Delabie, Jan

Abstract Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and anaplastic lymphoma kinase (ALK) status but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 patients with ALCL in the LLMPP (Lymphoma/Leukemia Molecular Profiling Project) and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple negative), and immunohistochemistry for phosphorylated STAT3Tyr705. RNA sequencing with unsupervised gene expression profiling in 393 patients identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (type I) or absence (type II) of phosphorylated STAT3Y705 expression. Type I ALCLs included ALK+ ALCL and a subset of triple-negative ALCLs (TN-I); type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3, whereas type II ALCLs were enriched for non–tyrosine kinase pathways, particularly epigenetic regulators such as EZH2. Immunohistochemistry showed overexpression of EZH2 and its trimethylated substrate H3K27. Prognosis in systemic ALCL was favorable for DUSP22-rearranged ALCL (5-year overall survival, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials, and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.

Abstract Molecular characterization of high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), is hindered by its rarity, evolving definition, and poor diagnostic reproducibility. To address this challenge, we analyzed 92 HGBCL-NOS tumors collected across Lymphoma/Leukemia Molecular Profiling Project sites. Leveraging comparison cohorts of diffuse large B-cell lymphoma, NOS (DLBCL-NOS) and Burkitt lymphoma (BL), and molecular frameworks described in these entities, our analysis revealed a heterogenous molecular landscape, reminiscent of DLBCL-NOS but with an enrichment of BL features. By cell-of-origin classification, 59% were germinal center B-cell–like (GCB), and 25% were activated B-cell–like (ABC). LymphGen, a genetic classifier for DLBCL-NOS, assigned a genetic subtype to 34% of HGBCL-NOS. Although classification rate was lower than in DLBCL-NOS (66%), assigned subtypes spanned the spectrum of LymphGen classes, including 31% of ABCs classified as MCD. Features differentiating HGBCL-NOS from DLBCL-NOS included MYC rearrangement (47% vs 6%); dark zone signature (DZsig) expression (45% vs 7%); and more frequent mutation of ID3, MYC, CCND3, and TP53, all common to BL. A genetic classifier that differentiates DLBCL-NOS from BL classified 53% of DZsig+ tumors as BL-like, and those classified as DLBCL-like were frequently BCL2-rearranged. Among DZsig− GCB tumors, 95% were DLBCL-like. Centralized pathology review reclassified almost half of tumors as DLBCL-NOS but did not identify a more homogenous HGBCL-NOS population, with no difference in features between confirmed and reclassified tumors. In conclusion, molecular testing enables a subset of HGBCL-NOS to be assigned to established categories. Based on rarity and diagnostic challenges, broader inclusion of HGBCL-NOS should be considered in biomarker-driven DLBCL trials.

