Salles, Gilles

Abstract We have recently reported that event-free survival at 24 months from diagnosis (EFS24) is a robust endpoint for assessment of disease related outcome for newly diagnosed patients with diffuse large B-cell lymphoma (DLBCL) treated with anthracycline-based immunochemotherapy (IC). Patients who achieve EFS24 following IC have a subsequent overall survival that is equivalent to the age and sex matched general population. The IPI was developed in the pre-rituximab era using OS/EFS endpoints. While the IPI is associated with outcome in the IC era, the original survival estimates for the IPI risk groups are outdated. The IPI is also a discrete risk classifier, with only 6 possible groups. Here we remodel the IPI using EFS24 as the endpoint in a large international cohort of patients treated with IC. This model provides an individual level 0-100% risk of failing to achieve EFS24 for each patient and can be used for patient prognosis, treatment stratification, and risk assessment. Methods Newly diagnosed DLBCL patients treated with immunochemotherapy from the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource, NCCTG N0489 clinical trial, GELA LNH2003B program, and a Lyon, France based hospital registry were included in the original EFS24 study. Patients with a relapse, progression, unplanned re-treatment, or death due to any cause within 730 days of diagnosis were considered a failure for the EFS24 endpoint. Patients alive and progression/relapse free at 730 days from diagnosis with no subsequent treatment following initial IC were considered a success for the EFS24 endpoint. Variables selected for potential model inclusion were the IPI components (age, stage, ECOG PS, number of extranodal sites, and LDH), as well as standard clinical variables typically reported in studies of DLBCL: presence of B-symptoms, bone marrow involvement, bulky disease (>10 cm), and sex. The IPI24, a prognostic model for EFS24, was developed using logistic regression models. Model building began by fitting the classic IPI score (0-5), then allowing individual IPI variables to carry different model weights and number of categories, followed by dropping insignificant variables, finally adding any additional significant variables. Model performance was assessed using bias corrected c-statistics, AIC, and R2 values. Model selection was done using the complete case set with multiple imputation on the full dataset used to refine parameter estimates for the final model. Results 1596 total patients were available for modeling. The median age was 62 (range 18-93) and 53% were male; 33% were IPI 0-1, 25% were IPI 2, 25% were IPI 3, and 17% were IPI of 4-5. The failure rate for achieving EFS24 was 29%. All variables but sex (p=0.33) were univariately associated with EFS24 (p<6.4x10-5). The original IPI had good prognostic ability for EFS24 (c=0.696, p=2.2x10-32). Allowing multiple categories for ECOG PS, stage, and age (continuous) improved model fit and prognostic ability (c=0.727). The number of extranodal sites was not significant when IPI variables were modeled separately and was removed from the model. Sex and bulky disease were additional significant variables when added to the model. Thus, the final IPI24 model contained the following risk predictors: male sex, ECOG PS (0 vs. 1 vs 2 vs 3-4), stage (I vs. II, vs. III vs. IV), LDH (elevated vs. normal range), bulky disease > 10cm, and age (continuous). This model resulted in improved fit and prognostic ability (c=0.749) over the standard IPI (c=0.696, bootstrap p<0.001). Model validation was performed on an additional 158 patients from the SPORE MER and showed good concordance (c=0.697). Additional validation is planned in a larger set of independent patients. Conclusion The IPI24, generated using standard clinical variables, yields an individual risk prediction for failing to achieve EFS24 following IC for treatment of DLBCL. The IPI24 has superior prognostic ability to the standard IPI and can be implemented in the clinic using a nomogram or as a simple electronic application. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Follicular lymphoma (FL) is the most common indolent NHL with a heterogenous natural history of disease and a median survival of 8 to 12 y, albeit ranging between 1 to 20 y. Casulo et al. (2015) identified a high-risk FL cohort of patients with progression of disease (POD) at ≤24 months following initiation of immunochemotherapy with R-CHOP. Idelalisib (Zydelig) is a first-in-class, highly selective, oral inhibitor of PI3Kd which is indicated for relapsed FL or SLL following receipt of at least two lines of systemic chemotherapy. Retrospective subgroup analysis of the idelalisib registrational trial NCT01282424 (101-09) of a cohort with early POD following immunochemotherapy was performed to assess possible activity of idelalisib in this population. Methods: A subset of 46 patients enrolled in study 101-09 were identified