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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 While the IPI is associated with outcome in the IC era, the original survival estimates for the IPI risk groups are outdated.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 Abstract 700 Studies conducted over the last 10 years suggest that 3–5% of the general population over age 40 harbor a clonal population of lymphocytes of CLL phenotype when evaluated using sensitive flow cytometry assays (Rawstron Blood 100:635, 2002; Ghia Blood 103:2337, 2003). This discovery led to creation of the new diagnostic entity, monoclonal B-cell lymphocytosis (MBL; Marti BJH 130:325, 2005), which is considered the precursor state to chronic lymphocytic leukemia (CLL) (Landgren NEJM 360:659, 2009). The current diagnostic criteria for CLL-like MBL specify patients have a clonal B-cell population with appropriate phenotype, an absolute B-cell count <5 × 10^9/L, and no characteristics of a lymphoproliferative disorder (e.g. lymphadenopathy, organomegaly, B-symptoms) while individuals with a clone of CLL phenotype and B-cell count >5 × 10^9/L or who have lymphadenopathy are classified as having CLL (Hallek Blood 111:5446, 2008). The natural history of MBL appears to differ based on the absolute B-cell count. From an epidemiologic perspective, the majority of individuals with MBL have very small clonal lymphocyte populations, B-cell counts within the normal range, and appear to be at low likelihood of developing CLL (Rawstron ASH 2009). Such individuals with “low count” MBL do not appear to be at increased risk of death (Rawstron ASH 2009). From a clinical perspective, most patients with MBL are identified due to lymphocytosis and have B-cell counts between 3–5 × 10^9/L. While such individuals with “high count” MBL progress to require treatment for CLL at a rate of ~1.5% per year (Shanafelt JCO 27:3959, 2009), whether their survival differs from that of age- and sex-matched individuals is unknown. Conceptually, patients with the precursor state to malignancy should have a risk of death similar to those without the precursor state unless they progress to develop malignancy. To determine the impact of clinically “high count” MBL on survival, we compared the survival of 300 patients with clinically recognized MBL to the age- and sex-matched population. In addition, to explore whether the current B-cell threshold used to segregate MBL from CLL optimally classifies risk, we also evaluated the survival of 94 patients with newly diagnosed Rai 0 CLL and a B-cell count between 5–10 × 10^9/L and 218 newly diagnosed Rai 0 CLL patients with an ALC > 10 × 10^9/L to the age- and sex-matched population. Although it appeared slightly lower, the survival of patients with clinical recognized MBL (n=300, median age 68.6) was not statistically different than age- and sex-matched individuals (p=0.14; Figure 1A). In contrast, the survival of patients with newly diagnosed Rai 0 CLL and a B-cell count between 5–10 × 10^9/L (p=0.03) and newly diagnosed Rai 0 CLL patients with an ALC > 10 × 10^9/L (p=0.00018) were both significantly shorter than expected for the age- and sex-matched population. While these data are consistent with the designation of clinically recognized MBL as a premalignant condition, a more nuanced story emerged when the individuals with clinically recognized MBL were stratified by the CD38 status of the B-cell clone. Although the survival of individuals with a CD38- clone (n=213; 78%) was not different than age- and sex-matched individuals (p=0.14), the survival for those with a CD38+ clone (n=59; 22%) was substantially shorter than the age- and sex-matched population (p=0.0002; Figure 1B). Collectively, these data provide additional clinical evidence in support of the 5 × 10^9/L B-cell threshold to distinguish between MBL and CLL as patients with a B-cell count between 5–10 × 10^9/L or higher are at increased risk of death compared to the age- and sex-matched population. However they also suggest that, in addition to absolute B-cell count, biologic characteristics of the B-cell clone influence survival in patients with clinically recognized MBL. Additional studies are needed to determine whether such characteristics should be considered when classifying individuals with B-cell clones <5 × 10^9/L as having a premalignant or malignant condition. Figure 1A: Survival of patients with clinically recognized MBL (n=300) compared to the age- and sex-matched general population Figure 1A:. Survival of patients with clinically recognized MBL (n=300) compared to the age- and sex-matched general population Figure 1B: Survival of patients with clinically recognized MBL by CD38 status compared to the age- and sex-matched general population. CD38- p=0.14, CD38+ p=0.0002 Figure 1B:. Survival of patients with clinically recognized MBL by CD38 status compared to the age- and sex-matched general population. CD38- p=0.14, CD38+ p=0.0002 Disclosures: Shanafelt: Celgene: Research Funding; Hospira: Research Funding; Genentech: Research Funding. Off Label Use: Lenalidomide. Kay:Celgene: Research Funding; Hospira: Research Funding; Genentech: Research Funding. Zent:Genzyme: Research Funding; Genentech: Research Funding; Novartis: Research Funding; G.S.K.: Research Funding.