ABSTRACTNodal follicular helper T‐cell (T FH) lymphoma of the angioimmunoblastic (AITL) subtype has a dismal prognosis. Using whole‐exome sequencing (n = 124), transcriptomic (n = 78), and methylation (n = 40) analysis, we identified recurrent mutations in known epigenetic drivers (TET2, DNMT3A, IDH2 R172 ) and novel ones (TET3, KMT2D). TET2, IDH2 R172 , DNMT3A co‐mutated AITLs had poor prognosis (p < 0.0001). Genes regulating T‐cell receptor (TCR) signaling (CD28, PLCG1, VAV1, FYN) or activation (RHOA G17V ) or regulators of the PI3K‐pathway (PIK(3)C members, PTEN, PHLPP1, PHLPP2) were mutated. CD28 mutation/fusion was associated with poor prognosis (p = 0.02). WES of purified, neoplastic T‐cell (CD3+PD1+) demonstrated high concordance with whole tumor biopsies and validated the presence of TET2 and DNMT3A in tumor and non‐lymphoid cells, but other mutations (CD28, RHOA G17V , IDH2 R172 , PLCG1) in neoplastic cells. Integrated DNA‐methylation and mRNA expression analysis revealed epigenetic alterations in genes regulating TCR, cytokines, PI3K‐signaling, and apoptosis. RNA‐seq analysis identified fusion transcripts regulating TCR‐activation (8%), revealed a restricted TCR‐repertoire (α = 87%, β = 72%), and showed the presence of Epstein–Barr virus transcriptome (73%). GEP demonstrated the association of B‐cells or dendritic cells in the tumor milieu with prognosis (p < 0.01). RNA‐seq and WES analysis of 12 AITL‐patient‐derived‐xenografts (PDX) showed that bi‐allelic TET2 and DNMT3A mutations or sub‐clonal mutations (PLCG1, PHLPP2) propagated in sequential passages, and gene signatures related to T FH and T CM (central‐memory) were well‐maintained through passages. Gene expression signatures associated with late PDX passages (3rd–5th) were enriched with proliferation and metabolic reprogramming‐related genes and predicted prognosis in an independent AITL series. Low PHLPP2 mRNA expression predicted poor prognosis (p = 0.05) and engineered PHLPP2 or TET2 loss in CD4+ T‐cells showed enhanced PI(3)K activation, thus uncovering a therapeutic target for clinical trials.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P&lt;0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P&lt;0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P&lt;0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P&lt;0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P&lt;0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction Plasmablastic lymphoma (PBL) is a rare aggressive subtype of lymphoma. It is more common in HIV positive patients, and is often associated with Epstein-Barr virus (EBV) and MYC rearrangements. PBL is characterized by markers of plasmacytic differentiation including CD138 and MUM1/IRF4, and loss of B-cell markers such as CD20, which suggests a cell of origin similar to that of multiple myeloma (MM) or activated B-cell-like (ABC) diffuse large B-cell lymphoma (DLBCL). The standard of care for PBL is CHOP-based chemotherapy but this is frequently not curative. Other than EBV-negative status, which defines a high-risk group, no other biological features have been discovered that are associated with outcomes. Methods Here, we provide a comprehensive analysis of the genomic and transcriptomic landscape of PBL using a collection of 176 exomes and genomes (n=55 new, contributed by Lymphoma/Leukemia Molecular Profiling Project [LLMPP] sites and centrally reviewed by the LLMPP pathology panel [WHO 2016]; n=121 previously published [PMID: 33225311, 33951889, 34465776, 34714908]), 65 transcriptomes (n=44 new; n=21 published) and 37 in situ single cell transcriptomic profiles (CosMx Spatial Molecular Imager) from archival diagnostic tissue biopsies. Somatic mutations (SNVs/Indels) were identified using an ensemble of four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Salmon and DESeq2 were used to identify differentially expressed genes. This multi-omics dataset was compared to other known B-cell malignancies including 92 MM, 238 Burkitt lymphomas (BL), and 208 DLBCLs. Cell segmentation of the CosMx data was performed using Baysor and downstream analyses were conducted using the Seurat workflow. For functional validation, cell viability assays (CellTiter-Glo Luminescent Assay) were conducted in the PBL cell line, PBL-1, and 4 control DLBCL cell lines. Results Within the cohort, 63% (106/169) of primary PBL cases were EBV-positive (EBV+) and 57% (82/144) harbored MYC rearrangements (MYC+). When stratifying patients by EBV and MYC rearrangement status, mutations in different genes were enriched in each group: STAT3 occurred in 55% (29/53) of EBV+/MYC+ PBLs; NOTCH1 in 19% (7/36) of EBV+/MYC-; MYC in 24% (6/25) and TP53 in 40% (10/25) of EBV-/MYC+ tumors; TET2 was mutated in 38% (9/24) and KRAS in 25% (6/24) of EBV-/MYC- PBLs. EBV-/MYC+ tumors represented a subgroup of patients with dismal outcomes. The 2-year overall survival (OS) of these patients was 22% compared to 61%, 78%, and 54% of EBV+/MYC+, EBV+/MYC-, and EBV-/MYC- PBL patients, respectively (log-rank test p-adj. &lt; 0.05). Consistent with previous studies, we observed recurrent mutations affecting JAK-STAT (STAT3, SOCS1, SOCS3, DUSP2) in 41% of cases and RAS-RAF (NRAS, KRAS, BRAF) signaling in 38% of all PBLs. In contrast to ABC-DLBCL, very few mutations occurred in the NF-κB signalling pathway. Accordingly, PBL showed marked downregulation of genes involved in B-cell receptor and NF-κB signaling. Consistent with the lack of reliance on these pathways, functional analyses in PBL-1 showed resistance to treatment to ibrutinib as well as PI3K- and MALT1-inhibitors. Moreover, constitutive expression of the IκBα super-repressor did not affect viability of PBL-1 cells. In situ single cell transcriptomic data analyses showed a sparse tumor microenvironment (TME) in PBL, with the primary non-malignant cell types being, on average, 10% macrophages, 7% NK/T cells, and 4% stromal cells of all represented cell types. EBV+/MYC+ tumors displayed a more abundant immune cell population with higher levels of macrophages (median 12%) and NK/T cells (median 9%), whereas EBV-/MYC- tumors were composed of the lowest levels of immune cells (median 4% macrophages and 1% NK/T cells). With respect to malignant cell phenotypes, we noted a novel population of SPP1-expressing cells that were enriched in EBV+/MYC+ tumors and a population of CD44-expressing cells that were enriched in EBV+/MYC- tumors. Conclusion Here we present novel subgroups of PBL with unique biology and clinical outcomes. We show that PBL does not rely on B-cell receptor signaling, instead relying on potentially targetable vulnerabilities in JAK-STAT, NOTCH, and RAS-RAF signaling according to EBV positivity and MYC rearrangement status. Lastly, we provide insight into the TME of PBL and demonstrate a novel population of SPP1 and CD44 expressing malignant cells in EBV+ tumors.

