Ziepert, Marita

Abstract Disease progression after frontline therapy for Diffuse large B‐cell lymphoma (DLBCL) is a clinically significant event. Patients who experience early progression or have refractory disease have especially poor outcomes. Simple, clinically applicable prognostic tools are needed for selecting patients for consideration for novel therapies and prognostication in the relapsed/refractory (R/R) setting. Model building was performed in patients from the Surrogate endpoints in aggressive lymphoma (SEAL) consortium with disease progression after frontline immunochemotherapy. The primary endpoint was overall survival (OS) measured from date of progression. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish National Lymphoma Register (LYFO) cohorts. Model performance was assessed using time‐dependent concordance indices (c‐statistic) and calibration with metrics evaluated at 2 years from progression. Note, 1234 of 5112 patients treated with frontline immunochemotherapy in the SEAL consortium developed progressive disease. Time to progression on immunochemotherapy and age at progression were strongly associated with post‐progression OS (both p < 0.001). A prognostic model was developed incorporating spline fit for both variables. The model had good concordance in the discovery (0.67) and validation sets (LYFO c = 0.64, MER c = 0.68) with generally good calibration. Time to progression on frontline therapy is strongly associated with post‐progression OS in DLBCL. We developed and validated a simple to apply clinical prognostic tool in the R/R setting. The useful prediction of expected outcomes in R/R DLBCL and can inform treatment decisions such as considerations for CAR‐T therapy as well as trial designs. The model is available in smartphone‐based point of care applications.

Abstract Great heterogeneity in survival exists for patients newly diagnosed with diffuse large B-cell lymphoma (DLBCL). Three scoring systems incorporating 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 evaluated 2124 DLBCL patients treated from 1998 to 2009 with frontline rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; or variant) across 7 multicenter randomized clinical trials to determine which scoring system best discriminates overall survival (OS). Median age was 63 years, and 56% of patients were male. Five-year OS estimates ranged from 54% to 88%, from 61% to 93%, and from 49% to 92% using the IPI, R-IPI, and NCCN-IPI, respectively. The NCCN-IPI had the greatest absolute difference in OS estimates between the highest- and lowest-risk groups and best discriminated OS (concordance index = 0.632 vs 0.626 [IPI] vs 0.590 [R-IPI]). For each given IPI risk category, NCCN-IPI risk categories were significantly associated with OS (P ≤ .01); the reverse was not true, and the IPI did not provide additional significant prognostic information within all NCCN-IPI risk categories. Collectively, the NCCN-IPI outperformed the IPI and R-IPI. Patients with low-risk NCCN-IPI had favorable survival outcomes with little room for further improvement. In the rituximab era, none of the clinical risk scores identified a patient subgroup with long-term survival clearly &lt;50%. Integrating molecular features of the tumor and microenvironment into the NCCN-IPI or IPI might better characterize a high-risk group for which novel treatment approaches are most needed.

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]

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value&lt;0.0001) with OS24 ranging from 18% in patients with TTP &lt;6 months compared to 69% in patients with TTP &gt; 36 months (Figure A). Additionally, age at PD (spline p-value&lt;0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles: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; 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; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Abstract Background We have previously reported and validated that the E-IPI, provided better discrimination of overall survival (OS) than the IPI for DLBCL patients > 60 years of age treated with R-CHOP (Advani et al., BJH 2010 and 12-ICML 2013 Abstract 222). Recent reports suggest that males > 60 years treated with R-CHOP have worse outcomes likely related to differences in rituximab clearance (Muller et al., Blood 2012). In this study, we explored if the addition of male sex to the E-IPI further improved risk stratification. Methods DLBCL patients > 60 years treated with R-CHOP on the E4494, RICOVER-60 and GELA 98-5 trials were included. A multivariate analysis was performed to assess whether male sex is an independent prognostic factor for OS in addition to the five E-IPI risk factors, E-IPI score, and E-IPI risk group. Male sex (S) was added as an additional risk factor to the E-IPI (S-EIPI, 0-6) and patients regrouped according to the number of risk factors: low (L)=0-1, low intermediate (LI)=2, high intermediate (HI)=3 and high (H)=4-6 based on the observed OS using Kaplan-Meier curves. C-statistic was used to compare the discrimination ability. The 5 year OS was estimated using the Kaplan-Meier method. Results 1079 patients (E4494, n=267; RICOVER-60, n=610; GELA 98-5, n=202) with a median follow-up of 8.9 years were included. In multivariate analyses, male sex was a significant independent predictor for OS adjusting for all five E-IPI factors, (HR 1.5, p < 0.0001), E-IPI score (HR 1.42, p < 0.001), and E-IPI risk group (HR 1.42, p < 0.001) (Table 1). C-statistic was 0.66 for S-EIPI and 0.64 for EIPI. Incorporation of sex into the E-IPI shifted 35% patients to higher risk groups, including 110 patients into the highest risk group (Table 2). The estimated 5 year OS for the S-EIPI L, LI, HI, and H risk groups was 87%, 70%, 58%, and 40%, respectively (Table 3). Compared to E-IPI, the 95% C.I. did not overlap among S-EIPI risk groups. Conclusion For DLBCL patients >60 years treated with R-CHOP, our results suggest the addition of male sex to the E-IPI may improve categorization of patients into well-defined clinically relevant risk groups. Patients with low risk S-EIPI have an excellent outcome (5 year OS 87%). Patients with high risk S-EIPI have a 5 year OS of only 40% and therapies beyond standard R-CHOP need to be explored. Disclosures: Horning: Genentech: Employment; Roche: Equity Ownership.

