Flowers, Christopher R.

AbstractIntroductionWhile most patients with mantle cell lymphoma (MCL) receive therapy shortly after diagnosis, a subset of patients with indolent‐behaving disease can safely defer treatment. In this subgroup, we evaluated the importance of treatment intensity in patients with MCL who defer initial therapy.MethodsOut of 1134 patients with MCL from 12 academic centers, we analyzed 219 patients who initiated therapy at least 90 days after diagnosis. Patients who received induction with high‐dose cytarabine and/or autologous stem cell transplantation (ASCT) in first remission were considered to have received intensive therapy (n = 88) while all other approaches were non‐intensive (n = 131).ResultsThere was no difference in progression‐free (PFS; P = .224) or overall survival (OS; P = .167) in deferred patients who received non‐intensive vs. intensive therapy. Additionally, univariate and multivariate Cox proportional hazards models were performed for PFS and OS. Treatment at an academic center (HR 0.43, P = .015) was associated with improved OS in both univariate and multivariate models, while intensity of treatment was not associated with improved OS in either model.ConclusionsThese results indicate that intensified initial treatment is not associated with improved survival after deferring initial therapy, although prospective studies are needed to determine which of these patients with MCL may benefit from intensive therapy.

Abstract Introduction: Although increased physical activity (PA) is associated with improved quality of life in cancer survivors, knowledge about the impact of PA on survival in lymphoma patients is limited. We studied the impact of usual adult PA and PA at 3 years after diagnosis on overall (OS) and lymphoma-specific (LSS) survival in lymphoma patients. Methods: Lymphoma patients were prospectively enrolled within 9 months of diagnosis into the Lymphoma SPORE Molecular Epidemiology Resource (MER) cohort. At baseline, patients reported their usual level of mild, moderate and strenuous PA during most of their adult life and (for survivors) again at the 3 year follow-up (FU3), which we used to calculate the Godin Leisure Score Index (LSI). At FU3, patients also self-reported change in PA since diagnosis as increase, decrease or no change. LSI was modeled as a continuous score (per 10-point change) and by tertile. Survival was measured as time from diagnosis (for baseline PA) and time from FU3 (for FU3 PA) until death due to any cause and due to lymphoma. We evaluated the association of PA with outcome using Kaplan-Meier curves as well as hazard ratios (HRs) and 95% confidence intervals (CI) from Cox models stratified by lymphoma subtype and adjusted for age and sex (and baseline PA for change models). Results: Of 4087 patients enrolled in the MER at the Mayo Clinic from 2002-2012, 3129 had baseline PA data (3060 evaluable for LSI), and 1845 (1395 evaluable for LSI) had FU3 PA data. In the baseline cohort, 57.6% of the patients were male. The median age at diagnosis was 62 years (range 18-92 years). The majority had ECOG performance status <2 (95%), no B-symptoms (85.8%) and advanced disease (63% stage III-IV) at diagnosis. The subtypes included were CLL/small lymphocytic (25.9%), follicular (18.1%), diffuse large B-cell (16.8%), marginal zone (8.1%), Hodgkin (6.9%), mantle cell (4.8%), T-cell (5%) and other (14.3%) lymphomas. The median LSI was 28 (range 0-57) at baseline and 25 (range 0-67) at FU3 with a median change in LSI of -2 (range -57 to 61). At a median follow-up of 84 months from diagnosis (IQR 59-119 months) in the cohort with baseline PA, there were 806 total deaths (271 in pts with FU3 PA) and 423 lymphoma deaths (104 in pts with FU3 PA). When analyzed as a continuous variable, baseline LSI was associated with OS (HR 0.95, 95% CI 0.91-0.99) and LSS (HR 0.95, 95% CI 0.90-1.01). LSI by tertiles was associated with OS (p<0.0001, Figure 1a), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.81, 95% CI 0.68-0.97) and LSS (HR 0.81, 95% CI 0.63-1.04). For prognosis after 3 years, continuous LSI at FU3 was also associated with superior OS (HR 0.84, 95% CI 0.77-0.92) and LSS (HR 0.76, 95% CI 0.65-0.89). LSI at FU3 by tertiles was associated with OS (p<0.0001, Figure 1b), and patients in the high LSI compared to the low LSI tertile had superior OS (HR 