Dixon, Jesse G.

7544 Background: Great heterogeneity in survival exists for patients (pts) newly diagnosed with aggressive BCL. Three scoring systems based on simple clinical parameters (age, lactate dehydrogenase, number/sites of involvement, stage, performance status) are widely used: the international prognostic index (IPI), revised-IPI (R-IPI), and National Comprehensive Cancer Network IPI (NCCN-IPI). We studied BCL pts treated with R-CHOP to determine which scoring systems best identifies subgroups with poor outcomes that might benefit from new approaches. Methods: Individual pt data from 7 multicenter trials (1998-2009) of pts with BCL (86% DLBCL) treated front-line with R-CHOP (or variant) were analyzed to determine whether IPI, R-IPI, or NCCN-IPI best discriminated overall survival (OS). The concordance index (c-index) from a proportional hazards model, stratifying on trial and induction therapy, quantified predictive accuracy of each scoring system. Results: 2561 pts (median age 63 yrs, 56% male) were classified into IPI, R-IPI, and NCCN-IPI risk groups (Table). With a median follow-up of 5 yrs, NCCN-IPI had the greatest absolute difference in OS estimates between the highest and lowest risk groups at 1, 3, and 5 yrs, and best discriminated OS (c-index = 0.631, Table). Conclusions: In an independent and large cohort of pts, NCCN-IPI performs best in risk-stratifying pts with aggressive BCL, readily distinguishing pts at high and low risk for treatment failure using clinical parameters (5-yr OS between 48 and 92%). Improvement over the simpler IPI appears incremental, and IPI may remain a valuable alternative. Work integrating molecular features of the tumor into the (NCCN-) IPI is in progress to define high risk groups where targeted novel approaches are needed most. [Table: see text]

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;

BackgroundSrc signaling is markedly upregulated in patients with invasive glioblastoma (GBM) after the administration of bevacizumab. The Src family kinase inhibitor dasatinib has been found to effectively block bevacizumab‐induced glioma invasion in preclinical models, which led to the hypothesis that combining bevacizumab with dasatinib could increase bevacizumab efficacy in patients with recurrent GBM.MethodsAfter the completion of the phase 1 component, the phase 2 trial (ClinicalTrials.gov identifier NCT00892177) randomized patients with recurrent GBM 2:1 to receive 100 mg of oral dasatinib twice daily (arm A) or placebo (arm B) on days 1 to 14 of each 14‐day cycle combined with 10 mg/kg of intravenous bevacizumab on day 1 of each 14‐day cycle. The primary endpoint was 6‐month progression‐free survival (PFS6).ResultsIn the 121 evaluable patients, the PFS6 rate was numerically, but not statistically, higher in arm A versus arm B (28.9% [95% CI, 19.5%‐40.0%] vs 18.4% [95% CI, 7.7%‐34.4%]; P = .22). Similarly, there was no significant difference in the median overall survival noted between the treatment arms (7.3 months and 7.7 months, respectively; P = .93). The objective response rate was 15.7% in arm A and 26.3% in arm B (P = .52), but with a significantly longer duration in patients treated on arm A (16.3 months vs 2 months). The incidence of grade ≥3 toxicity was comparable between treatment arms, with hematologic toxicities occurring more frequently in arm A versus arm B (15.7% vs 7.9%) (adverse events were assessed as per the National Cancer Institute Common Terminology Criteria for Adverse Events [version 4.0]). Correlative tissue analysis demonstrated an association between pSRC/LYN signaling in patient tumors and outcome.ConclusionsDespite upregulation of Src signaling in patients with GBM, the combination of bevacizumab with dasatinib did not appear to significantly improve the outcomes of patients with recurrent GBM compared with bevacizumab alone.