as having been diagnosed with FL and having received first-line immunochemotherapy, of which 37 experienced early POD, defined as starting second-line treatment within 24 months of initial first-line treatment. For the latter group, descriptive statistics of demographic and baseline characteristics and inter-treatment intervals in months as well as Kaplan-Meier estimates of progression-free survival (PFS) and overall survival (OS) following initiation of immunochemotherapy and idelalisib were calculated. Population: Demographic characteristics of these 37 patients included median (range) age at initiation of idelalisib of 64 (33-84) y and 18 (48.6%) females. Histologic grade at diagnosis included 33 (89.2%) with grades 1 or 2 as well as 4 (10.8%) with grade 3A, while 21 (56.8%) patients had a FLIPI score ≥3. The mean (s.d.) number of prior therapies was 3.4±1.4, with a range of 2 to 8, while first-line therapies included 21 (56.8%) patients who received R-CHOP-based regimens, 7 (18.9%) who received BR, and 5 (13.5%) who received R-CVP. Mean (s.d.) inter-treatment intervals included 12.5±6.1 months between first- and second-line for all patients, 9.7±9.3 months between second- and third-line for all patients, 11.9±12.0 months between third- and fourth-line for 24 (64.9%) patients, and 11.8±7.6 months between fourth- and fifth-line for 15 (40.5%) patients. Median (range) time from first-line therapy to idelalisib initiation was 30.3 (8.9-94.7) months; no patient received idelalisib as second-line therapy. Results: Best responses included 5 (13.5%) patients with CR, 16 (43.2%) with PR, 2 (5.4%) with SD, and 1 (2.7%) with PD; median duration of response for those with CR or PR was 11.8 months (95% CI: 3.8 months, not evaluable). There were 7 (18.9%) deaths and 21 (56.8%) PFS events in this group. Estimated probabilities of survival (s.e.) and progression-free status at 2 y following initiation of idelalisib were 79%±7% and 29%±10%, respectively. Median PFS was 11.1 months (95% CI: 5.5, 19.3 months). Estimated probability of survival (s.e.) at 5 years following initiation of first-line treatment was 79%±8%. Median overall survival from both initiation of first-line immunochemotherapy as well as with idelalisib was not reached during the course of this study. Conclusions: Idelalisib may have significant clinical activity in high-risk and doubly-refractive FL following early relapse status post first-line immunochemotherapy. Given the small size of the studied subset population which may not be representative, further characterization in additional patients is warranted to ensure the generalizability of this finding, including consideration of further investigational protocols featuring targeted therapies employed both as single agents and in combination. Figure 1. Overall Survival From Initiation of First-line Treatment Figure 1. Overall Survival From Initiation of First-line Treatment Figure 2. Overall Survival From Initiation of Idelalisib Figure 2. Overall Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Figure 3. Progression-Free Survival From Initiation of Idelalisib Disclosures Gopal: Gilead, Spectrum, Pfizer, Janssen, Seattle Genetics: Consultancy; Spectrum, Pfizer, BioMarin, Cephalon/Teva, Emergent/Abbott. Gilead, Janssen., Merck, Milennium, Piramal, Seattle Genetics, Giogen Idec, BMS: Research Funding; Millennium, Seattle Genetics, Sanofi-Aventis: Honoraria. Kahl:Roche/Genentech: Consultancy; Seattle Genetics: Consultancy; Millennium: Consultancy; Cell Therapeutics: Consultancy; Celgene: Consultancy; Infinity: Consultancy; Pharmacyclics: Consultancy; Juno: Consultancy. Flowers:Infinity Pharmaceuticals: Research Funding; Genentech: Research Funding; Spectrum: Research Funding; Millennium/Takeda: Research Funding; Seattle Genetics: Consultancy; Spectrum: Research Funding; Pharmacyclics: Research Funding; AbbVie: Research Funding; AbbVie: Research Funding; Gilead Sciences: Research Funding; Pharmacyclics: Research Funding; Onyx Pharmaceuticals: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Janssen: Research Funding; Acerta: Research Funding; Millennium/Takeda: Research Funding; Acerta: Research Funding; Infinity Pharmaceuticals: Research Funding; Janssen: Research Funding; Onyx Pharmaceuticals: Research Funding. Martin:Janssen: Consultancy, Honoraria; Acerta: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Bayer: Consultancy. Link:Genentech: Consultancy, Research Funding; Kite Pharma: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Celldex: Research Funding. Ye:Gilead: Employment. Koh:Gilead: Employment. Abella:Gilead: Employment. Barr:Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; Abbvie: Consultancy; Gilead: Consultancy. Salles:Calistoga Pharmaceuticals, Inc.; Celgene Corporation; Genentech, Inc.; Janssen Pharmaceutica Products, L.P.; Roche: Consultancy; Celgene Corporation; Roche and Gilead Sciences: Research Funding; Celgene Corporation; Roche: Speakers Bureau.

