Habermann, Thomas M.

Introduction: New Diffuse Large B-cell (DLBCL) treatments remain a clinical need despite the success of rituximab combined with CHOP chemotherapy (RCHOP), which results in durable responses in 60-70% of patients. Those refractory to, or who relapse following, first-line therapy have a very poor outcome, with only 20% surviving beyond 5 years. Rationally-targeted frontline strategies are needed, especially for those with high-risk disease. Significant advances have been made in the genomic classification of DLBCL, but none have impacted the design of phase III trials for untreated DLBCL patients. Building on new genetic profiling studies to personalize clinical treatment, an NCI initiative, could allow clinicians to add targeted therapies to the RCHOP backbone based on individual tumor signatures. The phase II ECOG-ACRIN1412 trial, which compared RCHOP combined with lenalidomide (R2CHOP) versus RCHOP showed significantly superior event free survival (EFS) and overall survival benefits for those treated with R2CHOP. Herein, we report the profile of a high-risk ABC/non-GCB subset of DLBCL driven by genomic alterations in inflammatory genes that are susceptible to front-line R2CHOP, but continue to experience poor outcome with RCHOP alone. Methods: We studied a total of 196 DLBCL patients. 47 were treated with R2CHOP from an investigator-initiated, open-label, single-arm phase II study (NCT00670358). 149 were newly diagnosed DLBCL cases treated with RCHOP, or R-immunochemotherapy (herein called RCHOP), and followed prospectively through the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic SPORE that served as a contemporary cohort. Patients from each treatment group were divided based on their event free survival at 24 months (EFS24). DNA alterations within these populations were identified through whole exome sequencing (WES). Variants were analyzed for their presence in EFS24 achievement or failure groups in both RCHOP and R2CHOP. Both tumor and germline sequencing was performed for 47/47 R2CHOP cases and 49/149 RCHOP cases. Gene expression data from the PanCan panel of 730 B-cell-related genes was analyzed to determine gene expression profiles characteristic of the high-risk/R2CHOP-profile on 59 available non-GCB DLBCL cases (45 RCHOP; 14 R2CHOP). A two-sided comparative marker analysis T statistic test was applied to assess what genes displayed differential expression based on achieving EFS24 in both R2CHOP and RCHOP populations. Positive values represented associations with achieving EFS24 and negative values were associated with cases of EFS24 failure. Results: Three genes were enriched in the RCHOP cases that failed EFS24 but achieved EFS24 with R2CHOP among non-GCB patients: PIM1, SPEN, and MYD88 (L265P), herein referred to as Responder Alterations. In R2CHOP cases, patients with a Responder Alteration had a better overall EFS (P = 0.051) compared to wild type patients. In contrast, RCHOP treated patients with a Responder Alteration in their tumor had a significantly worse overall EFS (P = 0.0004) compared to patients without a mutation. Together, PIM1, SPEN, or MYD88 (L265P) mutations were present in 38.0% (30/79) of all non-GCB cases. Collective R2CHOP and RCHOP EFS24 differential gene expression T values were significantly different for 18 previously-defined signatures. The R2CHOP cases that achieved EFS24 were enriched for genes involved in cell cycle, JAK-STAT, cytokine signaling, and NF-κB pathways based on these signatures and ontology analyses. Lastly, cases with WES and PanCan data were analyzed together to observe differential gene expression patterns between cases with and without signature mutations. These data suggest that R2CHOP disrupts tumors reliant on IRF4, NF-κB, and STAT transcription factors, leading to a loss of proliferative feedback systems. Conclusions: Our combined analysis of DNA and RNA across R2CHOP and RCHOP treatment cohorts identifies a high-risk non-GCB phenotype that encompasses approximately 38% of non-GCB patients, is capable of sustaining JAK-STAT and NF-κB signaling, and is sensitive to R2CHOP. Our study lays the groundwork for a precision therapy approach in DLBCL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care, RCHOP, and who would benefit from the addition of lenalidomide or other targeted agents. Disclosures Gandhi: Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Novak:Celgene Coorperation: Research Funding.