0.51, 95% CI 0.34-0.77) and LSS (HR 0.33, 95% CI 0.16-0.67). A change in LSI from baseline to FU3 PA was associated with superior OS (HR 0.84, 95% CI 0.76-0.92) and LSS (HR 0.74, 95% CI 0.64-0.87) after FU3. Change in LSI by tertiles was associated with OS (p=0.0007, Figure 1c). Compared to patients with stable LSI (change of LSI between -10 and 5 points from baseline, n=429), patients who increased LSI by > 5 (n=464) had improved OS (HR 0.67, 95% CI 0.45-1.00) and LSS (HR 0.47, 95% CI 0.23, 0.94), while patients who decreased LSI by >10 (n=477) had no association with OS or LSS. Self-reported change in PA from baseline to FU3 was associated with OS (p<0.0001, Figure 1d); compared to no change, an increase in PA was not associated with OS (HR 0.79, 95% CI 0.42-1.48) or LSS (OR 0.73, 0.26-2.07), while a reduction in PA was associated with inferior OS (HR 1.94, 95% CI 1.49-2.53) and LSS (HR 2.51, 95% CI 1.62-3.88). After excluding patients with any events through FU3, the associations for the PA change and OS remained significant for change in LSI (HR 0.91, 95%CI 0.83-1.00) and self-reported decrease in PA (HR 1.83, 95%CI 1.32-2.53). Conclusions: Patients with a higher level of usual PA during adult life have significantly better OS and LSS after lymphoma diagnosis compared to those who are less physically active. Higher PA in 3-year survivors is also associated with improved survival, while a reduction in physical activity is associated with worse outcomes. These data support an important role for PA in lymphoma survivorship and also provide a strong rationale for a PA intervention trial in lymphoma patients. Figure 1 Figure 1. Disclosures Cohen: LAM Therapeutics, Inc: Research Funding; Takada: Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bioinvent: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Research Funding; Novartis: 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; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees. Flowers: Spectrum: Consultancy; OptumRx: Consultancy; Acerta: Research Funding; Onyx: Research Funding; Janssen Pharmaceutical: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; National Cancer Institute: Research Funding; Burroughs Welcome Fund: Research Funding; Genentech/Roche: Consultancy, Research Funding; Gilead: Consultancy; Research to Practice: Research Funding; Millennium/Takeda: Research Funding; National Institutes Of Health: Research Funding; Prime Oncology: Research Funding; Educational Concepts: Research Funding; Abbvie: Consultancy, Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Seattle Genetics: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Celgene: Consultancy, Research Funding; Infinity: Research Funding; Bayer: Consultancy; Clinical Care Options: Research Funding. Cerhan: Janssen: Other: Scientific Advisory Board (REMICADELYM4001); Janssen: Other: Multiple Myeloma Registry Steering .

Abstract Limited data exist to describe the clinical features and outcomes for elderly patients with follicular lymphoma (FL). The Follicular Lymphoma Analysis of Surrogacy Hypothesis (FLASH) group performed a prospectively planned pooled analysis of individual patient data from first-line randomized controlled trials (RCTs) and examined associations between age (≤70 vs >70 years), clinical characteristics, and FL outcomes. We identified 18 multicenter clinical RCTs in the FLASH database that enrolled elderly patients (>70 years). Primary end points were early disease outcomes, CR24 and CR30, and progression-free survival (PFS) at 24 months (PFS24). Secondary end points were PFS and overall survival (OS). We identified 5922 previously untreated FL patients from 18 RCTs. Patients age >70 years (vs ≤70 years) more commonly had elevated lactate dehydrogenase, hemoglobin <12 g/dL, ECOG PS ≥2, and elevated β2-microglobulin. Median follow-up was 5.6 years. Patients >70 years did not differ from patients ≤70 years in rates of CR24, CR30, or PFS24. With a median OS of 14.6 years for all patients, median OS was 7.4 and 15.7 years for patients >70 and ≤70 years of age, respectively (hazard ratio = 2.35; 95% confidence interval = 2.03-2.73; P < .001). Age >70 years was a significant predictor of OS and PFS due to higher rates of death without progression, but not PFS24, CR24, or CR30. FL patients >70 years 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.