Abstract Abstract 991 Background: The outcome of patients with DLBCL has significantly improved with the addition of rituximab to CHOP chemotherapy. While standard clinical prognostic factors such as those included in the International Prognostic Index (IPI) still predict outcome in DLBCL in R-CHOP treated patients, predicting the outcome of patients might be further refined using biological factors. We therefore tested whether pretreatment serum cytokines, that measure the biological activity of the tumor as well as the host response, could provide additional prognostic information in DLBCL patients. Methods: Newly diagnosed DLBCL patients were prospectively enrolled in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER). Serum cytokines were measured from pre-treatment blood draws using a multiplex ELISA (Upstate Cell Signaling Solutions, Lake Placid, NY). Thirty cytokines, including pro-inflammatory, Th1 and Th2 associated cytokines, were analyzed using the Luminex-100 system Version 1.7 (Luminex, Austin, TX). Data analysis was performed using the MasterPlex QT 1.0 system (MiraiBio, Alameda, CA). The assay was also performed on research serum draws from 400 non-lymphoma epidemiology controls enrolled on a corresponding lymphoma etiology case-control study. The upper limit of normal for each cytokine was defined as the 95th percentile from the control distribution. Clinical data was abstracted using a standard protocol for the MER and patients were followed systematically for event-free (progression, retreatment, or death due to any cause) and overall survival (EFS and OS, respectively). Cox proportional hazards models were used to evaluate the association between cytokines and outcome. Principal components analysis (PCA) was performed on the combined DLBCL and control cytokine dataset, agnostic to clinical characteristics and outcome, to identify cytokine profiles. Results: 206 DLBCL patients diagnosed between September 2002 and February 2008 with available serum were included. The median age was 64 years (range 20–92) and 52% were male. At a medium follow-up of 47 months (range 5–94), 75 patients (36%) had relapsed and 63 patients had died (31%). A cytokine profile was determined based on the first two principal components (PC) of the PCA analyses with PC1 corresponding to a general (host) cytokine elevation (also seen in normal controls) and PC2 corresponding to an increase predominantly in cytokines that were commonly elevated in DLBCL patients. 75 DLBCL patients (36%) had a normal cytokine profile similar to the general population of controls; 114 DLBCL patients (55%) had elevation in tumor specific cytokines (CXCL10, CXCL9, IL-2R, IL-12, IL-8, and IL-10) and 17 (8%) had a general cytokine elevation. Patients with any elevation of cytokines (tumor specific or general elevation) had a significantly worse event-free (HR=3.96, 95% CI: 2.13–7.37, p<0.00001) and overall survival (HR=3.75, 95% CI: 1.99–7.09, p<0.0001) than those with a normal cytokine profile (see figure). While the cytokine elevations were associated with advanced stage, poor performance score and elevated LDH, they were not associated with advanced age or extranodal disease and the results remained significant after adjustment for the IPI and treatment in the Cox model (EFS HR=3.11, 95% CI: 1.66–5.85, p<0.0001; OS HR=2.93, 95% CI: 1.53–5.63, p=0.001). Elevated serum cytokines also identified patients who were unlikely to complete initial therapy or who relapsed early. In low-risk patients (IPI 0–2), only 3% of patients with normal serum cytokines relapsed in the first 12 months compared to 23% of patients with elevated cytokines. Similarly in the high-risk group (IPI 3–5), none of the patients with normal cytokines relapsed in the first year compared to 43% with elevated cytokines. Conclusion: Elevated pretreatment serum cytokines, predominantly related to the malignancy, are associated with an increased likelihood of disease relapse and an inferior survival in patients with DLBCL. Furthermore, the pretreatment cytokine profile may also identify DLBCL patients at high risk for early disease relapse. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 3881 Background: Chronic lymphocytic leukemia (CLL) is largely incurable neoplasm of the blood and has one of the highest familial risks of disease among cancers. Monoclonal B-cell lymphocytosis (MBL) with a CLL immunophenotypic profile (i.e., CLL-like MBL) is a precursor state to CLL. In the general adult population, the prevalence rate of MBL is 5–9% and increases with age. This is in contrast to the 13–18% rate of MBL in first-degree relatives of CLL patients from high-risk families (defined here as families with at least two confirmed living cases of CLL). Few longitudinal studies of MBL incidence exists. One study evaluated MBL status over time in a cohort of individuals with MBL from a rural valley in Northern Italy. Our work herein will be the first to evaluate the status of MBL over time in relatives of CLL patients from high-risk families. Methods: Using a cohort of 41 high-risk CLL families, we evaluated the MBL status and clonal blood B-cell counts in 94 relatives of CLL patients at two time points at least 1 year a part. High sensitivity, multi-color flow cytometry was performed on fresh or previously frozen blood samples in three laboratories: Duke University, the US Food and Drug Administration, and Mayo Clinic. The number of cell events collected for flow cytometry evaluation varied from 1 to 5 x105 cells. To assure concordance across flow cytometry laboratories, 15 samples were evaluated at multiple labs. The concordance of MBL calling was a 100%. We defined three monotypic B-cell subsets as CLL-like MBL (CD5+ CD20dim), atypical MBL (CD5+ CD20+), and non-CLL like MBL (CD5−CD20+). Results: Of the 94 relatives, the median age at consent was 57 