Hartert, Keenan T.

Abstract Non‐follicular low‐grade B‐cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: 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.

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.

Background:Diffuse Large B Cell Lymphoma (DLBCL) is characterized by both biologic and genetic heterogeneity. While significant therapeutic advances have been made, nearly 30% of cases fail frontline treatment. Recent data suggest that 5–10% of DLBCL can be categorized as double/triple hit lymphoma (DH/TH), defined as having concurrent MYC, BCL2 and/or BCL6 gene rearrangements, identified by fluorescence in‐situ hybridization (FISH) probes. According to the 2017 revision of the World Health Organization classification of lymphoid neoplasms, DH/TH DLBCL is now considered a high grade B‐cell lymphoma (HGBL). While gene rearrangements represent significant genetic events in cancer, copy number alterations (CNAs) also play an important role, often through genomic amplification of oncogenes or loss of tumor suppressors. Despite their similarities, the contributions of CNAs in MYC, BCL2, and BCL6, alone, or in combination with gene rearrangements, are largely unknown in DLBCL and could identify a group of previously uncharacterized patients that may have high expression of MYC, BCL2 and/or BCL6 driven by alternative mechanisms.Aims:The goal of the study is to define the landscape and prognostic significance of gene rearrangements and copy gains in MYC, BCL2, and BCL6 in a cohort of newly diagnosed DLBCL.Methods:A total of 176 cases of DLBCL with both FISH and CNA data were available for the study. FISH was performed on formalin‐fixed paraffin‐embedded sections using break‐apart probes to MYC, BCL2, and BCL6 in the Mayo Clinic Department of Lab Medicine and Pathology. Somatic copy number alterations were identified using whole exome sequencing and the Affymetrix OncoScan® FFPE assay on patient tumor FFPE tissue, and were interrogated using Nexus Copy Number 9.0 software. All patients were treated with immunochemotherapy and prospectively followed for event‐free and overall survival.Results:FISH identified 3 groups of previously described patients, DH/TH or HGBL (n = 11), translocation positive (single MYC, BCL2, BLC6, or double BCL2/BLC6, n = 86), and translocation negative (n = 79). Expanding upon the translocation positive subgroup, copy number analysis identified patients (19 of 86, 22%) with copy number gains in a second or third gene resulting in genomic hits at both MYC and BCL2/6; we classified these as alternative‐double/triple‐hit (A‐DH/TH). An example of this would be a MYC translocation plus a BCL2 and/or BCL6 gain (n = 5), or a BCL2 and/or BCL6 translocation plus a copy gain in MYC (n = 14). Similar interrogation of a publically available dataset (n = 250), revealed similar proportions of these patient groups in an independent cohort of DLBCL, validating our findings. Cell of origin classification of the A‐DH/TH cases revealed that they were 53% GCB. A‐DH/TH patients, in comparison to the translocation‐positive/copy‐gain‐negative cases, were at greater risk of an event (Cox HR = 1.5, 95% CI = 0.71–3.0) and the cases trended towards a worse overall survival (log‐rank P = 0.065; Cox HR = 2.0, 95% CI = 0.94–4.3). Patients with copy‐gains alone had similar outcomes as those with no copy‐gain or translocations, suggesting a central role for at least one translocation event in predicting outcome.Summary/Conclusion:Our data suggest that use of CNA data in combination with traditional FISH can identify a unique subset of DLBCL, A‐DH/TH, that are missed by standard clinical practice. More importantly, we find that these patients do poorly and may benefit from more aggressive treatment, although we do acknowledge that our population size was small and further validation is needed.image

Background:Recent large next generation sequencing studies have redefined the genomic landscape of diffuse large B‐cell lymphoma (DLBCL). While many of the more common genetic variants, primarily mutations, have been biologically characterized, little is known about newly emerging genetic aberrations. In particular, somatic copy number alterations remain poorly studied due to the large genomic regions they cover and the number of genes involved. This, in combination with lack of remission or early relapse in a portion of DLBCL, opens new opportunities to explore yet unidentified genetic contributions to lymphomagenesis.Aims:The goal of this study was to identify variants associated with failure to achieve event free survival at 24 months (EFS24) and define their biologic significance in DLBCL.Methods:Newly diagnosed DLBCL treated with standard immunochemotherapy were included in this study (n = 246). Copy number data was generated by using the molecular inversion probe OncoScan™ FFPE Assay Kit (Affymetrix, Santa Clara, CA, USA). OncoScan OSCHP files were analyzed using Nexus Copy Number 9.0 software (Biodiscovery, El Segundo, USA). Data interpretation and copy number calling was done using the human reference genome GRCh37/hg19. Files were analyzed using the Nexus FASST2‐Segmentation algorithm, which is based on a Hidden Markov Model approach for calling genetic events. For functional analysis, SOCS6 was overexpressed and studied in GCB‐DLBCL cell lines.Results:Upon review of genomic copy number data from DLBCL cases (n = 246), loss of 18q22.1‐q23 was a significant event in our cohort (p = 0.018), is found in 11% (n = 27) of DLBCL, and is enriched in germinal center DLBCL (GCB‐DLBCL) patients (18/107, 17%, p < 0.05) compared to activated B‐cell (ABC)/non‐GCB DLBCL patients (1/93, 1%). Furthermore, GCB‐DLBCL patients with a loss of 18q22.1‐q23 were significantly more likely to fail EFS24 (50%, p < 0.05) compared to GCB‐DLBCL patients without the loss (29%). Genes located at 18q22.1‐q23 include, TMX3, DOK6 and SOCS6. SOCS family proteins have been shown to play an important role in tumor biology, although little is known about SOCS6. SOCS6 gene expression is downregulated in a variety of solid tumors and is hypermethylated in some hematological malignancies. RNASeq data from DLBCL suggest variable expression across tumors. Using a panel of GCB cell lines we found that SOCS6 expression was low to negative, and publically available cell line data suggests this region is hypermethylated. Expression of SOCS6 was higher in a panel of mantle cell lymphoma and ABC‐DLBCL cell lines and this again correlated with their cell line methylation data, where no hypermethylation was found in those cell lines. Together, this data suggests that downregulation of SOCS6 through genomic loss or hypermethylation may have biologic importance. To explore the role of SOCS6, we restored expression in GCB cell lines and immuno‐fluorescence staining suggest localization to the nucleus and cytoplasm, as previously reported in other cell types. Studies to explore the role of SOCS6 in cytokine signaling and STAT family protein regulation are ongoing.Summary/Conclusion:Using genome copy number data from 246 DLBCL, we have found that loss of 18q22.1‐q23 (p = 0.018) is associated with GCB‐DLBCL and failure to achieve EFS24. Furthermore, we have identified that SOCS6 may have clinical and biologic significance in GCB‐DLBCL.