Flowers, Christopher R.

Abstract Background: Patients (pts) with DLBCL who receive first-line anthracycline based immunochemotherapy and remain in remission at 2 years have excellent outcomes with overall survival (OS) in one study that was comparable to the age and sex matched general population in cohorts from the US and France (Maurer et al, JCO 2014). To evaluate whether these findings are robust and generalizable, we assessed OS stratified by progression-free survival at 24 months (PFS24) using individual patient data from pts with DLBCL enrolled in 14 multi-center, international randomized clinical trials as part of the SEAL consortium. Methods: Central data alignment was performed by the SEAL statistics and data center. All enrolled patients treated with rituximab-containing anthracycline-based immunochemotherapy as part of initial induction therapy were included in this analysis using an intent-to-treat approach. PFS was defined as time from initiation of first-line therapy to progression or death. PFS24 was defined as being alive and progression-free 24 months (731 days) after initiation of therapy. Overall survival from PFS24 was defined as time from achieving PFS24 until death due to any cause. OS was compared to each patient's age-, sex-, and country- matched general population using expected survival and standardized mortality ratios (SMR). Country-level population rate tables were obtained from www.mortality.org where available. A per-study average rate table was used when country information was unavailable. OS beyond 9 years from treatment initiation was only available the time of analysis from two trials (13% of cohort) and thus follow-up from PFS24 was restricted to the first 7 years after PFS24 for this analysis. Results: Fourteen studies with 7975 patients were included in the SEAL database. 5853 (73%) received rituximab as part of induction therapy and used in this analysis. Median age was 62 years (range 18-92) with 56% >60 years, 54% were male, 57% had elevated LDH, 63% had stage III/IV disease, 13% had ECOG PS 2-4, and IPI was 0-1 in 36%, 2 in 25%, 3 in 23%, and 4-5 in 16%. At a median follow-up of 4.4 years, 1337 (23%) had disease progression, 1489 patients (25%) had died, and 5101 patients were evaluable for PFS24. 1423 (28%) of evaluable patients failed to achieve PFS24. These patients had poor outcomes after progression with median OS of 7.2 months (95% CI 6.8-8.1), 5-year OS of 19%, and SMR of 32.1 (95% CI: 30.0-34.4). Outcome was excellent (median unreached) in the 3678 patients who were progression-free at 24 months, with subsequent OS after achieving PFS24 approaching the age and sex matched general population (Figure). The observed vs. expected OS at 3, 5, and 7 years after achieving PFS24 was 93.1% vs. 94.4%, 87.6% vs. 89.5%, and 80.0% vs. 83.7%, respectively, the SMR was 1.22 (95% CI: 1.09-1.37). In subset analyses examining pts achieving PFS24, adverse prognostic characteristics at baseline (elevated LDH, advanced stage, ECOG PS 2-4, high IPI) were associated with greater decrement in OS from the general population based expected survival (Table). Conclusions: PFS24 from the start of initial therapy stratifies DLBCL pts into populations with distinct outcomes. DLBCL pts treated with rituximab containing anthracycline-based immunotherapy on large randomized clinical trials who are alive without progression at 24 months from start of initial therapy have excellent outcomes with survival that is marginally lower but clinically indistinguishable from the age-, sex-, and country-matched background population for at least 7 years after achieving PFS24. Further follow-up time will be needed to assess any long-term impact of late relapse and survivorship issues in this population. Table Table. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Cunningham:Amgen: Research Funding; Astra-Zeneca: Research Funding; Bayer: Research Funding; Celgene: Research Funding; Merrimack: Research Funding; Medimmune: Research Funding. Seymour:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Roche France: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees. Merli:Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees. Herbrecht:Servier: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Flowers:Gilead: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Acerta: Research Funding; ECOG: Research Funding; Mayo Clinic: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Millenium/Takeda: Research Funding; Infinity: Research Funding; TG Therapeutics: Research Funding; Genentech: Consultancy, Research Funding; NIH: Research Funding; AbbVie: Research Funding. Ou:Mayo Clinic: Employment. Sargent:Celgene: Research Funding. Coiffier:MorphoSys: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Background OSis the gold standard primary efficacy endpoint for evaluating treatment strategies for