Abstract Introduction: Diffuse large B-cell lymphoma (DLBCL) is an aggressive non-Hodgkin’s lymphoma (NHL) comprising 3 molecular subtypes: germinal center B-cell (GCB), activated B-cell (ABC) and class III. ABC patients (pts) have a poor prognosis. The immunomodulatory drug lenalidomide (Len) produces durable responses in pts with aggressive NHL (Witzig 2011), with preferential activity reported in non-GCB DLBCL (Hernandez-Ilizaliturri 2011). Methods: This randomized, multicenter, open-label, phase 2/3 study was conducted to determine the efficacy and safety of single-agent Len vs single-agent investigator’s choice (IC) in relapsed/refractory DLBCL pts who received ≥2 prior therapies, or were ineligible for stem cell transplantation or further combination chemotherapy. DLBCL subtype (GCB vs non-GCB) was determined by a central pathology lab using immunohistochemistry (IHC) per the Hans method (Hans 2004). Pts were stratified by subtype, then randomized 1:1 to receive Len (25 mg/day, 21 days of 28-day cycle) or IC (gemcitabine, rituximab, etoposide, or oxaliplatin) until progressive disease (PD), unacceptable toxicity, or voluntary withdrawal. In the event of radiologically confirmed PD, pts in the IC arm were allowed to cross over to Len. The primary endpoint for Stage 1 was overall response rate (ORR), as determined by an Independent Response Assessment Committee. Progression-free survival (PFS), overall survival (OS) and subtype analysis using gene expression profiling (GEP) were exploratory endpoints. Concordance of GEP and IHC was evaluated from 3 separate laboratories. Results: IHC subtyping agreement rate among the 3 laboratories ranged from 87.5%-97.9%, and sensitivity of IHC to detect ABC or GCB subtypes vs GEP ranged from 92.3%-100.0%. By IHC, 102 DLBCL pts (GCB, n=48; non-GCB, n=54) were treated with ≥1 dose of Len or IC (modified intent-to-treat population) in Stage 1. In this heavily pretreated population, >90.0% of pts received ≥2 prior systemic chemotherapies; 25 pts in Len and 32 pts in IC received ≥3 prior systemic chemotherapy regimens. Median age was 65 y in the IC arm vs 69 y in the Len arm. Twenty-nine pts crossed over from IC to Len after confirmed PD. All pts, regardless of subtype or therapy group, experienced ≥1 treatment-emergent adverse event, with neutropenia, anemia, and thrombocytopenia being the most common. Efficacy data are presented in the Table. Pts with GCB or non-GCB DLBCL (per IHC) treated with Len had similar ORR, but the data suggested greater improvements in PFS and OS with Len vs IC in the non-GCB pts. In an exploratory analysis of pts subtyped by GEP, ABC pts treated with Len vs IC-treated showed greater improvements in ORR, PFS, and OS compared with GCB pts. Prespecified criterion to advance to Stage 2 was a 2-sided 15% significance level in ORR in favor of Len based on IHC-defined subtype. The data did not fulfill this requirement, and Stage 2 was not opened. Conclusion: Len monotherapy showed clinical activity in heavily pretreated pts with DLBCL. The data suggest improved ORR, PFS, and OS with Len vs IC in the non-GCB population as defined by IHC, and the difference appears to be more pronounced in the ABC population as defined by GEP. Subtyping by GEP is warranted in further studies of Len in DLBCL. Abstract 628. Table 1.Table. Efficacy DataBy IHCBy GEPOverallGCBNon-GCBGCBABCLen(n=51)IC(n=51)Len(n=23)IC(n=25)Len(n=28)IC(n=26)Len(n=14)IC(n=16)Len(n=11)IC(n=16)ORR, % (95% CI)27.5 (15.9-41.7)11.8 (4.4-23.9) 26.1 (10.2-48.4)12.0 (2.5-31.2)28.6 (13.2-48.7)11.5 (2.4-30.2)21.4 (4.7-50.8)12.5 (1.6-38.3)45.5 (16.7-76.6)18.8 (4.0-45.6)P Value .079 .279 .179 .642 .206PFS, med wk (95% CI)13.6 (8.6-17.7)7.9 (6.3-9.0) 10.1 (8.3-22.3)29.0 (6.3-20.6)15.1 (8.3-24.1)7.1 (5.3-8.4)13.2 (8.3-24.9)7.1 (6.0-20.6)82.0 (7.3-NA)6.2 (4.3-10.1)P Value .041 .550 .021 .506 .105HR (95% CI) 0.64 (0.41-0.99) 0.82 (0.43-1.57) 0.50 (0.27-0.92) 0.77 (0.35-1.68) 0.44 (0.15-1.23)OS, med wk (95% CI)31.0 (16.6-41.3)24.6 (12.7-33.9) 30.0 (14.9-44.4)24.9 (13.7-58.3)32.3 (15.9-48.1)20.4 (10.3-33.9)30.0 (18.0-34.6)20.1 (13.7-36.9)108.4 (9.6-108.4)18.6 (6.6-48.0)P Value .673 .526 .253 .767 .144HR (95% CI) 0.91 (0.59-1.41) 1.23 (0.65-2.34) 0.70 (0.38-1.30) 1.12 (0.52-2.42) 0.47 (0.17-1.33) Abbreviations: CI, confidence interval; HR, hazard ratio; med, median; NA, not applicable/not available. Disclosures Czuczman: Celgene: Consultancy. Off Label Use: This abstract describes a clinical trial of lenalidomide, which is an orally-available immunomodulatory agent under investigation for treating patients with diffuse large B-cell lymphoma.. Davies:GlaxoSmithKlein: Research Funding; Hoffman La Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding. Wagner-Johnston:Celgene: Research Funding. Gascoyne:Celgene: Consultancy, Research Funding. Salles:Pfizer: Honoraria; Gilead: Honoraria; Jansen: Honoraria; Hoffman La Roche: Honoraria; Celgene: Honoraria; Mundipharma: Honoraria. Witzig:Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Zinzani:Mundipharma: Honoraria; Pfizer: Honoraria; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria; Millennium Takeda: Honoraria; Celgene: Honoraria; Teva: Membership on an entity's Board of Directors or advisory committees. Wright:Celgene: Research Funding. Staudt:Celgene Corporation: Research Funding. Repici:Celgene: Employment. Song:Celgene: Employment. Manzke:Celgene: Employment.

Abstract Background: One of the main complications in CLL is Richter syndrome (RS). RS derives from the rare transformation of chronic lymphocytic leukemia (CLL) into an aggressive lymphoma, most commonly of the diffuse large B-cell lymphoma (DLBCL) type. RS occurs in 2.2% to 8% of patients with CLL and the prognostic is poor with a median survival from 5 to 8 months. Detection of RS by imaging has resulted in conflicting and non-significant results. Aim: The objective of this study was to validate recent findings correlating FDG/PET imaging and histological features in CLL and to demonstrate that a tumoral maximum standardized uptake value > 10 measured on 18F-FDG-PET is a new valid marker to discriminate RS. Results: From June 2006 through December 2012, 240 patients from the Division of hematology of Centre Hospitalier Lyon Sud and Créteil and from the Mayo Clinic Rochester have been analyzed with a mean age of 62 years (21-91). Clinical, histological (confirm by biopsy) and biological parameters have been identified with 10% of the patients as having RS, 34% stable CLL disease; 42% of rapid CLL progression (histological features of progression defined as increased large cell number, large confluent proliferation centers or high proliferation rate assessed by Ki-67 but not meeting criteria for diffuse large B-cell lymphoma/RS) and 14% with others diseases (e.g. infection and or cancers). For patients with stable CLL disease, the median tumoral SUV max was 2 (range: 0-2.4). Among patients with a rapid progression of CLL, 90% had a tumoral SUV max <10 but greater than the liver median SUV max (4.5 range: 1-11). In contrast, 90% of patients with RS had a median tumoral SUV max >10 (12.9; range: 5-27). A statistically significant difference between SUV max of CLL patients with stable disease and RS was observed (2.2 vs. 12.9; p< 0.0001) and similarly for SUV max of CLL patients with rapid disease progression and RS (4.5 vs 12.9; p< 0.0001). Regardless of the RS prevalence (2.2% to 8%), statistical tests identified a threshold of tumor SUV max > 10 as the more discriminating cut off. Using this threshold, the sensitivity and specificity of PET to identify RS in our cohort are 91% and 95% respectively. Assuming an RS prevalence of 2.2%, positive predictive value (PPV) and negative predictive value (NPV) using the >10 threshold were 28.7% and 99.8%; for an 8% prevalence of RS, the PPV and NPV are 60.8 and 99.2% respectively.The proportion of correctly classified patients with RS is more accurate using a threshold of tumoral SUV max > 10 than 5 (2.2% RS prevalence: 94.8% versus 71.8%; 8% RS prevalence: 94.6% versus 73.5%). Finally analysis of the area under the curve (AUC) reveals a value of 0.95 (95% confidence interval: 0.89- 0.99). Recently, Falchi et al, reported on 332 patients with CLL classified as 95 RS, 117 rapid progression and 120 stable disease and demonstrated a strong correlation between histological features and PET imaging. A SUV max ≥10 strongly correlated with a shorter overall survival. Similarly, in our study we have shown excellent sensitivity and negative predictive value estimated at 100%, revealing the ability of 18F-FDG-PET-CT to rule out the diagnosis of RS if the tumor SUV max is less than 10. Conclusion: 18F-FDG-PET-CT is a valuable tool in the diagnostic evaluation of RS for patients with CLL. It is not only useful to identify RS syndrome and guide the site of biopsy but also to identify CLL patients who will experience more rapid disease progression. An SUV max > 10 is the optimal threshold to distinguish RS in CLL. Disclosures No relevant conflicts of interest to declare.

