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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.

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.

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.

Introduction: Follicular lymphoma (FL) is an indolent disease that undergoes histological transformation (HT) to aggressive diffuse large B-cell lymphoma (DLBCL) in 8-15% of patients. FLs frequently share genetic features with DLBCL, especially those of the germinal center B-cell-like (GCB) cell-of-origin (COO) and the EZB/C3 genetic subgroup, and approximately 80% of transformed FL (tFL) are classified as GCB. Our current understanding of the genetics of FL and tFL is based on a variety of studies, most of which have sequenced tumors in small case numbers or using targeted approaches such that the potential role of non-coding mutations and aberrant somatic hypermutation (aSHM) in predicting HT have not previously been fully explored. Methods: Whole genome sequencing (WGS) data from 212 FL (including 24 from patients that subsequently underwent HT) and 241 de novo DLBCL were analyzed. Simple somatic mutations (SSMs) were called using an ensemble of somatic variant callers, while structural variants (SVs) were called with Manta and copy number variants (CNVs) with Battenberg and GISTIC. Fluorescence in situ hybridization with break-apart probes (BA-FISH) was used to identify MYC, BCL2, and BCL6 translocations, and with IGH /BCL2 dual-fusion probes (DF-FISH) for a subset of FLs. To compare the genetics of FL and DLBCL, 83 significantly mutated genes (SMGs) were identified with dNdS, MutSigCV, HotMaps, and OncoDriveFML, and non-silent mutations were tabulated for their presence in each genome. For 38 hypermutated regions, we used a region-specific threshold to binarize the data to aSHM/no aSHM. Recurrent missense mutations in FOXO1, MYD88L265P, CREBBP lysine acetyltransferase (KAT) domain, EZH2Y646, MEF2B, and STAT6 were tabulated separately from other mutations in these genes. Using only the FL tumors from patients with no evidence of subsequent transformation and all available de novo DLBCLs, we trained a random forest classifier to separate these two entities using this set of 129 features, including MYC and BCL6 translocations. To validate this classifier, we fit a linear model to the number of FL votes from each discovery case, utilizing the 65 features (including 19 aSHM features) that were adequately sequenced in a validation cohort of 127 tFL. Statistical tests were corrected for multiple comparisons where necessary. Results: This large cohort of FL and DLBCL genomes has enabled the curation of an extensive list of novel and established FL driver genes and the identification of distinguishing genetic features among SMGs and CNVs. Loci that are significantly enriched for mutations in FL vs. DLBCL include the CREBBP KAT domain (OR 11.5, P < 0.0001), RRAGC (OR 9.61, P < 0.001), and ATP6V1B2 (OR 11.17, P < 0.001). Deletions of ARID1A (OR 4.74, P < 0.1), PTEN (OR 3.65, P < 0.01), and TNFRSF14 (OR 3.31, P < 0.01) were among the CNVs significantly enriched in FL. Out of 156 FLs, 24 (15%) were negative for BCL2 translocations by BA-FISH, but 4 (17%) of these had BCL2 translocations detected from WGS data. All 4 of these cryptic events were confirmed using IGH /BCL2 DF-FISH. Using a threshold of 0.7, the linear model separated discovery FL cases into a more DLBCL-like subgroup, termed dFL (n = 107), and a genetically homogeneous subgroup enriched for the FL-associated features, which we describe as constrained FL (cFL, n = 105). This separation is supported by more mutations in dFL vs cFL across several aSHM loci, including the transcription start sites for BCL6, BCL7A, DTX1, and ZFP36L1 (Figure 1), consistent with reduced exposure to the germinal center reaction in cFL. Within the targeted capture validation cohort of tFL, 30 (24%) tumors were classified as cFL and 97 (76%) as dFL. The tFL cohort was significantly depleted for mutations in the CREBBP KAT domain (OR 0.59, P < 0.05), and were significantly less frequently classified as cFL (OR 0.30, P < 0.0001) compared to the discovery FLs. Conclusions: The distinction between cFL and dFL is strongly driven by CREBBP KAT domain mutations and different rates of aSHM genome wide. Given the known early clonal nature of CREBBP mutations in FL and its role in regulating germinal center cycling, we speculate that CREBBP KAT mutations may limit the exposure of FL to the dark zone, reducing the opportunity for aSHM and creating an evolutionary constraint that may limit the opportunity for HT. This classification may serve as a useful biomarker to identify FLs at higher risk of HT. Figure 1 Figure 1. Disclosures Coyle: Allakos, Inc.: Consultancy. Grande: Sage Bionetworks: Current Employment. Slack: Seagen: Consultancy, Honoraria. Steidl: Curis Inc.: Consultancy; Trillium Therapeutics: Research Funding; Epizyme: Research Funding; Bayer: Consultancy; Seattle Genetics: Consultancy; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Scott: Janssen: Consultancy, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; Incyte: Consultancy; Rich/Genentech: Research Funding; Celgene: Consultancy; 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; Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy.