Citations

Abstract The purpose of this study was to explore epigenetic dysregulation resulting from deleterious SPEN mutations in diffuse large B cell lymphoma (DLBCL). Truncating (tr) SPEN mutations are among defining variants in LymphGen:BN2 and DLBclass:C1. We previously found SPEN mutations enriched in DLBCL that failed to reach EFS24, and inferior survival was observed in SPEN-tr vs all other BN2 and C1. Differential gene expression (DEG) in these groups identified >1000 DEGs including upregulation of IKZF3 and BCL2 and downregulation of MHC-II, overall suggesting an alternative, aggressive biology for a subset of BN2/C1 cases generally thought to have favorable outcomes. In healthy cells, SPEN is a dynamic epigenetic regulator with roles in chromatin formation and as a NOTCH co-repressor. We hypothesized that loss of SPEN reprograms the epigenetic landscape to drive the aggressive phenotype in affected DLBCL. Methods: OCI-LY3 cell line with CRISPR/Cas9-induced SPEN-tr mutations was analyzed by ATAC and RNA-seq. Diagnostic DLBCL FFPE tumor biopsies from the Molecular Epidemiology Resource (MER) were analyzed by 850k/EPIC methylation array (n=164). SPEN-depleted OCI-LY3 cells displayed significant (|Log2FC|>2) peaks of open (n=3083) and closed (n=1729) chromatin indicative of systemic epigenetic reprogramming, with enriched peaks (FDR<0.01, |Log2FC|>2) converging on pro-survival and immune evasion (ATP11C, BCL11B, RAG1/2 genes) and negatively enriched for cell cycle and apoptotic pathways (CDK6, TP63, CD80 genes). Chromatin accessibility featured enrichment for TCF3/4 and ZEB1 binding motifs and depletion of EBF1, REL, and NFKB1/2 motifs (FDR<0.0001, |Z-score|>5), suggesting a shift to an activated, post-germinal center program (via open TCF3/4 motifs) with reduced reliance on NF-kB signaling. Footprinting analysis further revealed decreased occupancy at NFKB2 and REL/B motifs, and reduced occupancy at HES1 with elevated occupancy at Rbpjl motifs (p<0.05, |Score|>0.2) indicating downregulation of NF-kB is in favor of non-canonical Notch transcription programming. Merging gene expression and accessibility, gene-peak concordance was observed for elevated CXCR4 and IL6 expression and decreased SOCS1, CD274 (PD-L1), and CCND1 expression (FDR<0.01, |Log2FC|>1), suggesting expression programs feature uncontrolled JAK/STAT signaling (via high IL6, low SOCS1) with immune evasion mechanisms independent of PD-L1. By methylation array, strong epigenetic reprogramming was observed in SPEN-tr DLBCL (n=8), where differential methylation analysis vs WT (n=146), vs BN2 (n=4), and vs C1 (n=15) samples revealed hypermethylation signatures (p<0.05, |FC|>1) indicative of reduced cell-cell signaling and antigen presentation pathways (including HLA-A/G/H). In conclusion, SPEN-depletion triggers systemic epigenetic reprogramming to enhance immune evasion and non-canonical Notch target genes. SPEN-tr DLBCL should be considered with heightened risk estimation and as prime candidates for personalized treatments beyond standard therapy. Citation Format: Janek S. Walker, Joseph P. Novak, Abigail R. Dropik, Zilin Xianyu, Michelle K. Manske, Matthew J. Maurer, Eric Mou, Rebecca L. King, Stephen M. Ansell, Thomas M. Habermann, Thomas E. Witzig, James R. Cerhan, Anne J. Novak. Deleterious SPEN mutations induce epigenetic reprogramming to promote survival and enhance immune evasion pathways in an aggressive subset of BN2/C1 DLBCL tumors [abstract]. In: Proceedings of the Fifth AACR International Meeting on Advances in Malignant Lymphoma: From Discovery to Clinical Impact; 2026 Jun 24-27; Philadelphia, PA. Philadelphia (PA): AACR; Blood Cancer Discov 2026;7(3_Suppl):Abstract nr A006.

