Krull, Jordan E.

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Description Follicular Lymphoma (FL) is a germinal center-derived malignancy representing approximately 20% of all non-Hodgkin’s lymphomas. Despite a relatively indolent disease course, FL is a biologically and clinically heterogeneous disease which poses significant clinical challenges due to its propensity for relapse and transformation into aggressive and untreatable forms of lymphoma. Here, we have leveraged computational techniques with WES, RNA-sequencing, and immune profiling data from FL tumors to gain further insight into the biologic basis for FL heterogeneity. We performed computational reconstruction of the B cell receptor repertoire on B cell sorted RNA-sequencing data from normal lymph nodes (n = 5) and FL tumors (n = 89). To gain a better understanding of FL B cell biology at the single cell level, we further investigated the clonal architecture of dominant malignant clonotypes in FL tumors using computational analysis of paired single cell V(D)J and 5’ GEX sequencing data from FL tumors (n = 21) and non-malignant lymph nodes (n = 3), allowing us to characterize the germinal center programs in malignant versus non-malignant B cells. Our data suggest that a subset of FL tumors consist of complex polyclonal B cell populations, some of which represent a pre-malignant FL precursor. Our data provide a deepened understanding of FL biology, including precursor cells, and contributes a refined transcriptomic characterization of malignant and non-malignant B cells in Follicular Lymphoma. Funding Sources This work was supported by the National Institutes of Health (R01 CA212162-01A1 to A.J.N and J.R.C., and P50 CA097274 to J.R.C., and A.J.N) Topic Categories Computational and Systems Immunology (COMP)

Introduction MYD88 L265P mutations (MUT) are found in 90% of patients with Waldenstrom macroglobulinemia (WM) and lead to the activation of pro-survival signaling. Limited literature has demonstrated that patients with MYD88 wild-type (WT) WM have poorer responses to conventional therapies and a higher likelihood of histological transformation. Increasingly the role of non-coding genomics in lymphomagenesis has demonstrated importance and currently, the epigenetic landscape of MYD88 WT WM remains underexplored. This study utilized a multi-omics approach to assess DNA methylation in MYD88 WT WM compared to MYD88 MUT patients and controls. Methods Bone marrow samples were prospectively collected from 20 subjects [3 MYD88 WT/CXCR4 WT WM, 12 MYD88 MUT WM and 5 normal controls] and positive selection for CD19+ and/or CD138+ B cells was performed. The analysis focused on the comparison between MYD88 WT WM to normal controls and MYD88-WT to MUT WM. Genome-wide DNA methylation analysis was performed, and CpG methylation ratios were segmented into 200-bp regions. Differentially methylated regions (DMRs) were determined using a Q-value <0.05 and absolute methylation difference ≥10%. Next, RNA-seq data was interrogated and differentially expressed genes (log2fold change>0.5 or <-0.5, false discovery rate<0.05) with corresponding promoter hypo- or hypermethylation were assessed. Ingenuity Pathway Analysis was used to determine potentially epigenetically regulated pathways using a corrected p<0.05. Results Comparing MYD88 WT WM samples to controls, a statistically greater number of hypermethylated DMRs were observed across genomic regions (hyper- vs hypomethylated): promoter (29129 vs 6300), 3'untranslated region (UTR) (2068 vs 1551), 5'UTR (10169 vs 735), intron (30344 vs 24511), exon (2224 vs 6855), CpG island (30372 vs 1240) and CpG shore (14477 vs 5872) (all p<0.001). Next, mRNA analysis demonstrated that 214 genes were differentially up-/downregulated with concordant promoter hypo-/hypermethylation. On the assessment of promoter-methylation-based pathways, multiple pathways involved in cell signaling, proliferation, gene regulation, cytokine and immune signaling and metabolism were found to be deregulated. Most pathways were found to be negatively enriched in MYD88 WT patients compared to controls including several members of interleukin (IL) signaling (IL-2, IL-3, IL,7) and regulators of cell proliferation and signaling including mTOR, PI3K/AKT, and p53. Positive enrichment of the PTEN, FGFR3 and ERK/MAPK pathways were observed. Underlying multiple pathways was promoter hypermethylation with corresponding mRNA downregulation of AKT3 and PIK3CA. Furthermore, promoter hypomethylation with transcript upregulation of PTEN was observed. Next, in comparison of MYD88 WT to MUT samples, a statistically greater number of hypermethylated DMRs were observed across genomic regions (hyper- vs hypomethylated): promoter (24829 vs 2544), 3'UTR (3534 vs 378), 5'UTR (6111 vs 605), intron (52292 vs 5360), exon (21066 vs 2187), CpG island (18334 vs 1551) and CpG shore (16351 vs 1957) (all p<0.001). Assessing mRNA expression, 354 genes were up-/downregulated with concordant promoter hypo-/hypermethylation. Pathway analysis revealed positive enrichment of the mitotic G1 phase to G1/S transition pathway with upregulation of the CDK4, LYN, and PSMA6 transcripts in MYD88 WT compared to MUT samples. Further, negative enrichment of cell signaling regulators including the RHO GTPase family, IL pathways (IL-4, IL-8, IL-13, IL-17) and integrin signaling were found in MYD88 WT compared to MUT samples. Underlying multiple pathways were downregulation with promoter hypermethylation of BCL2, an important regulator of apoptosis. Conclusion Genome-wide methylation analysis of MYD88 WT WM revealed a unique DNA methylation profile, with potential epigenetic alteration of key oncogenic transcripts and pathways. The negative enrichment of cell signaling and cytokine/inflammatory pathways in MYD88 WT samples compared to controls and MYD88 MUT WM suggests an epigenetic mechanism for immune evasion and potentially underlies the reduced response to conventional WM-directed therapies. Overall, this study reveals that MYD88 WT WM is epigenetically distinct from MYD88 MUT WM and regulators such as methylation may underlie the unique clinical and phenotypic properties observed.