DLBCL. Because current standard of care first-line regimens with rituximab plus chemotherapy produce median OS > 8 years, an endpoint assessed earlier would accelerate the evaluation of new therapies and potentially allow patients (pts) to more rapidly benefit from study results. The SEAL group formally evaluated PFS as a surrogate endpoint for OS in first-line therapy for DLBCL. Methods Through a systematic literature search and input from lymphoma experts on the SEAL committee, we identified multicenter, randomized controlled clinical trials that evaluated active treatment and enrolled at least 100 pts with previously untreated DLBCL or aggressive non Hodgkin lymphoma by WHO/REAL classification, published after January 1, 1995. Studies which enrolled only early stage (I or II) pts and studies that could not provide individual pt data (IPD) were excluded from surrogacy analysis. Potential surrogate candidates for OS included: 1) PFS, defined as the time from initiation of induction treatment to progressive disease, relapse or death due to any cause, and 2) PFS rate at 24 months after initiation of induction treatment (PFS24). These endpoints were derived consistently for all studies based on IPD. Surrogacy was evaluated by the correlation between surrogate candidates and OS. Pt-level correlation was measured by the rank correlation coefficient (ρ) and global odds ratio for PFS and PFS24, respectively, estimated by the Copula model. Trial-level correlation was evaluated by the correlation between trial-specific treatment effects on surrogate candidates and OS with two commonly used trial-level R2s: R2WLS (Sargent et al JCO 2005) and R2Copula (Burzykowski et al J R Stat Soc C 2001 and Burzykowski et al J R Stat Soc A 2004). For the R2 measures, a value closer to 1 indicates a stronger correlation. We pre-specified that if either trial-level R2 was >=0.80 and its 95% confidence interval (CI) excluded 0.60, the candidate would meet criteria for surrogacy. Results Thirteen of 14 studies collected to date in the SEAL database met all inclusion criteria. IPD for 7507 pts (median age 63 years, 54% male, 46% ECOG performance status 0, 8% IPI 0, 50% IPI 1-2, 64% stage III/IV, median follow-up time of 52 months) were analyzed. Among 13 studies, one tested maintenance treatment only and two tested both induction and maintenance regimens. One study included four treatment arms. Thus at the trial level, 17 two-arm comparisons were formed: 14 comparisons comparing induction regimens and 3 comparing maintenance; 15 comparisons involved rituximab. Longer PFS was highly correlated with longer OS at the pt level (ρ= 0.85, 95% CI 0.84-0.86), as was PFS24 (Global odds ratio = 61.1, 95% CI 52.6-69.6). The Table shows trial-level correlations, and the Figure shows trial-level association between estimated treatment effects (log(HR) [hazard ratio]) on PFS and OS. The trial-level correlation between PFS and OS was strong and met the pre-specified surrogacy criteria, with R2WLS of 0.83 (95% CI 0.58-0.94) and R2Copula of 0.85 (95% CI 0.72-0.99). Sensitivity analyses including only comparisons including rituximab or only induction regimens showed consistent results. The overall trial-level correlation between PFS24 and OS was reduced slightly, with R2WLS of 0.77 (95% CI 0.49-0.92) and R2Copula of 0.78 (95% CI 0.59-0.96). Performance of PFS24 was improved when restricting analysis to only including induction studies (see table). Conclusion This large IPD pooled-analysis demonstrates that PFS duration met the pre-specified criteria as a robust surrogate endpoint for OS, and can be considered as a surrogate endpoint for OS for frontline DLBCL trials evaluating immunochemotherapy. The earlier endpoint of PFS24 showed promise as a potential surrogate and further validation is warranted. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Flowers:Millenium/Takeda: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Gilead: Consultancy, Research Funding; NIH: Research Funding; Mayo Clinic: Research Funding; ECOG: Research Funding; Acerta: Research Funding; Infinity: Research Funding; AbbVie: Research Funding; Roche: Consultancy, Research Funding; Genentech: Consultancy, Research Funding. Ou:Mayo Clinic: Employment. Cunningham:Merrimack: Research Funding; Celgene: Research Funding; Bayer: Research Funding; Astra-Zeneca: Research Funding; Amgen: Research Funding; Medimmune: Research Funding. Pfreundschuh:Amgen: 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; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Seymour:AbbVie Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche France: Research Funding. Merli:Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees. Herbrecht:Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Coiffier:MorphoSys: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Sargent:Celgene: Research Funding.

