Classification Of Diffuse Large B-cell Lymphoma Research Articles

Advances in biology, diagnosis and treatment of DLBCL.

Diffuse large B-cell lymphoma (DLBCL), with approximately 150,000 new cases worldwide each year, represent nearly 30% of all cases of non-Hodgkin lymphoma (NHL) and are phenotypically and genetically heterogeneous. A gene-expression profile (GEP) has identified at least three major subtypes of DLBCL, each of which has distinct clinical, biological, and genetic features: activated B-cell (ABC)-like DLBCL, germinal-center B-cell (GCB)-like DLBCL, and unclassified. Different origins are associated with different responses to chemotherapy and targeted agents. Despite DLBCL being a highly heterogeneous disease, more than 60% of patients with DLBCL can be cured after using rituximab combined with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) to inhibit the growth of cancer cells while targeting the CD20 receptor. In recent decades, the improvement of diagnostic levels has led to a refinement classification of DLBCL and the development of new therapeutic approaches. The objective of this review was to summarize the latest studies examining genetic lesions and therapies for DLBCL.

Genomic Correlates of Radiosensitivity in Diffuse Large B Cell Lymphoma

Introduction Radiotherapy (RT) is beneficial for select patients with localized diffuse large B cell lymphoma (DLBCL), in both the early stage and relapsed/refractory settings. Although molecular subtyping plays a key role in classification of DLBCL, genomic predictors of local response to RT have yet to be adequately explored. To our knowledge, there are no existing studies investigating the genomic landscape of DLBCL patients treated with RT. Therefore, we sought to identify genomic signatures of radiosensitivity in localized DLBCL patients. Methods We analyzed an institutional database of DLBCL patients treated with RT for localized measurable disease treated between 2007-2022. All patients underwent next-generation sequencing using an institutional 505-gene panel (MSK-Impact). Clinical and radiographic characteristics were obtained from chart and imaging review. All patients had measurable disease on CT scan at time of RT and included both Ann Arbor stage I/II patients referred for consolidative radiotherapy and those with isolated sites of relapsed/refractory disease. Patients did not receive any systemic therapy between RT start and time of response assessment. The public NCI LymphGen algorithm was used to further classify genomic subgroups. Using Fisher's exact test and logistic regression, we tested for univariable correlation between specific gene alterations and LymphGen subgroups with initial radiation response, defined by Lugano criteria and decrease in maximum SUV. Results We identified 46 patients including 15 (33%) tumors treated in the early-stage setting (13% definitively without prior chemotherapy, 87% as consolidation), while 31 (60%) were treated in the localized relapsed/refractory setting. The majority were GCB subtype (57%) while 28% were non-GCB and 15% had missing immunohistochemistry data. The median number of prior lines of systemic therapy was 1 (0-2) in early stage and 1 (1-7) in localized relapsed/refractory diseases. The median RT dose was 36 Gy (4-54), with 38 (83%) tumors receiving at least 30Gy. The median lesion SUV at the time of RT was 4.11 (1.1-46.0). Complete response (CR) was achieved in 41% of tumors at the time of first imaging assessment (median 3.1 months) with a median of 90.3% (42-100%) decrease in SUV. The most commonly altered individual genes were KMT2D (35%), MYD88 (28%), and TP53 (35%). LymphGen classified 22% of cases as EZB subtype, 13% as MCD, 2% as BN2, 2% as ST2, and the remaining 61% were unclassified. Mutations in ETV6 (13% of tumors) were associated with a higher CR rate in response to RT (83% mutated vs 35% wildtype, p=0.068). Lesions from ETV6 mutated cases had similar SUVs (p=0.74) and sizes (p=0.92) as wildtype cases. Alterations in genes from the LymphGen MCD subgroup were associated with greater reductions in SUV (ETV6 p<0.0001, PIM1 p<0.0001, MYD88 p<0.001, and TNFAIP3 p=0.02) The MCD subgroup trended towards increased odds of CR post-RT (log odds 1.9, p=0.08) and greater reduction of SUV (p=0.07) compared to the EZB subgroup. Conclusions To our knowledge this is the first study evaluating the role of LymphGen subgrouping on radiotherapy response, and the first study to examine genomic alterations in DLBCL patients treated with radiation. Genomic biomarkers of radiosensitivity are needed to identify radioresponsive tumors which may benefit from better integration of RT into existing salvage paradigms such as CAR T cells. Our data suggest that alterations in MCD LymphGen subgroup genes (ETV6, PIM1, MYD88, TNFAIP3) may be associated with better response to radiotherapy. ETV6 is a strong hematopoietic transcription factor known to be altered in many human leukemias and myelodysplastic syndromes. Its role in DLBCL is not well defined, with preclinical studies suggesting a distinct role compared to leukemias. Due to the limited sample size, these findings are hypothesis generating and warrant further study with larger, prospective cohorts to validate the ability of genomic signatures to predict RT response.

