Xianyu, Zilin

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 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 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)

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.