Abstract Abstract 2686 Background: DLBCL is the most commonly occurring form of non-Hodgkin lymphoma and is a highly curable disease, but one that is universally fatal if untreated or improperly treated. In a series of studies, we have reported racial disparities in the clinical presentation and the treatment outcomes for patients (pts) with DLBCL in the United States (Shenoy Cancer 2010; Flowers CEBP 2012). These studies showed that black pts with DLBCL are diagnosed at an age a decade younger than whites, are more likely to have advanced stage disease, and are less likely to survive 5 years. One explanation is that black patients in the US less often receive standard of care therapy (Flowers CEBP 2012). However, in a cohort study of 533 white and 144 black patients with DLBCL managed at Emory and University of Alabama-Birmingham (UAB) black race predicted worse overall survival (OS) even when black and white pts received the same therapy (CHOP; Hazard ratio [HR] 1.8, p<0.001) suggesting a biological basis for some disparities(Flowers Leuk Lymph 2012). To address these findings, we examined whether known prognostic biological variants within DLBCL [germinal center B-cell-like (GCB) and activated B-cell like (ABC)] occurred differentially between black and white pts. Methods: DLBCL pts within our Emory-UAB cohort study provided formalin fixed material available for construction of tissue microarrays (TMAs). All pts were diagnosed with DLBCL from 1990–2010 and met the following criteria: age ≥18 years at diagnosis and available information on demographics, race, gender, initial clinical features at presentation, treatment, and treatment outcomes. H&E slides from each block for each case were reviewed to identify the appropriate zone(s) for cores. Sections placed in TMAs underwent immunohistochemistry (IHC) staining with BCL6, GCET1, CD10, FOXP1, and LMO2 antibodies. The percentages of positive cells were scored in 10% increments for each antibody, and recorded for each case by 3 hematopathologists blinded to the clinical and demographic data. The Choi algorithm was used to classify all patients into ABC and GCB subsets; other IHC algorithms were used as a backup. Descriptive statistics for the baseline characteristics of black and white pts were compared using Chi-square tests. Kaplan–Meier estimation was used to evaluate OS for the two groups and compared with the log-rank test. To evaluate prognostic factors and the effects of treatment on OS, Cox proportional hazards models were used controlling for age, sex, stage, LDH, performance status, presence of B-symptoms, race, treatment (R-CHOP vs. other), and ABC subtype. Results: Tissues for 26 black pts and 63 white pts meeting all eligibility criteria were evaluated in the TMA. There were significant differences in baseline characteristics between the two groups; 23% of black pts vs. 46% of whites were > 60 years of age (p=0.04), 73% had stage III/IV disease vs. 56% (p=0.03), and 77% had an LDH>ULN vs. 51% (p=0.04). There were no significant differences between the two racial groups in terms of sex, ECOG PS, presence of B-symptoms (38% vs. 29%p=0.46), extranodal sites (50% vs. 78% ≤ 1, p=0.27), IPI risk, or treatment received (RCHOP 46% vs. 40% p=0.93). By the Hans, Natkunam, Tally, and Choi algorithms black patients more commonly presented with the poor-risk ABC/non-GCB subtype (by Choi black 64% ABC vs. white 37%; p= 0.01, Table). After controlling for clinical confounders including age, sex, stage, LDH, performance status, presence of B-symptoms, race, treatment (RCHOP vs. other), and ABC subtype, being >60 years of age [HR 3.1 95% CI 1.3–7.2], being black (HR 3.5 95% CI 1.5–8.2), and receiving treatment other than RCHOP (HR 12.8, 95% CI 3.2–50.6) were associated with inferior OS. Conclusions: The rate of ABC DLBCL is significantly higher in black pts compared to white pts in this university-based cohort from the Southern United States. Additional studies confirming these findings in larger populations and examining the mutations associated with these differences are underway to address biological differences intrinsic to DLBCL that may in part explain comparatively adverse features and outcomes for black pts with DLBCL. Disclosures: Flowers: Celgene, Spectrum, Millennium, Gilead, Janssen: Research Funding; Genentech/Roche (unpaid), Millennium (unpaid), Celgene: Consultancy. Bernal-Mizrachi:Empire Genomics (not related to current work): Patents & Royalties. Sinha:Celgene: Research Funding. Jaye:Millenium Pharmaceuticals (For single lecture on immunohistochemical subtyping of large B cell lymphomas): Honoraria.