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.

Abstract Background Rituximab in combination with chemotherapy has been shown to improve overall survival (OS) in pts with FL compared with chemotherapy alone. The effect of MR was evaluated in randomized clinical trials (RCTs) with conflicting results regarding OS. In a previous systematic review and meta-analysis of summary (aggregate) data we demonstrated an OS benefit of MR treatment in pts with relapsed or refractory FL and an improved progression free survival (PFS) in both untreated and relapsed refractory FL. However, pts' disease and treatment characteristics may interact with the effect of MR. To improve identification of sub-groups of pts who may benefit from MR treatment, and to evaluate factors that interact with the effect of MR we conducted an international IPD (raw data) meta-analysis of RCTs of MR for pts with FL. We present the first analysis of this meta-database. Methods In June 2014, we updated an electronic search in Pubmed and the Cochrane Library, conference proceedings and database of clinical trials for randomized trials comparing MR to observation. The investigators of eleven trials that fulfilled inclusion criteria were invited to cooperate. Results of one trial were not reported and this trial could not be included in the meta-analysis. Seven study groups participated in this collaborative project and contributed data on individual pts, including disease characteristics, past treatment, response, survival and adverse events. We examined the effect of pt, disease and treatment characteristics by performing a series of Cox regression analyses stratified by trial. In a sensitivity analysis we combined the assessed log hazard ratios (HR) using a log-rank test for each study and combined the results using a fixed effect meta-analysis. Results We obtained data for 2323 patients randomized in 7 trials of MR compared to observation after induction therapy. The median follow up ranged from 28-114 months. The median age was 57 years (23 - 85 years), and 41% were older than 60 years. 50% of pts were women, 97% had WHO performance status 0-1; 1195 (53%) pts received CHOP/RCHOP, 453 (20%) CVP/R CVP; 1755 (77%) received rituximab containing induction; 33% of pts received 1st induction, 67% ≥2nd. Overall, pts less than 60 years of age (vs. ≥60 years) (p < 0.0001), and women (p = 0.001) had a better prognosis. There was no interaction between sex and the effect of MR (p = 0.65). Pts treated with MR had an improved OS compared to observation (HR 0.76, 95% CI 0.62-0.93, p=0.008, Figure). This was demonstrated also in a sensitivity analysis of 10 studies fulfilling inclusion criteria. There was no statistically significant heterogeneity of treatment effect between trials (p=0.15). In an analysis by treatment line (1st vs. ≥2nd), the estimated effect of MR among patients who received ≥2nd induction was HR of 0.70 (95% CI 0.55 - 0.91), compared to 0.90 (95% CI 0.65 - 1.25) among patients who received 1st induction. However, this difference was not statistically significant (p=0.25). Analysis by the inclusion of rituximab in induction demonstrated among patients who received rituximab in induction a HR of 0.82 (95% CI 0.63 – 1.06), and among those who received induction without rituximab a HR of 0.68 (95% CI 0.49 – 0.93). This difference was not statistically significant (p= 0.35). Analysis by chemotherapy regimen demonstrated an estimated effect for MR among patients who received RCHOP/CHOP of 0.79, 95% CI 0.58 – 1.06, and after RCVP/CVP of 0.65, 95% CI 0.42 – 1.02, with no statistically significant difference between these