Citations

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;

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.

Purpose Follicular lymphoma (FL) is an indolent cancer, with effective but rarely curative treatment options. As a standard study end point for first-line FL therapy, progression-free survival (PFS) requires extended follow-up (median PFS, > 7 years). To provide patients with earlier access to newer therapies, an earlier end point to expedite clinical trials is needed. Our objective was to formally assess the complete response rate at 30 months (CR30) after initiation of induction therapy as a potential surrogate end point for PFS in first-line FL therapy. Patients and Methods We analyzed individual patient data from 13 randomized multicenter trials of induction and maintenance regimens in first-line FL therapy published after 1990 and with sufficient data to evaluate whether CR30 could predict treatment effects on PFS. Correlation of the CR30 odds ratio with the PFS hazard ratio was evaluated by both linear regression (R2WLS) and bivariate copula (R2Copula) models. Prespecified criteria for surrogacy required either R2WLS or R2Copula ≥ 0.80, with a lower-bound 95% CI > 0.60. Results Data from eight induction and five maintenance randomized trials in 3,837 evaluable patients were analyzed. The prespecified surrogacy threshold was met, with an R2WLS of 0.88 (95% CI, 0.77 to 0.96) and an R2Copula of 0.86 (95% CI, 0.72 to 1.00). Multiple sensitivity and supplemental analyses supported the robustness of the findings. A minimum 11% absolute improvement in CR30 from a 50% control rate predicted a significant treatment effect on PFS (hazard ratio, 0.69). Conclusion This large, prospective, pooled analysis of randomized chemotherapy, immunotherapy, and chemoimmunotherapy trials demonstrates that CR30 is a surrogate end point for PFS in first-line FL treatment trials. Use of this end point may expedite therapeutic development with the intent of bringing novel therapies to this patient population years before PFS results are mature.

Abstract We assessed the cross-sectional relationship of age, menopausal years, body mass, previous estrogen use, and ethnic background to bone mineral status in a sample of 875 healthy postmenopausal women at the time they were recruited from the community to participate in a multicenter clinical trial. The women were 1-10 years postmenopause, 45-64 years of age, and had not received estrogen replacement therapy within 3 months of enrollment. Of the participants, 89% were white, 69% had a spontaneous menopause, and 53% had a history of previous estrogen replacement therapy. Bone mineral density (BMD) of the lumbar spine (L2-4) and proximal femur was measured by dual-energy x-ray absorptiometry. Results were consistent with a significant negative linear regression of BMD on age or years from menopause. Body mass index (BMI) correlated significantly with BMD at all sites (L 2-4 r = 0.28; femoral neck r = 0.34, p &lt; 0.0001). BMD adjusted for age and BMI were higher at both sites in women who had taken estrogen versus those who had not (L2-4 0.976 + 0.009 versus 0.932 + 0.01; femoral neck 0.740 + 0.006 versus 0.708 + 0.008, p &lt; 0.05). Adjusted BMD also increased with duration of ERT. Parity was negatively associated with L2-4 BMD (p = 0.03) but did not correlate significantly with BMD at the femoral neck. Black women had the highest L2-4 BMD, and Hispanic women had the highest femoral neck BMD, even when results were adjusted for age and BMI. When data were corrected for differences in bone size, these interethnic differences were no longer significant. We conclude that increased body mass is positively correlated with BMD, and this may confer a degree of skeletal protection to heavier postmenopausal women. Exposure for 5 years to exogenous estrogen is associated with significantly increased age- and BMI-adjusted BMD.