Improved Risk Prediction in DLBCL By Combining Clinical and PET Features with Interim PET Assessment

Background: Accurate detection of patients at high risk of treatment failure following frontline immunochemotherapy in diffuse large B-cell lymphoma (DLBCL) is of paramount importance as these patients might benefit from early treatment escalation. Recently, we introduced the IMPI prognostic model based on metabolic tumor volume (MTV), age and stage that outperformed the international prognostic index (IPI). However, radiomic features such as Dmax bulk and SUV peak as well as an early treatment response at interim PET (i-PET) as a measure of chemosensitivity using ΔSUVmax may have additional predictive value. We tested different models for risk prediction aiming at a dynamic risk tool in the era of evolving radiomic features in functional imaging. Methods: All patients within the PETRA database with newly diagnosed DLBCL, who were treated with R-CHOP and had available clinical data, baseline PET and i-PET scans were included. The optimal transformation of Dmax bulk, SUV peak and ΔSUV max was determined by choosing the best fitting Cox regression model with 3-year PFS as outcome, with highest R2 and lowest Akaike Information Criterion (AIC), while the cross-validated c-index was obtained as a measure for discrimination. Subsequently, risk models were developed using clinical, baseline PET and i-PET data. The best risk model was compared to the IMPI model and our subsequent ClinicalPET model, also incorporating radiomic features (MTV, IPI, age, SUV peak and D maxbulk) by determination of risk re-classification rates and by generating kaplan-meier (KM)-curves based on 60-30-10 PFS risk groups. Results: 1014 patients were included in the analyses. Adding i-PET reponse (ΔSUV max) to the IMPI model markedly improved outcome prediction (AIC 3177.44, c-index 0.72) and was superior to IMPI model alone (AIC 3247.09, c-index 0.68). By adding D maxbulk outcome prediction was further improved (AIC 3143.23, c-index 0.74), while SUV peak did not show significant impact on outcome (p=0.07). Compared to the IMPI and the ClinicalPET model, the new model combining baseline features (MTV, age and D maxbulk) with i-PET reponse (ΔSUV max) led to a sharper segregation of KM-curves with an improved rate of correct progression risk classification (22%; 95% confidence interval 12.1-31.1%). Conclusions: Adding i-PET reponse to baseline clinical and PET parameters optimizes risk classification in DLBCL enabling individualized risk assessment in early phase of frontline treatment and outperforms our previous IMPI model and ClinicalPET models.

Developing Deep Learning Pipeline of Whole-Slide Images for Enhanced Diffuse Large B Cell Lymphoma (DLBCL) Subtyping and Outcome Prediction: Leveraging Self-Attention Transformer for Training and Inference

