Cerhan, James R.

BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological phenotype with a relatively small number of malignant T-cells and an extensive infiltrate of normal cells including T-cells, B-cells, NK cells and FDCs. The malignant cell has a T follicular helper (Tfh) cell phenotype with increased expression of immune checkpoint molecules including PD-1 and ICOS. With the routine use of antibodies targeting immune checkpoint molecules, clinical results in limited numbers of AITL patients have thus far been mixed with some responding patients but also some who rapidly progress. Because both malignant T-cells and normal T-cells in AITL may be PD-1 positive, we conducted a study to compare the phenotype of normal T-cells and malignant T-cells within the tumor microenvironment. We also evaluated whether soluble immune checkpoint molecules in the peripheral blood differed between AITLs, controls, and other PTCL subgroups. METHODS: We designed a novel CyTOF antibody (Ab) panel comprised of 37 markers to identify and characterize cells of T, B, NK, macrophage and FDC lineages within a cohort of biopsy specimens from 6 AITL patients compared to 6 normal controls (2 LN, 4 tonsil). Soluble immune receptors and ligands were then measured by multiplex ELISA on peripheral blood from 8 AITLs, 5 controls, and 32 additional samples from PTCL subtypes including PTCL-NOS, ALCL and CTCL. RESULTS: Malignant T-cells in AITL specimens could clearly be identified by viSNE mapping based on CyTOF expression of CD4, CD5, BCL6, CD10 (variable), CXCR5 (variable), PD-1 and ICOS. Aberrant loss of CD3 and CD7 on malignant T-cells was commonly seen. Expression of other immune checkpoint molecules was assessed. Malignant cells expressed high levels of TIGIT and moderate levels of CD27, but malignant T-cell populations were negative for expression of TIM3, LAG3, and CTLA-4. A substantial proportion of normal T-cells also expressed immune checkpoint molecules including PD-1 and CD27, and a subset of these cells also expressed TIGIT and ICOS. A small minority of normal T-cells expressed TIM3, LAG3, and CTLA-4. As others have described, the normal Tfh cell population had an immunophenotype that was otherwise relatively similar to the malignant population, but distinguishable via viSNE mapping with our CyTOF Ab panel. Expression of PD-L1 could be detected on NK-cells, macrophages, and a few select populations of normal T cells, but not on B-cells or FDCs. Malignant T-cells did not express PD-L1. Due to the potential importance of immune checkpoint molecules in this disease, we then measured soluble checkpoint receptors and ligands in the serum. Soluble PD-1 was significantly increased in the peripheral blood of AITL when compared to normal controls (average 2637 pg/mL (SE = 512.1) in AITL vs 57.87 (SE = 16.88) in controls). Similarly, soluble PD-L1 was significantly increased in AITL compared to normal controls (13.8 (SE = 10.5) in AITL vs 0.94 (SE = 0.25) in controls). CONCLUSIONS: Malignant T-cells in AITL have a unique phenotype which was able to be reliably identified through use of viSNE mapping with our CyTOF Ab panel. Furthermore, malignant T-cells were found to consistently express additional immune regulatory molecules beyond PD-1 and ICOS, including TIGIT and CD27. However, a substantial subset of normal T-cells have a very similar phenotype suggesting that activation of T-cells in this disease will likely stimulate both normal and malignant T-cells. This may explain the rapid disease progression seen in a subset of AITL patients treated with PD-1 blockade. Furthermore, soluble PD-1 is significantly increased in the serum of AITL patients and may explain the overall lack of clinical efficacy of anti-PD-1 antibodies in treating AITL. Disclosures Cerhan: NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; LAM Therapeutics: Research Funding.

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL 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 and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding.