Abstract Over the past decade, classification systems using high-throughput genetic data have been developed to identify molecular subtypes of DLBCL. Although not a primary goal, these classifiers were also proposed as prognostic factors in their respective publications. To date, however, no study has directly compared these classifiers' ability to predict overall (OS) and event-free survival (EFS) in the same cohort. Herein, we sought to systematically compare predictive performance, benchmarked against the IPI, of several current established molecular classifiers and evaluate if any add significant independent prognostic information beyond the IPI. WES (including paired germline filter) and RNAseq data from newly diagnosed DLBCL patients (N=432; 2018 WHO criteria) in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER) were available for analysis (WES n=112, RNAseq n=40, or both n=280). The number of cases able to be classified (including as unclassified) for each classifier was as follows: IPI (432, 100%), cell-of-origin (COO, by Hans algorithm; 346, 80.1%), refined COO (by gene expression with Dark Zone Signature; 320, 100%), Haematological Malignancy Research Network (HMRN; 349, 89.0%), B-cell state (320, 100%), Lymphoma Ecotype (320, 100%), Lymphoma Microenvironment (LME; 320, 100%), Tumor microenvironment (TME26; 320, 100%), SubLymE (312, 97.5%), LymphGen (358, 91.3%), and DLBclass (223, 58.2%). OS was defined as time from diagnosis to all-cause death while EFS was defined as time from diagnosis to disease progression, initiation of second-line treatment, or all-cause death. We calculated Harrell's c-statistics for each Cox proportional hazards model evaluating the discriminative performance of each classifier in predicting OS/EFS. Using bootstrap validation, we obtained optimism-corrected c-statistics, which we report in the results. We performed likelihood ratio tests (LRT) comparing a univariable Cox model with IPI alone and models including both IPI and a given classifier to assess if a classifier provided independent prognostic information beyond the IPI. All patients were treated with immunochemotherapy (IC) with curative intent, 247 (57%) were male, median age at diagnosis was 65 years (IQR 55-72 years), 390 (96%) were White, 209 (60%) had GCB subtype by Hans, 22 (5.1%) had MYC double-hit by FISH (MYC and BCL2 or BCL6), and 162 (37.9%) had an IPI ≥ 3. The median follow-up was 7.0 years, 167 (39%) experienced an event, and 137 (32%) died. In a univariable Cox model, the c-statistic for IPI predicting OS was 0.66 (95% confidence interval [CI] 0.61-0.70). All other classifiers had c-statistics of ≤0.56. The c-statistic for IPI predicting EFS was 0.64 (95% CI 0.60-0.69), and all other classifiers had c-statistics of ≤0.58. The univariate DLBclass results for OS and EFS were not significantly impacted when restricted to cases with confidence >0.7 (n=136). In Cox models of IPI alone versus IPI plus an individual classifier, the only classifier that provided statistically significant, independent information for OS was TME26 (LRT p=0.02). Adjusting for IPI, patients who were TME26-negative had significantly inferior OS compared to TME26-positive cases (HR 1.63, 95% CI 1.07-2.47; p=0.02). For EFS, DLBclass (restricted to confidence >0.7) added significant prognostic information beyond IPI (LRT p=0.02). This finding was largely driven by C5 subtype; relative to C1 subtype and adjusting for IPI, C5 subtype had significantly inferior EFS (HR 5.53, 95% CI 1.81-17.0; p=0.003). In summary, we compared prognostic performance of multiple DLBCL molecular classifiers with respect to OS and EFS in a single real-world, prospective cohort of newly diagnosed, IC-treated DLBCL. Our data supports two key findings: all classifiers had modest performance for prognosis, and none individually outperformed the IPI in predicting OS nor EFS. Second, only TME26 and DLBclass (confidence >0.7) provided additional prognostic information beyond that captured by the IPI for OS and EFS, respectively. This finding suggests that the tumor immune microenvironment and characteristics of C5 DLBCLs (ABC-subtype, CD79B mutations) can potentially offer significant independent information for predicting survival and response to front-line immunochemotherapy. If replicated, these findings could inform risk-adapted treatment approaches and aid in identifying high-risk patients for enrollment in genomically-informed clinical trials.

