Diffuse Large B-cell Lymphoma Samples Research Articles

Exploratory screening for micro-RNA biomarkers in canine multicentric lymphoma

Lymphoma is one of the most frequent hematopoietic tumors in dogs and shares similar features with human counterparts. MicroRNAs (miRNA, small non-coding RNAs) are pivotal in gene regulation fine tuning and cancer hallmarks are influenced by their aberrant expression. Consequently, miRNA biomarkers may assist predicting therapeutic response and clinical outcome by providing less-invasive novel diagnostics tools. The aim of this study was to detect dysregulated miRNAs in lymphomatous lymph node tissues in comparison to lymph node material or PBMCs from healthy control dogs. Potential significant differences in miRNA expression profiles between four lymphoma entities were evaluated. A customized PCR array was utilized to profile 89 canine target miRNAs. Quantification was performed using qPCR, relative expression was determined by the delta–delta Ct method, and p-values were calculated with student’s t-test. In the 14 diffuse large B-cell lymphoma (DLBCL) patients, 28 and 24 different miRNAs were significantly dysregulated compared to lymph node material or PBMCs. Sixteen miRNAs occurred in both control groups, with 12 miRNAs being down- and four miRNAs being upregulated. The six peripheral T-cell lymphoma (PTCL) samples showed 24 and 25 dysregulated miRNAs when compared to the healthy controls. A combined analysis of DLBCL and PTCL samples revealed seven shared and 19 differently expressed miRNAs. Potential biomarkers in T- and B-cell lymphoma could be the miRNA-17/92 cluster and miRNA-181-family together with miRNA-34a and miRNA-150. Diagnostic utility of potential biomarkers must be validated in larger, prospective cohorts of canine lymphoma cases and in higher numbers of physiological patient material.

Abstract 2265: Distinct alternative splicing patterns in genetic subtypes of diffuse large B-cell lymphoma

Abstract Advances in sequencing techniques have made molecular subgrouping of Diffuse Large B-cell Lymphoma (DLBCL) based on DNA cahnges, which has led to the development of Lymphgen classification. By the way, although alternative splicing (AS) in cancer cells contributes to proliferation, invasion and metastasis, and immune escape, their role and association with DLBCL molecular subtypes are unknown. Therefore, we analyzed whole-exome sequencing (WES) and bulk RNA-Seq data paired in the public EGA dataset (EGAD00001003600) to explore the patterns of AS events that differ between Lymphgene subtypes. The WES data of 620 DLBCL samples that passed quality control were aligned to the reference using BWA-MEM, and variants were called with Mutect2. We used the information of copy number variants provided by cBioPortal. The RNA-Seq analysis was performed using the STAR-RSEM pipeline and differential splicing events were detected using rMATS to compare subtypes. Among 620 DLBCL samples, there were 19 MCDs, 95 EZBs, 22 BN2s, and 137 ST2s (50 samples with multiple genetic classes, and 291 samples unclassified). Mutations in spliceosome complex genes (ZRSR2 and SF1) were found in 14 and 27 samples, respectively. In BN2 and MCD subtypes, splicing events with significantly different exon inclusion levels were observed at a high frequency of 29 and 16 occurrences, respectively, compared to other subtypes. (P-value < 0.05). In contrast, retained intron is the only event observed in the EZB subtype (Table 1). Samples with mutations in ZRSR2 and SF1 showed four and two aberrant events, respectively, compared to samples without these mutations. Of note, rMATS detected an alternative 3’ splice site event in exon 6 of CD37 in the MCD. CD37 is involved in tumorigenesis in leukocytes and has been suggested as a potential biomarker in blood cancer, and its dysregulation may have influenced the poor prognosis of MCD. In summary, molecular subtypes of DLBCL had unique features in terms of alternative splicing. Functional consequences of these selective exon usages for each DLBCL subtype are being warranted. Citation Format: Sanyeowool An, Youngil Koh, Sung-Soo Yoon. Distinct alternative splicing patterns in genetic subtypes of diffuse large B-cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2265.

Abstract 4911: PAIRWISE: Deep learning-powered personalized medicine approach identifies actionable drug combinations in B cell lymphoma

Abstract Combination drug therapies offer promise for improving cancer treatment. However, identifying actionable drug combinations for specific cancer subtypes and individual patients remains challenging due to the vast number of possibilities. Existing approaches like high-throughput screening (HTS) and rule-based drug additions have limitations in their ability to predict clinically translatable synergistic combinations. Therefore, advanced machine learning focused on patient-centric modelling have the potential to optimize combination therapies. PAIRWISE is a patient-centric deep learning-powered drug combination recommendation platform. Given gene expression along with chemical structures and targets from a set of two drugs being applied, the output is the binary combination outcome. PAIRWISE introduces a new approach of representing tumor genomics using the unsupervised pretraining design on 11K TCGA tumors. The power of PAIRWISE is derived from knowledge-guided transfer learning by learning from in vitro experiments with drug-drug-cell interactions and cellular heterogeneity. PAIRWISE can accurately predict combination synergy with area under the curve (AUC) > 0.85 across 10 tissue types in a large dataset of 200K in vitro drug combinations and significantly outperform all state-of-the-art methods. Diffuse large B cell lymphoma (DLBCL) is the most common B cell non-Hodgkin lymphoma and remains incurable in around 40% of patients. Finding optimal combination therapy for each DLBCL patient is a critical unmet need. As independent validation, we applied PAIRWISE to an external HTS study of Bruton Tyrosine Kinase inhibitor (BTKi) combinations (Griner et al., 2014), confirming the reproducibility and accuracy of high-confidence predictions (AUC = 0.72). PAIRWISE was also applied to transcriptomic data of 29 DLBCL cell lines (Bal et al., 2022) and predicted BTKi-containing combinations across ABC subtypes with higher synergy scores than GCB (P = 0.02). This is consistent with expected better overall efficacy of BTK inhibitors in ABC subtype (Younes et al., 2019; Davis et al., 2010; Wilson et al., 2015). To predict opportunities in DLBCL, PAIRWISE was used to identify BTKi partners for an additional 211 anti-cancer drugs using 562 published ex vivo DLBCL samples (Schmitz et al., 2018). PAIRWISE effectively identified novel DLBCL sub-population with significantly higher response scores for predicted BTKi combinations and favorable survival. Notably, patients predicted as responsive had significantly better outcomes upon first-line chemotherapy treatments than non-responsive ones (P < 0.0001). Collectively, we demonstrate that PAIRWISE is a highly efficient and generalizable framework that enables confident predictions of pairwise drug combinations at disease subtype level, with the potential of informing treatment options and clinical trial design. Citation Format: Chengqi Xu, Jake Cohen-Setton, Zhouyang Shen, Marta Milo, Ilkay US, Ben Sidders, Jude Fitzgibbon, Heng Pan, Olivier Elemento, Krishna Bulusu. PAIRWISE: Deep learning-powered personalized medicine approach identifies actionable drug combinations in B cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4911.