Background: Deep learning algorithms can help to analyze whole-slide images (WSI) in lymphoma pathology, identifying deeper features and patterns that may not be easily discernible to human observers. This pilot project is focused on diffuse large B-cell lymphoma (DLBCL), a heterogeneous disease with diverse genetic alterations. By leveraging self-attention trained clusters and transformers, the project aims to identify patterns and associations between mutational status and overall survival, potentially enhancing personalized treatment strategies and improving data reliability. Methods: This study employed a computer vision pipeline learning system to classify DLBCL subtypes using self-discovery of discriminatory features from scanned WSI. The workflow is shown in Figure 1a. First, we segmented tiles or patches from the gigapixel-sized WSI of 223 lymphoma cancer biopsy slides sourced from The Cancer Genome Atlas (TCGA), DLBCL, and Stanford DLBCL-Morph datasets. to optimize extraction of relevant features. For feature extraction, we utilized self-supervised pretraining with a Vision Transformer (ViT) network on a dataset of 1,515,000 patches. These patches were grouped into morphologically similar clusters using K-means, representing various lymphoma proliferation patterns. Dimensionality reduction with UMAP allowed for computational efficiency and feature visualization. Extracted features were used to predict overall survival using Bag-of-Words (BoWs). We further enhanced the model by incorporating geometric information, including nuclear characteristics from HoverNet. We assessed the model's performance using key metrics such as area under the curve (AUC) and accuracy. Additionally, genomic mutations with a frequency of 10% or higher from the TCGA were incorporated for enhanced predictive capability, including PIM1, SGK1, CARD11, KMT2D, SOCS1, BTG1, MUC16 and FAT4. Correlations and hierarchical clustering were performed on mutations, patches, and outcomes (Figure 1b). Results: Using the self-trained ViT encoder as the backbone and random forest, we demonstrate an accuracy of 0.88 on a weakly supervised task with all samples. The performance is significantly improved when compared to ResNet-50 trained on ImageNet. In addition, saliency maps from multi-attention heads provide excellent interpretations of morphological characteristics, including tumor stroma, cell location and necrosis. With the feature embeddings extracted by ViT, 10 morphologically distinct clusters were identified. The algorithms identified morphologically similar and dissimilar clusters among tiles that represent variation in the distribution of lymphoma cells as seen in clustering results. There was a strong correlation between clusters one and four, the mutated genes, and an increased probability of poor outcomes. When this was visualized by the pathologist, cluster 4 showed a high level of necrosis in the WSI. Finally, using the segmented WSI cortex with clustering distribution, our method achieves an AUC of 0.77 for predicting the vital status of alive/dead as an outcome. Conclusions: Our pipeline effectively leverages WSI to employ machine learning and deep learning tools for disease classification and outcome prediction in DLBCL. By computationally analyzing the entire tumor landscape, it captures tumor heterogeneity and disease risk, establishing correlations between patch-level characteristics, genomic mutations and overall outcomes. The Vision Transformer (ViT) plays a pivotal role in this process, successfully identifying specific features in histopathology tissue by leveraging self-attention mechanisms, which enables precise feature extraction and accurate analysis for DLBCL classification. Notably, the ViT model achieves high performance across various tasks without the need for external labeled data, owing to its self-supervised learning capability that enables independent learning and extraction of meaningful information. Based on this discovery cohort from this dataset, we will update the analysis using a larger, independent external cohort from institutional archives, and the results will be presented at the annual meeting.

2024 Update: Advances in the risk stratification and management of large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with varying clinical outcomes. Our understanding of its molecular makeup continues to improve risk stratification, and artificial-intelligence and ctDNA-based analyses have the potential to enhance risk assessment and disease monitoring. R-CHOP and Pola-R-CHP are used in the frontline setting; chimeric antigen receptor therapy (CART) is now the new standard-of-care for most with primary refractory disease; both CART and autologous stem cell transplantation are utilized in the relapsed and refractory setting. In this review, we summarize the classification and management of DLBCL with an emphasis on recent advances in the field.

Association of single nucleotide variants in VEGFA and KDR with the risk and angiogenic features of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma subtype and dependent on angiogenesis (AG), whose main effectors are VEGFA and VEGFR2. Functional single nucleotide variants (SNVs) are described in VEGFA and KDR genes. However, it still unknown whether VEGFA − 2578C/A, −2489C/T, −1154G/A, −634G/C, −460C/T and KDR-604T/C, −271G/A, +1192G/A and +1719A/T SNVs act on DLBCL risk and angiogenic features. Genomic DNA from 168 DLBCL patients and 205 controls was used for SNV genotyping. Angiogenesis was immunohistochemically assessed in tumor biopsies, with reactions for VEGFA, VEGFR2, and CD34. VEGFA –1154GG genotype were associated with 1.6-fold higher DLBCL risk. KDR + 1192GG plus KDR + 1719 TT and KDR + 1192GG plus VEGFA − 2578CC combined genotypes are associated with 2.19- and 2.04-fold higher risks of DLBCL, respectively. VEGFA − 634GG or GC genotypes are associated with increased microvessel density and VEGFA levels. No relationship was observed between SNVs and cell-of-origin classification of DLBCL, but higher VEGFA and VEGFR2 were seen in non-germinal center tumors.

