Luan, Danny

ABSTRACT One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

Introduction Survival rates for people with mantle cell lymphoma (MCL) have improved in recent years. Among the potential factors accounting for this improvement is the approval of multiple new therapies over the past two decades, including bortezomib, bendamustine, ibrutinib, lenalidomide, acalabrutinib, and zanubrutinib. We hypothesized that the availability of multiple agents with non-overlapping mechanisms of action would result in significant improvements in overall survival following first-line therapy. Methods Patients diagnosed with MCL between 2002 and 2017 were identified using the SEER-Medicare linked database. Selected patients were >65 years of age at the time of diagnosis with MCL. Lines of therapy were determined using billing codes, with inpatient/outpatient claims used for traditional agents and Medicare Part D dataset used for oral agents. Next lines of therapy were defined based on claims for new anticancer agents separated from the end of the prior line by some certain number of days. Sensitivity analyses were performed varying the number of days between therapies. Overall survival (OS) was defined as initiation of initial therapy to death, while OS2 was defined as time from initiation of second-line therapy to death. Time to next therapy 1 (TTNT1) was defined as time from start of first-line therapy to death or start of second-line therapy, whichever came first. Cox models and Kaplan-Meier (KM) survival curves were generated for time-to-event analyses. Analyses were stratified by categories of years of first- and second-line therapies corresponding to eras of key drug approvals (e.g., 2002-2005, 2006-2009, 2010-2013, >2013). Results In total, 4,832 patients were included, among whom 3,840 patients (79.5%) had claims for first-line regimens and 1,648 (34.1% of total and 42.9% of patients with first-line regimens) had second-line regimens. Of the 4,832 patients, the majority were male (67.4%), White (94.6%), and non-Hispanic (94.7%), with good performance status (74.8%) and a Charlson Comorbidity Index <2 (64.8%). The median age at diagnosis was 75 (IQR 70-81). Patients with first- and/or second-line regimens did not differ significantly from the entire population of 4,832 patients with MCL in terms of baseline characteristics. In the first-line setting, use of bendamustine-rituximab (BR) increased from 2.3% of patients treated in 2006-2009 to 49.5% in 2014-2017. Similarly, use of single-agent BTK-inhibitors (BTKi) increased from nonexistent in patients treated in 2010-2013 to 13.8% in 2014-2017. In the second-line setting, use of single-agent BTKi similarly increased from 2.6% of those treated in 2010-2013 to 44.9% in 2014-2017. Median OS for the 3,840 patients with fist-line regimens was 4.4 years (95% CI, 4.1-4.5); median OS for those receiving first-line therapy in 2002-2005 was 3.5 years (3.1-3.9), compared to 3.7 (3.4-4.0), 4.5 (4.2-5.0), and not reached (4.8-NR) among those treated in 2006-2009, 2010-2013, and 2014-2017, respectively. Patients treated in 2014-2017 had significantly improved OS compared with those in 2002-2005 (log rank p<0.0001). Median TTNT1 overall was 2.5 years (2.4-2.6); median TTNT1 was 3.2 years (2.9-3.5) vs 2.3 years (2.1-2.6) for patients who received first-line therapy in 2014-2017 compared to 2002-2005 (log rank p<0.0001) ( Figure A). In addition, there was a significant difference across KM curves for OS2 stratified by categories of years of second-line therapy, with median OS2 of 2.5 (2.0-3.2) in patients treated in 2014-2017 compared to median OS2 of 2.1 (1.7-2.5) in 2010-2013 ( Figure B). Discussion In this analysis, we observed improvements survival of MCL patients over time, along with similar improvements in outcomes following first-line therapy, which may correlate with increased BR and BTKi use as first-line agents. Unexpectedly, improvements in survival following first-line therapy were considerably more modest. Potential explanations include lack of data from recent years during which we expect continued increased utilization of oral therapies vs unique impact of repeated therapies in the elderly. Overall, these data support the need for ongoing development of first- and subsequent-line therapies in MCL.

Abstract Cancer clinical trial eligibility criteria may create patient populations studied in trials that do not reflect the patient populations treated in the real-world setting. Follicular lymphoma (FL) is an indolent lymphoma with heterogeneous presentations across a broad range of individuals, resulting in many acceptable management strategies. We evaluated how first-line clinical trial eligibility criteria impacted the demographic makeup and outcomes of patients with FL for whom systemic therapy might be considered. We compared the characteristics of 196 patients with FL from a single institution to eligibility criteria from 10 first-line FL trials on clinicaltrials.gov. Next, we tabulated eligibility criteria from 24 first-line FL protocols and evaluated their impact on 1198 patients with FL with stages II to IV disease from the prospective Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO) cohort studies. We found that 39.8% and 52.7% of patients with FL might be excluded from clinical trials based on eligibility criteria derived from clinicaltrials.gov and protocol documents, respectively. Patients excluded because of renal function, prior malignancy, and self-reported serious health conditions tended to be older. Expanding stage requirement from III-IV to II-IV, and platelet requirement from ≥150 000 to ≥75 000 increased population size by 21% and 8%, respectively, in MER and by 16% and 13%, respectively, in LEO, without impacting patient demographics or outcomes. These data suggest that management of older individuals with FL may not be fully informed by recent clinical trials. Moreover, liberalizing stage and platelet criteria might expand the eligible population and allow for quicker trial accrual without impacting outcomes.

