Manske, Michelle K.

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.

Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: 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.

Abstract Massively parallel sequencing analyses have revealed a common mutation within the MYD88 gene (MYD88L265P) occurring at high frequencies in many non-Hodgkin lymphomas (NHL) including the rare lymphoplasmacytic lymphoma, Waldenström’s macroglobulinemia (WM). Using whole exome sequencing, Sanger sequencing and allele-specific PCR, we validate the initial studies and detect the MYD88L265P mutation in the tumor genome of 97% of WM patients analyzed (n=39). MYD88L265P was detected at lower frequencies in other indolent lymphomas including LPL (0%), MALT (4%), nodal MZL (5%) and splenic MZL (8%); all but one MYD88L265P was heterozygous. Due to the high frequency of MYD88 mutation in WM and other NHL, and its known effects on malignant B cell survival, therapeutic targeting of MYD88 signaling pathways may be useful clinically. However, while the effects of MYD88L265P on the activity of IRAK1/4 and NF-κB are have been studied previously, we are lacking a thorough characterization of the role of intermediary signaling proteins such as TRAF6 and TAK1 on the biology of MYD88L265P-expressing B cells. A better understanding of the proteins involved in MYD88L265P signaling may lead to the development of more targeted and effective therapeutic approaches. In an attempt to identify MYD88L265P –specific therapeutic targets we first wanted to characterize the role of intermediary signaling proteins that facilitate the downstream activation of NF-κB. Upon activation of TLRs or IL-1b receptors, MYD88 forms a homodimer and recruits IRAK1/4 and TRAF6 into a complex resulting in association and phosphorylation of TAK1 followed by activation of NF-κB. We monitored the formation of a complex comprised of MYD88, IRAK1, IRAK4 and TRAF6 and immunoprecipitation of either endogenous IRAK4 or IRAK1 revealed constitutive association of IRAK with TRAF6 and MYD88L265P. To assess if the formation of a MYD88L265P/IRAK/TRAF6 complex results in downstream activation of TAK1, constitutive TAK1 phosphorylation was measured and detected in all three cell lines that express MYD88L265P. An association between TAK1 and TRAF6, another measure of TAK1 activation, was also detectable. When a similar analysis of TAK1 was performed in DLBCL cells expressing wild-type MYD88, no phosphorylation of TAK1 was detected, nor was TAK1 associated with TRAF6. IRAK1, IRAK4, TAK1, TRAF6, and MYD88 were expressed at similar levels in all cell lines studied and therefore did not contribute the differences in MYD88 complex formation observed between cell lines. These studies were further confirmed using HEK 293T cells that were transduced with either a vector control plasmid or HA-tagged MYD88WT or MYD88L265P expression plasmids. Together, these studies suggest that MYD88L265P forms a complex with IRAK and TRAF6 resulting in constitutive activation of TAK1 and NF-κB. To confirm the significance of TAK1-mediated MYD88L265P signaling on lymphoma cell growth, the effect of the selective TAK1 inhibitor, (5Z)-7-Oxozeaenol, on cell proliferation was tested. All MYD88L265P-expressing cell lines were sensitive to TAK1 inhibition in a dose-dependent manner (0-10 μM). In contrast, NHL cells expressing MYD88WT were found to be insensitive to TAK1 inhibition. We next tested the impact of the TAK1 inhibitor on a MYD88L265P positive WM patient sample. Similar to what was seen in the WM cell lines, the TAK1 inhibitor inhibited WM cell growth and survival in a dose dependent manner. Additionally, the TAK1 inhibitor significantly reduced the level of IL-10 secreted by each of the cell lines. Together, these data suggest that MYD88L265P drives cell proliferation and cytokine secretion through a TAK1-dependent mechanism. In conclusion, we are the first to validate by NGS in a large patient cohort the high prevalence and specificity of MYD88L265P in WM. Cells harboring the L265P mutation but not wild-type MYD88 exhibit constitutive signaling leading to the hyperactivation of NF-κB. We have established the role of TAK1 as an integral component of MYD88L265P signaling in both WM and DLBCL cell. Our data suggest that targeting TAK1 clinically may be an effective strategy for the treatment of WM and other lymphomas driven by MYD88L265P signaling. Disclosures: Fonseca: millennium: Consultancy; amgen: Consultancy; Binding site: Consultancy; onyx: Consultancy; medtronic: Consultancy; Genzyme: Consultancy; Otsuka: Consultancy; Celgene: Consultancy; lilly: Consultancy; Onyx: Research Funding; cylene: Research Funding.