Purpose The development of cardiovascular disease (CVD) is a relatively rare but a clinically important adverse event in the treatment of lymphoma, particularly in individuals receiving anthracyclines. There are few studies assessing the role of germline genetic susceptibility as a predictor of CVD in this setting. We evaluated the association of 24 single nucleotide polymorphisms (SNPs) from candidate genes involved in anthracycline-induced cardiotoxicity, CVD, and venous thromboembolism with new-onset CVD in a prospective cohort of lymphoma patients treated in the modern era. Methods All patients were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma SPORE. Enrollment from 2002-2015 was offered to patients with newly diagnosed lymphoma who were age ≥18 years. Clinical, pathology and treatment data were abstracted using a standard protocol, and participants provided a peripheral blood sample, from which DNA was extracted. All patients were prospectively contacted every 6 months for the first 3 years from diagnosis and then annually thereafter to assess disease status, re-treatment and development of new morbidities, including CVD. Reported CVD events included congestive heart failure (CHF), coronary heart disease (CHD), arrhythmia, valvular heart disease (VHD), and other CVD. These events were identified during follow-up and validated against medical records. Genotyping was conducted using a custom Illumina iSelect platform with rigorous quality controls. For each SNP, Cox regression was used to estimate Hazard Ratios (HRs) and 95% confidence intervals (CI) with time to first CVD, using death without CVD as a competing risk. HRs were also obtained for time to CHF, using death without CVD as a competing risk. We also modeled these events for all patients and for patients receiving frontline anthracyclines. Each SNP was modeled as having a log-additive (per minor allele) effect in the regression model. An ordinal test was used to assess the trend, with a nominal P<0.05 considered statistically significant. Results There were a total of 3,063 newly diagnosed lymphoma patients (excluding chronic lymphocytic leukemia) with no history of CVD at time of lymphoma diagnosis, of which 1280 had genotype data available for analysis. The median age at diagnosis was 59 years (range, 18-95) and 56% were male. The most common subtypes were follicular (26.3%), diffuse large B-cell (23.2%), Hodgkin (11.3%), marginal zone (11.2%), mantle cell (6.3%) and T-cell (5.4%) lymphoma. Anthracycline-based chemotherapy as initial therapy was used in 52% of individuals. At a median follow-up of 6.9 years (range, 0.1-17.1), 363 (30.7%) patients died, and 173 (13.5%) had new-onset CVD after lymphoma diagnosis. There were 234 incident CVD events in the 173 patients: 49 CHD, 50 CHF, 27 VHD, 103 arrhythmias, and 5 other CVD. Results are shown in the Table. When assessing all CVD, F5 (rs4524) (HR=1.28, 95% CI=1.03-1.58) and F11 (rs4253399) (HR=1.30, 95% CI=1.06-1.61) were associated with CVD at P<0.05; both associations attenuated slightly when restricted to anthracycline-treated patients (F5 HR=1.18, 95% CI=0.89-1.57; F11 HR=1.23, 95% CI=0.92-1.65). F11 was more strongly associated with CHF (HR=1.55, 95% CI=1.03-2.34) and CHF in anthracycline-treated patients (HR=1.64, 95% CI=0.98-2.76). NCF4 (rs1883112) was marginally associated with lower risk of CVD overall (HR=0.81, 95% CI=0.66-1.00), and this association strengthened when restricted to anthracycline-treated patients (HR=0.67, 95% CI=0.49-0.91) and showed similar trends in HRs for CHF (HR=0.77, 95% CI=0.51-1.16) and CHF in anthracycline-treated patients (HR=0.72, 95% CI=0.41-1.28), although neither estimate was statistically significant at P<0.05. Finally, while XDH (rs2236168) was not associated with all CVD (HR=1.00, 95% CI=0.81-1.23), it was associated with CHF (HR=0.66, 95% CI=0.47-0.93) overall, but attenuated in anthracycline-treated patients (HR=0.81, 95% CI=0.55-1.18). Conclusions In this exploratory study of candidate SNPs from the literature, we found limited evidence for a role of germline genetic variability in predicting risk of CVD or CHF in a cohort of lymphoma patients, especially after accounting for multiple testing. To fully address this hypothesis, future studies will need larger sample sizes and more comprehensive genetic assessment. Disclosures Maurer: Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Nowakowski:NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

INTRODUCTION: EMZL is a heterogeneous disease with variable risk for relapse and progression. Based on age ≥70 years, stage III-IV and elevated LDH, Thieblemont et al (Blood. 2017) developed the MALT-IPI to identify high-risk patients. In this index, disease characteristics (stage and LDH) account for 66% while a disease nonspecific characteristic (age) for 33% of the index score. We reported (Am J Hematol. 2019) that EMZL with multiple mucosal sites (MMS) at diagnosis is characterized by shorter survival and increased incidence of higher grade transformation. To better recognize disease-attributable high-risk patients, we developed a new EMZL prognosis score chiefly based on patient's disease characteristics. METHODS: The revised (R)-MALT-IPI was developed using a retrospective data set of 405 EMZL patients treated at the University of Miami (UM) from 1995 to 2017. Cox proportional hazards regression analysis was conducted to evaluate the effect of the potential prognostic variables on progression-free survival (PFS) and overall survival (OS) and to develop the new index R-MALTI-IPI based on PFS. Model validation was performed in two independent cohorts of EMZL patients from the University of Iowa/Mayo Clinic Molecular Epidemiology Resource (MER) database (n=297) and the IELSG-19 study (n=400) used for the development of MALT-IPI. Performance of various prognostic indices was compared using AIC statistics, and