Abstract Background: Follicular lymphoma (FL) is the most common indolent NHL with a heterogenous natural history of disease and a median survival of 8 to 12 y, albeit ranging between 1 to 20 y. Casulo et al. (2015) identified a high-risk FL cohort of patients with progression of disease (POD) at ≤24 months following initiation of immunochemotherapy with R-CHOP. Idelalisib (Zydelig) is a first-in-class, highly selective, oral inhibitor of PI3Kd which is indicated for relapsed FL or SLL following receipt of at least two lines of systemic chemotherapy. Retrospective subgroup analysis of the idelalisib registrational trial NCT01282424 (101-09) of a cohort with early POD following immunochemotherapy was performed to assess possible activity of idelalisib in this population. Methods: A subset of 46 patients enrolled in study 101-09 were identified as having been diagnosed with FL and having received first-line immunochemotherapy, of which 37 experienced early POD, defined as starting second-line treatment within 24 months of initial first-line treatment. For the latter group, descriptive statistics of demographic and baseline characteristics and inter-treatment intervals in months as well as Kaplan-Meier estimates of progression-free survival (PFS) and overall survival (OS) following initiation of immunochemotherapy and idelalisib were calculated. Population: Demographic characteristics of these 37 patients included median (range) age at initiation of idelalisib of 64 (33-84) y and 18 (48.6%) females. Histologic grade at diagnosis included 33 (89.2%) with grades 1 or 2 as well as 4 (10.8%) with grade 3A, while 21 (56.8%) patients had a FLIPI score ≥3. The mean (s.d.) number of prior therapies was 3.4±1.4, with a range of 2 to 8, while first-line therapies included 21 (56.8%) patients who received R-CHOP-based regimens, 7 (18.9%) who received BR, and 5 (13.5%) who received R-CVP. Mean (s.d.) inter-treatment intervals included 12.5±6.1 months between first- and second-line for all patients, 9.7±9.3 months between second- and third-line for all patients, 11.9±12.0 months between third- and fourth-line for 24 (64.9%) patients, and 11.8±7.6 months between fourth- and fifth-line for 15 (40.5%) patients. Median (range) time from first-line therapy to idelalisib initiation was 30.3 (8.9-94.7) months; no patient received idelalisib as second-line therapy. Results: Best responses included 5 (13.5%) patients with CR, 16 (43.2%) with PR, 2 (5.4%) with SD, and 1 (2.7%) with PD; median duration of response for those with CR or PR was 11.8 months (95% CI: 3.8 months, not evaluable). There were 7 (18.9%) deaths and 21 (56.8%) PFS events in this group. Estimated probabilities of survival (s.e.) and progression-free status at 2 y following initiation of idelalisib were 79%±7% and 29%±10%, respectively. Median PFS was 11.1 months (95% CI: 5.5, 19.3 months). Estimated probability of survival (s.e.) at 5 years following initiation of first-line treatment was 79%±8%. Median overall survival from both initiation of first-line immunochemotherapy as well as with idelalisib was not reached during the course of this study. Conclusions: Idelalisib may have significant clinical activity in high-risk and doubly-refractive FL following early relapse status post first-line immunochemotherapy. Given the small size of the studied subset population which may not be representative, further characterization in additional patients is warranted to ensure the generalizability of this finding, including consideration of further investigational protocols featuring targeted therapies employed both as single agents and in combination. Figure 1. Overall Survival From Initiation of First-line Treatment Figure 1. Overall Survival From Initiation of First-line Treatment Figure 2. Overall Survival From Initiation of Idelalisib Figure 2. Overall Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Disclosures