Abstract 3648: The spatial organization of cells expressing MYC and BCL2 affects immune microenvironment composition and prognosis in DLBCL

Abstract Point process analyses are widely used in ecology and geography to understand the impact and relevance of spatial distribution of points in a complex system. To date, only few studies have explored point process analyses in the context of tumor heterogeneity. Malignancies often show variable patterns of cellular distribution, and the relationship of these topographic variables with underlying biological processes and clinical outcomes is not well understood. Here, we performed spatial point process analyses in diffuse large B-cell lymphoma (DLBCL), leveraging advances in multiplexed immunohistochemistry and cellular phenotyping, with downstream multi-omic and clinicopathological analyses. We have demonstrated that survival in DLBCL is strongly associated with the fraction of malignant cells showing co-expression of the oncogenes MYC and BCL2 in the absence of BCL6 (M+2+6-). These cells display non-random spatial organization within tumor infiltrates, deviating from a poisson distribution. We therefore modeled the spatial organization of M+2+6- cells within DLBCL, using x-y coordinate information from multiplexed fluorescent immunohistochemistry (mfIHC) in four independent cohorts (N=449 patients). We derived spatial point patterns, upon which Geyers point process models were applied. We observed that patients could be divided into two groups based on these: one group showed “clustered” spatial organization, while the other displayed a “dispersed” M+2+6- cell distribution. Cases with predominantly “dispersed” M+2+6- cells showed shorter overall survival in all analyzed cohorts (P < 0.05 in 4/4 cohorts). We performed multi-omic analyses to identify potential biological explanations for this association between topography and clinical outcome. Using digital spatial profiling of the transcriptome from CD20, CD3 and CD68 compartments in 47 DLBCL samples, we observed that lymphomas with an M+2+6- ‘dispersed’ phenotype had an immunologically cold microenvironment enriched in Tregs and exhausted CD4+ and CD8+ T cells. Through an integration with single-cell transcriptomic analyses (N=22 samples) we observed that malignant B-cells from cases with M+2+6- ‘dispersed’ phenotype expressed genes implicated in cell migration and adhesion, but also in potentially targetable immune checkpoints such as LAG3. In summary, by integrating multiplexed immunophenotyping and point process analysis with molecular profiling in the setting of DLBCL, we show that a dispersed pattern of distribution of the M+2+6- subclonal fraction uniquely confers poor prognosis, and associates with a potentially reversible immune cold microenvironment composition. This represents the first demonstration that the spatial distribution of malignant cell subpopulations in lymphoma can embody biological and clinical significance. Citation Format: Shruti Shridar, Michal Marek Hoppe, Patrick Jaynes, Gayatri Kumar, Siddham Jasoria, Ziwei Meng, Vaibhav Rajan, Kasthuri Kannan, Claudio Tripodo, Anand Devaprasath Jeyasekharan. The spatial organization of cells expressing MYC and BCL2 affects immune microenvironment composition and prognosis in DLBCL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3648.

A promising natural product in diffuse large B-cell lymphoma therapy by targeting PIM1.

Diffuse large B-cell lymphoma (DLBCL) is the most common and aggressive type of B-cell lymphoma. Unfortunately, about one-third of patients either relapse after the initial treatment or are refractory to first-line therapy, indicating a need for new treatment modalities. PIM serine/threonine kinases are proteins that are associated with genetic mutations, overexpression, or translocation events in B-cell lymphomas. We conducted an integrative analysis of whole-exome sequencing in 52 DLBCL patients, and no amplification, mutation, or translocation of the PIM1 gene was detected. Instead, analyses of TCGA and GTEx databases identified that PIM1 expression was increased in DLBCL samples compared to normal tissue, and high expression levels were associated with poor overall survival. Moreover, interference of PIM1 significantly suppressed DLBCL cell proliferation. In addition, we identified anwulignan, a natural small-molecule compound, as a PIM1 inhibitor. Anwulignan directly binds to PIM1 and exerts antitumor effects on DLBCL in vitro and in vivo by inducing apoptosis, cell cycle arrest, and autophagic cell death. Furthermore, we identified an effective synergistic combination between anwulignan and chidamide. Our findings suggested that PIM1 could be a therapeutic target and prognostic factor for DLBCL, and anwulignan holds promise for future development as a natural product for treatment.

STAT6 mutations enriched at diffuse large B-cell lymphoma relapse reshape the tumor microenvironment

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6D419 mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6D419N was retained in the nucleus longer than phospho-STAT6WT following IL-4 stimulation, and STAT6D419N recognized a more restricted DNA-consensus sequence than STAT6WT. Upon IL-4 induction, STAT6D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STATWT. The most significantly expressed genes induced by STAT6D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin’s lymphoma. To this end, in DLBCL, phospho-STAT6+ rrDLBCL cells had a greater proportion of infiltrating CD4+ T-cells than phospho-STAT6− tumors. Our findings suggest that STAT6D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.