concordance c-statistics by Harrell (CH) and by Gonen and Heller (CGH). RESULTS: Among the candidate variables tested in univariable analysis, the following were statistically significant predictors of shorter PFS: age >60, age ≥70, anemia (Hb<12g/dL), stage III-IV, ECOG PS ≥2, elevated serum LDH, number of extranodal sites >1, number of nodal sites >4, and presence of MMS at diagnosis, defined as EMZL with ≥2 different extranodal sites excluding spleen and bone marrow. A stepwise Cox regression analysis yielded a multivariable model with four independent predictors of shorter PFS: age >60 (HR=1.53, p=0.010), elevated LDH (HR=1.73, p=0.004), stage III-IV (HR=2.03, p=0.0003) and presence of MMS (HR=2.78, p<0.0001). Based on this, a new index R-MALT-IPI was developed with scores ranging from 0 to 5, calculated as a sum of 1 point for age >60, elevated LDH, stage III-IV, and 2 points for MMS. The R-MALT-IPI defined 4 risk groups: low-risk (score 0 (35%), reference group), low-medium risk (score 1 (39%), HR=1.91, p=0.005), medium-high risk (score 2 (13%), HR=3.77, p<0.0001), and high-risk (score 3+ (13%), HR=8.54, p<0.0001). When compared with MALT-IPI, R-MALT-IPI better stratifies and separates high risk patients (26%) into medium-high risk and high-risk patients with a median PFS of 5.8 years (2.9-9.1) and 1.8 years (1.3-2.6) respectively, compared to 2.6 years (1.8-4.7) in the high-risk MALT-IPI patients (16.8%). The R-MALT-IPI index also distinguished patients with different OS. For validation, we analyzed R-MALT-IPI index performance in independent Iowa/Mayo Clinic MER and IELSG-19 cohorts. Both R-MALT-IPI and MALT-IPI were useful in distinguishing PFS and OS in all the cohorts. In the UM training cohort, the concordance c-statistics' values for the two indices were similar: for PFS, CH=0.6893 and CGH=0.6611 for R-MALT-IPI, and CH=0.6551 and CGH=0.6367 for MALT-IPI; for OS, CH=0.7017 and CGH=0.6813 for R-MALT-IPI, and CH=0.7029 and CGH=0.67715 for MALT-IPI. In the validation cohorts, the concordance c-statistics' values for the two indices were also similar, but slightly lower than in the UM cohort for PFS. When comparing medium-high to high-risk R-MALT-IPI groups, there was a reduction of 4 years in median PFS in the UM cohort, and reduction in median EFS of 5.6 years in the MER cohort, an important difference between these risk groups identified by the R-MALT-IPI index. CONCLUSIONS: R-MALT-IPI is a new index for EMZL centered principally on disease characteristics. Overall, there is a similar prediction of PFS (EFS) by R-MALT-IPI and MALT-IPI indexes; however, R-MALT-IPI better recognizes a high-risk group accounting for 13% of EMZL patients with short median PFS and thus obviates the waiting period needed to recognize patients with shorter EFS24. Collaborative studies addressing best treatment approach for these high-risk EMZL patients are eagerly needed. Disclosures Alderuccio: Agios: Other: Immediate family member; Foundation Medicine: Other: Immediate family member; OncLive: Consultancy; Targeted Oncology: Honoraria; Puma Biotechnology: Other: Immediate family member; Inovio Pharmaceuticals: Other: Immediate family member. Thieblemont:Cellectis: Membership on an entity's Board of Directors or advisory committees; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Zucca:Kite, A Gilead Company: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant, Research Funding; AstraZenaca: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Merck: Research Funding; Celltrion Helathcare: Membership on an entity's Board of Directors or advisory committees; Abbvie: Other: Travel Grant. Lossos:NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen Scientific: Membership on an entity's Board of Directors or advisory committees.

INTRODUCTION Complete staging via bone marrow assessment is standard of practice for most newly diagnosed non-Hodgkin lymphoma (NHL) patients, though the methods and patterns of bone marrow assessment vary in clinical practice. The 2014 Lugano Classification recommends that in diffuse large B cell lymphoma (DLBCL), positron emission tomography-computed tomography (PET/CT) alone can designated advanced-stage disease in many, but not all cases. For most other NHL subtypes, obtaining bone marrow biopsy (BMB) remains recommended. We aimed to assess clinical practice patterns of marrow assessment and the performance of PET/CT in the clinical setting. METHODS The Lymphoma Epidemiology of Outcomes (LEO) cohort is a prospective cohort from 8 academic medical centers. All patients were enrolled in the cohort within 6 months of diagnosis, and prospectively followed. Patients with lymphoma enrolled in the cohort between July 2015 and June 2017 were included for study. Baseline clinical characteristics at diagnosis, whether BMB and/or PET/CT were performed, and the result of these assessments were extracted from the database. Bone marrow involvement on PET/CT was abstracted via review of radiology reports. No central review of images was utilized. RESULTS In total, 3,251 patients were included, consisting of the following subtypes: DLBCL (n=1,098), follicular lymphoma (FL n=730), mantle cell lymphoma (MCL n=302), T-cell lymphoma (TCL n=278), marginal zone lymphoma (MZL n=269), other NHL (n=505) and composite lymphoma (n=69). Other clinical characteristics are shown in table 1. Staging BMB was obtained in 78% of patients, PET/CT was obtained in 56%, both BMB and PET/CT were obtained in 46% while neither were obtained in 9% (table 1). There were significant differences in lymphoma subtype between patients having any bone marrow assessment vs no bone marrow assessment (p<0.0001). MCL (96%) and DLBCL (94%) most frequently had any bone marrow assessment, while MZL was least frequently assessed (84%). Patients undergoing any bone marrow assessment had higher median lactate dehydrogenase (247 U/L vs 206 U/L, p=0.0002). Among the 8 