Abstract Background: Follicular lymphoma (FL) is a germinal center–derived B cell malignancy characterized by the clonal expansion of a dominant malignant B cell population. While prior studies have identified transcriptional subtypes across patients, most analyses have treated B cells within individual tumors as a transcriptionally uniform population. However, this assumption has not been systematically tested using high-resolution, clonotype-resolved transcriptomic data. The broader landscape of intratumoral B cells—including those not belonging to the dominant clone—remains poorly defined, and their roles in disease biology are not well understood. These unexpanded, intratumoral B (ITB) cells may include residual non-malignant populations, early-stage malignant cells, or immune-modulating cells with distinct roles in the tumor microenvironment. It is also unknown whether their presence varies across patients or correlates with clinical features or immune microenvironmental interactions. Methods: We performed integrated single-cell 5′ RNA sequencing and B cell receptor (BCR) repertoire sequencing on 7 newly diagnosed FL 1-3A fresh-frozen lymph node biopsies (Dasari et al., Blood Cancer Discov 2025). Using a high-confidence approach that incorporates heavy and light chain pairing, V and J gene usage, and CDR3 nucleotide junction identity, we classified B cells into two groups: (1) cells belonging to the dominant expanded clone (FL B cells) and (2) all other B cells with detectable and non-expanded BCRs (ITB). These clonal identities were then integrated with single-cell transcriptomic profiles using Seurat and Immcantation pipelines. Differential expression analysis was conducted using edgeR to compare FL B cells to ITB cells across all FL biopsies. Results: After quality filtering, a total of 44,637 single cells were retained across all 7 samples (minimum per sample: 3,847 cells), including malignant and non-malignant B cells as well as diverse tumor microenvironment populations. The proportion of B cells assigned to the dominant FL clone varied substantially across tumors, ranging from 22% to 89%, indicating that some tumors contain a substantial population of ITB cells. In one tumor, we identified two expanded malignant B cell clones comprising 40.6% and 34.4% of all cells. These clones exhibited non-overlapping V and J gene usage (IGHV3-11/IGHJ5 vs. IGHV3-72/IGHJ3), distinct CDR3 junctions, and different immunoglobulin isotypes (IGHG1 vs. IGM). BCL2 was expressed in most cells within both clones (Clone 1: 87.8%, Clone 2: 67.0%), with mean log-normalized expression levels of 1.79 and 0.95, respectively. Both clones formed distinct UMAP clusters and showed transcriptional divergence from ITBs, with 453 differentially expressed genes (DEGs) identified for Clone 1 and 98 for Clone 2 (adjusted p<0.01, |log₂FC|>1, min.pct≥0.25). Additionally, 748 DEGs were identified between the two clones. This evidence of clonal divergence within a single FL tumor reinforces the need to reconsider intratumoral homogeneity in FL and suggests that multiple malignant clones may sometimes coexist. When comparing FL clones to ITB cells across tumors we found that FL clones exhibited a transcriptional program enriched for proliferation and cell cycle regulators, including MKI67 (p<0.005), E2F1 (p<0.0001), CDKN2A (p<0.0001), and CDKN2B (p<0.01), as well as elevated expression of AICDA (p<0.01), EZH2 (p<0.0001), and PD-L1 (p<0.05). As expected, the malignant cells also had higher expression of BCL2 (p<0.01) and CD10/MME (p<0.0001). In contrast, ITB cells demonstrated relatively higher expression of SELL (p<0.0001) and TNFRSF14 (p<0.05), which were significantly downregulated in the FL clone. Conclusions: Together, these findings challenge the prevailing notion that FL tumors are defined by a single homogeneous malignant B cell population. Instead, our data support a model in which multiple transcriptionally and functionally distinct B cell populations coexist within tumors, including dominant clones and a diverse population of ITB cells. This is, to our knowledge, the first study to comprehensively characterize these populations using integrated, lineage-aware single-cell multiomic profiling across FL patients. These insights may ultimately inform new strategies for understanding tumor evolution, immune evasion, and therapeutic targeting in follicular lymphoma.