Abstract Abstract 3927 Poster Board III-863 In previous studies we characterized the t(X;14)(p11.4;q32) translocation in a patient with MALT lymphoma and found that GPR34, an orphan G-protein coupled receptor (GPCR), was highly expressed due to its juxtaposition to the IGHSA2 switch region. As part of a larger MALT gene expression-profiling project, we have now acquired gene expression analysis on the patient carrying the t(X;14)(p11;q32) translocation and have confirmed overexpression of GPR34. We then measured GPR34 mRNA expression in a panel of MALT lymphomas (n=17) and found that GPR34 was expressed at levels higher than that seen in normal B cells (mean, 11.3 fold; median, 5.5; range, 1.4-64 fold). When analyzed separately, 70% (12/17) had an expression level greater than 3-fold over normal B cells. Of note, in a gastric MALT lymphoma specimen, we found a 64 fold increase in GPR34 mRNA expression. FISH studies performed on this specimen showed an extra intact GPR34 signal but no translocation involving IGH or GPR34, suggesting that other mechanisms, including gene dosage effect, can upregulate GPR34. Elevated expression of GPR34 mRNA was also detected in other histologic types of NHL, but not to the extent seen in MALT lymphoma. Taken together, these data suggest that GPR34 is commonly overexpressed in MALT lymphoma and that deregulation of GPR34 expression can occur independent of a t(X;14)(p11.4;q32) translocation. The receptor encoded by GPR34 is most similar to the PY2 receptor subfamily of GPCR and GPR34 mRNA transcripts are abundant in mast cells while lower levels were detected in other immune cells including B cells. Signals from GPR34 have been briefly described and the results to date suggest that overexpression of GPR34 results in an accumulation of inositol phosphates. To further characterize the impact of GPR34 overexpression on cell signaling, HeLa cells were transduced with a retroviral expression plasmid (pBMN-GFP) that expresses GPR34 and GFP. GFP expressing cells were isolated and overexpression of GPR34 mRNA was confirmed by PCR and GPR34 protein expression was detected by flow cytometry. When normalized to the isotype control, pBMN-GPR34 cells expressed 17-fold more GPR34 on their cell surface compared to the pBMN-vector control cells. To determine which signaling pathways were affected by GPR34 overexpression, pBMN-GPR34 or pBMN-vector control cells were transfected with an AP-1, CRE, NF-κB, E2F, SRE, NFAT, or ISRE- luciferase reporter plasmid. Upon normalization with renilla, pBMN-GPR34 expressing cells had increased luciferase activity (n=3) driven by AP-1 (5.35-fold), CRE (4.7), NF-κB (2.8-fold), and E2F (2.13) when compared to pBMN-vector control cells. ISRE, NFAT, and SRE mediated luciferase expression was similar in the GPR34 and control cells. AP-1 and CRE have been implicated in a large variety of cellular processes, including transformation, and both AP-1 and CRE activity is induced upon activation of MAP kinases. To determine if MAPK activity was also upregulated in GPR34 expressing cells, we analyzed the phosphorylation status of Erk1/2 in pBMN-GPR34 cells by western blot and found that Erk1/2 was constitutively phosphorylated in GRP34 expressing cells (1.8 fold increase) compared to vector control cells. Increased phosphorylation of PKC-α/β was also detected in pBMN-GPR34 cells (3.5 fold increase compared to control cells). To determine the biologic impact of GRP34 overexpression on cell growth, the proliferation rates of pBMN-control and pBMN-GPR34 cells were compared and it was found that proliferation of GPR34 expressing cells was 2.2 times higher than that seen in control cells. Because the MAPK kinase pathway was found to be active in the pBMN-GPR34 cells, we tested the effect of the MEK inhibitor PD98059 on proliferation and saw a dose dependent decrease in proliferation of GPR34 expressing cells. These results suggest that GPR34-mediated proliferation is Erk-dependent. In summary, these data suggest that deregulation of GPR34 is commonly found in MALT lymphoma and that overexpression of GPR34 results in activation of Erk1/2, phosphorylation of PKC, and results is AP-1 and CRE mediated transcription. Additionally, our data suggest that overexpression of GPR34 results in increased cell growth that is MAPK-dependent. Taken together, this studies indicate that overexpression of a GPCR, GPR34, may be a novel mechanism by which MALT, lymphoma, and potentially other subtypes of NHL, develop. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 2642 BAFF is essential for B cell maturation, and a lack of either BAFF or its primary receptor, BAFF-R, results in a severe depletion of T2 marginal zone and follicular B cells. Elevated serum BAFF levels have been correlated with an increased risk of developing non-Hodgkin's lymphoma (NHL), along with a more aggressive phenotype. A growing body of genetic evidence points toward an association between the development of human disease and variation in genes encoding BAFF and its receptors. Recently, we characterized a novel lymphoma-associated mutation in TNFRSF13C, the gene encoding BAFF-R. This mutation (BAFF-R H159Y) encodes a His159Tyr substitution in the C-terminus of BAFF-R adjacent to the TRAF3 binding motif. Signaling through BAFF-R H159Y results in increased NF-κB activity, elevated immunoglobulin production and increased association with TRAF2, TRAF3 and TRAF6 compared to wild type (WT) BAFF-R. We have detected this mutation in 6% of total NHL cases (n=129), and in 10% of follicular lymphoma (FL) cases (n=41) evaluated thus far. We previously reported that BAFF-R H159Y expressing mouse B cells exhibited significantly more resistance to Fas ligand (FasL) induced apoptosis compared to their cells expressing BAFF-R WT, and we propose here that BAFF-R H159Y mediated increases in PI3K activity may explain such an enhanced anti-apoptotic response. In this study we now show that BAFF stimulated HEK 293 cells stably expressing BAFF-R H159Y not only display significantly increased Akt phosphorylation when compared to BAFF-R WT expressing cells, but also demonstrate robust Akt phosphorylation in the absence of BAFF. BAFF-R H159Y-dependent Akt activation also led to activation of the downstream Akt targets mTOR and GSK3β and their phosphorylation was inhibited following treatment with the PI3- kinase inhibitor wortmannin. We next examined the impact of the BAFF-R H159Y mutation on expression of BAFF-target genes. Quantitative PCR analyses revealed that BAFF-R H159Y cells exhibited a pattern of gene expression indicative of promoting cell survival, displaying significantly higher levels of BCL2, BCL2L1 and PIN1, while down-regulating expression of the pro-apoptotic gene BIM. We recently reported that TRAF6 associates with BAFF-R, and that such binding is more pronounced in cells expressing BAFF-R H159Y. In order to investigate the role TRAF6 plays in mediating BAFF-R-dependent PI3K activity, we silenced TRAF6 expression in HEK 293 and Karpas 422 lymphoma cells using TRAF6 shRNA. Reduced TRAF6 protein expression resulted in a parallel decrease in BAFF-R WT mediated phosphorylation of mTOR in Karpas 422 cells and phosphorylation of both Akt and GSK3β was markedly reduced in BAFF-R H159Y expressing HEK 293 cells. Interestingly, TRAF6 knock-down did not affect NF-kB2 activation in either Karpas 422 or HEK BAFF-R expressing cells suggesting that Akt does not play a role in BAFF-R mediated activation of non-canonical NF-kB. Finally, preliminary co-precipitation studies indicate that Akt can be recruited to BAFF-R itself, and our initial observations suggest that such an association is significantly reduced in cells expressing BAFF-R H159Y. Taken together, these studies suggest that the BAFF-R H159Y mutation confers enhanced BAFF-R-dependent PI3K signaling and pro-survival gene expression independent of BAFF. Moreover, such enhanced P13K activation is partly dependent upon TRAF6, and decreased recruitment of Akt to BAFF-R H159Y may function to increase the amount of this PI3K target for activation. Thus, BAFF-R H159Y likely contributes to BAFF signaling irregularities in NHL patients harboring this mutation, and may predispose individuals to developing lymphoma regardless of their serum BAFF concentration. Disclosures: No relevant conflicts of interest to declare.