Targeting HDACs for diffuse large B-cell lymphoma therapy

Histone deacetylases (HDACs) are involved in tumorigenesis and progression, however, their role in diffuse large B-cell lymphoma (DLBCL) is not well understood. In this study, we examined the expression levels, mutations, and clinical significance of HDACs in DLBCL. Additionally, we investigated the therapeutic potential of Chidamide, a novel HDAC inhibitor, to provide scientific evidence for targeting HDACs in DLBCL patients. We extracted transcriptome data of DLBCLs––including 47 lymph node samples and 337 whole-blood-cell controls––from The Cancer Genome Atlas. Bioinformatic analyses of HDAC expression, mutation, and correlation with the clinical significance of DLBCL patients were performed with the Gene Expression Profiling Interactive Analysis, GENEMANIA, and web-based software including cBioPortal and WebGestalt. To examine the therapeutic effect of Chidamide, DLBCL cell lines (WSU-DLCL-2 and DB cells) were employed. Cell proliferation and apoptosis were analyzed with Cell Counting Kit-8 and flow cytometry assays. The impact of Chidamide treatment was also analyzed by RNA sequencing of treated DB cells. Western blot was used to explore the molecular mechanism of the cytotoxicity of Chidamide on DLBCL cell lines. The expression of some HDACs (HDAC1, 2, 3, 4, 6, 7, 8, and 9) were significantly higher in the lymph node samples of DLBCL than that in whole-blood-cell controls. Moreover, we found that the mutation rate of HDACs was also higher in DLBCL tissues, although the overall survival of DLBCL patients was not associated with HDAC expression. Chidamide was found to have a cytotoxic effect on DLBCL cells in a dose-dependent manner, while transcriptome analysis and western blot revealed that using it for treatment impacted several biological processes, including PI3K/AKT signaling, mTOR signaling, the cell cycle, and apoptosis pathways. Alterations of HDAC genes, including enhanced expression and mutations, are positively related to DLBCL. Targeting HDACs with specific inhibitors such as Chidamide may represent a potential therapeutic approach for DLBCL patients.

Performance of the cobas EZH2 mutation test on clinical samples from non-Hodgkin lymphoma patients

Objective To present the technical verification and clinical validation of the companion diagnostic assay, cobas® EZH2 Mutation Test (cobas EZH2 Test), targeting gain-of-function EZH2 mutations in follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL). The focus is on patient clinical samples proving that the test met the performance criteria required for FDA approval of a companion diagnostic test. Design Epizyme, Inc., Eisai Co., Ltd., and Roche Molecular Systems, Inc., collaborated to develop the cobas EZH2 Test on an RT-PCR platform. The assay design needed to detect the gain-of-function EZH2 mutations found in FL and DLBCL indications. Thus, the test was optimized for investigational purposes in a clinical trial setting. Part of its technical verification included testing of patient tumor samples with a documented diagnosis of FL and DLBCL procured from commercial vendors, and the clinical validation used patient samples from the Epizyme clinical study. Both the technical performance verification method correlation study (104 clinical commercially acquired samples) and the clinical validation accuracy study (341 patient samples from the therapeutic study) used next-generation sequencing as a reference method to establish true vs. false results by cobas EZH2 Test. The reproducibility study used a 15-member panel of DNA samples with varying EZH2 mutation status from procured clinical FL and DLBCL patient samples under multiple variables. Results Single and rare, infrequent double EZH2 mutations were detected in FL and DLBCL samples. Agreements between results from cobas EZH2 and sequencing were >98% from commercial clinical samples and from the therapeutic study clinical samples. The reproducibility study obtained 178 to 180 valid results for each panel member, with an overall invalid rate of 0.37%. The agreement for each per panel member was 100%. Conclusion cobas EZH2 Test data demonstrated that the test is reliable and will perform well in a commercial customer environment.

Unraveling the enigma of B cells in diffuse large B-cell lymphoma: unveiling cancer stem cell-like B cell subpopulation at single-cell resolution.

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent form of aggressive non-Hodgkin lymphoma. Despite receiving standard treatment, a subset of patients undergoes refractory or recurrent cases, wherein the involvement of cancer stem cells (CSCs) could be significant. We comprehensively characterized B cell subpopulations using single-cell RNA sequencing data from three DLBCL samples and one normal lymph tissue. The CopyKat R package was employed to assess the malignancy of B cell subpopulations based on chromosomal copy number variations. CIBERSORTx software was utilized to estimate the proportions of B cell subpopulations in 230 DLBCL tissues. Furthermore, we employed the pySCENIC to identify key transcription factors that regulate the functionality of B cell subpopulations. By employing CellphoneDB, we elucidated the interplay among tumor microenvironment components within the B cell subpopulations. Finally, we validated our findings through immunofluorescence experiments. Our analysis revealed a specific cancer stem cell-like B cell subpopulation exhibiting self-renewal and multilineage differentiation capabilities based on the exploration of B cell subpopulations in DLBCL and normal lymph tissues at the single-cell level. Notably, a high infiltration of cancer stem cell-like B cells correlated with a poor prognosis, potentially due to immune evasion mediated by low expression of major histocompatibility complex molecules. Furthermore, we identified key transcription factor regulatory networks regulated by HMGB3, SAP30, and E2F8, which likely played crucial roles in the functional characterization of the cancer stem cell-like B cell subpopulation. The existence of cancer stem cell-like B cells in DLBCL was validated through immunofluorescent staining. Finally, cell communication between B cells and tumor-infiltrating T cell subgroups provided further insights into the functional characterization of the cancer stem cell-like B cell subpopulation. Our research provides a systematic description of a specific cancer stem cell-like B cell subpopulation associated with a poor prognosis in DLBCL. This study enhances our understanding of CSCs and identifies potential therapeutic targets for refractory or recurrent DLBCL patients.