Background: Central nervous system (CNS) involvement by peripheral T cell lymphoma (PTCL) is a rare condition. Among primary CNS lymphomas, only 2% are secondary to PTCL, while the risk of CNS relapse in all cases of PTCL is estimated at 2% to 6%. Little is known about the presentation and outcomes of PTCL patients with CNS involvement given the rarity of this entity. In this study, we describe patient characteristics, histology, and clinical course of patients with CNS involvement by PTCL. Methods: The Mayo Clinic Lymphoma Database was used to identify PTCL patients with primary or secondary CNS involvement seen at our institution between 2000 and 2018. A total of 12 patients were identified and their medical records were reviewed for patient and disease characteristics, CNS-directed treatment modality, and outcomes. The Kaplan-Meier method was used for time-to-event analysis. Results: The median age at CNS diagnosis was 63 years (range 41 to 76) and 11 (93%) patients were male. The histological diagnoses were PTCL, NOS in 9 (75%) patients, enteropathy-associated T-cell lymphoma in 2 (17%) patients, and angioimmunoblastic T-cell lymphoma in 1 (8%) patient. Five patients presented with primary T-cell CNS lymphoma (all with a PTCL, NOS histology), while the remaining 7 (58%) patients also had systemic involvement. All patients presented with neurologic symptoms at the time of CNS involvement diagnosis including: focal motor deficits in 6 patients (unilateral upper extremity weakness, gait impairments, and hemiparesis), cognitive decline in 5 patients (memory impairments, reduced attention, and confusion), headache in 4 patients, and seizure in 3 patients. The CNS disease location included the brain parenchyma in 9 (75%) patients, leptomeninges in 1 (8%) patient, and lumbar plexus in 1 (8%) patient. One patient (8%) had positive CSF finding only without radiologic evidence of involvement. CSF analysis was performed in 11 patients. Elevated protein levels were noted in 3 (27%) patients, malignant cells in 2 (18%), and no clear abnormalities in the remaining 6 (55%) patients. Concomitant bone marrow involvement was seen in only 1 patient. Elevated LDH was seen in 2 patients. The a median LDH was 195 U/L (range 139 to 4,360) The most common CNS-directed therapies were: high-dose methotrexate (MTX)-based regimens in 8 (67%) patients, including high-dose MTX in combination with temozolomide (n=2), or cytarabine and thiotepa (n=2). Intrathecal MTX, temozolomide and dexamethasone, lenalidomide, high-dose steroids, and surgical resection were the treatment modality used for one patient each. At a median follow up of 18 months, eight (75%) out of 12 patients were not alive at the time of last follow up. The median overall survival (OS) from diagnosis was 16 months (95% CI: 2.8-173). The median progression free survival (PFS) from initiation of CNS-directed therapy was 9 months (95% CI: 1.6-33) (figure). Four patients had a PFS longer than 12 months. These 4 patients were treated with: temozolomide/dexamethasone, high-dose MTX, lenalidomide, and high-dose MTX followed by cytarabine/thiothepa. Conclusion: CNS involvement by T-cell lymphoma is a rare complication that carries a poor prognosis. Early onset of neurologic symptoms should trigger prompt investigation of CNS involvement. Despite the short OS and PFS, some patients may achieve a relatively longer disease free interval. Disclosures Bennani: Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Seattle Genetics: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Kite Pharma: Other: Advisory board. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Celgene: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: 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. Ansell:Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding. Paludo:Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding.