ABSTRACTDiffuse large B‐cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non‐PTR DLBCL. We found a significant increase in the frequency of TP53 (34% vs. 15%, p = 0.005) and ARID1A mutations (21% vs. 7%, p = 0.007) in PTR cases, with pathway analysis further demonstrating a downregulation of TP53 and an increase in chromatin modifying pathways. These results suggest that TP53 and ARID1A may be key mediators of PTR and important pathways contributing to the poor outcomes. We found that the current molecular classifiers were unable to identify PTR cases at diagnosis. However, our newly identified high‐risk signature identified 46% of PTR cases at diagnosis. Overall, these results contribute to our understanding of the genomic landscape of patients with primary treatment resistance.

Abstract Currently, the role of DNA methylation in the immunoglobulin M (IgM) monoclonal gammopathy disease spectrum remains poorly understood. In the present study, a multiomics prospective analysis was conducted integrating DNA methylation, RNA sequencing (RNA-seq), and whole-exome sequencing data in 34 subjects (23 with Waldenström macroglobulinemia [WM], 6 with IgM monoclonal gammopathy of undetermined significance [MGUS], and 5 normal controls). Analysis was focused on defining differences between IgM gammopathies (WM/IgM-MGUS) compared with controls, and specifically between WM and IgM-MGUS. Between groups, genome-wide DNA methylation analysis demonstrated a significant number of differentially methylated regions that were annotated according to genomic region. Next, integration of RNA-seq data was performed to identify potentially epigenetically deregulated pathways. We found that pathways involved in cell cycle, metabolism, cytokine/immune signaling, cytoskeleton, tumor microenvironment, and intracellular signaling were differentially activated and potentially epigenetically regulated. Importantly, there was a positive enrichment of the CXCR4 signaling pathway along with several interleukin (interleukin 6 [IL-6], IL-8, and IL-15) signaling pathways in WM compared with IgM-MGUS. Further assessment of known tumor suppressor genes and oncogenes uncovered differential promoter methylation of several targets with concordant change in gene expression, including CCND1 and CD79B. Overall, this report defines how aberrant DNA methylation in IgM gammopathies may play a critical role in the epigenetic control of oncogenesis and key cellular functions.