Background: Obesity is increasing worldwide, with the highest prevalence in the United States. High or low body mass index (BMI) is a well-established risk factor for increased all-cause mortality and also has been associated with cancer-specific mortality. However, the impact of BMI on survival following diagnosis with lymphoma currently remains controversial. We leveraged a prospective cohort of lymphoma patients to assess the relationship of BMI two years prior to diagnosis (BMI-2), at diagnosis (BMI-dx), and three-years post-diagnosis (BMI+3) with lymphoma-specific survival (LSS) as the primary endpoint and with event-free survival (EFS) and overall survival (OS) as secondary endpoints. Patient and Method: Patients were prospectively enrolled at lymphoma diagnosis to the SPORE Molecular Epidemiology Resource (MER) cohort at Mayo Clinic and University of Iowa from 2002-2015. BMI-2 and BMI+3 were self-reported in patient questionnaires, while BMI-dx was extracted from the medical chart. Patients with extreme BMI (BMI <14 and BMI ≥50) were excluded from the analysis. BMI change from BMI-2 to BMI-dx and from BMI-dx to BMI+3 was categorized as no change (-5% to 5%), decrease (>-5%), and increase (>+5%). Person-time at risk was assessed from lymphoma diagnosis until death or last follow-up, except for analyses of BMI change from BMI-dx to BMI+3, which started person-time at risk when the 3-year (+/- 6 months) follow-up questionnaire was returned. Cause of death was assigned by a study clinician. For all lymphoma patients combined and in the most common subtypes, we evaluated the association of BMI at each time point and change in BMI with EFS, LSS, and OS using hazard ratios (HRs) and 95% confidence intervals (CI) from multivariable adjusted Cox models. Results: A total of 4,009 lymphoma patients (including 670 diffuse large B-cell lymphoma [DLBCL], 689 follicular lymphoma [FL] and 1018 chronic lymphocytic leukemia/small lymphocytic lymphoma [CLL/SLL] and 1,632 others) with data on BMI-dx were included. Among them, 2,955 patients had BMI-2 and 2,004 had BMI+3 and were evaluable for change in BMI. The median age of all patients at diagnosis was 61 years (range 18-92 years), and 94% of patients had ECOG performance status <2. At the time of diagnosis, 28% were normal weight (BMI 18.5-25), 1% were underweight (BMI <18.5), 39% were overweight (BMI 25-30) and 32% were obese (BMI ≥30). With a median follow-up of 108 months from diagnosis (IQR 83-143 months), 1320 deaths were observed, 48% of which were due to lymphoma. Patients with FL who were obese at BMI-2 had significantly shorter LSS (HR: 3.02, 95%CI: 1.43-6.41, p=0.004). Associations between obesity at BMI-2 and LSS were not evident for DLBCL (HR: 1.04, 95%CI: 0.62-1.76, p=0.879) or CLL/SLL (HR: 1.10, 95%CI: 0.71-1.70, p=0.668) (Table). BMI-dx was not associated with LSS in any lymphoma patients, except that DLBCL patients who were underweight at BMI-dx (n=10) experienced shorter LSS (HR: 3.52, 95%CI: 1.22-10.1, p=0.020). This correlated significantly with presence of B symptoms (p=0.004) and may signify aggressive disease. Across all subtypes, >5% decrease in BMI from BMI-2 to BMI-dx was associated with significantly shorter LSS in patients with (HR: 2.02, 95%CI: 1.65-2.48, p<0.001). However, only for FL patients, >5% increase in BMI from BMI-dx to BMI+3 also was associated with significantly shorter LSS in subsequent years (HR: 3.74, 95%CI: 1.30-10.8, p=0.014). The associations reported for LSS generally were similar for EFS and OS. Conclusions: FL patients with obesity prior to diagnosis or who experienced increasing BMI after the diagnosis had significantly shorter LSS. The impact of weight control after the diagnosis of FL patient outcomes warrants investigation. Figure Disclosures Maurer: Celgene / BMS: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Kite: Research Funding; Bayer: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

Introduction: Lymphoma is the most common cancer among adolescents and young adults (AYAs). We examined changes in health-related quality of life (HRQoL) and its predictors in AYA patients (pts). Patients and Methods: We identified AYA pts (aged 18-39) enrolled 2002-2015 in a prospective cohort of pts with newly diagnosed lymphoma from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, part of the Lymphoma Epidemiology of Outcomes cohort. Enrollment could occur prior to or after initiation of treatment. We measured HRQoL using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at baseline, 12, and 24 months. FACT-G yields five HRQoL domain scores: emotional well-being (EWB), functional WB (FWB), physical WB (PWB), social/family WB (SFWB), and total FACT-G score (a sum of the domains). Pts completing <80% of the FACT-G questions were excluded. Linear mixed models with random subject intercepts estimated changes in FACT-G scores from baseline. The covariates in multivariate analysis were lymphoma subtype, stage, and treatment. Interaction effects between treatment (chemotherapy and/or radiation) and subtype were added to the model. We calculated effect sizes (ES) for the magnitude of mean change scores: 0.2, 0.5, and 0.8 were considered small, medium, and large ESs, respectively. Only ESs for mean score differences with p<0.05 are reported. Results: We identified 467 pts; median age at diagnosis was 30 years, median follow up was 5.9 years. 