene: Research Funding. Swern:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Kristen:Celgene: Employment, Equity Ownership. Flowers:Celgene, Prescription Solutions, Seattle Genetics, Millennium (unpaid), Genentech (unpaid) : Consultancy; Gilead, Spectrum, Millennium, Janssen: Research Funding.