Introduction: Molecular classifications have been developed to characterize the heterogeneity of diffuse large B cell lymphoma (DLBCL), with activation of BCL6 and Notch signaling pathways acting as driving pathogenic features in C1/BN2 subtype tumors. Among C1/BN2 defining variants are truncating/inactivating SPEN mutations. SPEN regulates the Notch signaling pathway by initiating formation of a repressive complex that facilitates transcriptional repression of Notch target genes. Our group has identified SPEN mutations enriched in DLBCL cases failing to achieve event-free status at 24 months (EFS24). This contrasted with generally favorable outcomes in C1/BN2 tumors, suggesting a potential high-risk molecular feature with unexplored significance. Presenting an opportunity to expand underlying mechanisms and risk stratification in C1/BN2 tumors, we aimed to use a functional genomic approach, combined with clinical data, to characterize the alternative biology stemming from SPEN truncating mutations. Methods: FFPE tumor biopsies from newly diagnosed DLBCL cases (N=444) enrolled in the Mayo Clinic/Iowa Lymphoma Molecular Epidemiology Resource (MER) program were analyzed by WES (n=404) and RNA-seq (n=321). Biologic and clinical variables were annotated for all cases. CRISPR/Cas9 introduced truncating mutations to the endogenous SPEN locus in OCI-LY3 & SU-DHL-2 ABC-type human cell lines to establish models of SPEN depletion. Functional in vitro analysis utilized standard methods for RNA-seq, cell proliferation, western blot, and flow cytometry. Results: WES identified 29/404 ndDLBCL (7.2%) harboring SPEN mutations; n=17 missense and n=12 nonsense or frameshift insertions/deletions (truncating variants - [trunc]), the later considered as the focal point of our study. Against all cases, SPEN trunc cases displayed enrichment for high-risk factors including non-GCB COO (p=0.06, OR=3.7, 95% CI [0.9, 22.2]) and primary refractory disease (PTR; p=0.02, OR=5.0, 95% CI [1.05, 19.9]), and demonstrated inferior OS (p=0.06, HR=2.2), where a majority of clinical progression events occurred within 12 months of diagnosis. Leveraging RNA-seq, we observed 11 of 11 SPEN trunc cases exhibiting a double-hit negative gene expression signature, 9/11 cases with inflammatory or depleted microenvironment (LME), and 8/11 with a signature associating with intermediate/high risk of EFS24 failure. Applying the LymphGen algorithm, 5/12 SPEN trunc cases received the BN2 classification, observing co-occurring mutations with CD70 (p<0.01, OR=9.68), BRCA2 (p=0.03, OR=5.84), TET2 (p=0.02, OR=4.91), IRF4 (p=0.02, OR=7.27), and PTPRD (p<0.01, OR=8.31) genes, and were mutually exclusive with ARID1A, BCL2, EZH2, FAS, HLA-B, IRF8, and TMEM30A mutations ( Figure 1A). Isolating SPEN trunc against BN2 cases without SPEN variants (n=14), despite similar clustering of non-GCB COO (p=0.7), PTR was exclusively observed in SPEN trunc cases, accompanied again by inferior OS (p=0.07, HR=4.2; Figure 1B). In the OCI-LY3 SPEN depletion model, GSEA analysis from RNA-seq revealed activation of E2F (NES=2.7; p=9.4E-20) and MYC target genes (NES=2.3; p=4.1E-6), while both OCI-LY3 and SU-DHL-2 models featured depletion of antigen presentation genes (NES= -1.8, p=5.7E-4; NES= -2.2, p=3.8E-7, respectively). A decrease of MHC class II proteins (HLA-DR, HLA-DP) was confirmed by flow cytometry in both models (p<0.01). Hypothesizing loss of SPEN enhances reliance on the BCL6-HDAC3 axis featured in BN2 tumors, we treated both SPEN depletion models with a class-I HDAC inhibitor highly selective for HDAC3, BRD-3308, and indeed observed a 1.4 and 2.4-fold increase in sensitivity compared to WT cells (p=0.07, p<0.01, respectively). Significance: We find SPEN trunc mutations in poor acting DLBCL cases with clinical and biologic characteristics that diverge from C1/BN2 biology. The enhanced rate of clinical progression highlights urgency to better characterize these tumors. Our data suggests loss of SPEN regulatory mechanisms may act cooperatively with C1/BN2 biology to facilitate tumor immune evasion and drive progression through Notch-mediated pathways. Importantly, in vitro evidence shows SPEN deficient lymphoma cells are susceptible to targeted therapeutics against dependent pathways, providing rationale for advancing individualized care strategies for affected patients.