Abstract Abstract 1570 B-cell non-Hodgkin lymphoma encompasses a heterogeneous group of B-lymphocyte-derived malignancies that are characterized by chromosomal translocations involving the immunoglobulin (IG) gene loci and specific histological subtypes of disease are associated with a different spectrum of IG translocations. Marginal zone derived B cell lymphomas encompass three distinct entities: extranodal marginal zone B lymphoma (MZL) of mucosa associated lymphoid tissue (MALT), nodal MZL (NMZL), and splenic MZL (SMZL). MALT lymphoma is genetically unique and four mutually exclusive chromosomal translocations have been identified in this disease thus far. However, the known translocations are only present in a subset of cases suggesting that additional uncharacterized translocations or other genetic events may exist that contribute to disease development. In previous studies we characterized the novel t(X;14)(p11.4;q32) translocation in a patient with MALT lymphoma and found that GPR34, an orphan G-protein coupled receptor (GPCR), was highly expressed due to its juxtaposition to the IGHSA2 switch region. We then measured GPR34 mRNA expression in tissue biopsies from a panel of MALT, NMZL, SMZL, lymphoplasmacytic (LPL), diffuse large B cell (DLBCL), follicular (FL), and mantle cell (MCL) lymphomas. Expression of GPR34 was detected in all tissues examined, but was significantly increased in MALT (37 fold), LPL (23 fold), NMZL (18 fold), and SMZL (21 fold) compared to normal CD19+ B cells. GPR34 mRNA expression was also analyzed by gene expression profiling (GEP) of a panel of lymphomas or control lymphoid tissue and we confirmed elevated expression of GPR34 in the t(X;14) case and detected elevated GPR34 mRNA expression in MALT, LPL, NMZL, SMZL, and ABC DLBCL. When we grouped specimens by high and low expression of GPR34, a clear molecular subtype of SMZL could be identified and suggests that GPR34 high expressing tumors have a unique GEP. To confirm the qPCR and GEP analysis of GPR34 expression, we analyzed surface expression of GPR34 by flow cytometry of normal and malignant tissues and found that expression of GPR34 was detected on all MALT and SMZL B cells with an average DMFI of 2.08 (DMFI range 1.26–3.18, n=12) and on the JeKo-1 lymphoma B cell line (DMFI= 4.75). Analysis of CD19+ cells from normal controls (n=3) revealed a low level of GPR34 on (average DMFI =1.12). Little is known about the function of GPR34 and to further characterize the impact of GPR34 overexpression on cell signaling, OCI-LY19 lymphoma B cells and HeLa cells were transduced with GPR34 or a vector control. To determine if MAPK activity, a pathway regulated by GPCRs, was upregulated, the phosphorylation status of ERK1/2 in GPR34 or control cells was analyzed and we found that ERK1/2 was constitutively phosphorylated at significantly higher levels in GRP34 expressing cells. To further explore which signaling pathways were affected by GPR34 overexpression, GPR34 or vector control cells were transfected with an AP-1, CRE, NF-kB, E2F, SRE, NFAT, or ISRE- luciferase reporter plasmid and GPR34 expressing cells had significantly increased luciferase activity driven by AP-1 (5.35-fold), CRE (4.7-fold), NF-kB (2.6-fold), and E2F (2.1) when compared to vector control cells. To determine the biologic impact of GRP34 overexpression, the proliferation rates of vector control and GPR34 cells were compared and we found that proliferation of GPR34 WT cells was significantly higher than that seen in the control. We next tested the effect of the MEK inhibitor on proliferation and saw a dose dependent decrease in proliferation of GPR34 expressing cells. We then tested the ability of GPR34 cells to generate colony formation in a soft-agar colony formation assay. After 3 weeks of culture, the GPR34 cells had formed significantly more colonies than the vector control suggesting that overexpression of GPR34 can result in increased anchorage-independent cell growth. Taken together, our results identify a novel translocation in MALT lymphoma, emphasize a novel role for GPR34 in regulation of gene expression and tumor cell growth, and suggest that MEK inhibitors may be useful in a subset of translocation–harboring or GRP34 high-expressing tumors. Disclosures: No relevant conflicts of interest to declare.