MicroRNA sequencing for biomarker detection in the diagnosis, classification and prognosis of Diffuse Large B Cell Lymphoma

Despite being considered a single disease, Diffuse Large B Cell Lymphoma (DLBCL) presents with variable backgrounds, which results in heterogeneous outcomes among patients, with 40% of them still having primary refractory disease or relapse. Thus, novel biomarkers are needed. In addition, multiple factors regarding its pathogenesis remain unclear. In this context, recent investigations point to the relevance of microRNAs (miRNAs) in cancer. However, regarding DLBCL, there is inconsistency in the data reported. Therefore, in this work, the main goals were to determine a miRNA set with utility as biomarkers for DLBCL diagnosis, classification, prognosis and treatment response, as well as to decipher the mechanism of action of deregulated miRNAs in the origin of the disease. We analyzed miRNA expression in a cohort of 78 DLBCL patients and 17 controls using small RNA sequencing and performed a miRNA-mRNA interaction network analysis. This way, we were able to define new miRNA expression signatures for diagnosis, classification, treatment response and prognosis, and we identified plausible mechanisms of action by which deregulated miRNAs could be involved in DLBCL pathogenesis. In summary, our study remarks that miRNAs could play an important role in DLBCL.

Immune-based therapies in diffuse large B-cell lymphoma

ABSTRACT Introduction Diffuse large B-cell lymphoma (DLBCL) is an aggressive and clinically heterogeneous malignancy originating from B-cells with up to 40% of patients experiencing primary refractory disease or relapse after first-line treatment. However, the past 5 years have seen a flurry of new drug approvals for DLBCL anchored upon new immune therapies, including chimeric antigen receptor (CAR) T-cells and antibody-based therapies. Areas covered This article summarizes recent advances in the treatment of DLBCL, including in the first line and relapsed and refractory setting (second-line and beyond). A literature search was conducted for publications relevant to the immunotherapeutic approach to DLBCL from 2000 through March 2023 within PubMed and articles were reviewed. The search terms were immunotherapy, monoclonal antibodies, chimeric antigen receptor modified T-cell (CAR-T), and classification of DLBCL. Relevant clinical trials and pre-clinical studies exploring the strengths and weaknesses of current immune therapies against DLBCL were chosen. We additionally explored how intrinsic differences amongst DLBCL subtype biology and endogenous host immune recruitment contribute to variable therapeutic efficacy. Expert Opinion Future treatments will minimize chemotherapy exposure and be chosen by underlying tumor biology, paving the way for the promise of chemotherapeutic free regimens and improved outcomes for poor-risk subgroups.

BTG2 SUPER‐ENHANCER MUTATIONS DISRUPT TFAP4 BINDING AND DYSREGULATE BTG2 EXPRESSION IN DIFFUSE LARGE B‐CELL LYMPHOMA

Introduction: Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy, remains incurable in ∼40% of patients. Coding-genome sequencing efforts identified several genes/pathways altered in this disease, as well as genetic subgroups of potential clinical relevance. However, the large non-coding portion of the genome remained largely unexplored. We recently identified a pervasive hypermutation mechanism targeting active super-enhancers (SEs) in >90% of DLBCL and leading to dysregulation of multiple genes, including well-known lymphoma oncogenes (Bal et al., Nature 2022). As evidence of oncogenic relevance, we demonstrated that mutational hotspots in the BCL6, BCL2 and CXCR4 SEs impair the binding of specific transcriptional repressors, preventing the gene negative regulation and creating oncogenic dependencies in DLBCL cells. Here we aimed to define the pathogenic role of mutations targeting the intragenic SE (iSE) of the BTG2 gene, the second most commonly mutated in DLBCL. BTG2 encodes a member of the B-cell translocation gene (BTG)/TOB family involved in transcriptional co-activation and modulation of mRNA abundance. BTG2 is also a recurrent target of somatic missense mutations (6%–11% of DLBCL), suggesting a major role in the pathogenesis of this disease. Methods: We screened 243 DLBCL cases for the presence of mutational hotspots within the BTG2-iSE, and combined in silico prediction, DNA-binding assays (Reverse-ChIP, electromobility-shift assay, ChIP-qPCR), RNA-seq and CRISPR/Cas9 editing approaches in isogenic BTG2 mutant versus WT DLBCL cell lines to identify transcription factors bound to the SE and disrupted by the mutation. Results: We identified a recurrent mutational cluster affecting the BTG2-iSE in 52/243 (21%) DLBCLs, with preferential enrichment in ST2 subgroup. CRISPR-Cas9 mediated correction of the mutation in 3 DLBCL cell lines led to counter selection and reduced BTG2 expression, consistent with oncogenic addiction. In silico motif prediction and in vitro DNA-binding assays, followed by validation in multiple isogenic DLBCL cell lines, identified TFAP4 as a major transcription factor that binds to the WT, but not to the mutated site. TFAP4 is an important regulator of B-cell proliferation and cell fate decisions, which can function as a transcriptional activator or repressor in germinal center B-cells, and acts downstream of/in parallel with c-MYC. Of note, introduction of SE hotspot mutations in WT DLBCL cells was associated with increased BTG2 expression, confirming a direct link between SE mutations and deregulated gene expression through escape from TFAP4-mediated suppression. Conclusions: These findings suggest a major role for BTG2 deregulation by SE aberrant somatic hypermutation in the pathogenesis and heterogeneity of DLBCL, with implications for precision classification and potential therapeutic targeting of DLBCL. Keywords: aggressive B-cell non-Hodgkin lymphoma, tumor biology and heterogeneity No conflicts of interests pertinent to the abstract.

The Immunology of DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy and is the most common type of malignant lymphoid neoplasm. While some DLBCLs exhibit strong cell-autonomous survival and proliferation activity, others depend on interactions with non-malignant cells for their survival and proliferation. Recent next-generation sequencing studies have linked these interactions with the molecular classification of DLBCL. For example, germinal center B-cell-like DLBCL tends to show strong associations with follicular T cells and epigenetic regulation of immune recognition molecules, whereas activated B-cell-like DLBCL shows frequent genetic aberrations affecting the class I major histocompatibility complex. Single-cell technologies have also provided detailed information about cell-cell interactions and the cell composition of the microenvironment of DLBCL. Aging-related immunological deterioration, i.e., immunosenescence, also plays an important role in DLBCL pathogenesis, especially in Epstein-Barr virus-positive DLBCL. Moreover, DLBCL in "immune-privileged sites"-where multiple immune-modulating mechanisms exist-shows unique biological features, including frequent down-regulation of immune recognition molecules and an immune-tolerogenic tumor microenvironment. These advances in understanding the immunology of DLBCL may contribute to the development of novel therapies targeting immune systems.

Pathobiology and its clinical relevance in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically and biologically heterogeneous disease. Remarkable effort has been exerted in the classification of DLBCL and the development of its corresponding treatment. The prognosis of patients with DLBCL receiving rituximab combination chemotherapy significantly improved in the early 2000s. However, approximately 40% of patients still develop recurrence, and the prognosis of these patients is extremely poor. Recently, the use of polatuzumab vedotin and CAR T-cell therapies has improved patient prognosis. However, it is extremely important to identify the patient group who can benefit from the efficacy of these treatments. In relation to this, the molecular pathogenesis of DLBCL should be further evaluated. Recent advancements in the genetic analysis technology have led to the discovery of unknown genetic abnormalities and gene expression patterns. The elucidation and subdivision of the molecular pathology based on these findings will be the foundation of future personalized medicine.

Towards a Unified Genetic Classification System for Diffuse Large B-Cell Lymphoma (DLBCL)