Introduction: Waldenström macroglobulinemia (WM) is a rare B-cell lymphoma that may be preceded by an asymptomatic IgM-monoclonal gammopathy of undetermined significance (MGUS). The mechanisms underlying the progression from IgM-MGUS to WM remain to be fully elucidated; however, the stepwise accumulation of genomic abnormalities has been suggested as a potential driver. Currently, the role of epigenetic regulation of DNA remains underexplored in this disease spectrum. Aberrant DNA methylation is a hallmark of oncogenesis that plays an important role in hematologic malignancies. The aim of this study was to assess differential DNA methylation in WM compared to IgM-MGUS and identify hyper- and hypomethylated pathways that may alter key cellular functions. Methods: This study included 15 patients with WM and 6 with IgM-MGUS. Bone marrow samples were prospectively collected and CD19+ and/or CD138+ B cells were positively selected. Subsequently, genome-wide DNA methylation analysis was performed. CpG methylation ratios were segmented into 200-bp regions and differentially methylated regions (DMRs) comparing WM to IgM-MGUS were identified. DMRs with a Q-value <0.05 and absolute methylation difference of ≥20% were considered significant and utilized for analysis. DMRs were annotated according to genomic regions of interest (promotors, 3'UTR, 5'UTR, introns, exons, CpG islands, CpG shores). Next, genes with significant promotor hyper- or hypomethylation were assessed using the Ingenuity Pathway Analysis. Pathways with a corrected p-value<0.05 were considered statistically significant and a Z-value was calculated based on the degree of differential methylation. Results: Genome-wide DNA methylation analysis revealed a significant number of DMRs (n= 15691, hyper: 8248; hypo: 7443) in WM compared to IgM-MGUS. Assessing genomic regions, promotors (hyper: 1379; hypo: 2403), introns (hyper: 1548; hypo: 2292), CpG shores (hyper: 587; hypo: 849) and 3'UTR (hyper: 66; hypo: 193) regions were observed to have a significantly greater number of hypomethylated DMRs (all comparisons, p<0.001) in WM compared to IgM-MGUS (Figure A). While exons (hyper: 1686; hypo: 1378), CpG islands (hyper: 2128; hypo: 82), and 5'UTR (hyper: 854; hypo: 246) regions had a significantly greater number of hypermethylated DMRs (all comparisons, p<0.001) (Figure A). Next, promotor methylation-based pathway analysis revealed differential methylation of multiple cellular pathways important in WM pathogenesis (Figure B). These included pathways involved in intracellular signaling and the regulation of cellular proliferation, such as the STAT3 and CXCR4 pathways which were hypomethylated, and the PTEN signaling pathway which was hypermethylated. Of interest, we have previously demonstrated the transcription factor, STAT3, to have an increased expression in WM and to be implicated in IgM secretion (Elsawa, 2011). Next, multiple cytokine signaling pathways were observed to be hypomethylated in WM compared to IgM-MGUS including members of the interleukin (IL) family (IL-1, IL-8, IL-15, IL-23), VEGF, and TGF-β. Of relevance, previous studies have demonstrated the elevation of IL-8, IL-15, and increased expression of angiogenic cytokines, including VEGF, in patients with WM (Lewicki, 2010; Anagnostopoulos, 2007). Furthermore, our group has also shown miRNA-based epigenetic regulation of IL-8 and IL-15 in WM compared to IgM-MGUS (Chohan, 2023). Lastly, several pathways critical in the regulation of the cell structure and the extracellular matrix (ECM) were found to be hypomethylated including the FGF, actin cytoskeleton, PAK, and FAK signaling pathways. Of note, the FGF/FGFR axis has been demonstrated to be dysregulated in WM, and its targeting has been shown to halt WM cell growth (Sacco, 2021). Conclusions: This study demonstrates differential DNA methylation of key genomic regions in WM compared to IgM-MGUS. Promotor methylation-based analysis reveals hypomethylation of several important cellular pathways involved in cytokine signaling, cell structure/ECM, and intracellular signaling. These results underscore the influence of epigenetics in the IgM-gammopathy disease spectrum and suggest the potential role of aberrant DNA methylation underlying the malignant progression from IgM-MGUS to WM.

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.

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.