Abstract Background: Patients (pts) with DLBCL who receive first-line anthracycline based immunochemotherapy and remain in remission at 2 years have excellent outcomes with overall survival (OS) in one study that was comparable to the age and sex matched general population in cohorts from the US and France (Maurer et al, JCO 2014). To evaluate whether these findings are robust and generalizable, we assessed OS stratified by progression-free survival at 24 months (PFS24) using individual patient data from pts with DLBCL enrolled in 14 multi-center, international randomized clinical trials as part of the SEAL consortium. Methods: Central data alignment was performed by the SEAL statistics and data center. All enrolled patients treated with rituximab-containing anthracycline-based immunochemotherapy as part of initial induction therapy were included in this analysis using an intent-to-treat approach. PFS was defined as time from initiation of first-line therapy to progression or death. PFS24 was defined as being alive and progression-free 24 months (731 days) after initiation of therapy. Overall survival from PFS24 was defined as time from achieving PFS24 until death due to any cause. OS was compared to each patient's age-, sex-, and country- matched general population using expected survival and standardized mortality ratios (SMR). Country-level population rate tables were obtained from www.mortality.org where available. A per-study average rate table was used when country information was unavailable. OS beyond 9 years from treatment initiation was only available the time of analysis from two trials (13% of cohort) and thus follow-up from PFS24 was restricted to the first 7 years after PFS24 for this analysis. Results: Fourteen studies with 7975 patients were included in the SEAL database. 5853 (73%) received rituximab as part of induction therapy and used in this analysis. Median age was 62 years (range 18-92) with 56% >60 years, 54% were male, 57% had elevated LDH, 63% had stage III/IV disease, 13% had ECOG PS 2-4, and IPI was 0-1 in 36%, 2 in 25%, 3 in 23%, and 4-5 in 16%. At a median follow-up of 4.4 years, 1337 (23%) had disease progression, 1489 patients (25%) had died, and 5101 patients were evaluable for PFS24. 1423 (28%) of evaluable patients failed to achieve PFS24. These patients had poor outcomes after progression with median OS of 7.2 months (95% CI 6.8-8.1), 5-year OS of 19%, and SMR of 32.1 (95% CI: 30.0-34.4). Outcome was excellent (median unreached) in the 3678 patients who were progression-free at 24 months, with subsequent OS after achieving PFS24 approaching the age and sex matched general population (Figure). The observed vs. expected OS at 3, 5, and 7 years after achieving PFS24 was 93.1% vs. 94.4%, 87.6% vs. 89.5%, and 80.0% vs. 83.7%, respectively, the SMR was 1.22 (95% CI: 1.09-1.37). In subset analyses examining pts achieving PFS24, adverse prognostic characteristics at baseline (elevated LDH, advanced stage, ECOG PS 2-4, high IPI) were associated with greater decrement in OS from the general population based expected survival (Table). Conclusions: PFS24 from the start of initial therapy stratifies DLBCL pts into populations with distinct outcomes. DLBCL pts treated with rituximab containing anthracycline-based immunotherapy on large randomized clinical trials who are alive without progression at 24 months from start of initial therapy have excellent outcomes with survival that is marginally lower but clinically indistinguishable from the age-, sex-, and country-matched background population for at least 7 years after achieving PFS24. Further follow-up time will be needed to assess any long-term impact of late relapse and survivorship issues in this population. Table Table. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Cunningham:Amgen: Research Funding; Astra-Zeneca: Research Funding; Bayer: Research Funding; Celgene: Research Funding; Merrimack: Research Funding; Medimmune: Research Funding. Seymour:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel support, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Roche France: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees. Merli:Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees. Herbrecht:Servier: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Flowers:Gilead: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Acerta: Research Funding; ECOG: Research Funding; Mayo Clinic: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Millenium/Takeda: Research Funding; Infinity: Research Funding; TG Therapeutics: Research Funding; Genentech: Consultancy, Research Funding; NIH: Research Funding; AbbVie: Research Funding. Ou:Mayo Clinic: Employment. Sargent:Celgene: Research Funding. Coiffier:MorphoSys: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Background OSis the gold standard primary efficacy endpoint for evaluating treatment strategies for DLBCL. Because current standard of care first-line regimens with