Introduction: Diffuse large B-cell lymphoma (DLBCL) is an aggressive B-cell neoplasm and the most common lymphoid malignancy worldwide. DLBCL, NOS is a genetically and biologically complex entity that remains uniformly treated at diagnosis despite the growing appreciation for subgroups with molecular distinctions. There are various approaches for assigning DLBCL into one or more molecular subgroups using either gene expression and/or mutation patterns. Several genetic classification systems have been proposed, each sharing commonalities. Distinctions between the definitions of individual classes and the ability to reproducibly assign individual patient samples to subgroups using each system remains a significant barrier to their implementation, further emphasized by up to 50% of patients that remain unclassified. The disparities among the genetic features considered by each classification scheme further limits the ability to consolidate, let alone adopt, these for research and clinical studies. We aimed to develop a classification system that unifies and extends the robustness of existing classification algorithms. Methods: We assembled whole genome sequencing (WGS) data from 900 tumors from DLBCL, Burkitt (BL), follicular (FL) and other mature B-cell neoplasms to comprehensively determine the set of significantly mutated genes (SMG) and common targets of aberrant somatic hypermutation (aSHM). These were intersected with the features used by the current DLBCL classification systems and a curated set of lymphoma genes to tabulate the mutation matrix. Common structural rearrangements involving oncogenes were determined from WGS data and the copy number state of focal high-level recurrent alterations was united with simple somatic mutations. For genes containing mutation hot-spots each was considered separately. Results: Clustering all cases using non-negative matrix factorization (NMF) analysis resulted in a solution with 7 distinct genetic subgroups (Figure 1). Many of these directly resemble clusters in the existing systems with either expanded coverage or refinement of a subgroup into two. First, the BNZ cluster was highly enriched for any aberration in BCL6, 3'UTR mutations in NFKBIZ, and truncating mutations in NOTCH2. Unlike the similar BN2/C1/NOTCH2 groups defined previously, the NFKBIZ mutations were weighted more highly than those of NOTCH2 in defining the BNZ subgroup. The cluster we named MCaP resembled the MCD/C5/MYD88 group. Tumors classified in this subgroup had evidence of aSHM with frequent non-coding mutations in PIM1,CD44, GRHPR, PIM2, and BCL11A among others. Another cluster, MP3, was enriched for coding mutations or aSHM in the MYC TSS (MYC_aSHM), which were generally correspondent with IG::MYC rearrangements. MP3 tumors were characterized by TP53 mutations, including hotspots, and significantly depleted for aberrations in BCL2, BCL6 and lower mutation rates at aSHM targets SGK1, PAX5, CIITA, RHOH and others. Notably, tumors in this subgroup were enriched for IRF4 translocations, suggesting potential overlap with large B-cell lymphoma with IRF4 rearrangement. Our approach further refined both the the ST2/C4 and EZB/C3/BCL2 classes into two smaller subgroups, aSEL/aSCI and EGB/ETB, respectively. The EGB tumors were enriched for GNAI2 mutations, and MYC translocations involving the BCL6 enhancer, and were more often associated with MEF2B hotspot mutations. Notably, ETB was significantly enriched for mutations in the lysine acetyltransferase (KAT) domain in CREBBP, while EGB was significantly enriched for non-KAT domain mutations. ETB tumors were further characterized by mutations in STAT6 and FOXO1 hotspot regions, MYC rearrangements with other non-IG partners, and TNFRSF14 mutations, largely resembling the mutational profile of follicular lymphomas. Conclusions: We present here a refined classification of DLBCL genetic subgroups that combines previously described classification algorithms and extends it to allow classification of more tumors into additional subgroups that preserve the major divisions of existing systems. This represents an important advancement that will facilitate further understanding of genomic complexity in this disease. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Abstract A12: Pervasive hypermutation of super-enhancer regions dysregulates oncogene expression in diffuse large B-cell lymphoma