enrolling medical centers, the frequency of bone marrow biopsy at diagnosis ranged from 53% to 86%, and the frequency of PET/CT at diagnosis ranged from 34% to 94% (p<0.0001, table 1). Among patients with DLBCL who had positive bone marrow by either PET/CT or BMB (N=107), 41% (44/107) were positive by BMB only, 19% (20/107) were positive by PET/CT only, and 40% (43/107) were positive by both. Figure 1 shows results of similar analyses for FL, MCL, MZL, and TCL. Using BMB as a gold standard for marrow involvement, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of PET/CT for marrow assessment in DLBCL is 49%, 90%, 68% and 91%, respectively. Data for other subtypes are shown in table 2. Patients undergoing both PET/CT and BMB had lower median LDH (231 U/L vs 254 U/L, p=0.0032) than those undergoing one or neither assessments. CONCLUSION Overall, 91% of all patients with newly diagnosed NHL had bone marrow assessments via either PET/CT, BMB or both. The significant variation in the frequency of BMB and PET/CT use among the 8 centers reflects a lack of clear consensus on staging practices regarding bone marrow assessment. The sensitivity of PET/CT for detection of marrow involvement was less than 50% across all subtypes. It is possible the sensitivity of PET/CT could be improved via a centralized review process of the images, as is performed in some clinical trials, however practical approaches for implementing such reviews in clinical practice have not been developed. Between 92-100% of all positive bone marrows were detected by BMB, except in cases with DLBCL in which 19% of positive bone marrows were missed by BMB but detected by PET/CT. Thus, in our analysis, BMB more reliably captured marrow involvement that PET/CT in all subtypes in which both assessments were performed. However, neither PET/CT nor BMB alone were able to identify all cases of bone marrow involvement and use of both modalities may be necessary in cases where marrow involvement would have clinically important implications. Disclosures Rutherford: Juno Therapeutics Inc: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria. Kahl:ADC Therapeutics: Consultancy, Research Funding; Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; BeiGene: Consultancy. Lossos:Janssen Scientific: Membership on an entity's Board of Directors or advisory committees; NIH: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees. Friedberg:Acerta: Other: Data & Safety Monitoring Committee; Bayer: Honoraria, Other: Data & Safety Monitoring Committee. Casulo:Celgene: Research Funding; Roche: Other: Travel, accommodation, expenses; Gilead: Honoraria, Other: Travel, accommodation, expenses. Cohen:Astra Zeneca: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Hutchison: Research Funding; Gilead/Kite: Consultancy; Bristol-Meyers Squibb Company: Research Funding; LAM Therapeutics: Research Funding; UNUM: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Genentech, Inc.: Consultancy, Research Funding. Nastoupil:TG Therapeutics: Honoraria, Research Funding; Novartis: Honoraria; Spectrum: Honoraria; Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Gilead: Honoraria; Bayer: Honoraria. Flowers:Denovo Biopharma: Consultancy; Spectrum: Consultancy; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; Acerta: Research Funding; Bayer: Consultancy; BeiGene: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; TG Therapeutics: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding; Millenium/Takeda: Research Funding; Celgene: Consultancy, Research Funding. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Martin:Sandoz: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; I-MAB: Consultancy; Janssen: Consultancy.

Abstract Background: Bendamustine and rituximab (BR) is a commonly chosen frontline treatment for grade 1-2 (G 1-2) follicular lymphoma (FL). This regimen resulted in significantly longer progression-free survival (PFS) and less toxicity compared to R-CHOP in the STiL trial [Rummel et al., 2013] and similar results were observed when comparing BR vs. R-CHOP/R-CVP in the confirmatory BRIGHT trial [Flinn et al., 2014, updated ASCO 2017]. Importantly, patients (pts) with grade 3A (G 3A) FL were excluded from these studies, yet the conclusions are often extrapolated to the treatment of G 3A pts. Notably, G 3A pts were included in the GALLIUM trial which used both bendamustine as well as CHOP backbones. Several retrospective studies of FL pts treated with frontline R-CHOP have demonstrated equivalent or improved overall survival (OS) for G 3A relative to G 1-2 FL (Koch et al., 2016, Mustafa et al., 2017, Yuan et al., 2017, Maeshima et al., 2013). Early disease progression after diagnosis and treatment with frontline R-CHOP has been identified as a strong predictor of poor OS in FL pts [Casulo et al. 2015], but has not been assessed in pts treated with frontline BR. We retrospectively evaluated outcomes of a large cohort of FL pts treated with frontline BR and stratified the results based on histologic grade and early vs. late progression. Methods: We reviewed medical records of adult (age >18) pts with FL treated with frontline BR at 18 US cancer centers. There was no central pathology review; each academic institution confirmed the diagnosis and grade of FL. Pts with unknown grade, grade 3B, or cutaneous FL were excluded. Baseline characteristics between grades were evaluated with the Chi-Square test for categorical variables and the Mann-Whitney U for continuous variables. Outcomes were calculated from time of initiation of BR. Patients were grouped according to whether or not they had progression of disease within 24 months of BR initiation (POD24). OS and PFS were estimated using the Kaplan-Meier method and compared using the log-rank test. Results: 687 pts were included (616 G 1-2, 71 G 3A). Median age at diagnosis was 60 years (range 21-94) with 53% men. The median time from