BTG2 Super-Enhancer Mutations Disrupt TFAP4 Binding and Deregulate BTG2 Expression in Diffuse Large B-Cell Lymphoma

Diffuse Large B-cell Lymphoma (DLBCL), the most frequent 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 prognostic and therapeutic significance. 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 DLBCLs and leading to dysregulation of multiple genes, including well-known lymphoma oncogenes (Bal et al., Nature 2022). This pervasive phenomenon exhibits hallmarks of AID activity, and we provided evidence of its oncogenic relevance by demonstrating that mutational hotspots in the BCL6, BCL2 and CXCR4 SEs impair the binding of specific transcriptional repressors, preventing their negative regulation and creating oncogenic dependencies in DLBCL cells. Here we aimed to define the pathogenic role of non-coding mutations targeting the intragenic SE (iSE) of the BTG2 gene, the second most commonly mutated in DLBCL (39% of samples analyzed, including 36/93 primary cases and 7/29 cell lines). BTG2 encodes a member of the B-cell translocation gene (BTG)/ Transducer of ErbB2.1(TOB) family involved in transcriptional co-activation and modulation of mRNA abundance. BTG2 is also targeted by somatic missense mutations in 6-11% of DLBCL samples, suggesting a major role in the pathogenesis of this disease, which has not been explored. In order to identify functionally relevant BTG2 SE mutations, we first screened an extended panel of 286 DLBCL samples for the presence of mutational clusters targeting predicted transcription factor binding motifs. We identified a 20bp sequence stretch that was significantly hypermutated in these tumors, as compared to other lymphoma types or to non-lymphoid tumors (n=64/286, 22%; vs 0/17 Burkitt Lymphomas; 18/284 Follicular Lymphomas; 1/240 Chronic Lymphocytic Leukemias, and 0/1596 other tumors; p<0.0001, Fisher's exact test). In particular, mutations of the C at position +904 to G or T were exclusively detected in 11% DLBCL samples (32/286, p<0.0001, Fisher's exact test). To define the consequences of BTG2 C904 mutations, we then investigated the impact of reverting the mutation to the wild type nucleotide in 2 mutant DLBCL cell lines, using the CRISPR-Cas9 technology. Correction of the mutation led to a significant counter selection of the corrected clones (~85% reduction in a clone recovery assay, compared to control; p<0.001, Fisher's exact test) and to reduced BTG2 mRNA expression (p<0.01, one-way ANOVA), suggesting oncogenic addiction. In silico motif prediction and in vitro DNA-binding assays using mutant vs wild type oligonucleotides in two different DLBCL cell lines, followed by mass spectrometry, identified the Transcription Factor AP4 (TFAP4) as a protein specifically bound to the wild type, but not to the mutant sequences. 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. We confirmed that the C904G and C904T SE mutation impaired TFAP4 binding in isogenic DLBCL cell lines by ChIP-qPCR and led to increased BTG2 expression (p<0.001, one-way ANOVA). Moreover, loss of TFAP4 in CRISPR-Cas9 edited DLBCL cells led to higher BTG2 expression levels, documenting TFAP4 as a direct negative regulator of BTG2 expression (p<0.001, one-way ANOVA). Consistently, single cell analysis of normal GC B cells revealed an inverse correlation between the expression pattern of TFAP4 and BTG2 in cells primed to undergo plasma cell differentiation vs cells committed to dark zone re-entry, suggesting that these two genes may function as a molecular switch in the cell fate decision of GC B cells. The programs modulated by BTG2 and TFAP4 in the GC, and disrupted in tumors carrying deregulated BTG2 alleles, will be presented, as defined by RNA-seq analysis of 3 isogenic GC-derived cell lines followed by interrogation of over 100 clinically annotated primary DLBCL biopsies. Together, these data identify TFAP4 as a novel regulator of BTG2 and reveal a previously unappreciated mechanism by which mutations in the BTG2 SE disrupt this circuit and contribute to lymphomagenesis through deregulating BTG2 expression.

Somatic Mutational Landscape and Transcriptomic Profiles of Lymphoproliferative Disorders with Epstein-Barr Virus Infection in Activated PI3K Delta Syndrome

Introduction: Germline gain-of-function variants in PIK3CD and PIK3R1 cause activated PI3K delta syndrome (APDS) types 1 and 2, respectively. Lymphoproliferative disorder (LPD) and susceptibility to various infections, including Epstein-Barr virus (EBV), are well-known features of APDS. EBV-associated LPDs are frequently observed in patients with APDS, and oligoclonal LPD might transform malignant lymphoma (ML). However, somatic molecular alterations in LPD and ML are not fully elucidated. Here, we report clinical characteristics and somatic mutational landscape and transcriptomic profiles of LPDs with EBV infection in APDS. Methods: LPD and ML in APDS diagnosed during 2011-2022 were recruited all over Japan. Clinical characteristics and outcomes were analyzed. EBV infection status was analyzed by EBER ISH and RNA sequencing (RNA-seq). Whole-exome sequencing (WES), RNA-seq, and single cell RNA-seq (scRNA-seq) were also performed. Results: Five LPD and 5 ML (borderline ML: n = 2, diffuse large B-cell lymphoma (DLBCL): n = 2, classical Hodgkin lymphoma: n = 1) cases were enrolled. Seven cases were APDS1 and 3 cases were APDS2. The median follow-up period was 2.9 (0.5-8.4) years. LPD cases were treated with rituximab monotherapy (n = 1), prednisolone (n = 3), and hematopoietic cell transplantation (HCT) (n = 1). Rituximab-combined chemotherapy was performed in borderline ML and DLBCL (n = 4) or ABVD regimen in Hodgkin lymphoma cases (n = 1). Sirolimus was used in two ML cases. HCT was performed in 9 cases. Two-year overall survival and event-free survival (events: relapse and death) were 78.8% and 70.0% in total LPDs, respectively, and 60% and 40% in ML cases. EBV was negative in 3 cases and positive in 2 cases of LPD cases, and positive in 4 cases, and negative in 1 case of ML cases. The median of 18 (10-48) and 42 (18-189) somatic variants were detected by tumor/germline pair WES in LPD (n = 5) and ML (n = 7, 2 samples were from the same patient at different time points), respectively. The recurrent variants were identified in ZFHX3 (n = 4), TRPM2 (n = 3), BEND3, CDH23, UBR4, ARAF, TET2, FBXO10, MAP4K1, KMT2C, KMT2D and PIK3R1 (n = 2) in LPD and ML cases. In transcriptome analysis, genes associated with lymphocyte activation including B-cell receptor signaling were upregulated in LPD, cell-cell adhesion, cellular response to TNF, and IL-18 signaling pathway were upregulated in ML samples. We performed correlation analysis to elucidate a relationship between EBV infection status and host transcriptomic profiles using RNA-seq data. Interestingly, in addition to cell-cell adhesion and inflammatory machinery, the upregulated molecules of the p53 pathway and negative regulation of the immune system were correlated with transcripts associated with EBV latency, such as EBNA, LMP-1, and LMP-2. In single-cell transcriptome analysis, LPD sample consisted of two large clusters of T cells and B cells with 10 subsets, and EBV-positive DLBCL sample consisted of one independent large cluster with 9 subsets. In LPD sample, components indicative of the normal lymphocyte differentiation process, including the germinal center, were preserved. The molecules involved in lymphocyte activation were upregulated in LPD subsets. In DLBCL sample, the genes associated with mitotic G2-M phases, chromatin organization and RNA metabolism were upregulated in the subsets in which EBV transcripts were more actively transcribed. Discussion and Conclusions: Our retrospective analysis demonstrated that the outcome of LPD and ML in APDS is not satisfactory. scRNA-seq data revealed that cells in LPD still retain the signature of normal lymph node. However, once transformed with EBV, EBV infection may affect epigenetic machineries including chromatin organizer followed by active RNA transcription involved in cell-cell adhesion, inflammatory machineries, molecules of p53 pathway and negative regulation of immune system in the host genome. As the outcome of ML is inferior to LPD, preemptive HCT would be an option for LPD cases.