53% of pts completed the baseline FACT-G assessment pre-treatment, 47% completed after treatment began. Pts assessed after treatment initiation had lower baseline total FACT-G (ES -0.25), FWB (ES -0.27), and PWB scores (-0.46); but baseline EWB was higher in pts assessed prior to treatment (ES 0.20). There was no association between HRQoL scores at baseline or over time and lymphoma subtype, stage, or treatment type, or interactions. Total FACT-G scores modestly improved over time, ES 0.32 at 1 year and ES 0.45 at 2 years after enrollment. EWB, FWB, and PWB also improved over time (ES 0.36, 0.44, 0.30 at 1 year; and 0.49, 0.56, 0.38 at 2 years, respectively). SFWB scores slightly worsened over time (ES -0.24 at 1 year and -0.12 at 2 years). Conclusions: AYA pts with lymphoma had higher baseline total FACT-G scores, FWB and PWB prior to therapy initiation compared to after initiation. HRQoL improved from diagnosis through the first 2 years after diagnosis, except for SFWB. Neither stage, lymphoma subtype, nor treatment type affected change in HRQoL. The lack of improvement in SFWB suggests social interventions and future studies should examine factors impacting SFWB in AYA pts. Disclosures Cohen: Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding; Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Bayer: Consultancy. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Introduction Primary mediastinal large B cell lymphoma (PMBCL) is a rare non-Hodgkin lymphoma (NHL) with a female predominance; often presenting with a large anterior mediastinal mass. Though PMBCL has clinical and molecular features overlapping with Hodgkin lymphoma, it is a distinct entity defined by the World Health Organization classification. PMBCL is heterogeneously treated, and most patients receive front line therapy with either rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP) with radiotherapy (RT), or the more intensive etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin with rituximab (EPOCH-R) regimen. Diagnosis of PMBCL is made using clinicopathologic criteria and radiographic imaging, however gene expression profiling (GEP) studies reveal a characteristic genotypic signature distinct from diffuse large B cell lymphoma (DLBCL). Molecular classification of PMBCL using the Lymph3Cx assay from formalin-fixed paraffin-embedded tissue (FFPE) is feasible, reproducible, and highly concordant in a training and validation cohort (Mottok et al. Blood 2018). Using a multicenter cohort of patients, we sought to estimate the rate of mis-match among patients with a clinical diagnosis of PMBCL using Lymph3Cx, and describe treatment selections and outcomes for each group. Methods Patients were identified from a cohort of patients with newly diagnosed NHL from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, and the Lymphoma Epidemiology of Outcomes cohort. Patients were enrolled between 2002-2019, and included if they had clinically defined PMBCL. FFPE was retrieved from hematopathology archives of participating academic centers. All diagnoses of PMBCL were based on expert hematopathology review at the time of therapy, and all cases underwent classification by GEP using the Lymph3Cx assay. Lymph3Cx was performed in the clinical lab at the Mayo Clinic in Arizona: Contiguous unstained sections were deparaffinized and macrodissected to enrich for tumor content before RNA isolation;100-200 ng of total RNA was used in an nCounter Elements XT, hybridized, and processed the following day using the nCounter FLEX system. Raw counts were processed through the Lymph3Cx algorithm and results reported as probability of PMBCL (≥0.90 as PMBCL, ≤0.10 as DLBCL all other results "Unclear PMBCL/DLBCL") (A. Mottok et al, Blood, 2018). For cases classified as DLBCL, the Lymph2Cx cell-of-origin classifier results was reported (Scott et al, JCO, 2016). Time to event endpoints were described with Kaplan-Meier plots by groups defined by mismatch status and compared with a logrank test. Binary outcomes will be presented with 90% exact confidence intervals. Results Fifty patients were identified. Median age was 35 years (range 19-70). Sixty four percent were women. Median follow up was 47 months. Treatments included R-CHOP (44%), EPOCH-R (44%), and MACOP-B [methotrexate with leucovorin rescue, doxorubicin, cyclophosphamide, vincristine, prednisone, and bleomycin] (6%), other (4%). Ten patients (20%) had events (defined as progression or death). Three patients in the entire cohort (6%) died. The Kaplan-Meier estimated survival at 47 months (median follow-up) is 92%. The Lymph3Cx assay yielded gene expression data of sufficient quality in 47/50 cases (94%, 90% CI=85.2, 98.3%). Of 47 cases clinically identified as PMBCL, 5 unclear were DLBCL/PMBCL and 1 was Germinal Center B cell subtype of DLBCL. Among these 6 patients, 4 received R-EPOCH (66%), 1 received R-CHOP (16.6%). One patient had missing treatment data. One patient had an event requiring subsequent therapy; all patients remain alive. Conclusions Using 47 patients with PMBCL defined by histology, clinical and radiographic findings, and molecular features, we demonstrate high concordance between clinical phenotype and molecular genotype of PMBCL by Lymph3Cx. Among the 6 patients not classified as PMBCL, most received R-EPOCH. Differences in outcome by mis-match status await longer follow-up and further accrual of subjects to our data base. Our data suggest molecular genotyping may have a role in mediastinal presentations of large cell lymphoma to optimize treatment decision making. Disclosures Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Flowers:AbbVie: Consultancy, Research Funding; Kite: Research Funding; Burroughs Wellcome Fund: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; TG Therapeutics: Research Funding; Eastern Cooperative Oncology Group: Research Funding; V Foundation: Research Funding; Bayer: Consultancy; National Cancer Institute: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy. Friedberg:Acerta Pharma - A member of the AstraZeneca Group, Bayer HealthCare Pharmaceuticals.: Other; Astellas: Consultancy; Bayer: Consultancy; Kite Pharmaceuticals: Research Funding; Portola Pharmaceuticals: Consultancy; Roche: Other: Travel expenses; Seattle Genetics: Research Funding.

AbstractHealth‐related quality of life (HRQoL) in patients with chronic lymphocytic leukemia (CLL) is important in guiding treatment decisions. However, the impact of CLL treatment initiation on HRQoL is unclear. We assessed HRQoL using the FACT‐Leu and EQ‐5D‐3L questionnaires in the Connect® CLL Registry, a large, US‐based, multicenter, prospective observational study of CLL patients enrolled between 2010 and 2014, prior to the introduction of novel therapies. Among 889 patients initiating first‐line therapy with chemoimmunotherapy or rituximab monotherapy, questionnaire completion rates were 95.7% and 95.8% at enrollment, and 70.8% and 69.4% at 12 months, for FACT‐Leu Total and EQ‐5D‐3L, respectively. For 849 patients completing all five FACT‐Leu components, average total scores were 135.7 at enrollment and 141.6 at 12 months. Among 526 patients with FACT‐Leu Total scores at enrollment and 12 months, clinically meaningful (≥11‐point) improvements or reductions were observed in 179 (34.0%) and 88 (16.7%) patients, respectively. Mean EQ‐5D‐3L index scores were 0.87 at enrollment and 12 months. Among 513 patients completing EQ‐5D‐3L at enrollment and 12 months, clinically meaningful (≥0.06‐point) improvements or reductions were observed in 125 (24.4%) and 116 (22.6%) patients, respectively. In the Connect® CLL Registry, HRQoL remained stable or slightly improved after 12 months of follow‐up.

Abstract Optimal treatment of chronic lymphocytic leukemia (CLL) remains unclear. The Connect CLL Registry, a United States–based multicenter prospective observational cohort study, enrolled 1494 patients between 2010 and 2014 from predominantly community-based settings. Patients were grouped by line of therapy (LOT) at enrollment. With a median follow-up of 46.6 months (range, 0-63.0 months), median overall survival (OS) was not reached in LOT1, 63.0 months (95% confidence interval [CI], 46.0-63.0 months) in LOT2, and 38.0 months (95% CI, 33.0-47.0 months) in LOT≥3. Bendamustine and rituximab (BR; 33.5%); fludarabine, cyclophosphamide, and rituximab (FCR; 21.4%); and rituximab monotherapy (18.5%) were the most common regimens across LOTs. Median event-free survival (EFS) was similar in patients treated with BR (59.0 months) and FCR (55.0 months) in LOT1; median OS was not reached. In multivariable analysis, BR or FCR vs other treatments in LOT1 was associated with improved EFS (hazard ratio [HR], 0.60; P < .0001) and OS (0.67; P = .0162). Using the Kaplan-Meier product limit, ibrutinib vs other treatments improved OS in LOT2 (HR, 0.279; P = .009), LOT3 (0.441; P = .011), and LOT≥4 (0.578; P = .043). Prognostic modeling of death at 2 years postenrollment identified 3 risk groups: low (mortality rate, 6.2%), medium (14.5%), and high (27.4%). The most frequent adverse events across LOTs were pneumonia (11.6%) and febrile neutropenia (6.2%). These data suggest that advantages of LOT1 FCR over BR seen in clinical trials may not translate to community practice, whereas receiving novel LOT2 agents improved outcomes. This trial was registered at www.clinicaltrials.gov as NCT01081015.