diagnosis to treatment with BR was 1 month (range 0-139). The median duration of follow up for the entire cohort was 39 months. Characteristics including age, gender, ethnicity, ECOG, stage, LDH, and FLIPI are shown in the Table, and did not significantly differ between G 1-2 and G 3A pts. Rates of complete response (CR) and partial response (PR) to BR were similar between G 1-2 and G 3A (72%/22% vs. 66%/21%, respectively, p=0.114) but progressive disease (PD) was higher in G 3A pts (11.9%) relative to G 1-2 (4.8%, p=0.025). As shown in the Figure (A and B), 3-year PFS and OS was significantly longer for G 1-2 vs. G 3A pts (75% vs. 65%, log rank p= 0.035 and 90% vs. 86%, log rank p=0.007, respectively). Seventy-six (12.3%) of the 616 G 1-2 pts and 13 (18.3%) of the 71 G 3A pts experienced POD24 (p=0.19). For pts with both G 1-2 and G 3A FL, POD24 was associated with inferior OS (3-year OS 95% vs. 57% for G 1-2 pts, log rank p<0.0001 and 3-year OS 93% vs. 37% for G 3A pts, log rank p<0.0001) compared to patients without POD24. Conclusions: G 3A FL pts have an inferior OS and are significantly more likely to have PD to frontline BR compared to G 1-2, which differs from the published experience with R-CHOP. Similar to outcomes after frontline treatment with R-CHOP, POD24 after frontline BR for FL is associated with very poor OS. Future studies are needed to address optimal frontline management of pts with G3A as well as for pts with any grade FL with POD24 after frontline BR. Disclosures Nastoupil: Novartis: Honoraria; Genentech: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; TG Therappeutics: Research Funding; Karus: Research Funding; Merck: Honoraria, Research Funding; Spectrum: Honoraria; Janssen: Research Funding; Juno: Honoraria. Cerhan:Jannsen: Other: Scientific Advisory Board; Celgene: Research Funding; Nanostring: Research Funding. Maurer:Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Research Funding. Smith:Genentech: Research Funding; Incyte Corporation: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Merck Sharp and Dohme Corp.: Consultancy, Research Funding; Acerta Pharma BV: Research Funding; Seattle Genetics: Research Funding. Lossos:Affimed: Research Funding. Portell:TG therapeutics: Research Funding; Amgen: Consultancy; Kite: Research Funding; AbbVie: Research Funding; BeiGene: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Research Funding; Infinity: Research Funding. Calzada:Seattle Genetics: Research Funding. Cohen:Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: 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; Janssen: Research Funding; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Research Funding; BioInvent: Consultancy. Ghosh:Spectrum: Consultancy; SGN: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics, an Abbvie Company: Consultancy, Research Funding, Speakers Bureau; TG Therapeutics: Honoraria, Research Funding; Celgene: Consultancy; Juno: Consultancy, Research Funding; Abbvie: Consultancy, Speakers Bureau; Genentech: Research Funding; F. Hoffman-La Roche Ltd: Research Funding; PCYC: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Forty seven Inc: Research Funding. Barta:Janssen: Membership on an entity's Board of Directors or advisory committees; Merck, Takeda, Celgene, Seattle Genetics, Bayer: Research Funding. Caimi:Kite Pharma: Other: Advisory Board Participation; Genentech: Other: Advisory Board PArticipation, Research Funding; Celgene: Speakers Bureau; Kite Pharma: Other: Advisory Board Participation. Danilov:Gilead Sciences: Consultancy, Research Funding; Aptose Biosciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Astra Zeneca: Consultancy; Genentech: Consultancy, Research Funding; Takeda Oncology: Research Funding; TG Therapeutics: Consultancy; Verastem: Consultancy, Research Funding. Martin:AstraZeneca: Consultancy; Seattle Genetics: Consultancy; Janssen: Consultancy; Kite: Consultancy; Gilead: Consultancy; Bayer: Consultancy. Kamdar:Seattle Genetics: Speakers Bureau; Genentech: Consultancy. Kahl:AstraZeneca: Consultancy; Acerta: Consultancy; Juno: Consultancy; CTI: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Seattle Genetics: Consultancy; Abbvie: Consultancy; Genentech: Consultancy; ADC Therapeutics: Consultancy. Hill:Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract Background: Waldenström macroglobulinemia (WM) is an indolent B-cell lymphoma with a heterogeneous disease course. For nearly a decade, the International Prognostic Scoring system (IPSS), requiring 5 variables (platelet count, hemoglobin, age, Beta 2 microglobulin and immunoglobulin M size) has been used to prognosticate patients with active WM. In this study, we attempt to propose a simplified prognostic model for patients with active/symptomatic WM. Methods: All patients with active WM diagnosed between 01/01/1996 and 12/31/2017 and evaluated consecutively at Mayo Clinic, Rochester were included in the study after Institutional Review Board approval. Active WM was defined as ≥10% marrow infiltration with lymphoplasmacytic cells plus monoclonal IgM of any size requiring systemic therapy. The follow up and overall survival (OS) were calculated from the time of active WM until date of last follow up or death. Baseline parameters at active disease were used to assess prognostic factors for OS using a univariate followed by a multivariate (MV) Cox regression analysis. Independent variables on the MV analysis were given a score proportional to their risk ratios (RR). All time-to-event analyses were performed using the Kaplan-Meier method and the survival curves were compared using log-rank test. Discriminatory ability of the IPSS and the currently proposed prognostic system was assessed using Harrell's concordance index wherein a higher c-statistic is associated with a better discriminatory ability. Results: We identified 889 patients with active WM. The median follow up