sub-groups (p = 0.50). Conclusions This is the first report of the collaborative group of maintenance therapy for FL patients. Based on individual patient data, this study demonstrated an improved OS with MR therapy for patients with FL. A sensitivity analysis with all 10 eligible trials supported these results. Results support the effectiveness of MR among patients after their 1st induction as well as after ≥2nd induction, and irrespective of inclusion of rituximab in induction, and the chemotherapy regimen. These results are consistent with those of the summary data meta-analysis that was previously published. The wider CI and lack of statistical significance within sub-groups could be attributed to the sample size. Figure: Pooled HR of death of MR compared to observation in patients with follicular lymphoma. In one study there were no events in one group and it is not represented in the forest plot. Figure:. Pooled HR of death of MR compared to observation in patients with follicular lymphoma. In one study there were no events in one group and it is not represented in the forest plot. Disclosures Vidal: Roche: unrestricted grant Other. Shpilberg:Roche: Consultancy, Research Funding.

Abstract Abstract 3637 Background: A recent large genome-wide association study confirmed the important role of the major histocompatibility complex in classical Hodgkin Lymphoma (cHL) susceptibility (rs6903608), but also identified significant associations with genetic variants outside of the HLA region at chromosomal regions 2p16.1 (REL, rs1432295), 8q24.21 (PVT1, rs2608053, rs2019960), and 10p14 (GATA3, rs501764, rs485411). These single nucleotide polymorphisms (SNPs) are localized in or near important genes involved in lymphoma pathogenesis: REL, which encodes for c-rel, is a member of NFkB pathway; PVT1, which is downstream of the MYC locus, encodes for non-coding microRNAs, some of which have oncogenic properties; and GATA3, which is a critical transcriptional factor for priming lymphoid T-cells for Th2 phenotype, is aberrantly expressed in HL. We investigated whether these six SNPs described in cHL etiology also influence the outcome of newly diagnosed cHL patients in two independent cohorts. Patients and Methods: The first cohort consisted of 342 cHL patients from the Groupe d'Etude des Lymphomes de l'Adulte (GELA) who were prospectively enrolled between 1998 and 2002. The second cohort consisted of 259 patients prospectively enrolled in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). DNA for genotyping was extracted from a blood samples collected at diagnosis, and genotyping was conducted using Taqman technology (ABI Prism 7000, Applied Biosystems) for HLA-DRA, PVT1, GATA3 and PCR-based restriction fragment length polymorphism for REL in GELA cohort and Illumina iSelect for the 6 SNPs in MER cohort. Both cohorts were followed for disease progression, re-treatment and death. In the GELA series, the median age was 32 years (range, 15–93), 57% were male, and 72% had an Ann Arbor stage I–II. Histology was nodular sclerosis in 84%, and 29% of available cases (n=40/140) were EBV positive. In the MER series, the median age was 38 years (range, 18–89), 53% were male, and 53% had an Ann Arbor stage I-II. Nodular sclerosis was the most common subtype (62%) and 23 of 115 available cases were EBV positive (20%). The International Prognostic Score (IPS) was 0–1 in 46% and 52% of GELA and MER patients, respectively. ABVD regimen was used for 60% and 88% of the GELA and MER patients, respectively. The 5-year progression free survival (PFS) was 83% and 75% in GELA and MER cohorts, respectively. We estimated the prognostic value of each individual SNP for PFS for allelic and genotypic (dominant and recessive) modes of transmission in each series. In order to increase power, we then performed a meta-analysis of the two studies. Results: Consistent with prior reports, the minor allele frequencies for REL (rs1432295), HLA-DRA (rs6903608), PVT1 (rs2608053), PVT1 (rs2019960), GATA3 (rs501764) and GATA3 (rs485411) were 0.44, 0.50, 0.43, 0.29, 0.22, 0.28 in the GELA and 0.46, 0.41, 0.46, 0.27, 0.25, 0.31 in the MER series, respectively. In GELA, GATA3 (rs501767) was associated with PFS in the