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Myeloid ecotropic virus insertion site 1 (MEIS1) is essential for normal hematopoiesis and is a critical factor in the pathogenesis of a large subset of acute myeloid leukemia (AML). Despite the clinical relevance of MEIS1, its regulation is largely unknown. To understand the transcriptional regulatory mechanisms contributing to human MEIS1 expression, we created a knock-in green florescent protein (GFP) reporter system at the endogenous MEIS1 locus in a human AML cell line. Using this model, we have delineated and dissected a critical enhancer region of the MEIS1 locus for transcription factor (TF) binding through in silico prediction in combination with oligo pull-down, mass-spectrometry and knockout analysis leading to the identification of FLI1, an E-twenty-six (ETS) transcription factor, as an important regulator of MEIS1 transcription. We further show direct binding of FLI1 to the MEIS1 locus in human AML cell lines as well as enrichment of histone acetylation in MEIS1-high healthy and leukemic cells. We also observe a positive correlation between high FLI1 transcript levels and worse overall survival in AML patients. Our study expands the role of ETS factors in AML and our model constitutes a feasible tool for a more detailed understanding of transcriptional regulatory elements and their interactome.

Burkitt lymphoma (BL) accounts for most pediatric non-Hodgkin lymphomas, being less common but significantly more lethal when diagnosed in adults. Much of the knowledge of the genetics of BL thus far has originated from the study of pediatric BL (pBL), leaving its relationship to adult BL (aBL) and other adult lymphomas not fully explored. We sought to more thoroughly identify the somatic changes that underlie lymphomagenesis in aBL and any molecular features that associate with clinical disparities within and between pBL and aBL. Through comprehensive whole-genome sequencing of 230 BL and 295 diffuse large B-cell lymphoma (DLBCL) tumors, we identified additional significantly mutated genes, including more genetic features that associate with tumor Epstein-Barr virus status, and unraveled new distinct subgroupings within BL and DLBCL with 3 predominantly comprising BLs: DGG-BL (DDX3X, GNA13, and GNAI2), IC-BL (ID3 and CCND3), and Q53-BL (quiet TP53). Each BL subgroup is characterized by combinations of common driver and noncoding mutations caused by aberrant somatic hypermutation. The largest subgroups of BL cases, IC-BL and DGG-BL, are further characterized by distinct biological and gene expression differences. IC-BL and DGG-BL and their prototypical genetic features (ID3 and TP53) had significant associations with patient outcomes that were different among aBL and pBL cohorts. These findings highlight shared pathogenesis between aBL and pBL, and establish genetic subtypes within BL that serve to delineate tumors with distinct molecular features, providing a new framework for epidemiologic, diagnostic, and therapeutic strategies.

Burkitt lymphoma (BL) accounts for the majority of pediatric non-Hodgkin lymphomas (NHL) and is relatively rare but significantly more lethal when diagnosed in adults. The global incidence is highest in Sub-Saharan Africa, where Epstein-Barr virus (EBV) positivity is observed in 95% of all tumors. Both pediatric (pBL) and adult (aBL) cases are known to share some driver mutations, for example MYC translocations, which are seen in > 90% of cases. Sequencing efforts have identified many common somatic alterations that cooperate with MYC in lymphomagenesis with approximately 30 significantly mutated genes (SMG) reported thus far. Recent analyses revealed non-coding mutation patterns in pBL that were attributed to aberrant somatic hypermutation (aSHM). We sought to identify genomic and molecular features that may explain clinical disparities within and between aBL and pBL in an effort to delineate BL subtypes that may allow for the stratification of patients with shared pathobiology. Through comprehensive sequencing of BL genomes, we found additional SMGs, including more genetic features that associate with tumor EBV status, and established three new genetic subgroups that span pBL and aBL. Direct comparisons between pBL and aBL revealed only marginal differences and the mutational profiles were consistently better explained by EBV status. Using an unsupervised clustering approach to identify subgroupings within BL and diffuse large B-cell lymphoma (DLBCL), we have defined three genetic subgroups that predominantly comprise BL tumors. Akin to the recently defined DLBCL subgroups, each BL subgroup is characterized by combinations of common driver mutations and non-coding mutations caused by aSHM. Two of these subgroups and their prototypical genetic features (ID3 and TP53) had significant associations with patient outcomes that were different among the aBL and pBL cohorts. These findings highlight not only a shared pathogenesis between aBL and pBL, but also establish genetic subtypes within BL that serve to delineate tumors with distinct molecular features, providing a new framework for epidemiological studies, and diagnostic and therapeutic strategies.

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.

Background Diagnosing glaucoma in patients with high myopia is still a challenge despite the rapid develop of optical coherence tomography (OCT) in the past 2 decades. One of the reasons was the different geographic anatomies of the retinal nerve fibre layer (RNFL) among the high myopes and the emmetropes. This often resulted in errors when RNFL thicknesses were analysed. As diagnosis of glaucoma relied on the detection of focal RNFL thinning in OCT, this could lead to wrong diagnosis. This study aimed to compare the distributions of superotemporal and inferotemporal retinal nerve fibre bundles among these 2 groups of population. Methods Retrospective case-control study. Myopic group comprised subjects with a spherical equivalent of ≤-6D and axial length ≥ 26 mm, whereas non-myopic control group consisted of subjects with a spherical equivalent of 0 ± 0.5D and axial length 24 ± 0.5 mm. Cirumpapillary OCT images were retrieved from all subjects and the distances between the peaks of the 2 retinal nerve fibre bundles was measured. Results One-hundred-thirty-four highly myopic eyes from 74 subjects were included in the myopic group and 188 non-myopic eyes from 94 subjects were recruited as control group. The mean inter-peak distance of the myopic group 8.89 ± 1.00 mm and that of the non-myopic group was 6.81 ± 0.61 mm. The mean inter-peak distance was significantly larger in the myopic group (p

Background Cannabis contains Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) as the primary constituents responsible for pharmacological activity. However, there are numerous additional chemically-related structures to Δ9–THC and CBD that are pharmacologically active and may influence the pharmacological properties of Δ9-THC and CBD. This study chemically characterized the cannabinoid constituents in a series of cannabis chemovar extracts and investigated the potential cannabinoid entourage effect in two behavioral assays. Methods Six chemovar extracts were compared to pure Δ9-THC, CBD and morphine for effects on the following behavioral assays in mice: hot plate and tail suspension. The battery of behavioral tests was conducted post intravenous administration of cannabis chemovar extract. Cannabinoid profiles of extracts were analyzed using high performance liquid chromatography. Cannabis extracts were administered at equal doses of Δ9-THC to investigate the role of their cannabinoid profiles in modulating the effects of Δ9-THC. Dose response curves were fit using a log[inhibitor] vs response three parameter model and differences between group means were determined using a one-way ANOVA followed by a post hoc test. Results Cannabis chemovars tested in this study exhibited substantially different cannabinoid profiles. All chemovars produced dose-dependent immobility in the tail suspension assay and dose-dependent antinociception in the hot plate assay. The maximum antinociceptive effect and ED50 was comparable between cannabis chemovars and Δ9-THC. Two cannabis chemovars produced significantly greater immobility in the tail suspension test, with no significant differences in ED50. Conclusions Commercially available cannabis chemovars vary widely in cannabinoid content, but when equalized for Δ9-THC content, they produce similar behavioral effects with two exceptions. These findings provide only limited support for the entourage hypothesis. Further studies are necessary to characterize the nature of these pharmacological differences between cannabis chemovars and pure Δ9-THC.