was 8.2 years (95% CI: 7.5-9 years). The baseline characteristics of the patients are shown in Table 1. At the time of diagnosis of WM, 16% of patients had previously documented MGUS and 16% patients had smoldering (asymptomatic) WM. The course of WM was complicated by systemic amyloidosis in 8% (n=69), transformation to high-grade lymphoma in 4% (n=38), symptomatic hyperviscosity in 13% (n=118), myelodysplastic syndrome in 2% (n=17) and Bing-Neel syndrome in 1.4% (n=12) of patients. Of the patients tested, cryoglobulinemia was present in 17% (n=48), cold agglutinin disease in 34% (n=43) and Coombs positive hemolytic anemia in 9% (n=34) patients. The median OS was 10.6 years (95% CI: 9.7-11.4 years). The factors affecting OS are shown in Table 2. While the patients' MYD88L265P mutation status was not found to be prognostic for OS, age >65 years [RR 1.8 (95% CI: 1.2-2.8); p=0.01], LDH > upper limit of normal [RR 2.5 (95% CI: 1.3-4.6); p=0.003] and serum albumin <3.5 g/dL [RR 1.6 (95% CI: 1.1-2.5); p=0.02] were independently prognostic. Owing to comparable RRs, each of these were assigned a score of 1 point and a prognostic model with a minimum score of 0 and maximum score of 3 was constructed. A score of 0 was considered as low risk WM and 1 as intermediate risk WM. The patients with scores of 2 and 3 were analyzed together to form the high risk cohort due to comparable OS for these two groups. The patient outcomes based on this prognostic model are depicted in Figure 1. Using the new prognostic model (n-=341), the median OS for the low risk group (n=71, 21%) was 14.6 years (95% CI: 9 years- not reached) compared to 11 years (95% CI: 8.1-14.7 years) for intermediate risk (n=140, 41% ) and 7.2 years (95 % CI: 5.4-10 years) for the high risk group (n=130, 38%), p<0.0001. The data for the IPSS-WM were available in 319 patients in the entire cohort. The OS for low risk IPSS (n=56) was 14.6 years (95% CI: 12.1 years-NR), 15.6 years (95% CI: 8.3-18.5 years) for intermediate risk IPSS (n=131) and 9.1 years (95% CI: 6.8-11.2 yeas) for the high risk IPSS group (n=132; p=0.009). Among the 220 patients having data for IPSS-WM as well as our proposed model, the current three variable model held its prognostic significance (p=0.0004). The Harrell's c-index was 0.647 (95% CI 0.592-0.702) for our prognostic model and 0.58 (95%CI: 0.521-0.638) for IPSS-WM. Common frontline treatment comprised of rituximab monotherapy (n=277, 33%), rituximab with alkylator or purine analog combination (n=255, 30.5%) and other therapies, including alkylators/purine analog monotherapy, ibrutinib, proteasome inhibitors, etc., in 37% patients. Conclusion: A simpler, clinically useful, risk stratification model based on age, serum albumin and serum LDH with a potentially higher discriminatory value than IPSS-WM may be used to prognosticate patients with WM. This model requires external validation. Disclosures Ansell: Pfizer: Research Funding; Affimed: Research Funding; Celldex: Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Merck & Co: Research Funding; Regeneron: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Gertz:Abbvie: Consultancy; Prothena: Honoraria; Medscape: Consultancy; annexon: Consultancy; Amgen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; janssen: Consultancy; Physicians Education Resource: Consultancy; Ionis: Honoraria; spectrum: Consultancy, Honoraria; celgene: Consultancy; Research to Practice: Consultancy; Apellis: Consultancy. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lacy:Celgene: Research Funding. Dingli:Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding. Kumar:Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: 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, Research Funding; Janssen: 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, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding.

Abstract Introduction Waldenstrom macroglobulinemia (WM) is a rare disease accounting for only 1-2% of all hematologic malignancies and with an overall annual, age-adjusted incidence of approximately 3.5 to 5.5 cases per million-person years. With approximately 13,000 active hematologist/oncologists in the US, most of who are in the community oncology setting, it's estimated that a hematologist/oncologist will only diagnose a WM case approximately every 8 years. Therefore, the clinical experience of a general hematologist/oncologist in the management of WM is likely very limited compared to more prevalent malignances. Previous studies in the setting of rare cancers suggest a correlation between higher volume of care and improved outcomes. Therefore, we explored the volume-outcome relationship in WM using the National Cancer Data Base (NCDB). Methods Patient-level data from the NCDB, a nationwide, facility-based, database covering approximately 70% of all newly diagnosed cancer cases in the US, was queried for all new WM cases diagnosed between 2004 and 2014. Only patients requiring treatment were included. Treatment facilities were divided into quartiles based on the average annual volume of newly diagnosed cases of WM seen. Cox regression was used to analyze the association between facility WM volume and survival, adjusted by demographics (sex, age, race), socioeconomic status (income, education, insurance type), geography (area of residence, treatment facility type, travel distance), comorbidity factors (Charlson-Deyo score), and year of diagnosis. Time-to-event analysis was calculated from frontline therapy initiation date using the Kaplan-Meier method and the log-rank test. Results A total of 3,732 patients with WM treated in 831 facilities were included. The median age at diagnosis was 70 years and 75% of the patients were treated within 20 miles from their residency zip code. Patient characteristics per treatment facility volume quartile are shown in table 1. The median annual facility volume was 1 new