Abstract Rearrangements that place the oncogenes MYC, BCL2, or BCL6 adjacent to superenhancers are common in mature B-cell lymphomas. Lymphomas with diffuse large B-cell lymphoma (DLBCL) or high-grade morphology with both MYC and BCL2 rearrangements are classified as high-grade B-cell lymphoma with MYC and BCL2 rearrangements (“double hit”; HGBCL-DH-BCL2) and are associated with aggressive disease and poor outcomes. Although it is established that MYC rearrangements involving immunoglobulin (IG) loci are associated with inferior outcomes relative to those involving other non-IG superenhancers, the frequency of and mechanisms driving IG vs non-IG MYC rearrangements have not been elucidated. Here, we used custom targeted capture and/or whole-genome sequencing to characterize oncogene rearrangements across 883 mature B-cell lymphomas including Burkitt lymphoma, follicular lymphoma, DLBCL, and HGBCL-DH-BCL2 tumors. We demonstrate that, although BCL2 rearrangement topology is consistent across entities, HGBCL-DH-BCL2 have distinct MYC rearrangement architecture relative to tumors with single MYC rearrangements or with both MYC and BCL6 rearrangements (HGBCL-DH-BCL6), including both a higher frequency of non-IG rearrangements and different architecture of MYC::IGH rearrangements. The distinct MYC rearrangement patterns in HGBCL-DH-BCL2 occur on the background of high levels of somatic hypermutation across MYC partner loci in HGBCL-DH-BCL2, creating more opportunity to form these rearrangements. Furthermore, because 1 IGH allele is already disrupted by the existing BCL2 rearrangement, the MYC rearrangement architecture in HGBCL-DH-BCL2 likely reflects selective pressure to preserve both BCL2 and B-cell receptor expression. These data provide new mechanistic explanations for the distinct patterns of MYC rearrangements observed across different lymphoma entities.

Background: Anaplastic large cell lymphomas (ALCLs) are CD30-positive T-cell lymphomas that share pathologic features but differ in clinical presentation, outcome, and molecular features. The World Health Organization (WHO) and International Consensus Classification (ICC) classify ALCLs by presence or absence of ALK rearrangements (R) and clinical presentation (systemic, cutaneous [c], or breast implant-associated [BIA]). ICC, but not WHO, recognizes DUSP22-R as defining a new genetic subtype of ALK- ALCL. The classifications otherwise do not reflect additional molecular heterogeneity in genetics (e.g., TP63-R) or therapeutic vulnerabilities (e.g., JAK-STAT3 pathway activation). Methods: ALCLs (N=689) underwent expert consensus review (WHO/ICC) through the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). All cases also underwent genetic subtyping (ALK, DUSP22, TP63, and triple-negative [TN]) using fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC), as well as IHC for phospho-STAT3 Tyr705 (pSTAT3). RNAseq was performed and evaluable in 393 cases; the remaining cases had insufficient tissue, tumor content, RNA yield or quality, and/or sequencing data quality. Sequenced and non-sequenced sub-cohorts had similar demographics and subtype distribution. Results: Unsupervised gene expression profiling (GEP) identified 2 main molecular types of ALCL. Type I ALCLs predominantly included ALK+ ALCLs, BIA-ALCLs, and a subset of TN ALCLs (designated TN-I), whereas Type II ALCLs predominantly included ALCLs with DUSP22-R, TP63-R, or both (double-hit; DH), and the remaining TN ALCLs (TN-II). Type I ALCLs were strongly associated with pSTAT3 expression (74.2±26.4% positive malignant cells vs 9.9±22.7% for Type II; P&lt;0.0001). An independently derived pSTAT3 staining threshold of 30% assigned Type I vs II with 91% accuracy. A third cluster of ALCLs, mostly cALCL, showed an epithelial GEP signature rather than a lymphoma signature; these cases were assigned to Types I or II based on pSTAT3 IHC. Distinct sub-signatures were identified for ALK+, DUSP22-R, TP63-R, and BIA ALCL (Fig. 1), but not ALK- ALCL or cALCL. Gene set enrichment analysis showed Type I ALCLs to be enriched for JAK-STAT3 signaling genes (normalized enrichment score [NES], 2.22; FDR&lt;0.0001) and related pathways, such as TNFα-NFκB signaling (NES, 2.21; FDR&lt;0.0001). In contrast, Type II ALCLs were enriched for cell cycle, DNA repair, epigenetic, and metabolic pathway genes, but not for major tyrosine kinase-mediated signaling pathway genes. Enriched epigenetic pathways included chromatin modifying enzymes (NES, -1.86; FDR=0.002) and histone methylation (NES, -1.71; FDR=0.002). EZH2 was the most overexpressed gene in Type II ALCLs (fold-change, 7.74; FDR=8.84×10 -306). At the protein level, EZH2 IHC H-scores were 275±45 in Type II and 168±70 in Type I ALCLs (P&lt;0.0001); H3K27me3 H-scores were 170±81 and 76±63, respectively (P&lt;0.001). The top metabolic gene set involved cholesterol biosynthesis (NES, -1.88; FDR=0.002). Overall survival (OS) data were available in 257 systemic ALCL patients (145 sequenced and 112 non-sequenced; cALCL and BIA-ALCL were excluded). Non-sequenced TN ALCLs were assigned to TN-I or TN-II using pSTAT3 IHC. The results supported earlier data indicating favorable prognosis of DUSP22-R ALCL (5 y OS, 95%) and ALK+ ALCL (87%), intermediate prognosis of TN ALCL (TN-I, 52% and TN-II, 38%; P=NS), and poor prognosis of TP63-R/DH ALCL (0%)(P&lt;0.0001; Fig. 2). Conclusions: Two overarching molecular types of ALCL exist, predominantly associated with the presence (Type I) or absence (Type II) of the JAK-STAT3 signaling program. Distinct GEP signatures exist for ALK+ ALCL and BIA-ALCL (predominantly Type I), and DUSP22-R ALCL and TP63-R ALCL (predominantly Type II). TN ALCLs lacking ALK-R, DUSP22-R, and TP63-R can be stratified into TN-I and TN-II subtypes. pSTAT3 IHC has &gt;90% accuracy as a surrogate for GEP-based subtyping. ALK- ALCL and cALCL cluster by molecular subtype rather than by defining GEP signatures. Type II ALCLs are enriched for targetable epigenetic and metabolic pathways, including EZH2/histone methylation and cholesterol biosynthesis. This molecular classification is diagnostically, prognostically, and potentially therapeutically relevant, and can be applied using FISH and IHC in routine practice and in the clinical trial setting.