Introduction: Genome- and transcriptome-wide analyses continue to enhance our understanding of the molecular pathogenesis of cancer. In lymphomas, this has enabled the identification of hundreds of recurrently mutated genes, highlighting genetic heterogeneity and relationships both within and among clinical entities. While the growing availability of lymphoma genomic data sets can be leveraged to integrate genomic analyses into diagnostic testing and clinical trials, the ability to rapidly process genomic data sets in a reproducible manner serves as a barrier to this goal. To this end, we developed a suite of tools Lymphoid Cancer Research modules (LCR-modules) to facilitate the discovery of novel drivers and molecular features in lymphoma cancers and perform quantitative comparisons between disease entities. We demonstrate here how this toolkit enabled a meta-analysis of lymphoma genomic data involving genome-wide profiles of 3330 patients. Methods: We assembled a collection of whole genome, whole exome, and RNA sequencing data from a combination of controlled-access repositories and ongoing projects at BC Cancer. The scope of genomic analysis of mature B-cell lymphomas (GAMBL) project includes cell lines and patient tumors from all common mature B cell neoplasms, comprising a total of 4612 samples from 3330 patients. To facilitate the project, we developed a suite of open-source and custom bioinformatics tools (https://github.com/LCR-BCCRC/lcr-modules) that leverages the Snakemake workflow management system and includes lymphoma-centric modules for the discovery and annotation of common mutation types, analysis of B-cell receptor repertoires and discovery of novel aSHM targets and relevant non-coding mutations, and RNA-seq analysis with batch correction and normalization. Individual modules are configured to create an automated, scalable, and reproducible workflow that runs each step as dictated by the availability of new data. The cohort-level integrative analysis and comparisons across entities are handled by our custom R package GAMBLR, which facilitates open-ended data analysis and custom visualizations. Results: Simple somatic mutations (SSM) were detected using a workflow that utilizes four algorithms to identify high-confidence variants with validated default thresholds for filtering of germline variants and common FFPE-associated artifacts, allowing for processing of samples without matched normal tissue. This automated and reproducible workflow facilitated the discovery of novel genes significantly mutated across lymphomas and broadened our understanding of the scope of aberrant somatic hypermutation (aSHM) and other non-coding mutations. Specifically, HNRNPU, STAT3, TFAP4, RRAGC were found to be mutated at relatively low frequencies, and their presence is a distinct feature of certain lymphomas or novel genetic subgroups within lymphoma types (Figure 1A). The aSHM analysis and discovery of novel hypermutated regions is handled by a custom tool Rainstorm. As a result, we were able to detect sites preferentially hypermutated in a single entity, such as the transcription start site of BACH2, mutated at lower rates than the other common target sites but significantly more in BL compared to other entities (Figure 1B). Combining aSHM at target sites discovered using our toolkit with other genetic features allowed us to explore and establish novel genetic subgroups within Burkitt lymphoma and follicular lymphoma. SV analysis can be conducted using Manta, GRIDSS, and JaBbA modules with downstream processing in GAMBLR. In B-cell lymphomas, the most common SVs identified using the automated workflow were targeting MYC, BCL2, and CCND1. Unsurprisingly, the most common translocation partner among B-cell lymphomas was the immunoglobulin heavy chain, but the novel BCL6-FOXP1, CD274-BACH2, BCL6-RHOH translocations in DLBCLs and MYC-BCL6 translocations in BLs were identified, among others (Figure 1C). Conclusions: We present here the modularized workflow for scalable and automated analysis of genomic and transcriptomic data and demonstrate that it can be successfully deployed across thousands of tumour samples for the discovery of known and novel lymphoma biology. This represents an important advancement in reproducibility that will facilitate clinical translation of genomic discoveries. Figure 1 Figure 1. Disclosures Grande: Sage Bionetworks: Current Employment. Coyle: Allakos, Inc.: Consultancy. Steidl: AbbVie: Consultancy; Trillium Therapeutics: Research Funding; Epizyme: Research Funding; Seattle Genetics: Consultancy; Curis Inc.: Consultancy; Bayer: Consultancy; Bristol-Myers Squibb: Research Funding. Scott: Abbvie: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; Celgene: Consultancy; AstraZeneca: Consultancy; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. . Morin: Epizyme: Patents & Royalties; Celgene: Consultancy; Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees.