3502 Background: DFS with 3 years median follow-up (3yDFS) was validated as a surrogate for OS with 5 years median follow-up (5yOS) in adjuvant chemotherapy CC trials prior. Recent data showed improved survival after recurrence and OS, over time, in pts who received adjuvant FOLFOX. Hence, re-evaluation of the association between DFS and OS, as well as the optimal follow-up of OS to aid its utility in future adjuvant trials is needed. Methods: Individual patient data from 8 randomized adjuvant studies conducted from 1998-2009 were included; 3 trials tested anti-VEGF or anti-EGFR agents. Trial-level surrogacy examining the correlation of treatment effect estimates (i.e. hazard ratios) of 3yDFS and 5y to 8yOS was evaluated using both linear regression (R2WLS) and Copula bivariate (R2Copula) models. For the R2, a value closer to 1 indicates a stronger correlation. Prespecified criteria for surrogacy required either R2WLS or R2Copula ≥ 0.80 and neither < 0.7, with lower-bound 95% Confidence Interval (CI) > 0.60. The rank correlation coefficient (ρ) quantified the individual-level surrogacy. Results: Total of 18,886 pts were analyzed, with median age 60, 54% male, 83% stage III, 59% > 12 nodes examined. Median follow-up for survival ranged from 5 to 10 years across trials. Trial level correlation between 3yDFS and OS remained strong (R2WLS ≥0.74; R2Copula ≥ 0.89) and increased as the median follow-up of OS extended longer (see table). Analyses limited to stage III pts and/or trials tested biologics showed consistent results. Conclusions: 3yDFS remains a validated surrogate endpoint for 5yOS in adjuvant trials in CC pts per prespecified criteria. The correlation was strengthened with more than 6 years of follow-up for OS. [Table: see text]

3535 Background: CRC is known to be a heterogeneous disease. This study quantifies within pt heterogeneity in early lesion change rate (LCR) in the era of targeted agents compared to chemo alone and its potential impact on survival. Methods: Pts with 2-10 lesions measured at baseline were included. For each lesion, the early LCR was defined as the change in size (shrinking or growing) from baseline to 12 weeks on treatment. Within pt heterogeneity in early LCR among lesions was estimated by standard deviation (STD). A larger value of STD indicates larger variation of LCR per pt. Stratified multivariate Cox models were used to assess the associations between LCR STD with overall survival (OS). Adjusted hazard ratios (HRadj) and 95% confidence intervals (CIs) are reported. Results: Data were available on 9,092 mCRC pts (median age 61; 60% male, 55% ECOG PS 0; 61% 2+ metastatic sites) enrolled in 16 1st-line randomized trials, with 44%, 42%, and 10% of pts received chemo alone, + a VEGF inhibitor (VEGFi) or an EGFR inhibitor (EGFRi), respectively. LCR heterogeneity is the highest among pts received EGFRi but lowest among pts received VEGFi (Table). Overall, higher heterogeneity is associated with worst OS (HRadj1.22, 95% CI (1.16, 1.27)). The effect is most pronounced in pts received VEGFi (interaction p=0.0012). Conclusions: There was heterogeneity observed in lesion size changes within pts. Its magnitude varies across treatment approaches, and was associated with poor survival. This preliminary result reveals the great potentials to define novel response endpoint and refine treatment decision-making by incorporating heterogeneities in lesion changes. [Table: see text]

Purpose Overall survival (OS) is the definitive and best-established primary efficacy end point to evaluate diffuse large B-cell lymphoma (DLBCL) therapies, but it requires prolonged follow-up. An earlier end point assessed post-treatment would expedite clinical trial conduct and accelerate patient access to effective new therapies. Our objective was to formally evaluate progression-free survival (PFS) and PFS at 24 months (PFS24) as surrogate end points for OS in first-line DLBCL. Patients and Methods Individual patient data were analyzed from 7,507 patients from 13 multicenter randomized controlled trials of active treatment in previously untreated DLBCL, published after 2002, with sufficient PFS data to predict treatment effects on OS. Trial-level surrogacy examining the correlation of treatment effect estimates of PFS/PFS24 and OS was evaluated using both linear regression ( R2WLS) and Copula bivariable ( R2Copula) models. Prespecified criteria for surrogacy required either R2WLS or R2Copula ≥ 0.80 and neither < 0.7, with lower-bound 95% CI > 0.60. Results Trial-level surrogacy for PFS was strong ( R2WLS = 0.83; R2Copula = 0.85) and met the predefined criteria for surrogacy. At the patient level, PFS strongly correlated with OS. The surrogate threshold effect had a hazard ratio of 0.89. Surrogacy was consistent across comparisons with or without rituximab and with rituximab maintenance trials. Trial-level surrogacy for PFS24 was relatively strong ( R2WLS = 0.77; R2Copula = 0.78) but did not meet prespecified criteria. At the patient level, PFS24 significantly correlated with OS. The surrogate threshold effect had an odds ratio of 1.51. Conclusion This large pooled analysis of individual patient data supports PFS as a surrogate end point for OS in future randomized controlled trials evaluating chemoimmunotherapy in DLBCL. Use of this end point may expedite therapeutic development with the intent of bringing novel therapies to this patient population years before OS results are mature.