Abstract Coding-genome sequencing efforts have identified several genes/pathways altered in Diffuse Large B-cell Lymphoma (DLBCL), including new potential therapeutic targets. However, the mutational contribution of the non-coding genome of DLBCL remains largely unexplored. To identify functionally relevant non-coding mutations targeting regulatory domains, we integrated enhancer (E)/super-enhancer (SE) identification, by ChIP-seq analysis of the hallmark H3K27 histone mark, with whole genome sequencing (WGS) and RNA-seq analysis in 29 DLBCL cell lines representative of the major DLBCL subtypes, along with germinal center (GC) B cells representing the normal counterpart of DLBCL. Following validation in 93 normal/tumor DLBCL biopsies, we found that active SEs are specifically hypermutated (≥3 somatic mutations/Kb) in 99% of DLBCLs, as compared to the same loci when not active as SE (in total, 159 hypermutated SEs, with at least 2/case). As evidence of oncogenic relevance, we have shown that the hypermutated SEs linked to the BCL6 and BCL2 proto-oncogenes prevent the binding and transcriptional downregulation by transcriptional repressors. Genetic correction of selected mutations using the CRISPR/Cas9 technology restored repressor DNA-binding, downregulated target gene expression, and led to the counter-selection of cells harboring corrected alleles, indicating oncogenic dependency from the SE mutations. A third recurrently hypermutated SE was the CXCR4-SE, affected in 19% of cases. CXCR4 encodes for a G-protein coupled chemokine receptor involved in cell migration within the GC, and is mutationally activated in 40% of Waldenström macrobulinemia (WM), a post-GC lymphoproliferative disease. We identified a mutational hotspot preventing the binding of the NR3C1 glucocorticoid receptor in 7.5% of DLBCL cases. Correction of the mutations in cell lines led to counter selection and reduced expression of CXCR4. Conversely, the introduction of a mutation disrupting NR3C1 binding in WT DLBCL cells increased CXCR4 expression. Together, these results are consistent with dependency of the mutant cells on the CXCR4-SE mutation, and confirm a direct link between SE mutations in this region and deregulated gene expression through escape from NR3C1-mediated suppression. In primary cases, mutations in the NR3C1 binding site of the CXCR4-SE were observed in the absence of NR3C1 coding mutations and correlated with increased CXCR4 transcript levels, as documented by RNA-seq. Together with the oncogenic role of CXCR4 in WM, these findings reveal this chemokine receptor as a potential oncogenic target in DLBCL. Collectively, these findings reveal a new layer of genetic alterations, which identifies novel mechanisms of dysregulation for known oncogenes, as well as new dysregulated genes and pathways, with implications for precision classification and therapeutic targeting of DLBCL. * Co-senior authors. Citation Format: Elodie Bal, Rahul Kumar, Mohammad Hadigol, Antony B. Holmes, Laura K. Hilton, Jui Wan Loh, Kostiantyn Dreval, Jasper Wong, Sofija Vlasevska, Clarissa Corinaldesi, Rajesh Kumar Soni Soni, Katia Basso, Ryan D. Morin, Hossein Khiabanian, Laura Pasqualucci, Riccardo Dalla-Favera. Pervasive hypermutation of super-enhancer regions dysregulates oncogene expression in diffuse large B-cell lymphoma [abstract]. In: Proceedings of the Third AACR International Meeting: Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2022 Jun 23-26; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2022;3(5_Suppl):Abstract nr A12.

Genetic Subtyping and Phenotypic Characterization of the Immune Microenvironment and MYC/BCL2 Double Expression Reveal Heterogeneity in Diffuse Large B-cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1-high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Diffuse large B-Cell lymphoma: from novel molecular classifications to tailored targeted therapies

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease comprising multiple genetic subtypes that translates and impacts clinical outcomes after standard chemoimmunotherapy. Our initial understanding of the complex biological subtypes of DLBCL began with the identification of cell of origin (COO), and now has evolved to include even more specific subtypes defined by genetic signatures and mutations. These newer classifications lend themselves to the application of precision-based medicine, allowing us to tailor new treatment platforms that target specific oncogenic drivers in order to improve DLBCL outcomes. Essential to this is the development of genetic assays and tools that are reliable and readily available to assist in the application of these molecular classifications to real-world use. In this review, we discuss the history of DLBCL classification systems and their implication on clinical investigation as well as novel therapeutic options in DLBCL.

Diffuse large B-cell lymphoma genetics - simplifying the subtyping.

Diffuse large B-cell lymphoma genetics - simplifying the subtyping.

Prognostic value of CD10, BCL6 and MUM1 in diffuse large B-cell lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is considered the commonest subtype of non-Hodgkin lymphoma (NHL) in the world. It is a refractory disease with a high mortality rate due to frequent relapses. Several prognostic parameters are now widely studied for risk stratification and achieving a better outcome. Objectives: In this study, we aim to assess the prognostic value of immunohistochemical expression of CD10, BCL6, and MUM1 independently as surrogate markers for cell of origin (COO) classification of DLBCL and their correlation with clinicopathological characters and survival. Patients and methods: This is a retrospective study conducted on 63 cases of DLBCL, NOS. Full-faced sections were constructed and immunostained for CD10, BCL6, and MUM1. Results: CD10 expression was associated with early-stage (P=0.003), normal serum LDH level (P=0.022), absence of B symptoms (P=0.019), low international prognostic index (IPI) and age-adjusted-IPI (P=0.001) and also associated with longer progression free survival (PFS) (P=0.006). BCL6 expression was associated with centroblastic variant (P=0.005), good ECOG performance status (P=0.038) and low IPI (P=0.004) and also associated with better overall survival (OS) (P=0.028) and PFS (P=0.018). MUM1 expression was associated with advanced-stage (P=0.002) while no significant association was detected with other clinicopathological parameters or survival. Conclusion CD10, BCL6, and MUM1 can be used independently as prognostic immunohistochemical markers for DLBCL that may denote the clinical behavior of the disease and further patients' outcomes.