Myeloid ecotropic virus insertion site 1 (MEIS1) is essential for normal hematopoiesis and is deregulated in a large subset of acute myeloid leukemia (AML) by yet unknown mechanisms. We previously identified 3 candidate enhancer regions: enhancer region 1 (E1) at -2 kb upstream; enhancer region 2 (E2) at +10.6 kb downstream inside intron 6; and enhancer region 3 (E3) +140 kb downstream of the translation start site. In the current study, we utilized CRISPR-Cas9 genome editing to further characterize these enhancers in a human AML cell line and identify the key transcription factors (TFs) associated with their function. To efficiently track MEIS1 expression levels, a GFP reporter, a P2A self-cleaving peptide tag and a hemagglutinin tag at its translation start site was introduced in a MEIS1 high expressing human AML cell line, U937. Then we introduced random mutations (Indels) along the MEIS1 locus utilizing a CRISPR-Cas9 mediated genome editing vector system in mono-allelic MEIS1-GFP-tagged U937 cells with special focus on the previously identified enhancer regions to find the key sequences important to the function of the MEIS1 enhancer regions. Two targeted regions yielding the highest proportion of GFP - cells corresponded to the E2 enhancer region within intron 6 and were referred to as E2.1 and E2.2. Using chromosome conformation capture (3C) assay, we detected a significantly decreased interaction (p=0.0022) between the promoter and the intron 6 region surrounding the E2 region in E2.2 targeted cells compared to the parental cells. Moreover, our data indicated that the DNA sequence within E2.2 is highly critical to this region's enhancer function which is further influenced by the larger genomic region surrounding the E2.1 gRNA targeted site. To identify TFs binding to the E2 region, we further scrutinized the E2.2 indel region for loss of TF binding sites. We performed TF prediction analysis and performed a protein pull down-mass spectrometry experiment to identify TF candidates. The overlap yielded a list of 7 TFs, each of which we targeted via CRISPR/Cas9. Reduction in GFP levels was only observed for FLI1 locus targeting but not for the other 6 TFs. Concordant reduction in MEIS1 and FLI1 levels were confirmed by immunoblotting. Additionally, chromatin immunoprecipitation (ChIP) followed by quantitative PCR revealed significant FLI1 enrichment at the promoter and at 3 sites surrounding the E2.2 region (p=0.0004) compared to 4 control regions scattered along the MEIS1 locus. Given a previous study indicating MEIS1 upregulation of FLI1 in normal hematopoiesis, we hypothesised that a positive feedback loop may exist between FLI1 and MEIS1 in AML. Since MEIS1 levels are frequently elevated in normal karyotype AML (CN-AML), we used the murine Hoxa9/Meis1 AML model as a surrogate for CN-AML and performed Meis1 ChIP-seq analysis. We detected direct Meis1 binding to the intronic region of the mouse Fli1 gene as well as other ETS factor loci, in Hoxa9/Meis1 cells. To better understand the clinical relevance of FLI1 in AML, we analyzed the Beat AML dataset. High FLI1 transcript levels correlated with adverse overall survival in CN-AML (p=0.044). Additionally, we observed a trend towards worse outcome with high FLI1 in the NPM1-mutated CN-AML subtype (p=0.069). We also observed a similar correlation in CN-AML for another ETS factor, ELF1, which we had previously shown to bind and upregulate MEIS1 expression in AML, suggesting a broader unrecognized role for ETS factors in AML. In summary, we have developed a rapid flow cytometry-based readout for the fine dissection and characterization of the cis-regulatory elements and associated TFs critical for MEIS1 transcription via CRISPR-Cas9 genetic manipulation. Our study revealed FLI1 as the candidate key regulator of MEIS1 expression and a positive correlation between FLI1 mRNA levels and worse overall survival in MEIS1-high AML subgroups. Disclosures No relevant conflicts of interest to declare.