Abstract Background: Differences in outcomes between clinical trial and real-world populations in follicular lymphoma (FL) may be partially explained by differences in characteristics between clinical trial and real-world patients. We hypothesized that certain eligibility criteria may be responsible for populations with distinct demographic/clinical characteristics. In this study, we compiled a list of eligibility criteria from front-line FL trials and evaluated their impact on the potentially eligible FL patients derived from two national cohort studies. We also evaluated whether criteria were duplicative and whether liberalizing criteria might enlarge the pool of eligible patients. Methods: Eligibility criteria were abstracted from 21 first-line clinical trials in FL in the FLASH database. We identified FL patients in two prospective cohort studies: Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO). Descriptive statistics were used to characterize patients included and excluded by various eligibility criteria. Chi-square tests and two-sample t-tests were used to compare categorical and continuous variables, respectively. To study the relative impact of individual criteria, we used a step-wise approach to quantify the number of additional patients excluded with each individual criterion after applying other criteria. Results: Eligibility criteria included in at least one-third of studies included stage (present in all 21 trials), renal function (18), HIV/AIDS status (17), performance status (16), history of other malignancies (16), hepatic function (16), and cardiac function (15), other serious health conditions (13), pregnancy status (10), neuro/psych function (10), age (10), metabolic disease status (9), CNS involvement (9), birth control (8), pulmonary function (8), WBC count (8), HBV status (8), platelet count (7), measurable disease (7), HCV status (7), active infection status (7), and CD20 status (7). We included 738 patients with newly diagnosed FL undergoing first-line therapy from MER and 703 patients from LEO. In MER, the median age was 60 years; 87% were White vs 2% non-White, 80% were non-Hispanic vs 2% Hispanic, and 68% were stage III/IV. In LEO, the median age was 60 years; 88% were White vs 11% non-White, 87% were non-Hispanic vs 11% Hispanic, and 67% were stage III/IV. Exclusion criteria impacting more than 10% of patients included stage (31 in LEO vs 29% in MER), self-reported serious health conditions (17 vs 31%), prior cancer diagnosis (14 vs 11%), performance status (10%), and platelet count (10%). Patients excluded due to the following criteria tended to be older: renal function (median 70 vs 60 years in MER), prior malignancy (67 vs 58 years), and self-reported serious health conditions (66 vs 58 years). This pattern was consistent in LEO (Table). No eligibility criteria significantly impacted race/ethnicity. Given the potential for multiple eligibility criteria to exclude the same patients, we used a step-wise approach to evaluate the impact of individual criteria on patients remaining after exclusion by the larger group of criteria, and whether liberalizing criteria impacted patient numbers and/or demographics. We found that only self-reported serious health conditions significantly impacted numbers (excluding 43% of patients), whereas hematologic parameters and performance status excluded only 5 and 1% of patients, respectively, with no impact on age or race/ethnicity. We also found that liberalizing stage requirement from III-IV to II-IV could increase enrollment by 14% in MER (12% in LEO), and liberalizing platelet requirement from ≥150,000 to ≥100,000 could increase enrollment by 11% (15% in LEO). Moreover, liberalizing eligibility criteria had no impact on age or race/ethnicity of the patient pool or on EFS (Figure). Conclusions: We found that excluding patients with prior malignancies, poor renal function, and self-reported serious health conditions appears to inadvertently exclude older patients. Other serious health conditions appeared to exclude a significant number of patients but did not impact patient demographics. Liberalizing certain criteria, including stage and platelet requirement, could potentially increase trial accrual, but would not likely correct the older patient deficit. These findings may serve in developing consensus eligibility criteria for future first-line FL trials. Figure 1 Figure 1. Disclosures Flowers: Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Allogene: Research Funding; Biopharma: Consultancy; Cellectis: Research Funding; EMD: Research Funding; Genentech/Roche: Consultancy, Research Funding; Epizyme, Inc.: Consultancy; Genmab: Consultancy; Iovance: Research Funding; Guardant: Research Funding; Burroughs Wellcome Fund: Research Funding; Sanofi: Research Funding; Takeda: Research Funding; Spectrum: Consultancy; Gilead: Consultancy, Research Funding; Amgen: Research Funding; Xencor: Research Funding; Nektar: Research Funding; SeaGen: Consultancy; Ziopharm: Research Funding; TG Therapeutics: Research Funding; 4D: Research Funding; Acerta: Research Funding; Denovo: Consultancy; Novartis: Research Funding; Pfizer: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Morphosys: Research Funding; Adaptimmune: Research Funding; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; BeiGene: Consultancy; AbbVie: Consultancy, Research Funding; Kite: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Pharmacyclics: Research Funding. Link: MEI: Consultancy; Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding. Friedberg: Acerta: Other: DSMC ; Bayer: Other: DSMC ; Novartis: Other: DSMC . Cohen: Genentech, Takeda, BMS/Celgene, BioInvent, LAM, Astra Zeneca, Novartis, Loxo/Lilly: Research Funding; Janssen, Adaptive, Aptitude Health, BeiGene, Cellectar, Adicet, Loxo/Lilly, AStra ZenecaKite/Gilead: Consultancy. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Lossos: Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy; NCI: Research Funding; Verastem: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; NIH grants: Research Funding; University of Miami: Current Employment. Nastoupil: Bayer: Honoraria; Gilead/Kite: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; ADC Therapeutics: Honoraria; IGM Biosciences: Research Funding; Takeda: Honoraria, Other: DSMC, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Epizyme: Honoraria, Research Funding; Denovo Pharma: Other: DSMC; Caribou Biosciences: Research Funding; Bristol Myers Squibb/Celgene: Honoraria, Research Funding; MorphoSys: Honoraria. Maurer: Nanostring: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; BMS: Research Funding. Cerhan: NanoString: Research Funding; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Genentech: Research Funding. Martin: ADCT: Consultancy.