Follicular helper T-cell lymphoma of the angioimmunoblastic type (AITL) is associated with dismal prognosis. We performed functional genomic approaches including whole-exome sequencing (WES; n=119), transcriptomic (n=78) and methylation (n=40) analysis. We identified recurrent mutations in known epigenetic drivers ( TET2, DNMT3A, IDH2 R172), and also identified novel ones (TET3, KMT2D). Somatic mutation of all three epigenetic drivers ( TET2, IDH2, and DNMT3A) was associated with poor prognosis (p&lt;.001). Mutations in genes regulating T-cell receptor (TCR) signaling ( CD28 VAV1, FYN, PLCG1) or activation ( RHOA G17V), and regulators of the PI3K pathway (PIK(3)C members, PTEN,PHLPP-1/-2) were also found. Genome-wide DNA-methylation analysis integrated with mRNA expression profiling also revealed epigenetic alterations in genes regulating TCR-RHOA/B/C or PI3K-signaling. TET2 loss was noted in 85% AITLs and was significantly associated with RHOA G17V, CD28 and IDH2 R172mutations. AITLs lacking RHOA G17V tended to have mutations regulating the JAK-STAT pathway ( JAK2, JAK3, STAT1, STAT3, SOCS1). RNA-seq analysis identified fusion transcripts in genes regulating TCR activation (8%), revealed a restricted TCR repertoire in the majority of cases (a=87%, b=72%), and showed the presence of Epstein-Barr virus transcriptome (73%). GEP demonstrated association of B-cells in the tumor-milieu with better prognosis (p=.006), while dendritic cells were associated with worse prognosis (p=.001), which was further validated by immunohistochemistry using CD20, CD68, and CD163 antibodies. RNA-seq and corresponding WES analysis of 12 AITL patient-derived-xenografts (PDX) showed that bi-allelic TET2 mutations, DNMT3A mutations or sub-clonal mutations ( PLCG1 PHLPP2) werepropagated in sequential passages. Gene signatures related to T FH (follicular helper) and T CM (central memory) were also well-maintained in secondary passages in PDX models. Gene signatures of late PDX passages (3 rd-5 th) were enriched with genes related to proliferation and metabolic reprogramming, and in an independent cohort of AITLs, high expression of T3/T5 related signatures was associated with worse outcome (p=0.02/p=0.009). Low mRNA expression of PHLPP2 predicted poor prognosis (p=.03) and engineered PHLPP2 loss showed enhanced PI(3)K activation and FOXO1 inactivation in CD4+ T-cells in-vitro. Thus, we defined the genomic landscape for AITL, which is largely characterized by epigenetic alterations, TCR signaling and PI3K/AKT dysregulation, which may be amenable for therapeutic targeting.

Abstract 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..