Introduction: Plasmablastic lymphoma (PBL) is an aggressive B-cell lymphoma that predominantly occurs in patients with HIV or other causes of immunodeficiency. Frequent infection by the Epstein-Barr virus (EBV) and MYC translocations have been described as major features contributing to the pathogenesis of PBL. Prior studies examining the genetic landscape of PBL have largely relied on targeted capture-based sequencing approaches. As such, the molecular features of PBL remain to be fully explored. Here, we provide a comprehensive description of the genomic landscape of PBL using whole-genome and whole-exome sequencing to identify commonly perturbed pathways. Method: Archival diagnostic fresh frozen and formalin fixed paraffin embedded tissue biopsies from 58 PBL tumours were accrued from Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites, including 15 tumours from Ramis-Zaldivar et al., Haematolgica 2021. MYC rearrangements were identified by break-apart fluorescent in situ hybridization (FISH) and rearrangement partner was determined in a subset of tumours from capture or genome sequencing data using structural variant callers Manta, GRIDSS, and Delly. High confidence somatic mutations (SNVs/Indels) were identified in data from whole-genome (n=5) or whole-exome (n=53) sequencing through an ensemble voting approach utilizing four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Mutation frequencies in known lymphoma-related genes were compared to activated B-cell (ABC)-DLBCL (Schmitz et al., NEJM 2018), as the closest tumour entity in terms of putative cell-of-origin differentiation stage, to identify differences in genetic aberrations. Candidate somatic copy number alterations (CNAs) were identified from exome and genome sequencing data, using CopywriteR and ControlFREEC, respectively, and high-confidence CNAs were determined using GISTIC2.0. Results: Within the study cohort, 81% of patients were male with a median age of 59 years (range 11-88). HIV and EBV statuses were available for 47% of patients and 95% of tumours, respectively, with 49% (13/27) of the patients being HIV+ and 69% (38/55) of tumours being EBV+. MYC rearrangement was observed in 60% (35/58) of PBLs, with IGH as the partner gene in 88% (21/24) of tumours. The most frequently mutated genes were STAT3 (38%), TP53 (22%), NRAS (21%), and TET2 (16%), consistent with previous studies, however novel mutations were seen in DUSP2 (21%), KLHL6 (16%), and BHLHE41 (16%). Recurrent CNAs included amplifications in 1q, whole gains of 7, 8q24, 11p12 and deletions affecting 4p16, 5p15, 10q11.22. While the mutational landscapes were similar between samples with and without a MYC translocation, the MYC-translocated PBLs showed more frequent amplification of 1q32.1. When stratifying by EBV status, STAT3 and SOCS1 mutations were more frequent in EBV-positive tumours, whereas TP53, TET2, KRAS, and MMRN2 mutations were associated with EBV-negativity. In comparison to ABC-DLBCL, PBLs were significantly enriched in STAT3 and NRAS mutations, and lacked common mutations affecting the NF-κB pathway (eg. MYD88, CD79B, and NFKBIZ 3' UTR mutations). Mutations in genes that are frequently mutated in ABC-DLBCLs, such as those associated with plasma cell differentiation (eg. PRDM1) or a memory B-cell fate (eg. TBL1XR1), were also not mutated in PBLs. Finally, genetic alterations associated with immune evasion, such as deletion of MHC I and II and mutations in B2M, CIITA, and CD58, were rarely observed. Conclusion: These data present a comprehensive overview of the genomic landscape of PBLs in a large cohort. We show frequent mutations involving the JAK-STAT and MAPK pathways, wherein the genetic landscape can be differentially characterized by EBV status and MYC rearrangement status. We show that PBLs are genetically distinct from ABC-DLBCLs, with absence of mutations in genes affecting the NF-κB pathway, immune evasion, and driving a memory B-cell fate. Disclosures Slack: Seagen: Consultancy, Honoraria. Raess: Scopio Labs: Research Funding. Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Savage: Servier: Consultancy, Honoraria; Roche: Research Funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; Astra-Zeneca: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; AbbVie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Takeda: Other: Institutional clinical trial funding; Beigene: Other: Institutional clinical trial funding; Genentech: Research Funding. Steidl: Seattle Genetics: Consultancy; Curis Inc.: Consultancy; Bayer: Consultancy; Epizyme: Research Funding; Trillium Therapeutics: Research Funding; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Morin: Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Epizyme: Patents & Royalties. Scott: Abbvie: Consultancy; AstraZeneca: Consultancy; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. ; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling..