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

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

Abstract Recent next generation sequencing of paired normal and tumor lymphoma tissue highlights a significant role for the MYD88L265P mutations in Waldenstrom Macroglobulinemia (WM) and diffuse large B-cell lymphoma (DLBCL). In recent work we have found that MYD88L265P signaling is constitutively active in both WM and DLBCL cells leading to heightened MYD88L265P, IRAK and TRAF6 oligomerization and TAK1 and NF-kB activation. However, little else is know about the full impact of the mutation, especially in combination with other genetic events. Recent mouse models suggest that MYD88L265P alone may not be sufficient to induce tumor formation and that the pathway requires additional genetic hits. Interplay between MYD88L265P and other oncogenic events are further supported by the fact that TNFAIP3 (A20) inactivation often accompanies MYD88L265P. These data, combined with the finding that loss of TNFAIP3 promotes accumulation of MYD88L265PB-cells in a mouse model, suggest a potential relationship between these genetic events, and more importantly, a possible clinical impact. The aim of this study was to generate a human cell line model which mimics the effect of MYD88L265P andTNFAIP3 loss to further understand the cellular consequence of these genomic alterations in lymphoma. To mimic the TNFAIP3 loss in combination with the MYD88L265P mutation,we used the WM cell line MWCL1, which harbors a heterozygous mutation in MYD88. For the TNFAIP3 specific knock out, we used the transcription activator-like effector nucleases (TALENs) genome editing approach to induce a double stranded break in exon 5 of the TNFAIP3 gene. TALENs were expressed in MWCL1 cells by transient transfection, and single-cell clones were isolated. Sanger sequencing of the TNFAIP3 gene was performed to identify clones that carried the deletion and all clones were screened by Western blot to confirm knock down of A20. We successfully generated a panel of MWCL1 cell lines that harbor a heterozygous deletion in the TNFAIP3 gene. Western blot analysis confirm a 2.4 fold reduction in A20 expression in the heterozygous cell line (MWCL1A20-/A20+), Because A20 has been shown the be a central negative regulator of NF-κB activation, we first measured the impact of A20 loss on phosphorylation of NF-κB p65 and found that MWCL1A20-/A20+ cells had higher base line levels of pNF-κB compared to the wild type (WT) cell line (1.77 fold). Furthermore, time course stimulation experiments with the TLR ligand lipopolysaccharide (LPS) showed that MWCL1A20-/A20+ cells had a higher amount of pNF-κB after 30 (3 fold) and 60 (1.5 fold) minutes of stimulation. We next looked at expression of NF-κB target genes by quantitative real time PCR and found that IL-6 (2.5 fold; p≤ 0.05) and CXCL10 (IP10) (4 fold; p≤ 0.05) were upregulated in MWCL1A20-/A20+cells compared to WT. In summary, we have established a new WM cell line model which mimics the effect of the MYD88L265P mutation in combination with a heterozygous loss of the TNFAIP3 gene. We show that loss of TNFAIP3 results in a higher baseline and TLR-stimulated phosphorylation of NF-κB as well as in higher expression IL-6 and IP10. Overall, this cell line will be a useful tool to study biological consequences of TNFAIP3loss in WM patients as well as novel therapeutic strategies. Disclosures Ansell: BMS, Seattle Genetics, Merck, Celldex and Affimed: Research Funding.