Diffuse Large B-Cell Lymphoma Has a Low Frequency of dMMR and High Frequencies of DNA Mismatch Repair Protein High Expression Associated with Lower T-Cell Infiltration

Background: DNA mismatch repair (MMR) deficiency (dMMR), which leads to genomic and microsatellite instability, is a biomarker that predicts response to immunotherapies and has variable prognostic effects for chemotherapies in solid tumors. The dMMR can be detected using gene sequencing or immunohistochemistry for four essential MMR proteins: MSH6, MSH2, MLH1, and PMS2. Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma. Previous studies have suggested the role of MMR gene variants in DLBCL lymphomagenesis, yet the prognostic role of dMMR in DLBCL has not been well studied. Methods: We performed targeted next-generation sequencing and immunohistochemistry for MSH6, MSH2, MLH1, and PMS2 in a large cohort of DLBCL patients treated with standard chemoimmunotherapy. Gene expression profiling (GEP) was performed using the Affymetrix GeneChip Human Genome HG-U133 Plus 2.0 microarray (data in GSE31312). Fluorescent multiplex immunohistochemistry (mIHC) was performed using MultiOmyx multiplexing immunofluorescence staining protocols and antibodies against 13 immune markers. We investigated the frequencies of dMRR and MMR protein expression and correlated the mutation status and expression levels with patient survival and DLBCL biology, including the number of mutated genes, GEP data, previously analyzed DLBCL biomarkers, and immune cell immunophenotypes. Results: MMR gene mutations and loss of MMR protein expression were infrequent in DLBCL (Figure A) and did not show a significant prognostic impact. MMR proteins were commonly expressed in DLBCL samples and the germinal centers of reactive tonsil controls (Figure B), with higher mean and median percentages of tumor cells expressing MSH6 and MLH1 proteins than in the solid tumor samples used for comparison. High expression of MMR proteins was associated with Ki-67, MYC, and p53 overexpression as determined by immunohistochemistry. GEP analysis identified significantly upregulated genes involved in the mitotic cell cycle and DNA metabolism and prominent downregulated immune gene signatures in DLBCL with high MMR protein expression. Fluorescent mIHC confirmed the associations with decreased T cell abundance in MSH6/MSH2/MLH1/PMS2 highly expressing DLBCL (Figure C) independent of p53 and MYC expression status, whereas MSH6/ MLH1 mutations were associated with increased T cell frequencies. High versus low expression of MSH6, MLH1, and PMS2 showed significant unfavorable prognostic effects in DLBCL when the optimal cutoffs were used for high expression. However, when the median percentages were used as cutoffs, the prognostic effects were not significant; nonetheless, MSH6 and PMS2 high expression showed significant adverse prognostic effects in the MYC¯ DLBCL subset. Interestingly, when we used Ki-67, MYC + or BCL2 + percentages in DLBCL assessed by immunohistochemistry to subtract MMR protein percentages and then correlated the differences to survival, we found significant favorable prognostic impact by higher percentage expression differences between MMR proteins and BCL2, or by intermediately higher differences between MMR proteins and MYC. For MSH2/MLH1/PMS2, the higher percentages compared with MYC/BCL2 showed no significant correlations with T cell infiltration levels, whereas the higher differences between MSH6 and MYC/BCL2 were associated with significantly lower T cell frequencies (Figure D). Conclusions: This study revealed that high expression of MMR proteins is a common feature of DLBCL associated with lower tumor-infiltrating T cells, MYC and p53 overexpression, and context-dependent prognostic effects. In contrast, dMMR and loss of MMR proteins are infrequent and have no significant prognostic effects in DLBCL treated with standard chemoimmunotherapy. These results may have implications for understanding DLBCL biology and the low efficacy of PD-1 blockade immunotherapy in DLBCL.

Whole Exome Sequencing Analysis of Diffuse Large B-Cell Lymphoma That Progressed to Central Nervous System