Background: While there is evidence in the literature of increased prevalence of hypogammaglobulinemia in chronic lymphocytic leukemia (CLL), there are no studies evaluating the prevalence of hypogammaglobulinemia in newly diagnosed diffuse large B cell lymphoma (DLBCL) or the relationship between hypogammaglobulinemia and presentation or outcomes. The objective of this study was to examine the prevalence of hypogammaglobulinemia in newly diagnosed DLBCL patients and to test the hypothesis that DLBCL patients with baseline hypogammaglobulinemia have a distinct clinical profile and outcome. Methods: We obtained banked frozen sera from 200 newly diagnosed, treatment-naïve, DLBCL patients from the Lymphoma SPORE Molecular Epidemiology Resource (MER), a prospective cohort study conducted at the Mayo Clinic and the University of Iowa. IgG/A/M levels were measured using immunoturbidimetric assay whereas IgE level was measured using electrochemiluminescence immunoassay; deficiency was defined using standard reference ranges. IgE levels were considered deficient if &lt;2 UI/ml. The associations between Ig deficiencies and clinical factors were evaluated with Wilcoxon rank sum and chi-squared (Fisher's exact, where appropriate) tests. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death, and EFS24 was defined as EFS at 24 months after diagnosis (achieve or failure to achieve EFS24). The association of Ig levels with EFS24 was estimated using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression, and with EFS, overall survival (OS), and lymphoma-specific survival (LSS) was estimated using Kaplan-Meier curves and hazard ratios (HR) and 95% CI from Cox regression. Results: The mean age (SD) of the cohort was 65.6 (13.4) years, 54% were males and 98% of the patients were white. Over a median follow-up of five years, there were 59 (29.5%) deaths. The prevalence of hypogammaglobulinemia, defined as any deficiency, in newly diagnosed, treatment-naïve DLBCL was 22.1% (44/199) in our cohort, and the most common Ig deficiency was for IgG (&lt;700 mg/dL, 13.5%), followed by IgM (&lt;40 mg/dL, 9.0%), IgE (&lt;2 UI/ml, 7.5%) and IgA (&lt;70 mg/dL, 4.0%) (Table1). There were no statistically significant differences between Ig deficient and non-deficient patients in terms of age at diagnosis, gender, stage, cell of origin, or MYC double hit status. However, median LDH levels were higher in Ig deficient patients (228 vs 194, p&lt;0.01). Any immunoglobulin deficiency was associated with inferior EFS (HR 1.94, 95% CI 1.16-3.24) (Figure 1) and OS (HR 2.02, 95% CI 1.17-3.49), and these associations were not attenuated after adjustment for the international prognostic index (IPI). Any Ig deficiency was also associated with failure to achieve EFS24 (OR=2.13, 95% CI 1.00-4.60) after adjusting for IPI. Conclusions: To the best of our knowledge, this is the first study to report the prevalence of hypogammaglobulinemia in treatment naïve DLBCL. We found that any Ig deficiency was not uncommon in our cohort and it was associated with an inferior EFS and OS in DLBCL patients. The prevalence of hypogammaglobulinemia in DLBCL patients seems to be lower than has been described in CLL patients. While the underlying relationship between these two immunologic disorders deserves further study, our findings highlight the interaction between global immune dysfunction and emergence of a clonal B cell process. Disclosures Nowakowski: Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Introduction: Front-line immunochemotherapy (IC) with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is expected to cure 60-70% of newly diagnosed DLBCL. Up to one third of newly diagnosed DLBCL patients will have relapsed or refractory (r/r) disease.1 Current standard of care for these patients is salvage chemotherapy and, if chemosensitive, to be followed by high dose chemotherapy with hematopoietic cell rescue (autoHCT).2,3 Event-free survival at 24 months (EFS24) after frontline IC is associated with excellent long-term outcomes with overall survival (OS) similar to that of age- and sex-matched controls.4 In comparison, those achieving EFS24 following autoHCT for relapsed or refractory disease may have increased risk for late-mortality compared to the general population with advanced age and intensive salvage therapy contributing to the risk of late complications.5 We sought to better characterize EFS24 after autoHCT to determine the utility of this end-point for informed clinical decisions, patient management, and future clinical trials. Methods: Patients were prospectively enrolled onto the Molecular Epidemiology Resource (MER) of the University of Iowa/ Mayo Clinic Specialized Program of Research Excellence (SPORE). Patients were followed for relapse, retreatment, and death; all events were validated by medical record review. For this analysis patients were included if they were consented within 9 months of initial diagnosis of DLBCL between 2002-2012, had received anthracycline-based IC (R-CHOP, or similar), and eventually had undergone autoHCT for r/r DLBCL. Patients with primary central nervous system (CNS) lymphoma or post-transplant lymphoproliferative disorders (PTLD) were excluded. Overall survival (OS) was defined as time from autoHCT until death due to any cause. Event-free survival (EFS) was defined as time from autoHCT until progression, relapse, retreatment, or death due to any cause. OS from achieving EFS24 after autoHCT was compared to age- and sex-matched general US population. Results: 108 patients underwent autoHCT for relapsed DLBCL between 2002 and 2017. Median age at autoHCT of the patients was 60 years (range 20-78) and 72 (67%) were male. The most common transplant conditioning regimen was BEAM (82%). At a median follow-up after autoHCT of 85 months (range 1-171), 72 patients (67%) had an event and 64 patients (59%) had died. Kaplan-Meier estimates for EFS and OS at 24 months from the time of autoHCT were 49% (95% CI: 40-59) and 61% (95% CI: 52-71), respectively. 52 patients had a progression within 24 months of autoHCT; OS from progression was poor (median OS=2.8 months, 95% CI: 1.8-6.0; 5 year OS = 9%, 95% CI: 2-22). 48 patients achieved EFS24 after autoHCT; median OS from achieving EFS24 was 136 months (95% CI: 92-NE) and 5 year OS was 79% (95% CI: 68-93). This was inferior to the background population (Figure 1, SMR=3.64, 95% CI: 2.11-6.27, p<0.0001). Eight patients had a progression after achieving EFS24 from autoHCT. OS from progression in these 8 patients (median OS=27.3 months, 95% CI: 14.4-NE; 5 year OS = 16%, 95% CI: 3-93) was improved compared to progression within 24 months of autoHCT (p=0.072, figure B). Cause of death (COD) in EFS24 achievers was progression of lymphoma (n=6), infection (n=1), secondary malignancy related to therapy (n=3), heart disease (n=1), and unknown (n=1). Conclusions: Patients achieving EFS24 after salvage chemotherapy and autoHCT have a favorable long-term prognosis; however, overall survival remained inferior to the general population. Most common COD after achieving EFS24 was progression of lymphoma. In spite of this, EFS24 remains a valuable end-point for informed clinical decisions, patient management, and future clinical trials. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Farooq:Celgene: Honoraria; Kite Pharma: Research Funding.

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: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: 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.