Introduction: Burkitt lymphoma (BL) accounts for approximately 50% of all pediatric non-Hodgkin lymphomas compared to 1-2% in adults. Adult BL (aBL) remains a poorly understood entity and its relationship to pediatric BL (pBL) and to DLBCL has not been fully elucidated. The variable treatment outcomes between these entities necessitate a more thorough understanding of the genetic and molecular features underlying their biology to enable better prognostication and more effective treatments. We sought to comprehensively determine genetic features shared with DLBCL and those that are unique to BL, to further delineate genetic subgroupings within each entity. Methods: Samples for this study were collected through the Burkitt Lymphoma Genome Sequencing Project (BLGSP). We sequenced the tumor genomes of 139 pBL and 92 aBL, consisting of both EBV-positive (EBV+) and EBV-negative (EBV-) BLs, and compared these to the genomes of 252 DLBCL patients. All cases were analyzed for simple somatic mutations (SSM), recurrent copy number variations (CNV), structural variations (SV), aberrant somatic hypermutation (aSHM), and SSM hotspots. Mutations were used as features for the identification of genetic subgroups using non-negative matrix factorization (NMF) clustering. Results: Clustering of BL and DLBCL revealed six distinct genetic subgroups (Figure 1) with three primarily representing DLBCLs (DLBCL-1, DLBCL-2, and DLBCL-3) and three predominantly comprising BLs (M53-BL, IC-BL, and DGG-BL). The DLBCL-predominant subgroups partially overlapped with those previously described and resembled features of EZB and ST2-like subgroups. The frequency of aBLs within these subgroups was higher than that of pBL patients (p=0.0005). The new cluster M53-BL consists of both pBL (9/27) and aBL (13/27) samples and is characterized by the highest prevalence of mutations in TP53 accompanied by the paucity of other driver mutations but without the aneuploidy associated with the A53 subgroup described in DLBCL. Enrichment of EBV- samples in this cluster further corroborate our previous findings of TP53 mutations being associated with EBV- BL. IC-BL is characterized by mutations in ID3, CCND3, and SMARCA4. In contrast, DGG-BL, where 65% of the cluster consisted of EBV+ BL samples, had mutations in DDX3X, GNA13, and GNAI2. Using a linear model, we compared the rates of aSHM in BL genomes from all clusters and identified the DLBCL-3 cluster to harbor the highest aSHM rates at common sites while the M53-BL cluster harbored the lowest rates. To further establish the biological basis of unique clusters within BL, we conducted differential gene expression analyses between the two major BL genetic subgroups, DGG-BL and IC-BL. We identified a total of 86 differentially expressed genes between the two clusters (p.adj < 0.01 and |log2foldChange| > 1). Among the genes with the strongest differential expression were IRF4, SERPINA9, and TNFRSF13B. Each of these are notable as their expression is a component of the DLBCL cell-of-origin and double-hit signature classifiers. Further, we found IRF4 expression to be one of the strongest predictors of cluster membership, with high IRF4 expression associated with IC-BL membership. Using TP53 and ID3 mutations as a proxy for M53-BL and IC-BL clusters in aBL, we found mutations in TP53 to be associated with significantly inferior progression free survival (PFS) at 2 year follow up, while mutations in ID3 were associated with overall better PFS at 2 year follow up. Conclusion: This work identifies novel genetic subgroups within BL with characteristic genetic and gene expression differences and some bearing relationship to DLBCL subgroups. The three subgroups with predominantly BL samples (DGG-BL, IC-BL, and M53-BL) each comprised a mixture of aBL and pBL samples, confirming similar molecular features in these entities. The IC-BL cluster is associated with mutations in ID3 and CCND3, high IRF4 expression, and ID3 mutated cases exhibited significantly better outcomes. M53-BL is associated with TP53 mutations and inferior PFS in aBL, representing a subset of patients to be considered for novel treatment approaches. These findings highlight shared pathogenesis between aBL and pBL and establish genetic subtypes within BL that delineate cases with distinct molecular and clinical features. This provides a new framework for new diagnostic and therapeutic strategies. Figure 1 Figure 1. Disclosures Abramson: Seagen Inc.: Research Funding; Allogene Therapeutics: Consultancy; Astra-Zeneca: Consultancy; Incyte Corporation: Consultancy; BeiGene: Consultancy; Kymera: Consultancy; Kite Pharma: Consultancy; Novartis: Consultancy; Bluebird Bio: Consultancy; C4 Therapeutics: Consultancy; Morphosys: Consultancy; Genmab: Consultancy; EMD Serono: Consultancy; Bristol-Myers Squibb Company: Consultancy, Research Funding; AbbVie: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy. Bartlett: Pharmacyclics: Research Funding; Millennium: Research Funding; Merck: Research Funding; Kite, a Gilead Company: Research Funding; Janssen: Research Funding; Genentech: Research Funding; Forty Seven: Research Funding; Celgene: Research Funding; Bristol Myers Squibb: Research Funding; Autolus: Research Funding; Seagen: Consultancy, Research Funding; Roche/Genentech: Consultancy; ADC Therapeutics: Consultancy, Research Funding. Casper: EUSA Pharma: Consultancy. Gerrie: Roche: Research Funding; AbbVie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Astrazeneca: Honoraria, Research Funding; Sandoz: Honoraria. Grande: Sage Bionetworks: Current Employment. Mullighan: Illumina: Membership on an entity's Board of Directors or advisory committees; Pfizer: Research Funding; AbbVie: Research Funding; Amgen: Current equity holder in publicly-traded company. Noy: Epizyme: Consultancy; Rafael Parhma: Research Funding; Morphosys: Consultancy; Targeted Oncology: Consultancy; Medscape: Consultancy; Pharmacyclics: Consultancy, Research Funding; Janssen: Consultancy, Honoraria. Scott: NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; AstraZeneca: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling--licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. . Morin: Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Epizyme: Patents & Royalties.