Background: Recent sequencing analyses have identified distinct genetic subtypes of diffuse large B-cell lymphoma (DLBCL). The MCD subtype, characterized by MYD88 L265P and CD79B gene mutations, is frequently found in DLBCL that progresses to the central nervous system (CNS), known as secondary CNS lymphoma (sCNSL) (Ollila, Blood 2021). In addition, the MCD subtype is also frequently found in primary CNS lymphoma (PCNSL) (Radke, Nat commun 2022). Therefore, we hypothesized that specific genetic alterations that are present in the initial DLBCL tumors are associated with their progression to the CNS. We aimed to investigate i) the clinical impact of genetic subtypes of DLBCL and ii) genomic mutations that may be associated with sCNSL. To this end, genetic alterations in DLBCL samples at diagnosis that later developed into sCNSL were evaluated using whole-exome sequencing (WES). Methods: A total of 44 patients with DLBCL who developed sCNSL after receiving standard rituximab-containing chemotherapy between 2009 and 2020 were identified from five institutions. sCNSL was diagnosed on the basis of imaging and/or pathological findings. Genomic DNA (gDNA) was extracted from formalin-fixed, paraffin-embedded tumor samples obtained at the initial diagnosis of DLBCL. Finally, 26 of the 44 samples with sufficient gDNA were subjected to WES at BGI Genomic Co. Ltd. Single-nucleotide variants and short indels were detected using Mutect2 and filtered as described in Figure 1. Genetic subtypes were determined using the LymphGen algorithm (https://llmpp.nih.gov/lymphgen/index.php). The association between the clinicopathological features and genetic subtypes was also evaluated. In addition, recurrently mutated genes (>10% in our cohort) were identified and their alteration frequencies were compared with those in a representative general DLBCL cohort (N=574, available in the article by Schmitz, NEJM 2018) and PCNSL cohort (N=30, whole genome sequencing performed, available in the article by Radke, Nat Commun 2022) (Figure 1a). Results: The median age of the 26 patients was 69 years (range, 34-87). At the time of CNS progression, 54, 27, and 19% of the patients had parenchymal CNS disease, extraparenchymal CNS disease, and both, respectively. Immunohistological analysis showed that the non-germinal center B-cell-like (GCB) type, as defined using the Hans algorithm, and dual expression of MYC and BCL2 (DEL) were identified in 65% and 69% of patients, respectively. The median overall survival (OS) from CNS relapse was 16 months (95% confidence interval, 2-30). WES analysis showed that the median mean coverage depth of the 26 samples was 140 (range, 53-299). The median mapping rate was 94% (range, 80-99). As expected, the most frequently mutated genes in our cohort were MYD88 (50%), PIM1 (50%), BTG2 (31%), and CD79B (31%), corresponding to the MCD subtype. The LymphGen algorithm classified the 26 samples into MCD (n=12, 46%), EZB (n=5, 19%, including one with a composite feature of ST2), BN2 (n=1, 4%) and “not classified” (n=8, 31%) (Figure 1b). The CNS-IPI risk, DEL status, and CNS relapse sites did not differ among the genetic subgroups. In addition, univariate analysis showed that non-GCB status, DEL status, and genetic subtype at initial diagnosis did not influence OS. Next, we shortlisted 38 genes whose mutations were identified in three or more samples (>10%) of our cohort as well as in >10% of the PCNSL cohort (by Radke, Nat Commun 2022). We further selected genes that were less frequent (<3%) in the general DLBCL cohort and identified several genes, such as MUC16 and FAT4, that were more enriched in PCNSL and our sCNSL cases (Figure 1a). Metascape analysis (https://metascape.org/gp/index.html#/main/step1) revealed that the top three enriched processes associated with these genes were sensory organ development, cell adhesion via plasma membrane adhesion molecules, and morphogenesis of epithelium. Conclusion: Our study confirmed that MCD was a major genetic subtype of DLBCL that developed sCNSL. We identified recurrently mutated genes that were common in both sCSNL and PCNSL but not in the general DLBCL cohort; these genes may be associated with CNS progression. Validation of our results and investigation of the functions of these genes will help to understand the mechanisms of CNS relapse and develop more accurate markers to predict sCNSL. *Suzuki T and Yokomori R equally contributed to this study.

A CD5 Gene Signature Identifies Diffuse Large B-Cell Lymphomas Sensitive to Brutonʼs Tyrosine Kinase Inhibition

Introduction: Diffuse large B-cell lymphomas (DLBCLs) with a non-germinal center B cell-like (non-GCB) cell-of-origin are frequently driven by genetic alterations that culminate in constitutive B-cell receptor (BCR) signaling, which has inspired the exploration of Bruton's tyrosine kinase inhibitors (BTKi) in these lymphomas. However, the phase III PHOENIX study that randomized untreated, non-GCB DLBCL patients to R-CHOP plus placebo or ibrutinib failed to meet its primary endpoint of event-free survival (Younes et al. 2019), which suggests that cell-of-origin alone is an insufficient biomarker to predict BTKi sensitivity in DLBCL. More recently, a DLBCL genetic classifier termed LymphGen has identified distinct subtypes (MCD and N1) of non-GCB DLBCL that benefit from the addition of BTKi to R-CHOP (Wilson et al. 2021). However, genetic classifiers are complex and difficult to implement in routine clinical settings and may fail to capture all DLBCLs that benefit from BTKi. Therefore, we sought to identify a straightforward biomarker of BTKi responsiveness in DLBCL with greater precision than cell-of-origin but with broader inclusivity than current genomic platforms, such as LymphGen. We hypothesized that CD5 - a surrogate marker of BCR activation - may effectively identify BCR-driven, non-GCB DLBCLs that are sensitive to BTKi therapy, and evaluated the extent to which CD5 protein expression and a transcriptionally defined CD5 gene signature could accurately identify BCR-activated DLBCLs with potential susceptibility to BTKi-based therapies. Methods: CD5 immunohistochemistry (IHC) was performed on a cohort of 406 diagnostic DLBCL samples, which were considered CD5+ if >=30% of lymphoma cells exhibited unequivocal membranous staining. A majority of DLBCL samples had available RNA-sequencing and targeted mutational sequencing data. A comparison of differentially expressed genes between CD5+ and CD5- DLBCLs was performed in order to construct a 60-gene CD5 signature (CD5sig), which was applied to large genomic DLBCL datasets, including pre-treatment biopsies from patients enrolled on PHOENIX (n = 584) to evaluate the utility of the CD5sig in identifying DLBCLs that benefitted from the addition of ibrutinib to R-CHOP. Results: Twenty-six of 406 DLBCLs were identified as CD5+ by IHC (6% of all DLBCLs; 12% of non-GCB DLBCLs). CD5 IHC+ DLBCLs were majority non-GCB cell-of-origin and were associated with inferior progression-free survival (PFS) to R-CHOP (50% 3-year PFS), compared with CD5 IHC- DLBCLs, consistent with previous reports. Gene set enrichment analysis revealed that CD5 IHC+ DLBCLs exhibited transcriptional features of BCR activation, and mutational analysis demonstrated that CD5 IHC+ DLBCLs were enriched for CD79B BCR-activating mutations known to correlate with BTKi sensitivity. Many CD5 IHC+ DLBCLs, however, lacked canonical BCR-activating mutations or were classified as “Other” by LymphGen. A CD5 gene signature (CD5sig; Figure 1A) was developed that recapitulated these findings in independent DLBCL datasets (NCI, Duke), where ~13% of non-GCB DLBCLs were classified as CD5sig+. Together, these results suggest that CD5 signature expression captures DLBCLs with both a genetic and non-genetic basis for BCR dependence. Supporting this notion, CD5sig+ DLBCL patients (< 60 years) derived a selective and striking event-free and overall survival advantage from the addition of ibrutinib to R-CHOP in the PHOENIX study ( Figure 1B), independent of LymphGen classification. Conclusions: We demonstrate that CD5 IHC and a novel CD5 gene signature identify high-risk, BCR-driven DLBCLs. Importantly, the CD5 signature also identifies DLBCL patients with a selective survival advantage to BTK inhibitor-based therapy, independent of LymphGen classification. In conclusion, the CD5 signature expands upon LymphGen classification as a biomarker of BTKi response by accurately identifying DLBCLs with both genetic and non-genetic bases for BTKi response. The CD5 signature and/or CD5 IHC should be prospectively evaluated in BTKi-based clinical trials for non-GCB DLBCLs.