Gopal: Gilead, Spectrum, Pfizer, Janssen, Seattle Genetics: Consultancy; Spectrum, Pfizer, BioMarin, Cephalon/Teva, Emergent/Abbott. Gilead, Janssen., Merck, Milennium, Piramal, Seattle Genetics, Giogen Idec, BMS: Research Funding; Millennium, Seattle Genetics, Sanofi-Aventis: Honoraria. Kahl:Roche/Genentech: Consultancy; Seattle Genetics: Consultancy; Millennium: Consultancy; Cell Therapeutics: Consultancy; Celgene: Consultancy; Infinity: Consultancy; Pharmacyclics: Consultancy; Juno: Consultancy. Flowers:Infinity Pharmaceuticals: Research Funding; Genentech: Research Funding; Spectrum: Research Funding; Millennium/Takeda: Research Funding; Seattle Genetics: Consultancy; Spectrum: Research Funding; Pharmacyclics: Research Funding; AbbVie: Research Funding; AbbVie: Research Funding; Gilead Sciences: Research Funding; Pharmacyclics: Research Funding; Onyx Pharmaceuticals: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Janssen: Research Funding; Acerta: Research Funding; Millennium/Takeda: Research Funding; Acerta: Research Funding; Infinity Pharmaceuticals: Research Funding; Janssen: Research Funding; Onyx Pharmaceuticals: Research Funding. Martin:Janssen: Consultancy, Honoraria; Acerta: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Bayer: Consultancy. Link:Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Celldex: Research Funding. Ye:Gilead: Employment. Koh:Gilead: Employment. Abella:Gilead: Employment. Barr:Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Abbvie: Consultancy; Gilead: Consultancy. Salles:Calistoga Pharmaceuticals, Inc.; Celgene Corporation; Genentech, Inc.; Janssen Pharmaceutica Products, L.P.; Roche: Consultancy; Celgene Corporation; Roche and Gilead Sciences: Research Funding; Celgene Corporation; Roche: Speakers Bureau.

Abstract Abstract 4591 Introduction: Clinicians and investigators appreciate the value of measuring HRQOL for monitoring CLL and the impact of treatments, and commonly use ECOG performance status (PS) and clinician-reported patient fatigue as surrogates for HRQOL in clinical practice. However, limited data exist on the relationships between PS, fatigue, and HRQOL in CLL patients (pts) undergoing treatment in clinical practices. We examined the associations between these measures and 3 psychometrically validated, patient-reported, HRQOL instruments: the Brief Fatigue Inventory (BFI), EQ-5D, and Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu). Methods: Data were collected as part of Connect CLL®, a prospective observational registry initiated in March 2010 involving US practices. Data on pt demographics and clinical characteristics were provided by clinicians. HRQOL was self-reported by pts at enrollment using the BFI, EQ-5D, and FACT-Leu. Mean BFI, EQ-5D and FACT-Leu scores were analyzed by ECOG PS and clinician-reported fatigue. Differences in HRQOL scores between sub-cohorts were assessed by ANOVA. Results: HRQOL data were reported by 604 pts enrolled from 10 academic, 148 community, and 3 government centers. Pts were predominantly male (62%) and white (90%); mean age was 70 (standard deviation 11) years. BFI data (scale: 0 [no fatigue] - 10 [worst fatigue]) indicated that on average pts report that global fatigue, fatigue severity and fatigue-related interference worsen by ECOG severity (Table 1) and are statistically associated with clinician-reported fatigue (Table 2). Mean EQ-5D overall HRQOL as measured by a Visual Analogue Scale (VAS) from 0 (worst) to 100 (best) worsens by ECOG severity and is significantly worse in pts with fatigue. Mean EQ-5D domain scores (scale: 1 [no problem], 2 [some problems], 3 [incapacity]) indicated that pain/discomfort, mobility and usual activities increase in severity as ECOG worsens and in pts with fatigue. FACT-Leu domains except social/family were statistically worse with worse ECOG PS and in pts with fatigue. Conclusions: Initial results