Lifelong production of the many types of mature blood cells from less differentiated progenitors is a hierarchically ordered process that spans multiple cell divisions. The nature and timing of the molecular events required to integrate the environmental signals, transcription factor activity, epigenetic modifications, and changes in gene expression involved are thus complex and still poorly understood. We now show that the more primitive types of human cells in this system display a unique repressive H3K27me3 signature that is retained by mature lymphoid cells but is lost in terminally differentiated monocytes and erythroblasts. Additional intervention data implicate that control of this chromatin state change is a requisite part of the process, whereby normal human hematopoietic progenitor cells make lymphoid and myeloid fate decisions.

Cell-fate determination is influenced by interactions between master transcription factors (TFs) and cis-regulatory elements. Hepatocyte nuclear factor 4 alpha (HNF4A), a liver-enriched TF, acts as a master controller in specification of hepatic progenitor cells by regulating a network of TFs to control onset of hepatocyte cell fate. Using analysis of genome-wide histone modifications, DNA methylation, and hydroxymethylation in mouse hepatocytes, we show that HNF4A occupies active enhancers in hepatocytes and is essential for active histone and DNA signatures, especially acetylation of lysine 27 of histone 3 (H3K27ac) and 5-hydroxymethylcytosine (5hmC). In mice lacking HNF4A protein in hepatocytes, we observed a decrease in both H3K27ac and hydroxymethylation at regions bound by HNF4A. Mechanistically, HNF4A-associated hydroxymethylation (5hmC) requires its interaction with ten-eleven translocation methylcytosine dioxygenase 3 (TET3), a protein responsible for oxidation from 5mC to 5hmC. Furthermore, HNF4A regulates TET3 expression in liver by directly binding to an enhancer region. Conclusion: In conclusion, we identified that HNF4A is required for the active epigenetic state at enhancers that amplifies transcription of genes in hepatocytes.

Background: Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information which is obfuscated by the data structure complexity. Results: bioSyntax ( https://biosyntax.org/ ) is a freely available suite of biological syntax highlighting packages for vim, gedit, Sublime, VSCode, and less. bioSyntax improves the legibility of low-level biological data in the bioinformatics workspace. Conclusion: bioSyntax supports computational scientists in parsing and comprehending their data efficiently and thus can accelerate research output.

Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information that is obfuscated by the complexity of the data structures. bioSyntax (http://bioSyntax.org) is a freely available suite of syntax highlighting packages for vim, gedit, Sublime, and less, which aids computational scientists to parse and work with their data more efficiently.

Information generation, representation and control in a manufacturing enterprise are often done through interactions of a variety of application software. Application software such as resource planning, electronic design automation, computer aided design and manufacturing, product data management, various database and supply-chain management applications are essential for an enterprise to be successful in this information age. For an enterprise to operate effectively, individual application must communicate and interact with each other, and user groups can share and exchange pools of information. Although client/server technology can break some of the barriers, with applications being shared among common databases and open system platforms, it does not imply that the applications are capable of inter-operating data and document can be efficiently managed and distributed over the manufacturing enterprise at large. XML provides a tool to integrate data from different sources and eliminate client-extensions to achieve universal access. In this paper, we will incorporate XML in a manufacturing enterprise to provide a cross-organizational support for a board range of users, across hardware and software boundaries conveniently and cost effectively.

This paper presents a study on the characterization of the sound quality of transient sounds via fundamental psychoacoustic measures. Specifically, the overall subjective perception of annoyance for transient sounds was studied. Through magnitude estimation and paired comparison jury evaluation experiments, the subjective annoyance magnitudes of 15 transient sounds were determined. For each sound, several objective psychoacoustic measures were calculated, and using simple linear regression models, the relationships between these objective measures and the subjective annoyance magnitudes were investigated. Examined psychoacoustic measures included loudness, sharpness, roughness, fluctuation strength, tonality, and a new loudness‐based measure of impulsiveness. The new impulsiveness measure is based on the summation of the magnitudes of impulse‐induced peaks in the loudness time history for a sound (calculated according to DIN 45631/A1). The models were analyzed using several statistical measures of model s...