ordinal model (HR=1.66, 95%CI 1.11–2.49, P=.01), while there were suggestive associations for PVT1 (rs2608053) (HR=1.76, 95%CI 0.95–3.26, P=.07) and REL (rs1432295) (HR=0.63, 95%CI 0.37–1.06, P=.08) in the dominant model. In the MER, PVT1 (rs2608053) was associated with PFS in the ordinal model (HR=1.42, 95%CI 0.99–2.03, P=.06), while there was a suggestive association with HLA-DRA (rs6903608) in the dominant model (HR=0.64, 95%CI 0.39–1.05, P=.08) for whole cohort but also for EBV negative cHL (HR=0.41, 95%CI 0.17–0.99, P=.05). In a meta-analysis of the two studies, PVT1 (rs2608053) was significantly associated with PFS in the ordinal (HR=1.34, 95%CI 1.04–1.73, P=.02) and the dominant (HR=1.88, 95%CI 1.20–2.95, P=.006) models. Further adjustment for IPS had minimal impact on these associations (ordinal HR=1.30, 95%CI 1.00–1.70; dominant HR=1.78, 95%CI 1.13–2.83). Patients with PVT1(rs2608053) GG, GA, AA genotypes had a 5-year PFS rate of 83%, 74% and 73% in GELA and 86%, 70%, and 73% in the MER, respectively. Conclusions: The non-HLA cHL susceptibility locus PVT1 (rs2608053) was associated with PFS in two independent cohorts of cHL cases, and after adjustment for the IPS. While associations with GATA3 and REL were only observed in one of the studies, their evaluation in other studies is warranted. Functional studies of PVT1 in cHL pathogenesis should be pursued. Disclosures: No relevant conflicts of interest to declare.

Abstract We previously identified five single nucleotide polymorphisms (SNPs) at four susceptibility loci for diffuse large B-cell lymphoma (DLBCL) in individuals of European ancestry through a large genome-wide association study (GWAS). To further elucidate genetic susceptibility to DLBCL, we sought to validate two loci at 3q13.33 and 3p24.1 that were suggestive in the original GWAS with additional genotyping. In the meta-analysis (5662 cases and 9237 controls) of the four original GWAS discovery scans and three replication studies, the 3q13.33 locus (rs9831894; minor allele frequency [MAF] = 0.40) was associated with DLBCL risk [odds ratio (OR) = 0.83, P = 3.62 × 10−13]. rs9831894 is in linkage disequilibrium (LD) with additional variants that are part of a super-enhancer that physically interacts with promoters of CD86 and ILDR1. In the meta-analysis (5510 cases and 12 817 controls) of the four GWAS discovery scans and four replication studies, the 3p24.1 locus (rs6773363; MAF = 0.45) was also associated with DLBCL risk (OR = 1.20, P = 2.31 × 10−12). This SNP is 29 426-bp upstream of the nearest gene EOMES and in LD with additional SNPs that are part of a highly lineage-specific and tumor-acquired super-enhancer that shows long-range interaction with AZI2 promoter. These loci provide additional evidence for the role of immune function in the etiology of DLBCL, the most common lymphoma subtype.

AbstractEpidemiologic studies show an increased risk of non‐Hodgkin lymphoma (NHL) in patients with autoimmune disease (AD), due to a combination of shared environmental factors and/or genetic factors, or a causative cascade: chronic inflammation/antigen‐stimulation in one disease leads to another. Here we assess shared genetic risk in genome‐wide‐association‐studies (GWAS).Secondary analysis of GWAS of NHL subtypes (chronic lymphocytic leukemia, diffuse large B‐cell lymphoma, follicular lymphoma, and marginal zone lymphoma) and ADs (rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis). Shared genetic risk was assessed by (a) description of regional genetic of overlap, (b) polygenic risk score (PRS), (c)"diseasome", (d)meta‐analysis.Descriptive analysis revealed few shared genetic factors between each AD and each NHL subtype. The PRS of ADs were not increased in NHL patients (nor vice versa). In the diseasome, NHLs shared more genetic etiology with ADs than solid cancers (p = .0041). A meta‐analysis (combing AD with NHL) implicated genes of apoptosis and telomere length.This GWAS‐based analysis four NHL subtypes and three ADs revealed few weakly‐associated shared loci, explaining little total risk. This suggests common genetic variation, as assessed by GWAS in these sample sizes, may not be the primary explanation for the link between these ADs and NHLs.