Nielsen, Tina

SummaryProgression‐free survival (PFS) has been the regulatory primary end‐point for recent phase III trials in first‐line follicular lymphoma (FL), but requires prolonged follow‐up. Complete response (CR) at 30 months after initiation of induction treatment was validated as surrogate end‐point for PFS. Our objective was to further evaluate surrogacy of CR measured by [18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging at the end of induction (EoI). Individual patient data were analysed from 1505 patients from five randomized trials. Trial‐level surrogacy examining the association between treatment effects on EoI‐PET‐CR and PFS was evaluated using linear regression () and bivariate Copula () models. Although EoI‐PET‐CR strongly predicted PFS at a prognostic level, the trial‐level assessment did not show strong correlation (, confidence interval [CI]: 0.20–0.88; , CI: 0.0–0.82). The high uncertainty in estimation was possibly due to the small number of trials and the population of patients with available PET data. Maintenance therapy affecting PFS beyond induction treatment, but not EoI‐PET‐CR end‐point, may have distorted the association between treatment effects. However, there will probably be a number of additional trials approaching completion with available PET response data. Refined evaluation of PET‐CR based surrogate end‐points is still warranted.

Abstract Observational studies and stand-alone trials indicate that patients with follicular lymphoma (FL) who experience disease progression within 24 months of front-line chemoimmunotherapy (POD24), have poor outcomes. We performed a pooled analysis of 13 randomized clinical trials of patients with FL in the pre- and postrituximab eras to identify clinical factors that predict POD24. Logistic regression models evaluated the association between clinical factors and POD24. Cox regression evaluated the association between POD24 as a time-dependent factor and subsequent overall survival (OS). A landmark analysis evaluated the association of POD24 with OS for the subset of patients who were alive at 24 months after trial registration. Patients without progression at 24 months at baseline had favorable performance status (PS), limited-stage (I/II) disease, low-risk FL International Prognostic Index (FLIPI) score, normal baseline hemoglobin, and normal baseline β2 microglobulin (B2M) level. In a multivariable logistic regression model, male sex (odds ratio [OR], 1.30), PS ≥2 (OR, 1.63), B2M (≥3 mg/L; OR, 1.43), and high-risk FLIPI score (3-5; OR, 3.14) were associated with increased risk of progression before 24 months. In the time-dependent Cox model and the 24-month landmark analysis, POD24 was associated with poor subsequent OS (hazard ratio, 4.85 and 3.06, respectively). This is the largest pooled analysis of clinical trials data validating POD24 as a robust indicator of poor FL survival and identified clinical predictors of early death and progression that can aid in building comprehensive prognostic models incorporating clinical and molecular predictors of POD24.

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.

Introduction: Most patients with newly diagnosed FL treated with rituximab (R) alone or R + chemotherapy will experience prolonged progression-free survival and overall survival (OS), but it remains unclear what factors have the greatest influence on FL-associated and other causes of death in this patient population. We utilized individual patient data from 13 first-line randomized clinical trials from the FLASH database to perform a comprehensive multistate survival analysis to examine and quantify the relationships between clinical characteristics, treatment response, and early, intermediate, and late FL outcomes. Methods: The multistate survival analysis model defined states for "Alive after beginning Induction Treatment (TX)", "Alive after beginning Maintenance TX", "Death due to FL", and "Death from Other Causes" (Figure 1). We used the Aalen-Johansen estimator, a generalization of the Kaplan-Meier estimator, to calculate the likelihood of being in each model state and estimate the course of FL over time. Making no assumptions on the probability distributions and capable of coping with censored observations, the Aalen-Johansen estimator is a convenient and reliable nonparametric estimator for clinical data. Results: Among 7,465 FL patients with median age 56 (range 18-90) years, 49.2% were female; 28.7% Stage I-III, 71.3% Stage IV, and FLIPI was 0-1 (20.0%), 2 (36.8%), ≥ 3 (43.2%). Following initiation of induction treatment, 2-, 5- and 10-year death rates were 1.7%, 3.8%, and 5.8% due to FL, and 0.7%, 2.1%, and 4.8% from other causes (Figure 2). Death rates at 2, 5, and 10 years due to FL and other causes for subgroups based on clinical characteristics and treatment response are shown in Table 1. Notably, patients > 70 years and patients with FLIPI ≥ 3 had worse outcomes and patients achieving CR at 18, 24, and 30 months experienced improved outcomes. Conclusion: This is the largest study using data from randomized trials to quantify the impact of clinical factors on early, intermediate and late mortality by cause of death. We demonstrated that age > 70 years and FLIPI ≥ 3 were linked to increased FL-associated death and response to TX distinguished patients with favorable and poor outcomes. Future analyses should quantify the impact of predictors on the rate/time of FL outcomes in multivariable models. Disclosures Salles: Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. Hoster:Janssen: Research Funding; Roche Pharma AG: Other: Travel Support. Hiddemann:Bayer: Research Funding; Gilead: Consultancy, Honoraria; Vector Therapeutics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria. Herold:Roche: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Gilead: Honoraria; Celgene: Honoraria. Morschhauser:Servier: Consultancy; Gilead: Consultancy; Janssen: Honoraria; Roche/Genentech: Consultancy; BMS: Honoraria; Celgene: Honoraria. Rummel:Roche Pharma AG: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Kimby:AbbVie,: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Gilead: Other: educational lectures. Vitolo:Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche: Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Gyan:Pfizer: Honoraria. Ladetto:Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria; Abbvie: Honoraria; Acerta: Honoraria; Sandoz: Honoraria. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Flowers:AstraZeneca: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; AbbVie: Consultancy, Research Funding; Optimum Rx: Consultancy; V Foundation: Research Funding; Pharmacyclics/Janssen: Consultancy, Research Funding; Bayer: Consultancy; Gilead: Consultancy, Research Funding; TG Therapeutics: Research Funding; Denovo Biopharma: Consultancy; Acerta: Research Funding; National Cancer Institute: Research Funding; Millenium/Takeda: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Spectrum: Consultancy;