years (range 42–93) and 39% were male. At the initial MBL screening, 70 (74%) relatives had normal immunophenotype (NIPT) and 24 (26%) had MBL (1 atypical MBL, 3 non-CLL like MBL, and 20 CLL-like MBL). Seven of the 24 individuals with MBL had monoclonal B-cell counts available at baseline screening; median = 147.4 cells/uL (range 22.7–1663). The 94 relatives underwent repeat evaluation with a median follow up time of 1.8 years (range 1–6). At the second evaluation, we found an increase in the prevalence of MBL, with 11 of the 70 (12%) initially NIPT relatives developing a new MBL clone (10 CLL-like, 1 non-CLL like). These 11 incident MBL cases were from 9 families with 2 families each having 2 relatives changing MBL status. The median number of monoclonal B-cells in these 11 incident MBL cases was 0.58 cells/uL (range 0.38–60.3). The 23 out of the 24 relatives with baseline MBL (20 CLL-like MBL and 3 non-CLL like MBL) had a persistent MBL clone. Only 6 of these 23 individuals had monoclonal B-cell counts available at both time points, and among these 6, the clonal B-cell counts decreased at follow-up screening; median difference −64.1 cells/uL (range −1204.3– 10.5). The second evaluation of the initial atypical MBL relative had too few clonal cells for flow assessment. Conclusion: We found that our CLL-like MBL clones persisted over the period of observation in relatives from high-risk CLL families; this finding is similar to that previously reported using a population-based study conducted in Northern Italy. Notably, 12% of relatives from high-risk CLL families developed incident MBL during just 2 years of follow up. Although these MBL clones are all low count (< 1500 cells/uL), these data provide further evidence that MBL is a marker of inherited predisposition to CLL. It will be important to see how our rate of change of MBL status compares to that of the general population as we continue to follow our cohort of CLL families. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 3903 INTRODUCTION: Monoclonal B-Cell Lymphocytosis is an asymptomatic hematologic condition characterized by the presence of small populations of clonal B-cells in the blood or bone marrow occurring in 3–5% of the general population over age 40. Although MBL is considered the precursor state to CLL and other sub-types of indolent non-Hodgkin Lymphoma, most individuals with MBL will not develop a B-cell malignancy. It is unknown, however, whether individuals with MBL may be at risk for other adverse outcomes associated with CLL, such as increased risk of infection. To explore this aspect, we evaluated the risk of hospitalization for infection among patients with clinically recognized MBL and compared this to a cohort of general medicine patients and a cohort of CLL patients. METHODS: The Mayo Clinic Rochester (MCR), a tertiary referral center in rural southeastern Minnesota, is also the primary hematology center for southeastern Minnesota, northern Iowa, and western Wisconsin. There are no metropolitan areas or hematology specialty centers within a 50 mile radius. To explore the natural history of infection in a local, community dwelling cohort of individuals with clinically identified, CLL-phenotype MBL, we used the Mayo Clinic CLL database to identify all patients with newly diagnosed MBL seen in the MCR Hematology Division between January 1999 and December 2009. Analysis was limited to MBL cases who resided within 50 miles of MCR. The comparison cohort consisted of 689 adult patients also resided within 50 miles of MCR who were seen for a general medical examination between September 2002 and December 2009 and who were enrolled as controls in a case-control study of non-Hodgkin lymphoma. Rates of infection among individuals with MBL were also compared to a cohort of 174 patients with newly diagnosed CLL diagnosed in the same time interval and who resided within 50 miles of MCR. All hospitalizations in these 3 groups were audited to document hospitalization with infection including cross-referenced with the electronic Mayo Clinic infection database, which contains all culture results obtained from hospitalized patients at MCR. Serologies and viral assays (e.g. CMV viral load) were also reviewed. Patients whose cultures were negative but who were given a clinical diagnosis of infection (e.g. pneumonia) and who were treated with a full course of antibiotics were considered to have a culture negative infection. Among the cases, only hospitalizations after CLL or MBL diagnosis were considered. RESULTS: In May 2010, there were 524 individuals in the Mayo Clinic MBL/CLL Database with the diagnosis of CLL-phenotype MBL made between January 1999 – December 2009. Of these, 154 (29%) patients resided within a 50 mile radius of Mayo Clinic and were considered a local, community-based cohort of MBL patients. Median age at MBL diagnosis for these 154 patients was 70 years (range 45–94). After median follow-up of 4.2 years (range 0.3–10.6 years), 25 (16%) individuals with MBL were hospitalized with infection as compared to 20/689 (3%) individuals in the control population (odds ratio MBL relative to control=6.5; p<0.001) and 32/174 (18%) in the cohort of patients with newly diagnosed CLL (OR MBL relative to CLL=0.9; p=0.61) (Figure 1). The 25 MBL patients hospitalized for infection were hospitalized a total of 39 times (median 1 hospitalization for infection per individual). A specific infectious organism was identified by cultures or serologies in 8/39 (21%) hospitalizations among individuals with MBL, 10/27 (37%) hospitalizations for infection among controls, and 60 (63%) of the 89 hospitalizations for infection in CLL cases. Finally we performed a pooled multivariable analysis of all patients (n=1017) adjusting for age, sex, presence of MBL, diagnosis of CLL, and whether patients with CLL or MBL received treatment