Large-Scale Genomic Analysis of Mutational Hotspots in Burkitt Lymphoma and Diffuse Large B-Cell Lymphoma

Introduction: The diverse pathways that are deregulated during the malignant transformation of B-cells have been identified for many of the common mature B cell neoplasms, but a comprehensive description of the driver mutations that alter the function of proteins in these pathways remains incomplete. Specific variants may be distinguishing features of disease or largely restricted to disease subtypes, while other variants may be more widespread between subtypes. Mutations that are prevalent in certain immune cancers can be found at a lower frequency in other subtypes, which can affect the relevance of targeted therapeutics. Manual curation of hotspots is crucial to minimize noise and enhance statistical power for detecting hotspots present at lower frequencies, as certain regions in the genome are more susceptible to sequencing artifacts and data sourced from multiple cohorts can be prone to batch effects. Through the integration of mutational variants between Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), and by employing manual validation of mutational variants to reduce sequencing noise, it is possible to identify novel mutational hotspots within the two subtypes. Methods: Mutational hotspot analyses were performed on a combined cohort consisting of BL and DLBCL samples that had undergone either whole genome sequencing (644 samples) or whole exome sequencing (1996 samples), for a total of 2640 samples. Samples were sourced from a combination of in-house and external datasets, comprising 28 datasets in total. Simple somatic mutations (SSMs) were identified using four variant callers: Strelka2, MuTect2, SAGE, and LoFreq. Mutational hotspots and significantly mutated genes (SMGs) were identified from coding region variants with HotMAPS, OncodriveCLUSTL, and OncodriveFML using a consensus approach of 2/3 tools. The curation of hotspots is performed by manual inspection of hotspot loci from the automated generation of IGV snapshots. Results: A large number of SMGs and hotspots were identified due to the size of our analysis cohort. HotMAPS identified 180 genes at a q-value of 0.01, OncodriveCLUSTL identified 219 genes at a q-value of 0.001, and OncodriveFML identified 105 genes at a q-value of 0.01. A total of 106 SMGs were identified by at least two tools, with 35 SMGs identified by all three tools (Figure 1). Among these genes, only 10 have not yet been incorporated in the Cancer Genome Census which may be used to estimate the quality of driver genes returned by these tools. Comparing to our curated list of genes previously associated with either BL or DLBCL, 39 of the genes with two votes and only 2 of the 35 genes with three votes are novel. HotMAPS and OncodriveCLUSTL identified a total of 207 and 254 hotspots, of which 109 and 110 hotspots were identified in the genes called by two or more tools, respectively, with overlapping HotMAPS and OncodriveCLUSTL hotspots occurring in 65 genes. Of the coding SSMs occurring in genes with two votes, 13% of hotspot mutations (HSMs) existed in both HotMAPS and OncodriveCLUSTL hotspots (Figure 2). Within the 207 hotspots called by HotMAPS, 177 (86%) were supported by variants from genome and capture samples, with the remaining 30 hotspots (14%) being exome-specific. Within these 207 hotspots, 160 (77%) were identified in both matched and unmatched-normal samples, while 47 (23%) were only identified in unmatched samples. Of the 254 hotspots identified by OncodriveCLUSTL, 191 (75%) were supported by both genome and capture samples and 63 (25%) were exome-specific. Both matched and unmatched samples contributed to 151 (59%) of the OncodriveCLUSTL hotspots and 103 hotspots (41%) were found only in unmatched samples. Conclusions: All but two of the 35 genes identified by all three tools have been previously associated with DLBCL or BL as driver genes, which highlights the robustness of using this suite of tools to identify novel driver genes and mutational hotspots. Out of the 106 genes identified by at least two tools, 39 have not yet been classified as driver genes and may be novel NHL drivers. Manual inspection of the hotspots identified by HotMAPS and OncodriveCLUSTL is performed to expand curated blacklists and whitelists of mutational hotspots to minimize the impact of low-quality variants and improve power for detecting hotspots with lower mutation frequencies.

Changes in the <i>MIR-143</i. gene methylation pattern in the tumor tissue of the diffuse large B-cell lymphoma

The study of DNA methylation status in malignant lymphomas is a new field of research in oncohematology. The aim of this study was to quantify the level of methylation of the MIR-143 gene in the tumor tissue of patients with diffuse large B-cell lymphoma (DLBCL). Material and methods. The study included 81 tumor samples of DLBCL (26 of germinal and 55 of non-germinal origin) and 11 biopsies of reactive lymph nodes. To quantify the methylation of the MIR-143 gene, the method of direct bisulfite sequencing by Sanger was used with the calculation of the average level of methylation of the analyzed CpG-sites. Results. The average level of MIR-143 methylation in tumor samples was significantly lower than the values in reactive lymph nodes (64.43 ± 19.92 и 76.27 ± 4.92 %, respectively, p = 0.049), did not depend on the immunohistochemical subtype of the tumor and showed a greater spread of values. In the lymphoma samples, there was predominant hypomethylation of one of the four analyzed CpG-dinucleotides within the boundaries of the analyzed fragment. Conclusion. The data on a change in the pattern of the MIR-143 gene methylation in the tumor tissue of DLBCL were obtained. To establish the role of microRNA in the pathogenesis of DLBCL further studies aimed at clarifying the mechanisms of epigenetic regulation of MIR-143 expression in lymphoma cells and identification of this microRNA targets are required.

