Multiomics analysis of IgM monoclonal gammopathies reveals epigenetic influence on oncogenesis via DNA methylation.

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.

MicroRNA and long non-coding RNA analysis in IgM monoclonal gammopathies reveals epigenetic influence on cellular functions and oncogenesis.

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Molecular classification and identification of an aggressive signature in low-grade B-cell lymphomas.

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.

DEK regulates B-cell proliferative capacity and is associated with aggressive disease in low-grade B-cell lymphomas.

This study sheds light on the pivotal role of the oncoprotein DEK in B-cell lymphoma. We reveal DEK expression correlates with increased tumor proliferation and inferior overall survival in cases diagnosed with low-grade B-cell lymphoma (LGBCL). We also found significant correlation between DEK expression and copy number alterations in LGBCL tumors, highlighting a novel mechanism of LGBCL pathogenesis that warrants additional exploration. To interrogate the mechanistic role of DEK in B-cell lymphoma, we generated a DEK knockout cell line model, which demonstrated DEK depletion caused reduced proliferation and altered expression of key cell cycle and apoptosis-related proteins, including Bcl-2, Bcl-xL, and p53. Notably, DEK depleted cells showed increased sensitivity to apoptosis-inducing agents, including venetoclax and staurosporine, which underscores the therapeutic potential of targeting DEK in B-cell lymphomas. Overall, our study contributes to a better understanding of DEK's role as an oncoprotein in B-cell lymphomas, highlighting its potential as both a promising therapeutic target and a novel biomarker for aggressive LGBCL. Further research elucidating the molecular mechanisms underlying DEK-mediated tumorigenesis could pave the way for improved treatment strategies and better clinical outcomes for patients with B-cell lymphoma.

Follicular lymphoma B cells exhibit heterogeneous transcriptional states with associated somatic alterations and tumor microenvironments.

Follicular lymphoma (FL) is an indolent non-Hodgkin lymphoma of germinal center origin, which presents with significant biologic and clinical heterogeneity. Using RNA-seq on B cells sorted from 87 FL biopsies, combined with machine-learning approaches, we identify 3 transcriptional states that divide the biological ontology of FL B cells into inflamed, proliferative, and chromatin-modifying states, with relationship to prior GC B cell phenotypes. When integrated with whole-exome sequencing and immune profiling, we find that each state was associated with a combination of mutations in chromatin modifiers, copy-number alterations to TNFAIP3, and T follicular helper cells (Tfh) cell interactions, or primarily by a microenvironment rich in activated T cells. Altogether, these data define FL B cell transcriptional states across a large cohort of patients, contribute to our understanding of FL heterogeneity at the tumor cell level, and provide a foundation for guiding therapeutic intervention.

Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.

Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.

Molecular Clusters and Tumor-Immune Drivers of IgM Monoclonal Gammopathies.

PURPOSE: IgM monoclonal gammopathy of undetermined significance (MGUS) and Waldenström macroglobulinemia (WM) represent a disease spectrum with highly varied therapeutic management, ranging from observation to chemoimmunotherapy. The current classification relies solely on clinical features and does not explain the heterogeneity that exists within each of these conditions. Further investigation is warranted to shed light on the biology that may account for the clinical differences. EXPERIMENTAL DESIGN: We used bone marrow (BM) clonal CD19+ and/or CD138+ sorted cells, matched BM supernatant, and peripheral blood serum from 32 patients (7 MGUS, 25 WM) to perform the first multi-omics approach including whole-exome sequencing, RNA sequencing, proteomics, metabolomics, and mass cytometry. RESULTS: We identified three clusters with distinct pathway activation, immune content, metabolomic, and clinical features. Cluster 1 included only patients with WM and was characterized by transcriptional silencing of genes involved in cell cycle and immune response, enrichment of mitochondrial metabolism, infiltration of senescent T effector memory cells, and aggressive clinical behavior. Genetic/structural alterations of TNFAIP3 were distinct events of this cluster. Cluster 2 comprised both MGUS and WM patients with upregulation of inflammatory response, senescence and glycolysis signatures, increased activated T follicular helper and T regulatory cells, and indolent clinical behavior. Cluster 3 also included both MGUS and WM patients and exhibited intermediate features, including proliferative and inflammatory signaling, as well as glycolysis and mitochondrial metabolism. CONCLUSIONS: We have identified three distinct molecular clusters, suggesting a potential biologic classification that may have therapeutic implications.

Multiomic Analysis Identifies a High-Risk Metabolic and TME Depleted Signature that Predicts Early Clinical Failure in DLBCL.

PURPOSE: 60-70% of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients avoid events within 24 months of diagnosis (EFS24) and the remainder have poor outcomes. Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. PATIENTS AND METHODS: Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis followed by integration with clinical and genomic data was used to identify a multiomic signature associated with high risk of early clinical failure. RESULTS: Current DLBCL classifiers are unable to discriminate cases who fail EFS24. We identified a high risk RNA signature that had a hazard ratio (HR, 18.46 [95% CI 6.51-52.31] P < .001) in a univariate model, which did not attenuate after adjustment for age, IPI and COO (HR, 20.8 [95% CI, 7.14-61.09] P < .001). Further analysis revealed the signature was associated with metabolic reprogramming and a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. CONCLUSION: This novel and integrative approach is the first to identify a signature at diagnosis that will identify DLBCL at high risk for early clinical failure and may have significant implications for design of therapeutic options.