Introduction: The use of PET-CR as a surrogate endpoint would expedite the development of novel therapies and enable better estimates of sample size based on early outcomes of a trial. Previous studies have reported an association between end-of-therapy (EOT) PET results and long-term progression-free (PFS) and overall survival (OS) in diffuse large B-cell lymphoma (DLBCL) patients receiving standard first-line chemoimmunotherapy. We have also previously shown that the overall predictability of PET-CR for PFS and OS in these trials is similar to that in 18 literature-based studies. (15-ICML 2019, P195). To assess the potential for PET-CR as a surrogate endpoint in registration trials, we conducted a prospectively designed individual patient-level-data meta-analysis of available clinical trials. Methods: We synthesized patient-level data from three prospective phase II and III trials (GOYA [NCT01287741], GATHER [NCT01414855], MAYO [NCT00670358]) conducted in previously untreated DLBCL patients, using a Bayesian hierarchical model. We considered the two treatment arms in GOYA (GOYA-R-CHOP [rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone] and GOYA-G-CHOP [obinutuzumab, cyclophosphamide, doxorubicin, vincristine, and prednisone]) separate; hence a total of four arms, excluding patients without baseline PET scans. We investigated the relationship between PET-CR and long-term survival outcomes overall and within patient subgroups. We used Kaplan-Meier plots to compare survival endpoints by PET-CR status. We considered hypothetical RCTs that have PET-CR and PFS as endpoints to show how our model of the relationship between PET-CR and PFS can be used to predict the trial outcome. Results: We included 1496 patients (GOYA-R, GOYA-G, GATHER, MAYO, respectively: 665, 669, 100, 62). EOT PET-CR status was determined by the Lugano criteria in GOYA and by IHP in the other two studies. The overall rate of PET-CR was 72%; respectively 72%, 73%, 62% and 74% per arm. Panel A of the Figure shows Kaplan-Meier plots of PFS by PET-CR status. The Bayesian modeled hazard ratio (HR) comparing PET-CR versus nonCR was 0.13 (95% CI: 0.10, 0.15). The model can be used in planning a clinical trial as follows. Suppose a new therapy improves the PET-CR rate from 70% to 85%. Based on our model the expected HR for PFS treatment effect would be 0.71 (95% CI: 0.58, 0.84). The trial sample size required to demonstrate such an improvement with 90% power is about 650 patients, assuming an accrual rate of 50 patients per month and a minimum follow-up time of 12 months. Panel B of the Figure shows the model-based PFS HR and its CI (shaded) as it depends on the PET-CR rate of the new therapy. This plot also shows the estimated total trial sample size (in red), again assuming the relationship between PET-CR and PFS in our model. However, our model may not apply for the new therapy; and the trial could have an adaptive design with final sample size tailored to the accruing information about PET-CR and PFS and their relationship for the therapies in the trial. Conclusions: Achieving an EOT PET-CR in newly diagnosed DLBCL is highly predictive of favorable outcome in the populations we considered. The estimated HR is 0.13 for PFS, PET-CR versus nonCR, and it is 0.10 for OS. Based on our model, a treatment that improves PET-CR rate could be reasonably expected to have a benefit on PFS and OS, for the populations we considered. Whether a new therapy, with a different mechanism of action, that improves PET-CR rate will extend PFS in a clinical trial is less clear. An adaptive trial could be initiated based on our model as a hypothesis. This hypothesis can be verified or updated using accruing information in the trial itself. Our model can also help in planning clinical trials for re-estimating sample size during the trial and considering early stopping for futility. Further work should investigate the applicability of PET-CR in predicting PFS and/or OS for treatments that have different mechanisms of action and for other populations of patients. Acknowledgements: This work was funded by Genentech/Roche. Disclosures Berry: Berry Consultants, LLC: Consultancy, Employment, Equity Ownership, Other: Berry Consultants, LLC is a company that provides statistical design and analysis services to pharmaceutical companies (including Genentech/Roche), medical device companies, U.S. NIH cooperative groups, patient advocacy groups, and international consortia. Broglio:Berry Consultants LLC: Employment. Ward:Hoffmann La Roche: Employment, Equity Ownership. Mattiello:F. Hoffmann-La Roche Ltd: Employment. Sahin:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. McGlothlin:Berry Consultants, LLC: Consultancy, Employment. Elliott:Berry Consultants, LLC: Employment. Sehn:Lundbeck: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Janssen-Ortho: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Merck: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; Astra Zeneca: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; Verastem: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen-Ortho: Honoraria; Takeda: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria. Trněný:Abbvie: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Celgene: Consultancy; Janssen: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; F. Hoffmann-La Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; MorphoSys: Consultancy, Honoraria. Vitolo:Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; F. Hoffmann-La Roche: Speakers Bureau. Martelli:Servier: Honoraria; F. Hoffman-La Roche, Celgene, Janssen, Sandoz, Novartis, Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria; F. Hoffman-La Roche, Celgene, Janssen, Sandoz, Novartis, Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kostakoglu:F. Hoffman-La Roche: Consultancy; Genentech: Consultancy. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding.

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.