for CLL during follow-up. All factors were independently associated with risk of hospitalization for infection [Age (per year, OR=1.02, p=0.02), sex (male, OR=2.6, p=0.001), presence of MBL (OR=3.3, p<0.001), diagnosis of CLL (OR=3.0, p=0.002), and treatment for CLL (OR=3.3, p<0.001)]. CONCLUSIONS: In this cohort study, patients with newly diagnosed clinical-MBL had a 6.5 fold risk of hospitalization for infection relative to the control cohort. MBL was an independent risk factor for hospitalization for infection after controlling for age, sex, and treatment. Disclosures: Zent: GlaxoSmithKline: Research Funding; Genentech: Research Funding; Genzyme: Research Funding. Kay:Biothera: Research Funding; Clegene: Research Funding; Cephalon: Research Funding; Genentech: Research Funding; Glaxo Smith Kline: Research Funding; Hospira: Research Funding; Novartis: Research Funding; Supergen: Research Funding; Calistoga: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Emergent Biosolutions (Formerly Trubion): Membership on an entity's Board of Directors or advisory committees.

Abstract Gene expression profiling has shown that diffuse large B-cell lymphoma (DLBCL) clusters into three major subtypes based on similarity in expression patterns to their cell of origin (COO): germinal center B-cell-like (GCB-DLBCL), activated B-cell-like DLBCL (ABC-DBLCL) and primary mediastinal B-cell lymphoma (PMBCL). These subtypes of DLBCLs are associated with distinctly different overall survival rates after standard immunochemotherapy. However, clinical and prognostic heterogeneity remains within COO subsets and strategies are needed to further stratify patients to identify and target high-risk subsets. A comprehensive genomic analysis of COO on a clinically defined set of DLBCL cases has not been performed and the aim of this study was to use whole-exome sequencing (WES) data from 58 paired tumor-normal DLBCL samples to assess association of known DLBCL genomic alterations with cell COO as well as for identification of novel and relevant genetic biomarkers. To investigate genomic alterations associated with DLBCL subsets, we analyzed WES and genome wide copy number data from 58 paired tumor-normal DLBCL tumors. Gene expression profiling or Hans classification was performed to determine DLBCL COO subtype; 31 patients were classified as GCB and 27 as non-GCB. The WES data were used to 1) assess the association of known DLBCL genomic alterations with COO, and 2) identify novel alternations associated with COO. Statistical analysis was performed and the data were ranked by significance (p≤ 0.05) within each DLBCL subtype. In total, 45 genomic abnormalities were analyzed for their association with either GCB, non-GCB or both. Mutations in CREBBP, EZH2, MEF2B, FOXO1 and REL have also been reported as GCB driver mutations and we observed GCB patients with these mutations, but the mutation clustering was not wholly associated with GCB. MYC double-hits were exclusively found in the GCB-subtype group. For the non-GCB cases we found that mutations in MAP2K3 and MYD88 were significantly associated with this subtype(p< 0.05). In addition to mutational patterns, we identified several copy number alterations (CNA) across both groups. Chromosomal losses in GCB patients were found at chromosomes 10q11.21-10q24.23, 4q12-4q35.2, 3q12.1-3q29, 4p12-4p16.3, 10p11.21-10p15.3, and 14q11.2-14q24.3 whereas gains were localized to 7q11.1-7q36.3, 7p11.2-7p22.3, and 1q21-1q32.1 (p < 0.05). No CNA was observed to directly associate with non-GCB patients, however, a loss at 9p21 and gains at 9q24.1 and 18q21.33 trended with the non-GCB subtype, supporting previous reports. Loss at 10q23.31 or a gain in 2p13-2p12 have been reported as being specific for GCB and our data confirmed the association of 10q23.31 with GCB while a gain at 2p13-2p12 (REL)was found in both subtypes. To further understand genomic differences between DLBCL subtypes, we evaluated the relative percentage of each genomic feature. 18/45 (40%) were only observed in GCB patients whereas 2/45 (5%) were specific to the non-GCB subtype. The majority (25/45, 55%) overlapped between the two subtypes. Throughout our analysis we noted that 7 non-GCB cases lacked any of the driver mutations analyzed in the study. While all cases carried mutations, they consisted of low frequency mutations that were not specifically associated with COO. 2/7 cases had a gain at 9p24.1 that included CD274 and JAK2. Because 9p24.1 gains have not been fully defined in DLBCL, we reviewed all cases and identified 4 (7%) with a 9p24.1 gain, 3 of which were non-GCB and 1 GCB. One non-GCB case was EBV+ and none of the cases showed evidence of PMBCL. Of the 9p24.1 cases, three had RNAseq data available and we found that PDL1 and JAK2 expression was elevated (12 fold p< 0.01 and 7 fold p< 0.01, respectively) when compared to the 9p24.1 normal cases (n=32). While outcome was not the focus of the study, we did note that 6/7 cases that lacked driver mutations achieved event free survival at 24 months (EFS24). Taken together, this analysis has further characterized the genetic profile of each COO subtype and has identified novel GCB CNAs which require independent replication. Additionally, we identify a subgroup of non-GCB DLBCL patients that do not harbor known driver mutations and require further genomic study to better resolve the biology of these tumors. Together, these data provide insight on the genetic heterogeneity of DLBCL and identify genetic variants that may inform subtype specific therapy. Disclosures Nowakowski: Morphosys: Research Funding; Celgene: Research Funding; Bayer: Consultancy, Research Funding. Rimsza:NCI/NIH: Patents & Royalties: L.M. Rimsza is a co-inventor on a provisional patent, owned by the NCI of the NIH, using Nanostring technology for determining cell of origin in DLBCL..