rituximab plus chemotherapy produce median OS > 8 years, an endpoint assessed earlier would accelerate the evaluation of new therapies and potentially allow patients (pts) to more rapidly benefit from study results. The SEAL group formally evaluated PFS as a surrogate endpoint for OS in first-line therapy for DLBCL. Methods Through a systematic literature search and input from lymphoma experts on the SEAL committee, we identified multicenter, randomized controlled clinical trials that evaluated active treatment and enrolled at least 100 pts with previously untreated DLBCL or aggressive non Hodgkin lymphoma by WHO/REAL classification, published after January 1, 1995. Studies which enrolled only early stage (I or II) pts and studies that could not provide individual pt data (IPD) were excluded from surrogacy analysis. Potential surrogate candidates for OS included: 1) PFS, defined as the time from initiation of induction treatment to progressive disease, relapse or death due to any cause, and 2) PFS rate at 24 months after initiation of induction treatment (PFS24). These endpoints were derived consistently for all studies based on IPD. Surrogacy was evaluated by the correlation between surrogate candidates and OS. Pt-level correlation was measured by the rank correlation coefficient (ρ) and global odds ratio for PFS and PFS24, respectively, estimated by the Copula model. Trial-level correlation was evaluated by the correlation between trial-specific treatment effects on surrogate candidates and OS with two commonly used trial-level R2s: R2WLS (Sargent et al JCO 2005) and R2Copula (Burzykowski et al J R Stat Soc C 2001 and Burzykowski et al J R Stat Soc A 2004). For the R2 measures, a value closer to 1 indicates a stronger correlation. We pre-specified that if either trial-level R2 was >=0.80 and its 95% confidence interval (CI) excluded 0.60, the candidate would meet criteria for surrogacy. Results Thirteen of 14 studies collected to date in the SEAL database met all inclusion criteria. IPD for 7507 pts (median age 63 years, 54% male, 46% ECOG performance status 0, 8% IPI 0, 50% IPI 1-2, 64% stage III/IV, median follow-up time of 52 months) were analyzed. Among 13 studies, one tested maintenance treatment only and two tested both induction and maintenance regimens. One study included four treatment arms. Thus at the trial level, 17 two-arm comparisons were formed: 14 comparisons comparing induction regimens and 3 comparing maintenance; 15 comparisons involved rituximab. Longer PFS was highly correlated with longer OS at the pt level (ρ= 0.85, 95% CI 0.84-0.86), as was PFS24 (Global odds ratio = 61.1, 95% CI 52.6-69.6). The Table shows trial-level correlations, and the Figure shows trial-level association between estimated treatment effects (log(HR) [hazard ratio]) on PFS and OS. The trial-level correlation between PFS and OS was strong and met the pre-specified surrogacy criteria, with R2WLS of 0.83 (95% CI 0.58-0.94) and R2Copula of 0.85 (95% CI 0.72-0.99). Sensitivity analyses including only comparisons including rituximab or only induction regimens showed consistent results. The overall trial-level correlation between PFS24 and OS was reduced slightly, with R2WLS of 0.77 (95% CI 0.49-0.92) and R2Copula of 0.78 (95% CI 0.59-0.96). Performance of PFS24 was improved when restricting analysis to only including induction studies (see table). Conclusion This large IPD pooled-analysis demonstrates that PFS duration met the pre-specified criteria as a robust surrogate endpoint for OS, and can be considered as a surrogate endpoint for OS for frontline DLBCL trials evaluating immunochemotherapy. The earlier endpoint of PFS24 showed promise as a potential surrogate and further validation is warranted. Figure Figure. Disclosures Shi: Mayo Clinic: Employment. Flowers:Millenium/Takeda: Research Funding; TG Therapeutics: Research Funding; Pharmacyclics, LLC, an AbbVie Company: Research Funding; Gilead: Consultancy, Research Funding; NIH: Research Funding; Mayo Clinic: Research Funding; ECOG: Research Funding; Acerta: Research Funding; Infinity: Research Funding; AbbVie: Research Funding; Roche: Consultancy, Research Funding; Genentech: Consultancy, Research Funding. Ou:Mayo Clinic: Employment. Cunningham:Merrimack: Research Funding; Celgene: Research Funding; Bayer: Research Funding; Astra-Zeneca: Research Funding; Amgen: Research Funding; Medimmune: Research Funding. Pfreundschuh:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Spectrum: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Seymour:AbbVie Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Jaeger:Roche: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Haioun:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sandoz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghesquieres:Mundipharma: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche France: Research Funding. Merli:Teva Pharmaceuticals Industries: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees. Herbrecht:Cell Therapeutics, Inc: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees. Flament:Celgene: Employment, Equity Ownership. Fu:Celgene: Employment, Equity Ownership. Coiffier:MorphoSys: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Sargent:Celgene: Research Funding.