Genomics of diffuse large B cell lymphoma.

Next generation sequencing (NGS) technology has revealed the heterogeneity of diffuse large B-cell lymphoma (DLBCL) from a mutation perspective. Accordingly, the conventional cell of origin-based classification of DLBCL has changed to a mutation-based classification. Mutation analysis delineates that B-cell receptor pathway activation, EZH2 mutation, and NOTCH mutations are distinctive drivers of DLBCL. Moreover, the combination of RNA expression data and DNA mutation results suggests similarity between DLBCL subtypes and other non-Hodgkin lymphomas. NGS-based dissection of DLBCL would be the cornerstone for precision treatment in this heterogeneous disease in the near future.

Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications.

Diffuse large B-cell lymphoma (DLBCL) represents a group of tumors characterized by substantial heterogeneity in terms of their pathological and biological features, a causal factor of their varied clinical outcome. This variation has persisted despite the implementation of rituximab in treatment regimens over the last 20 years. In this context, prognostic biomarkers are of great importance in order to identify high-risk patients that might benefit from treatment intensification or the introduction of novel therapeutic agents. Herein, we review current knowledge on specific immunohistochemical or genetic biomarkers that might be useful in clinical practice. Gene-expression profiling is a tool of special consideration in this effort, as it has enriched our understanding of DLBCL biology and has allowed for the classification of DLBCL by cell-of-origin as well as by more elaborate molecular signatures based on distinct gene-expression profiles. These subgroups might outperform individual biomarkers in terms of prognostication; however, their use in clinical practice is still limited. Moreover, the underappreciated role of the tumor microenvironment in DLBCL prognosis is discussed in terms of prognostic gene-expression signatures, as well as in terms of individual biomarkers of prognostic significance. Finally, the efficacy of novel therapeutic agents for the treatment of DLBCL patients are discussed and an evidence-based therapeutic approach by specific genetic subgroup is suggested.

Molecular Subtyping of Diffuse Large B-Cell Lymphoma Using a Novel Quantitative RT-PCR Assay

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease. Cell-of-origin classification in DLBCL has identified activated B cell (ABC) and germinal center B cell (GCB) as two major subtypes. Patients with the ABC subtype show reduced overall survival with standard therapies. Development of a quantitative RT-PCR-based lymphoma cell-of-origin (LCOO) assay to determine ABC, GCB, and unclassifiable subtypes in formalin-fixed, paraffin-embedded tissue (FFPET) DLBCL samples is reported. The LCOO classifier was trained on two DLBCL cohorts with validation performed by using an analytical grade assay in an independent cohort of 60 FFPET DLBCL samples. In the validation cohort, LCOO classification was 88.1%, 84.7%, and 84.7% concordant with microarray, immunohistochemistry (Hans classification), and Lymphoma Subtyping Test, respectively. Importantly, LCOO and Lymphoma Subtyping Test assays commonly assigned subtypes in 17 (94.4%) of 18 ABC samples and 34 (89.5%) of 38 GCB DLBCL samples from this cohort. Progression-free survival and overall survival of ABC and GCB subtypes, as classified by all platforms, were not significantly different in the validation cohort. LCOO classification using publicly available microarray gene expression from two independent data sets (414 fresh frozen and 474 FFPET DLBCL biopsies) revealed a significantly worse outcome for the ABC subtype compared with that of the GCB subtype. Thus, a sensitive, reproducible, LCOO assay developed on an easy to standardize quantitative RT-PCR platform may be an important clinical tool for DLBCL cell-of-origin classification.