Abstract Diffuse Large B Cell Lymphoma (DLBCL), the most common subtype of aggressive B-cell Non-Hodgkin Lymphoma (NHL), accounts for 30-40% of newly diagnosed NHL cases. Despite recent progress improving our understanding of molecular features of DLBCL, there is still a high relapse rate (~45%) among patients who undergo standard treatment (R-CHOP). Several groups have identified reproducible molecular clusters of DLBCL although, the biologic significance of many of the specific gene mutations within these clusters is poorly understood. DTX1 mutations are a feature of the BN2/C1 DLBCL classifiers and are among the mutations with unknown significance. BN2/C1 DLBCL are a mixture of ABC, GCB, and Unclassified cell of origin (COO) with a common signature being the presence of genetic alterations in genes known to regulate the NOTCH pathway; yet, these tumors lack transcriptional signatures of traditional NOTCH activation, suggesting a unique and undefined biology driven through non-canonical NOTCH pathway activation. DTX1 is an E3 ubiquitin ligase which directly binds the intracellular domain of the NOTCH receptor (NICD), targeting it for degradation, resulting in suppression of NOTCH pathway activity. We therefore hypothesize that DTX1 mutations lead to NOTCH pathway dysregulation and are a critical driver for a subset of BN2/C1 DLBCLs. Using whole exome sequencing (WES) data from 404 newly diagnosed DLBCL patients, we found 8% of patients with mutations in DTX1. Alterations in DTX1 were localized to the NICD binding domain, WWE1. In silico Alphafold modeling of DTX1 WWE1-interaction with NICD revealed that patient mutations clustered within predicted interacting surfaces with NICD. These findings lead to the hypothesis that mutations in the DTX1 WWE1 domain suppress its ability to bind to and degrade NICD, enhancing NOTCH pathway activation in DLBCL. To address this question, we first compared DTX1 protein expression by immunoblot in DTX1 WWE1 mutant (OCI-Ly3 and SUDHL2) and wildtype (WT) (OCI-Ly7, OCI-Ly19, SUDHL6, and U2932) DLBCL cell lines. We found higher expression of NICD1 (p=0.03) in OCI-Ly3 and NICD2 (p<0.0001) in SUDHL2 cell lines compared to WT. This increase was accompanied by an increase in NOTCH surface expression with higher NOTCH1 (p<0.0001) and NOTCH2 (p<0.0001) expression on OCI-Ly3 and SUDHL2 cells, respectively, compared to WT. To remove variability across cell lines we next used CRISPR to generate DTX1 knock out (KO) models using WT DTX1 DLBCL cell lines U2932 and OCI-Ly19. Analysis of NICD and NOTCH receptor protein expression confirmed our initial finding, showing an 8.5 and 4.8-fold increase of NICD1 and a 6 and 2.5-fold increase of NICD2 expression in the U2932 and OCI-Ly19 DTX1 KO cell lines, respectively, compared to empty vector (EV) controls. An increase in surface NOTCH1, and to a lesser extent NOTCH2, was seen in U2932 (p=0.01 and p=0.07, respectively) and OCI-Ly19 (p=0.001 and p=0.2, respectively) KO cells compared to EV controls. To identify pathways affected by DTX1 mutations, we next performed RNAseq on U2932 EV and KO cell lines. Of interest, we did not detect any significant changes in classical NOTCH driven genes (Hes1, Hey1, etc.), supporting prior studies on BN2/C1 tumors. However, we did identify potential non-canonical NOTCH-target genes that may contribute to lymphomagenesis. Gene expression in KO cells showed upregulation of genes involved in germinal center (GC) processes (AICDA, IRF4; p<0.001) and cellular growth and survival (IL6R, CCND2, STAT5B, BCL2; p<0.0001). Measurement of IL6R by flow cytometry and CyclinD2 by immunoblot further verified DTX1-mediated upregulation of IL6R (p<0.01) and CyclinD2 (p<0.01) for U2932 KO cells compared to EV control. We also observed downregulation of genes contributing to cell death (ex. BAX, CASP8, FAS; p<0.05) in KO cells compared to EV. Together, these novel findings support our hypothesis that DTX1 WWE1 mutations lead to dysregulation of the NOTCH pathway by driving overexpression of NICD and NOTCH, resulting in dysregulated expression of proteins involved in cell cycle, GC reaction, and apoptosis. We further provide new biologic insight into BN2/C1 DLBCL, ultimately highlighting a path toward novel therapeutic targets and the advancement of personalized medicine, as patients harboring these mutations may benefit from new therapies targeting inflammatory pathways, such as CELMoDs.