Expression of "DNA damage response" pathway genes in diffuse large B-cell lymphoma: The potential for exploiting synthetic lethality.

Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) are two of the most prevalent non-Hodgkin's lymphoma subtypes. Despite advances, treatment resistance and patient relapse remain challenging issues. Our study aimed to scrutinize gene expression distinctions between DLBCL and FL, employing a cohort of 53 DLBCL and 104 FL samples that underwent rigorous screening for genetic anomalies. The NanoString nCounter assay evaluated 730 cancer-associated genes, focusing on densely tumorous areas in diagnostic samples. Employing the Lymph2Cx method, we determined the cell-of-origin (COO) for DLBCL cases. Our meticulous analysis, facilitated by Qlucore Omics Explorer software, unveiled a substantial 37% of genes with significantly differential expression patterns between DLBCL and FL, pointing to nuanced mechanistic disparities. Investigating the impact of FL disease stage and DLBCL COO on gene expression yielded minimal differences, prompting us to direct our attention to consistently divergent genes in DLBCL. Intriguingly, our Gene Set Enrichment Analysis spotlighted 21% of these divergent genes, converging on the DNA damage response (DDR) pathway, vital for cell survival and cancer evolution. Strong positive correlations among most DDR genes were noted, with key genes like BRCA1, FANCA, FEN1, PLOD1, PCNA, and RAD51 distinctly upregulated in DLBCL compared to FL and normal tissue controls. These findings were subsequently validated using RNA seq data on normal controls and DLBCL samples from public databases like The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, enhancing the robustness of our results. Considering the established significance of these DDR genes in solid cancer therapies, our study underscores their potential applicability in DLBCL treatment strategies. In conclusion, our investigation highlights marked gene expression differences between DLBCL and FL, with particular emphasis on the essential DDR pathway. The identification of these DDR genes as potential therapeutic targets encourages further exploration of synthetic lethality-based approaches for managing DLBCL.

Histone regulator KAT2A acts as a potential biomarker related to tumor microenvironment and prognosis of diffuse large B cell lymphoma

BackgroundRecent studies have indicated that epigenetic alterations contribute significantly to lymphoma pathogenesis. A type of epigenetic regulation known as histone acetylation plays a crucial role in transcriptional regulation in eukaryotic cells. Specifically, a significant effect of histone acetylation modifications on the abnormal progression and microenvironment of diffuse large B-cell lymphoma (DLBCL) has been observed.MethodsTo provide insight into the significance of histone acetylation-related genes, we developed a HAscore model for analyzing histone acetylation patterns in DLBCL samples. Furthermore, KAT2A, a regulator of histone acetylation, was knocked down in DLBCL cell lines to investigate its role in proliferation, cell cycle, and apoptosis.ResultsThe HAscore model has been demonstrated to provide insight into the significance of these patterns, showing that patients with a low HAscore have distinct tumor immune microenvironments and poorer prognoses. Besides, KAT2A was identified as a potential biomarker related to immune infiltration and malignant pathways in DLBCL.ConclusionAccording to these findings, it is evident that the histone acetylation pattern score model is helpful in describing the immune status of DLBCL and that KAT2A may be used as a biomarker for its treatment.

Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle.

Diffuse large B-cell lymphoma (DLBCL) exhibits remarkable heterogeneity but still remains undiagnosed in identifying the subpopulation of DLBCL to predict the prognosis and guide clinical treatment. Molecular subgroups were identified in gene expression data from GSE10846 by a consensus clustering algorithm. And gene set enrichment analysis, immune infiltration, and the proposed cell cycle algorithm were applied to explore the biological functions of different subtypes. Meanwhile, univariate and multivariate Cox regression analyses were used to evaluate independent prognostic factors of DLBCL. Finally, the prognostic model, including some key genes screened by Lasso regression, Random Forest algorithm, and point-biserial correlation, was constructed by an optimal classifier from seven machine learning algorithms and validated by another three external datasets (GSE34171, GSE87371, GSE31312). Comprehensive genomic analysis of 1,143 DLBCL samples identify 2 molecularly, prognostically relevant subtypes: immune-enriched (IME) and cell-cycle-enriched (CCE). Then a new predictive model including seven key genes (SERPING1, TIMP2, NME1, DCTPP1, RFC4, POLE2, and SNRPD1) was developed with high prediction accuracy (88.6%) and strong predictive power (AUC = 0.973) based on the Support Vector Machine (SVM) algorithm in 414 patients from GSE10846. The predictive power was similar in another three testing sets (HR > 1.400, p < 0.05). This model could evaluate survival independently with strong predictive power compared with other clinical risk factors. Our study constructed a reliable model to predict two new subtypes of DLBCL patients, which could guide the implementation of individualized treatment.

Factors determining whether diffuse large B-cell lymphoma samples are detected by flow cytometry.

Flow cytometry (FCM) is widely used in the diagnosis of mature B-cell neoplasms (MBN), and FCM data are usually consistent with morphological findings. However, diffuse large B-cell lymphoma (DLBCL), a common MBN, is sometimes not detected by FCM. This study aimed to explore factors that increase the likelihood of failure to detect DLBCL by FCM. Cases with a final diagnosis of DLBCL that were analysed by eight-colour FCM were retrospectively collated. Clinical, FCM, histopathological and genetic data were compared between cases detected and cases not detected by FCM. DLBCL cases from 135 different patients were analysed, of which 22 (16%) were not detected by FCM. In samples not detected by flow cytometry, lymphocytes were a lower percentage of total events (p = 0.02), and T cells were a higher percentage of total lymphocytes (p = 0.01). Cases with high MYC protein expression on immunohistochemistry were less likely to be missed by FCM (p = 0.011). Detection of DLBCL was not different between germinal centre B-cell (GCB) and non-GCB subtypes, not significantly affected by the presence of necrosis or fibrosis, and not significantly different between biopsy specimens compared to fine-needle aspirates, or between samples from nodal compared to extranodal tissue. The study identifies several factors which affect the likelihood of DLBCL being missed by FCM. Even with eight-colour analysis, FCM fails to detect numerous cases of DLBCL.