from Connect CLL® indicate that HRQOL worsens with worsening ECOG PS, especially in physical / functioning domains, pain/discomfort, and mobility, and worsens across multiple domains among pts whose physicians reported fatigue. Future analyses should be conducted on how HRQOL, PS and fatigue may change over time with changes in CLL, and how they are influenced by therapies. These results may serve as baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Flowers:Genentech/Roche (unpaid): Consultancy; Celgene: Consultancy; Millennium/Takeda: Research Funding; Wyeth: Research Funding; Novartis: Research Funding. Weiss:Celgene: Membership on an entity's Board of Directors or advisory committees. Lamanna:Celgene: Membership on an entity's Board of Directors or advisory committees. Farber:Celgene: Membership on an entity's Board of Directors or advisory committees. Kipps:Igenica: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Abbot Industries: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; GSK: Research Funding; Gilead Sciences: Consultancy, Research Funding; Amgen: Research Funding. Lerner:Celgene: Membership on an entity's Board of Directors or advisory committees. Kay:Celgene: Membership on an entity's Board of Directors or advisory committees. Sharman:Celgene: Membership on an entity's Board of Directors or advisory committees. Grinblatt:Celgene: Membership on an entity's Board of Directors or advisory committees. Flinn:Celgene: Membership on an entity's Board of Directors or advisory committees. Kozloff:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Kahn:Celgene Corporation: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Keating:Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: Limited data exist to describe the clinical features and outcomes for elderly pts with FL. The FLASH group performed a prospectively planned pooled analysis of IPD from first-line RCTs and examined associations between age (<= 70 vs. > 70 years), clinical characteristics and FL outcomes. Methods: We identified 18 randomized controlled multicenter clinical trials in the FLASH database which enrolled elderly pts (> 70 yrs). From these 18 studies, 5922 previously untreated FL pts were included for this analysis. Complete response (CR) at 24 and 30 months were defined as whether the pt achieved and remains in CR at 24 months and 30 months after initiation of induction treatment. PFS24 is defined as proportion of pts progression-free and alive at 24 months post-randomization. Time to progression (TTP) and overall survival (OS) were defined as time from randomization to the date of progression (censoring for death without progression) and date of death due to any cause, respectively. Progression-free survival (PFS) was defined as time from randomization to the date of progression or death, due to any cause, whichever comes first. Non-lymphoma death was defined as death without prior progression. Early disease outcomes, i.e. CR24, CR30, and PFS24 were primary outcomes; secondary outcomes were TTP, OS, and PFS. For time-to-event outcomes, Kaplan-Meier method and log-rank test were used for univariate estimation and comparison; stratified Cox proportional hazard modeling was used for multivariable analyses. Cumulative incidence methods were used to model PFS while treating disease progression as the primary event of interest and non-lymphoma death as a competing risk. For binary outcomes, multivariable logistic regression was used to estimate the association between age groups and outcomes. Variable adjusted in the regression models are FLIPI score without the age component, ECOG performance status (>= 2 vs < 2), and rituximab use. Results: Among 5922 previously untreated FL pts from 18 RCTs, 63% (n = 3728) were <= 60, 28% (n = 1652) 61-70, and 9% (n = 542) > 70 yrs. Pts age > 70 (vs. <= 70) more commonly had elevated LDH (42% vs 36%, p = 0.02), hemoglobin < 12 g/dL (27% vs 19%, p < 0.0001), ECOG PS >= 2 (8.8% vs 5.0%, p = 0.0004), and elevated β2-microglobulin (68% vs 49%, p < 0.001), less often had > 4 lymph nodes involved (54% vs 65%, p < 0.001), and had similar FLIPI scores without age component (p = 0.17). There were no significant differences between groups in: Ann Arbor stage (94% vs 95% stage III/IV) or rituximab use (62% vs 58%). Median survival follow-up was 5.6 yrs. Pts > 70 yrs did not differ from pts <= 70 in rates of CR24 (27% vs 30%, p=0.32), CR30 (29% vs 31%, p=0.55) or PFS24 (63% vs. 67%, p=0.10). Median TTP was 3.8 and 4.0 years for pts > 70 and <= 70, respectively (HR=1.08, 95% CI 0.96-1.23, p=0.20). With a median OS of 14.6 years for all pts, median OS was 7.4 and 15.7 years for pts > 70 and <= 70, respectively (HR=2.35, 95% CI=2.03-2.73, p<0.001). PFS was statistically significantly different but clinically similar at median 3.1 and 3.8 years for pts > 70 and <= 70, respectively (HR=1.26, 95% CI =1.12-1.41, p<0.001). Further investigation showed the difference in PFS was due to higher incidence of non-lymphoma death in pts > 70 with no difference in disease progression (Figure). In regression models, adjusting for rituximab use, ECOG PS >= 2, and FLIPI score without the age component, age > 70 was a significant predictor of OS and PFS (due to higher incidence of non-lymphoma death), but not PFS24, CR24 or CR30 (Table). Conclusions: FL pts > 70 yrs treated on trials have similar early disease outcomes to younger patients. There is no disease-specific outcome difference between age groups. Age alone should not disqualify patients from standard treatments or RCTs. Disclosures Flowers: Genentech: Consultancy, Research Funding; NIH: Research Funding; Mayo Clinic: Research Funding; TG Therapeutics: Research Funding; Millenium/Takeda: Research Funding; ECOG: Research Funding; Acerta: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Roche: Consultancy, Research Funding; AbbVie: Research Funding; Infinity: Research Funding; Gilead: Consultancy, Research Funding. Ou:Mayo Clinic: Employment. Shi:Mayo Clinic: Employment. Hochster:Genentech: Consultancy, Other: Consultancy fees for advisory boards for GI cancer and bevacizumab with amounts within Yale University de minims guidelines. Brice:Roche: Honoraria; Takeda Pharmaceuticals International Co.: Honoraria, Research Funding; Bristol Myers-Squibb: Honoraria; Gilead: Honoraria; Seattle Genetics: Research Funding. Hiddemann:Roche: Other: Grants; Roche: Membership on an entity's Board of Directors or advisory committees; Genentech: Other: Grants. Marcus:Takeda: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau. Kimby:Abbvie: Consultancy, Honoraria; Jansen: Consultancy, Honoraria; Celgene: Honoraria; Baxalta: Consultancy; Gilead: Honoraria. Herold:Gilead: Other: Personal fees from member advisory board; Celgene: Honoraria; Genentech: Other: Grants; Roche: Honoraria, Other: Grants. De Bedout:Celgene: Employment. Nielsen:Hoffmann-La Roche: Employment. Morschhauser:Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Janssen: Honoraria. Rummel:Mundipharma GmbH: Other: Personal fees, Research Funding; Roche Pharma AG: Other: Personal fees, Research Funding. Vitolo:Gilead: Other: Honoraria for lectures; Takeda: Other: Honoraria for lectures; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Honoraria for lectures; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: Honoraria for lectures. Salles:Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Gilead: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding; Mundipharma: Honoraria. Sargent:Celgene: 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.

Key Points Early progression of disease within 2 years of initial therapy independently predicts inferior survival in CLL. Early progression of disease is a robust clinical end point and a useful posttreatment risk stratification tool.