Abstract Background: There are a number of ongoing clinical trials assessing next-generation immunochemotherapy (IC) regimens (i.e. RX-CHOP) in DLBCL. In addition to standard clinical trial workup, some trials require central pathology review and/or molecular phenotyping before treatment assignment and/or initiation of therapy. There is concern that these studies may be biasing patient selection due to missing patients with aggressive disease who require immediate therapy and cannot delay their treatment to enroll on a study. Here we examine clinical characteristics and outcome stratified by the time from diagnosis to initiation of therapy from a large observational cohort of patients with DLBCL from the R-CHOP era. Methods: Patients were prospectively enrolled in the University of Iowa / Mayo Clinic SPORE Molecular Epidemiology Resource (MER) within 9 months of diagnosis and followed for relapse, retreatment, and death. Clinical management at diagnosis and subsequent therapies were per treating physician. This analysis includes patients with stage II-IV DLBCL or primary mediastinal B-cell lymphoma (PMBCL) who underwent front-line anthracycline based IC; patients with primary CNS lymphoma, PTLD, or a component of low-grade lymphoma were excluded. Time from diagnosis to treatment was defined as the time from date of first lymphoma-containing biopsy to the initiation of IC therapy; delayed therapy was defined as initiating therapy more than 14 days after diagnosis. Event-free survival was defined as time from diagnosis until progression, retreatment, or death due to any cause. EFS24 was defined as progression free status 24 months from diagnosis. Results: 720 patients with stage II-IV newly diagnosed DLBCL or PMBCL and treated with IC were enrolled in the MER from 2002-2012. Median age at diagnosis was 62 years (range 18-92) and 399 patients (55%) were male. 541 patients (75%) had stage III/IV disease and IPI at diagnosis was 0-1 in 166 patients (23%), 2 in 221 patients (31%), 3 in 222 patients (31%) and 4-5 in 111 patients (15%). 233 of 395 patients (59%) were GCB per Hans. At a median follow-up of 73 months, (range 0-163), 349 (49%) patients had an event and 267 patients died (37%); 37% of patients failed to achieve EFS24. Median time from initial lymphoma diagnosis to initiation of IC was 14 days (range 0-79, IQR=8-23). Patients with delay in therapy (>14 days from diagnosis) were more frequently female (50%, p=0.0051) and older than patients who initiated therapy within 14 days from diagnosis (median age at diagnosis of 63 years vs. 60 years, p=0.0008). Patients with delay in treatment initiation had universally less aggressive disease characteristics, including earlier stage, non-elevated LDH, 0-1 extranodal sites, absence of B-symptoms, lower ECOG PS, and lower IPI (see table). In addition, patients with delayed therapy were enriched for GCB subtype per Hans algorithm (p=0.056). Patients with initiation of therapy within 14 day of diagnosis had significantly worse outcome (EFS24 failure=44%) compared to patients with delayed time to initiation of therapy (EFS24 failure=28%, p<0.0001, figure), which remained significant after adjusting for either IPI or aaIPI (both p<0.005). The association between delayed time to initiation of therapy and EFS24 was observed in both GCB (EFS24 failure = 42% vs. 27%, p=0.019) and non-GCB (EFS24 failure = 46% vs. 27%, p=0.014) subsets by Hans. The lower event rate in delayed therapy patients results in an approximately 10% loss of power if the study was powered based on all patients regardless of timing from diagnosis to therapy. Conclusions: Patients with delayed therapy from diagnosis have less aggressive clinical characteristics compared to patients who initiate treatment within 14 days from diagnosis. Studies with a lengthy trial work-up including central pathology review period may be selecting patients with less aggressive disease based on the patient's ability to delay treatment to complete study enrollment requirements. Furthermore, selection of patients with less aggressive disease may result in underpowered studies due to a lower event rate (fewer events) than expected. This retrospective analysis would suggest that trials in DLBCL should consider streamlined enrollment and therapy initiation to avoid potential selection bias and loss of power. Further assessment of implications on trial design and outcomes by cell of origin in this setting is ongoing. Table Table. Figure Figure. Disclosures Maurer: Kite Pharma: Research Funding; Celgene: Research Funding. Ansell:BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding. Nowakowski:Morphosys: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Research Funding.

Abstract While extensive, the genetic studies of DLBCL tumors to date have primarily focused on somatic mutations that are associated with development of lymphoma, i.e., driver mutations. Identification of key genetic events that impact and/or predict response to therapy and clinical outcome of lymphoma patients has not been accomplished. In an exploratory study, we used whole-exome sequencing to identify novel biomarkers that can predict which patients have an aggressive form of DLBCL, and therefore would likely benefit from a different and more aggressive treatment plan. Using whole-exome sequencing data from 54 newly diagnosed DLBCL patients, we evaluated the association of somatic coding single nucleotide (cSNV) and copy number (CNV) variants with aggressive DLBCL. All patients were treated with R-CHOP or immunochemotherapy, and disease aggressiveness was based on relapse, with patients classified as having aggressive disease (AD) (n=13, defined as treatment failure, relapse, or progression within 24 months of diagnosis) versus non-aggressive disease (n=41, defined as achieving at least 24 months of relapse-free survival). An in-house algorithm, patternCNV was used to detect somatic CNVs. Logistic regression was used to assess statistical significance. In the cSNV analysis, 24 genes were found to be significantly (p ≤ 0.05) associated with AD. Of these genes, CIITA (p=0.01) was previously identified as a mutational target in DLBCL and is a recurrent gene fusion partner in lymphoid cancers. Metacore analyses showed that seven of these genes, including CIITA, can be placed in the same regulatory network centered around CREB1. Interestingly, the CREB binding protein (CREBBP) is a known target of pathogenic mutations in DLBCL. In the CNV analyses, we identified 245 gene amplifications and 209 gene deletions associated with AD (p ≤ 0.05). Deleted genes were localized in three major chromosomal regions, 6p22.3-6p22.1 (26 genes), 6q13-6q16.3 (31 genes), and 6q21-6q24.2 (86 genes). In the CNV amplification analysis, the genes were localized in two major chromosomal regions, 3q12.3-3q29 (90 genes) and 19q13.12-19q13.43 (122 genes). We also quantified the association of each regional deletion/amplification with patient outcome. As expected, each deleted region was univariately associated with time to relapse: 6p22.3-6p22.1 (HR = 7.19, 95% CI: 2.66-19.46, p=1.33x10-5), 6q13-6q16.3 (HR = 4.44, 95% CI: 1.74-11.32, p=6.84x10-4), and 6q21-6q24.2 (HR = 5.42, 95% CI: 2.12-13.87, p=9.23x10-5) as well as the two amplification regions: 3q12.3-3q29 (HR = 4.60, 95% CI: 1.49-14.24, p=4.27x10-3) and 19q13.12 -19q13 (HR = 13.79, 95% CI: 4.39-43.34, p=2.81x10-8). Deletions at 6q21-6q24.2 have been previously reported in DLBCL and two tumor suppressor genes associated with lymphoma pathogenesis are localized in the region, TNFAIP3 (p=0.14) and PRDM1 (p=0.02), the latter of which was significantly associated with AD. However, the genes in the 6p21 locus that were most significant (p=0.002) were SLC22A16, GPR6, SOBP, MATTL24, DDO, and REV3L. SLC22A16, which also had cSNVs in two tumors, is an organic cation transporter that has been shown to transport chemotherapeutic drugs including doxorubicin. Successful drug response has been correlated with the level of activity and expression of this transporter in tumor cells. To determine if expression of SLC22A16 would have an impact on doxorubicin transport, and may therefore impact response to R-CHOP we overexpressed either an empty vector control or SLC22A16 in OCI-Ly7 DLBCL cells, which do not express endogenous SLC22A16 mRNA. We found that OCI-Ly7 cells expressing SLC22A16 have increased 14C-doxorubicin uptake and are more sensitive to doxorubicin-induced cell death when compared to the empty vector control cells. These data suggest that loss of SLC22A16 expression through gene deletion could impact the ability of DLBCL cells to respond to therapy and indicates an aggressive form of the disease. In summary, our data are the first to use whole exome sequencing to identify genetic variants associated with aggressive DLBCL, which will require replication in additional samples. Nevertheless, our data highlight the role for somatic CNVs in patient outcome and we biologically validate the potential impact of deletions in SLC22A16 at 6p21. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2486 In patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL), deletion of 17p13 (17p-) resulting in loss of one allele of TP53 and 11q22 (11q-) resulting in loss of one allele of ATM predicts a significantly poorer prognosis. These lesions are nearly always monoallelic in patients with CLL and their consequences are considerably influenced by the integrity of the remaining allele. A dysfunctional mutation in the remaining allele, resulting in more aggressive disease, ultimately occurs in most patients with 17p- (> 80%) but fewer patients with 11q- (30%). Because ATM and TP53 are integral components of the p53 pathway, we hypothesize that patients with clonal 17p- and 11q- in the same CLL cell would have a worse prognosis than patients with only one of these deletions. To test this hypothesis, we compared the clinical outcomes (overall survival (OS) and time to treatment (TTT)) of patients with 17p- and 11q- in the same cell to patients with either 17p- or 11q- or neither defect. Methods: We used the Mayo Clinic CLL Database and the Cytogenetics laboratory database to identify all CLL patients seen in the Division of Hematology from 1/1/1995-3/29/2012 who had FISH analysis. Data was extracted on demographics, date of first (sentinel) occurrence of 11q- and 17p-, treatment, and vital status. FISH studies were performed on uncultured blood or bone marrow by standard methods using the Mayo Clinic CLL FISH panel of centromere specific control probes and probes for 6q (MYB), 11q (ATM), 13q14, and 17p (TP53) deletions, trisomy 12 and IGH. Patients with both 11q- and 17p- were further tested with the Abbott Molecular (Des Plaines, IL) ATM/TP53 combination probe set, to determine if the 11q and 17p deletions were present in the same cells. Patients were grouped into 4 cohorts: 1) clonal 17p- and 11q-; 2) 17p- only; 3) 11q- only; and 4) neither 17p- nor 11q-. OS and TTT were calculated and Kaplan-Meier curves were drawn from sentinel FISH date until their last known alive date or death date for OS and until treatment or last known untreated date for TTT. We censored all data on 3/29/2012 and analyzed using log-rank tests from date of sentinel FISH. Sanger sequencing of exons 4–9 of TP53 was done in samples from 15 patients with clonal 17p- and 11q- with an available sample collected within 1 year of the sentinel FISH test and without treatment in that interval. All mutations were confirmed by a repeat assay and analyzed for their predicted effect using the UMD p53 database. Results: We studied 2234 CLL patients with a median follow-up of 4.8 years (range 0 – 29.6 years). Twenty (1%) patients had clonal 17p- and 11q-, 159 (7%) had 17p-, 251 (11%) had 11q-, and 1804 (81%) had neither 17p- nor 11q-. Median OS from sentinel FISH study (Figure 1) was significantly shorter in the clonal 17p- and 11q- group (1.9 years), 17p- group (3.2 y), and 11q- group (4.7 y) compared to the patients with neither 17p- nor 11q- (9.2 y) (p < 0.0001). OS was significantly shorter in the patients with clonal 17p- and 11q- cells vs those with 17p- (p=0.048). TTT was also significantly different among the 4 groups (p<0.0001) with median time to treatment of 0.07 y in the clonal 17p- and 11q-, 0.2 y in 17p-, 0.7 y in 11q-, and 5.6 y in the neither 17p- nor 11q- group. TP53 analysis performed in samples from 15 patients with clonal 17p- and 11q- showed that 10 patients had mutations in the remaining TP3 allele predicted to be deleterious to normal p53 protein function. Median OS was 2.1 years for those with TP53 mutations and 0.9 years for those without mutations (p = 0.03) and median TTT was 0.25 years for those with TP53 mutations and 0.05 years for those without mutations (p = 0.049). Conclusion: “Double hit” clonal deletions of 17p and 11q occurred in 1% of this large cohort of CLL patients and were associated with a significantly poorer prognosis. We recommend that testing for this “double hit” phenomenon, by use of an ATM/TP53 combination probe set, should thus be performed in all patients with 17p- and 11q- on FISH analysis because these patients have a poorer prognosis than those with either 17p- or 11q- and could be candidates for novel therapies. The finding that dysfunctional TP53 mutations in the remaining allele did not worsen prognosis in patients with clonal 17p- and 11q- suggests that monoallelic deletion of both ATM and TP53 in a clone of CLL cells is sufficient to disable p53 pathway function in these patients. Future study of clonal 17p- and 11q- CLL could thus be informative in this regard. Disclosures: Kay: Celgene: Research Funding. Shanafelt:Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding. Zent:Biothera: Research Funding; Genzyme: Research Funding; Genentech: Research Funding; Novartis: Research Funding; GlaxoSmithKline: Research Funding.

Abstract Introduction: Ibrutinib has shown remarkable safety and efficacy in CLL. Due to CYP3A4 (3A4) metabolism, the concurrent use of 3A4 inhibitors/inducers with ibrutinib should be avoided. Ibrutinib is also associated with bleeding complications, in part due to effects on collagen-mediated platelet aggregation, thus caution is advised with the simultaneous use of anticoagulant (AC) and antiplatelet agents. It is unknown what proportion of CLL patients starting ibrutinib therapy in routine practice are on these medications. We conducted a retrospective study to evaluate these aspects in a large cohort of CLL pts treated with ibrutinib outside the context of clinical trials. Methods: Following IRB approval, consecutive CLL pts who initiated ibrutinib off-protocol at Mayo Clinic, Rochester, MN from November 2013 through July 2015 were evaluated. A medication review for drug-drug interactions was performed by a pharmacist on all pts. Baseline characteristics, concomitant medications, and toxicity were recorded. Time to toxicity and ibrutinib discontinuation were analyzed by Kaplan Meier and cumulative incidence methods accounting for competing risk of death, respectively. Results: Ninety-six CLL pts started ibrutinib. Median age was 66 years and 64 (67%) were male. Ibrutinib indications included: relapsed/refractory CLL (n=84), treatment-naïve CLL with del17p (n=8), and CLL with Richter's transformation (n=4). Approximately 80% had unmutated IGHV and 45% had del17p or del11q on FISH. Upon initial pharmacy consult prior to ibrutinib, the median number of concomitant medications was 9 (range, 1-31). Sixty (63%) pts were taking concurrent medications increasing their risk of ibrutinib toxicity and 4 (4%) pts were on drugs potentially reducing ibrutinib efficacy. At ibrutinib initiation, 16 (17%) pts were on concomitant 3A4 inhibitors. This included 9 (9%) on moderate 3A4 inhibitors (fluconazole, diltiazem, imatinib, cyclosporine) and 7 (7%) on strong 3A4 inhibitors (voriconazole, posaconazole, clarithromycin, itraconazole). In 4 pts, the 3A4 inhibitor was switched to another agent or discontinued to allow standard 420 mg daily dosing. Ibrutinib was initiated at 140 mg daily in 7 pts on moderate 3A4 inhibitors and at 140 mg every 48 hours in 5 pts on strong 3A4 inhibitors. Four (4%) pts were on strong 3A4 inducers (carbamazepine, rifampin, rifabutin) at the initiation of ibrutinib. The 3A4 inducer was discontinued prior to ibrutinib start in 3 pts, while the initial ibrutinib dose was decreased to 140 mg every 48 hours in the remaining patient (also on 2 strong 3A4 inhibitors). During the course of ibrutinib, an additional 8 (8%) pts were started on 3A4 inhibitors/inducers which necessitated dose modifications. Upon commencing ibrutinib, 9 (9%) pts were on concomitant AC (6 warfarin, 3 enoxaparin). Due to significant bleeding risk, warfarin was switched to enoxaparin in 2 pts, to aspirin (ASA) in 1 and warfarin was discontinued in 1. In 2 pts, an alternative AC could not be used so ibrutinib was begun at 140 mg daily and titrated upward. Twenty-nine (30%) pts were on ASA (3 [3%] also on clopidogrel) at ibrutinib initiation. Nineteen (20%) pts were on selective serotonin release inhibitors (SSRIs), and 9 (9%) were on NSAIDs. Thirteen (13%) pts were on fish oil and 24 (25%) were on herbal medications; all of which were discontinued prior to starting ibrutinib. Ten (10%) pts had clinically significant bleeding on ibrutinib including 4 (4%) requiring hospitalization (1 subdural hematoma). Of these 4 pts, 1 was on enoxaparin, 2 were on a SSRI and 1 a NSAID. Six pts had minor bleeding necessitating a dose reduction of ibrutinib to 140-280 mg daily. After 16 months follow-up, the risk of bleeding was 21% (95% CI: 1-37%, Figure). After a median follow-up of 7.6 months, 73 (76%) pts remain on ibrutinib. There was no difference in rates of discontinuation of ibrutinib between patients who were on 3A4 inhibitors/inducers versus those who were not. Conclusion: In the "real-world" setting, 2 out of 3 CLL patients commencing ibrutinib therapy is on a concomitant medication with a potentially clinically significant drug-drug interaction with ibrutinib. These findings have implications for the practicing hematologist who must maintain a high degree of vigilance when prescribing ibrutinib to CLL pts. We highly recommend a formal medication review by a clinical pharmacist in all patients initiating ibrutinib. Figure 1. Figure 1. Disclosures Ding: Merck: Research Funding. Kay:Hospira: Research Funding; Tolero Pharma: Research Funding; Genentech: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding. Shanafelt:Glaxo-Smith_Kline: Research Funding; Pharmactckucs: Research Funding; Celgene: Research Funding; Genentech: Research Funding; Cephalon: Research Funding; Janssen: Research Funding; Hospira: Research Funding; Polyphenon E Int'l: Research Funding.