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: Diffuse large B cell lymphoma (DLBCL) patients experience favorable outcomes following frontline immunochemotherapy, however, 30-40% of patients will exhibit primary refractory disease or relapse (rrDLBCL). Recent studies have expanded upon well-defined characteristics of newly diagnosed DLBCL (ndDLBCL) to elucidate genomic landscapes underlying rrDLBCL; yet, high-throughput examination of resistance and relapse mechanisms within rrDLBCL subtypes, including the newly defined dark zone signature (DZsig) addition to cell of origin (COO), have yet to be described. To expand upon the current tumor etiology and define subtype-specific driving mechanisms, we describe a multi-omic analysis leveraging clinical and biologic variables in rrDLBCL. Methods: Tumor biopsies (N=228) collected at the time of relapse were obtained from n=85 participants in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource (MER), n=73 in the Mayo Clinic Biobank, and n=38 from CC-122-DLBCL-001 or n=32 CC-122-ST-001 trials. Tumor samples were analyzed by WES (n=158) or WGS (n=70) and by RNAseq (n=145). Comparative analysis against ndDLBCL MER samples (N=444) utilized WES (n=404) and RNAseq (n=321). Chi Square or Fisher's exact test was used for enrichment analysis of categorical variables, while Wilcoxon or Kruskal-Wallis test was used for continuous variables. Results: rrDLBCL samples were enriched for ABC (46% vs 33% in ndDLBCL, p=0.01), and fewer GCB (27% vs 40%, p=0.006). Amongst rrDLBCL patients, variance in overall survival (from time of original diagnosis; log-rank p=0.001) was observed between COO subtypes, with poor outcomes in patients with DZsig (median 15.0 mo) and GCB (36.5 mo) compared to ABC tumors (74.4 mo). Patients with progression or relapse within 6 months featured a more even COO distribution (ABC - 22%, GCB - 34%, DZsig - 28%) than patients with early (6-24 mo; 51%, 22%, 22%) or late relapses (>24 mo; 62%, 19%, 4%), which were predominantly ABC. Comparing mutation frequency within subtypes, rrDLBCL GCB tumors were enriched for B2M (41% vs 15% in ndDLBCL, p=0.002), EP300 (19% vs 7%, p=0.059), CIITA (19% vs 5%, p=0.02), PRKCB (11% vs 2%, p=0.035), CD79B (11% vs 0%, p=0.004), and CD83 (8% vs 1%, p=0.049) mutations, and CNV events including gains at 8q24.21 (MYC locus; 48% vs 14%, p<0.001) and 18q21.33 (BCL2, TCF4 locus; 36% vs 15%, p=0.021), and losses at 17p13.1 (TP53 locus; 40% vs 19%, p=0.035). rrDLBCL DZsig tumors featured enrichment for BCL6 (22% vs 3%, p=0.025), and SPEN (17% vs 3%, p=0.062) mutations and 2q22.2 losses (LRP1B locus; 20% vs 0%, p=0.019). Despite differential mutation and CNV profiles, gene expression analysis revealed minimal variance within COO subtypes in rrDLBCL and ndDLBCL, however upregulation of lymphoma driving genes including MYBPC2 (LogFC=1.68, FDR<0.001), MYOM2 (LogFC=1.33, FDR=0.007), MYC (LogFC=0.61, FDR=0.04), and CELSR2 (LogFC=0.53, FDR=0.02) were observed in ABC rrDLBCL. Increased frequency of LymphGen classifier A53 was observed in ABC (43% vs 17%, p=0.005) and GCB (40% vs 14%, p=0.009) rrDLBCL. Using CIBERSORTx to infer lymphoma microenvironment (LME) cell type abundance, an LME-depleted class was evident in rrDLBCL tumors, including reduction of CD8+ T cells (effect size (r)=-0.78, p<0.001) and T follicular helper cells (r=-0.48, p=0.012) in GCB rrDLBCL and reduction of T follicular helper cells (r=-0.43, p=0.023) and Tregs (r=-0.32, p=0.065) in DZsig rrDLBCL. Significance: We present an update to the genomic landscape of rrDLBCL, highlighting GCB and DZsig tumors with differential biology and advanced clinical course. Our data supports increased implementation of sequencing efforts in rrDLBCL to more broadly evaluate relapse mechanisms, with optimism that a more detailed understanding of r/r disease may guide trials for targeted treatment strategies. For example, rrDLBCL patients may be strong candidates for MYC pathway targeting agents (BETi), or a Lenalidomide-based regimen (R2 or Tafa-Len), as Lenalidomide has been shown to reprogram the LME via PD-L1 expression in addition to downregulation of MYC and MYC target genes. Or, due to enrichment of variants affecting anti-apoptotic proteins including BCL6 or BCL2 in DZsig and GCB rrDLBCL, a venetoclax-based therapeutic may be considered. Further targeted strategies should be considered as development of a more personalized approach remains an unmet need.

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: 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.