WM patient/year (range 0.1 to 21). The median follow-up from frontline treatment was 5 years. The unadjusted median OS by facility volume was: Q1: 6.5 years (95% CI: 5.7-7.4), Q2: 7 years (95% CI: 6.3-8.2), Q3: 8.2 years (95% CI: 7.1-8.9), and Q4: NR (95% CI: 8.5-NR), p<0.0001 (figure 1). The estimated 5-year OS by facility volume was: Q1: 56%, Q2: 61%, Q3: 64%, Q4: 71%, p<0.0001. Multivariate analysis including all variables showed in Table 1 demonstrated that facility volume was independently associated with all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower-quartiles facilities had a higher incremental risk of death (Q3 hazard ratio [HR], 1.07 [95% CI: 0.88 to 1.29] p=0.46; Q2 HR, 1.34 [95% CI: 1.11 to 1.63] p=0.002; Q1 HR, 1.52 [95% CI: 1.23 to 1.88] p=<0.0001). Conclusion Our results suggest that a volume-outcome relationship exits in WM as patients treated initially at higher-volume facilities had a lower risk of mortality. Although differences in the underlying disease biology, referral patterns after initial therapy, or cumulative treating-physician experience could not be assessed, these potential biases would only underestimate the magnitude of the volume-outcome relationship reported. Disclosures Ansell: Celldex: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding. Gertz:Medscape: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Ionis: Honoraria; Physicians Education Resource: Consultancy; Prothena: Honoraria; Teva: Consultancy; Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; annexon: Consultancy; Apellis: Consultancy. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Ailawadhi:Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding; Celgene: Consultancy; Takeda: Consultancy. Reeder:Affimed: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Backgroud. The treatment landscape of mantle cell lymphoma (MCL) has significantly changed in last decades. Improvement in diagnosis and understanding of disease biology has been coupled with emergence of new therapeutic options, including targeted agents. While MCL outcome data comes primarily from clinical trials (CT), the impact of therapeutic advances on pattern of care (POC) and outcome in MCL in the general population is not well characterized. This study sought to characterize changes in pattern of care and outcomes of patients with MCL in a prospective observational series in the rituximab era. Methods. The study included consenting adult patients with newly diagnosed MCL that were prospectively enrolled into the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE) Molecular Epidemiology Resource, from 09/01/2002 to 06/30/2015. Demographic, clinical and prognostic factors were abstracted, and all patients were actively followed for re-treatment, relapse and death. Since bendamustine-rituximab (BR) regimen starting to be used in 2010, we defined patients enrolled from 09/01/2002 to 12/31/2009 as era 1 and those enrolled 01/01/2010 to 06/30/2015 as era 2. Baseline characteristics and outcomes, evaluated in terms of event free survival (EFS), overall survival (OS) and cause of death, were analyzed and compared between the two eras identified. Results. 348 patients with newly diagnosed MCL were enrolled. Five patients had no available data for front-line treatment and were excluded. The analysis was thus conducted on 343 patients: 169 patients were diagnosed in era 1 with a median follow-up of 131.2 months vs. 174 in era 2 with a median follow-up of 58.9 months. Baseline clinical characteristics and MIPI score were similar across the two eras ( Table 1). Frontline induction treatment was significantly different in the two eras. BR use was 0 vs 49 (28.2% ), R-CHOP/CHOP like regimen in 89 (52.7%) vs 45 (25.9%), high-dose cytarabine (HiDAC)-based therapy in 1 (0.6%) vs 28 (16.1%), intensified regimens (HyperCVAD) in 16 (9.5%) vs 8 (4.5%), other regimens (including R-cladribine, R-fludarabine-mitoxantrone, rituximab monotherapy) in 35 (20.7%) vs 13 (7.5%) patients in era 1 vs era 2 respectively. Non-systemic treatment (observation, surgery or radiation only) was performed in 25 (14.8%) vs 31 (17.8%), while 9 (8%) vs 12 (7.7%) patients were enrolled in clinical trials, in era 1 vs era 2 respectively. Autologous stem cell transplantation (ASCT) as consolidation of first line treatment or use of Rituximab maintenance was not different between Era 1 and Era 2. Among the entire cohort of 343 MCL patients, 3y-EFS and 3y-OS were 51.9% (95% IC 46.7-57.6) and 73.5 (95% CI 68.8-78.4), respectively (Figure 1). 3y-EFS was 45.9% (95% CI 39.0-54.1%) vs 58.4 (95% CI, 51.2-66.6%) in Era 1 vs Era 2 (HR 0.69 (0.51-0.92), p=0.006), 3y-OS was 70.9% (95% CI, 63.4-78.1%) vs 76.1% (95% CI, 69.7-83.1%) in Era 1 vs Era 2 (HR 0.87 (0.61-1.24), p=0.26), respectively (Figure 1). In a univariate analysis, high risk simplified MIPI was prognostic of lower EFS and OS in both the era groups. Conclusion. The BR regimen entered front line therapy of MCL resulting in decline of R-CHOP use in the MER. While rates of ASCT remain similar over the two eras, high dose cytarabine usage in induction therapy has increased. While POC in MCL continue to evolve, the introduction of bendamustine and high-dose citarabine based regimens resulted in an improvement in EFS but not OS in this observational cohort-based analysis. Findings in this study are important for design and planning of future clinical trials incorporating novel agents in induction therapy of MCL. Figure 1. Figure 1. Disclosures Cohen: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; AbbVie: 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; Janssen: Research Funding; Bristol-Myers Squibb: Research Funding; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BioInvent: Consultancy; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cerhan:Celgene: Research Funding; Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding.

Abstract Background: The majority of subjects with a diagnosis of primary central nervous system lymphoma (PCSNL) have diffuse large B-cell lymphoma (DLBCL) as their histology. However, there are a sub-set of PCSNL patients who present with other histologies. This retrospective study was conducted to evaluate histological morphology, risk factors and clinical course in subjects with non-diffuse large B-cell primary CNS lymphoma. Methods: We used the Mayo Clinic Lymphoma Database to search for patients with PCSNL during the time period from January 1995 to June 2018. One hundred and fifty nine patients met the criteria and underwent record review. One hundred and nine patients were excluded as they had PCSNL with DLBCL histology (N=47, 43%), secondary central nervous system lymphoma (N=56, 51%) and post-transplant lymphoproliferative disorders (PTLD) with DLBCL morphology (N=6, 5%). We identified fifty patients with PCSNL Non-DLBCL histology, by evaluating the biopsy reports. We abstracted clinical data and outcomes for these groups through medical chart review. Kaplan-Meier analysis was used to estimate survival. Results: The mean age of the study population was 70 ± 11.3 years and 66% were male. Of the 50 patients, the most common histology was low grade CNS lymphoma (N=14, 28%) while Hodgkin's lymphoma (N= 1, 2%), Burkitt's lymphoma (N= 1, 2%) and histiocytic lymphoma (N= 1, 2%) were the least common. The frequencies of other PCSNL Non-DLBCL histology are demonstrated in Table1. Most patients showed good ECOG performance status of 0-2 and four patients had ECOG performance status of grade 3 (N=4, 8%). Six of the patients were immunocompromised (N=6, 12%), PPD positive tuberculosis; corticosteroids and PTLD with DLBCL histology were the causes for the immunocompromised state. The location of the CNS lymphoma included brain (N=24, 50%); spinal cord (N=9, 18%); orbits (N=5, 10%); deep structures which included brainstem, cerebellum, cerebellopontine angle and cauda equina (N=5, 10%); leptomeninges (N=4, 8%) and neural involvement which included sciatic, peroneal and facial nerve (N=3, 6%). CSF examination revealed increased protein levels (N=10, 20%), malignant cells (N=11, 22%), negative cytology (N=14, 28%) and unknown or not done (N=15, 30%). Most patients presented with neurological symptoms such as seizures, peripheral neuropathy (numbness/weakness/tingling), headache, confusion & cognitive disturbances (memory changes), gait disturbances (difficulty with balance/ataxia), visual disturbances (blurry vision) and paraplegia, but none of them presented with B-cell symptoms such as fever, night sweats and weight loss. Treatment modalities included chemotherapy, radiation and surgery. Fourteen patients underwent radiation therapy (N=14, 28%); three of the patients had undergone surgery of which the patient with peripheral T-cell lymphoma had undergone a complete excision, the patient with marginal zone lymphoma had undergone debulking with subtotal craniotomy and the patient with MALT lymphoma had undergone resection with gamma knife. Table 2 demonstrates the various chemotherapy drugs used and the distribution of frequency of biopsy, radiation and surgical treatment in the different pathology of PCSNL Non-DLBCL. The median overall survival period using Kaplan-Meier analysis showed that Burkitt's Lymphoma had the least median time survival of less than a month and PTLD had the highest median survival of 146.25 months (p=0.0017). Table 3 shows the median survival time for all the different pathology's of PCSNL Non-DLBCL. Conclusion: This retrospective study reinforces the critical need for a histologic diagnosis when a patient is diagnosed with PCNSL. PCSNL is considered to be DLBCL but there are other histology's that could potentially be PCSNL and the treatment should be tailored to the individual patient's histology. Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: Clinical tumor lysis syndrome (TLS), which manifests most often as acute kidney injury (AKI) is a common and devastating complication of rapidly proliferating hematologic malignancies. Epidemiologic studies of attributable risk factors for AKI in this setting are small, focus on only aggressive lymphomas rather than the more prevalent intermediate risk disease subtypes, and predate contemporary risk stratification, diagnostic criteria, and supportive care. Additionally, early administration of rasburicase to enhance uric acid (UA) elimination has been adopted without robust evidence in support of its impact on clinical outcomes, specifically, prevention of AKI. Methods: This was a retrospective cohort study of adult lymphoma patients at intermediate or high risk for TLS treated at Mayo Clinic between 2009 and 2017. Patients were excluded if they had AKI at hospital admission, received pre-admission rasburicase, or were on dialysis. Consensus definitions were used to stratify TLS risk and stage AKI. The incidence of new AKI in the setting of TLS was described along with predictors of its development, including early rasburicase use. Results: Of the 461 patients with a new lymphoma diagnosis during the study timeframe at intermediate or high risk of TLS, 78 were excluded, primarily due to prevalent AKI at hospital admission. The 383 included patients were predominantly at intermediate risk for TLS (73%) and had a diagnosis of Diffuse Large B-cell Lymphoma (64%). Incidence of new onset AKI during hospitalization was 6% and 91% of cases were stage 1. All new onset AKI occurred within the first 5 days of hospital admission. Independent predictors of new AKI included age > 60 years, history of chronic kidney disease or cardiovascular disease, and UA >8 mg/dL at any time during hospital admission. Rasburicase was given to 48 patients, 6 (13%) of whom still went on to develop AKI. Rapid UA reduction with rasburicase did not impact the risk of developing AKI (HR 2.3; p = 0.11). Uric acid level at the time of administration did not modify the effect of rasburicase on prevention of AKI (p = 0.36 for the interaction term). Conclusion: In conclusion, for lymphoma patients at intermediate or high risk for TLS, if not present at admission, AKI only infrequently developed during the course of a patient's hospitalization. Predictors of AKI included advanced age, comorbid conditions, and elevated UA levels. Early rasburicase administration, regardless of UA level, did not alter AKI risk. Disclosures No relevant conflicts of interest to declare.