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease characterized by multiple low-frequency alterations including somatic mutations, copy number alterations (CNAs) and chromosomal rearrangements. We sought to identify previously unrecognized low-frequency genetic events, integrate recurrent alterations into comprehensive signatures and associate these signatures with clinical parameters. For these reasons, our multi-institutional international group assembled a cohort of 304 primary DLBCLs from newly diagnosed patients, 87% of whom were uniformly treated with state-of-the-art therapy (rituximab-containing CHOP regimen) and had long term followup. Tumors were subjected to whole exome sequencing with an extended bait set that included custom probes designed to capture recurrent chromosomal rearrangements. In this cohort, 47% of samples had available transcriptional profiling and assignment to associated disease subtypes. Analytical pipelines developed at the Broad Institute were used to detect mutations (MuTect), CNAs (Recapseq+Allelic Capseq) and chromosomal rearrangements (dRanger+Breakpointer) and assess clonality (Absolute). To analyze formalin-fixed paraffin-embedded tumors without paired normals we developed a method which utilized 8334 unrelated normal samples to stringently filter recurrent germline events and artifacts. Significant mutational drivers were identified using the MutSig2CV algorithm and recurrent CNAs were assessed with GISTIC2.0. In addition, we utilized a recently developed algorithm, CLUMPS2, to prioritize somatic mutations which cluster in 3-dimensional protein structure. With this approach, we identified > 90 recurrently mutated genes, 34 focal amplifications and 41 focal deletions, 20 arm-level events and > 200 chromosomal rearrangements in the DLBCL series. Of note, 33% of the mutational drivers were also perturbed by chromosomal rearrangements or CNAs, underscoring the importance of a comprehensive genetic analysis. In the large DLBCL series, we identified several previously unrecognized but potentially targetable alterations including mutations in NOTCH2 (8%) and TET2 (5%). The majority of identified chromosomal rearrangements involved translocations of potent regulatory regions to intact gene coding sequences. The most frequently rearrangements involved Ig regulatory elements which were translocated to BCL2, MYC, BCL6 and several additional genes with known roles in germinal center B-cell biology. After identifying recurrent somatic mutations, CNAs and chromosomal rearrangements, we performed hierarchical clustering and identified subsets of DLBCLs with comprehensive signatures comprised of specific alterations. A large subset of tumors shared recurrent alterations previously associated with follicular lymphoma including mutations of chromatin modifiers such as CREBBP, MLL2, and EZH2 in association with alterations of TNFRSF14 and GNA13 and translocations of BCL2. This cluster was enriched in GCB-type DLBCLs and contained a subset with select genetic alterations associated with an unfavorable outcome. An additional cohort of tumors was characterized by alterations perturbing B-cell differentiation including recurrent BCL6 translocations or alterations of PRDM1. A subset of these DLBCLs had alterations of NOTCH2 and additional pathway components or mutations of MYD88 in association with TNFAIP3, CD70 and EBF1, a master regulator of B-cell differentiation. An additional group of DLBCLs exhibited frequent MYD88 mutations in association with alterations of CD79B, PIM1, TBL1XR1 and ETV6 and BCL2 copy gain; these tumors were highly enriched for ABC-type DLBCLs. This coordinate signature and additional alterations of p53 pathway components were associated with outcome. We explored bases for the identified genetic alterations in DLBCL by performing an in silico mutational signature analysis. The most frequent mutational signatures were those of spontaneous deamination (aging) and AID with rare cases of microsatellite instability. We also assessed the clonality of identified genetic features to define cancer cell fraction and establish the timing of specific genetic events. The comprehensive genetic signatures of clinically annotated DLBCLs provide new insights regarding approaches to targeted therapy. Disclosures Link: Kite Pharma: Research Funding; Genentech: Consultancy, Research Funding. Rodig:Perkin Elmer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Pfreundschuh:Boehringer Ingelheim, Celegene, Roche, Spectrum: Other: Advisory board; Roche: Honoraria; Amgen, Roche, Spectrum: Research Funding. Shipp:Gilead: Consultancy; Sanofi: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding.