Rimsza, Lisa M.

Abstract BACKGROUND: Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin lymphoma with variable clinical outcomes. Commonly used risk stratification tools (Ki67 IHC, MIPI) in newly diagnosed MCL are not frequently used when selecting therapy, resulting in treatment choice being dictated by age and co-morbidities rather than disease biology. The MCL35 risk score was developed as a more reliable measure of proliferation and has been shown to be prognostic and can risk stratify younger transplant eligible MCL patients into three groups with significantly different overall survival (OS; Scott et al. 2017; Holte et al. 2018) but has not been evaluated in older transplant ineligible patients. We report results evaluating the prognostic value of the MCL35 assay in older MCL patients (≥65) treated with frontline bendamustine/rituximab (BR). METHODS: Archived tissue samples from 119 patients age ≥65 years treated with BR from collaborating Lymphoma/Leukemia Molecular Profiling Project (LLMPP) sites and the LEO/MER cohort were collected and analyzed using the MCL35 assay and stratified into three distinct risk groups (low, standard, and high risk). Association between MCL35 proliferation scores and OS were estimated by the Kaplan-Meier method and hazard ratios were calculated. Associations between Ki67, s-MIPI, p53 IHC status, morphology and OS were also evaluated. RESULTS: The MCL35 assay was run on tissue samples from 119 patients. Median patient age was 74 (range 65-93) and 69.5% were male. Ki67 was <30% in 29 patients (24%) and ≥30% in 90 patients (76%). Simplified MIPI (s-MIPI) score was 0-3 in 21 patients (24%), 4-5 in 42 patients (48%) and ≥6 in 25 patients (28%). Thirty-one did not have sufficient data to calculate a s-MIPI score. MCL35 was low risk in 51 patients (43%), standard risk in 39 patients (33%) and high risk in 29 patients (24%). Eleven patients had blastic morphology, 7 had pleomorphic morphology and the remainder were classic morphology (n=56). Of 57 samples with p53 IHC staining 7 (12.3%) were positive. At a median follow up of 33.4 months, 82 patients were alive and 35 had died. Patients with high risk MCL35 score had inferior OS compared to low risk (HR 2.27, 95% CI: 1.03-5.00; p=0.042) while standard risk was not statistically significant compared to low risk (HR 0.87, 95% CI: 0.37-2.0; p=0.740)(Figure 1). Ki67 IHC using a cutoff of ≥ 30% and 10%-29% was not significantly associated with OS compared to Ki67 <10% ( Ki67 ≥ 30% vs. Ki67 < 10%, HR 0.87, 95% CI: 0.12-6.41; p=0.892, Ki67 ≥ 10%-29% vs. Ki67 < 10%, HR 0.32, 95% CI: 0.04-2.83; p=0.303), however high s-MIPI score (≥6) (s-MIPI ≥6 vs. s-MIPI 0-3, HR 3.86, 95% CI 1.20-12.5; p=0.024) and positive p53 IHC (HR: 9.51, 3.26-27.7; p <0.001) were both associated with poor OS. Eighteen cases were blastic/pleomorphic by morphology, 12 of which were in the high-risk group by MCL35, and this subset also had worse survival than classic MCL (p=0.0052). CONCLUSIONS: These results suggest high risk MCL35 score is a prognostic biomarker of poor OS in patients >65 with MCL treated with BR. Conversely, Ki67 was not significantly associated with OS in these patients. Additional clinical validation using a larger sample size from the E1411 study is planned. If similar results are found, the MCL35 assay in combination with s-MIPI and p53 status may have utility in stratifying patients into risk adapted treatment arms in future prospective clinical trial designs. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cohen: Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo, BeiGene, Adaptive: Consultancy; Genentech, BMS/Celgene, LAM, BioINvent, LOXO, Astra Zeneca, Novartis, M2Gen, Takeda: Research Funding. Hill: Celgene (BMS): Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Incyte/Morphysis: Consultancy, Honoraria, Research Funding. Raess: Scopio Labs: Research Funding. Scott: Celgene: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; 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. ; Rich/Genentech: Research Funding; Janssen: Consultancy, Research Funding; Incyte: Consultancy; Abbvie: Consultancy; AstraZeneca: Consultancy. Rimsza: NanoString Technologies: Other: Fee-for-service contract.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

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..

Abstract Objectives: We identified a case of follicular lymphoma (FL) that transformed into a morphologic diffuse large B-cell lymphoma (DLBCL), which by gene expression profiling showed a primary mediastinal (PMBL)-like gene expression profile (GEP) (Lymph3Cx; Blood 2018;132:2401-5). A search identified 4 additional transformed FL (tFL) cases with a PMBL-like GEP, which we further studied to determine how similar these tFLs were to classic cases of PMBL. Methods: The morphology and previously reported immunophenotype were reviewed, and CD30, CD23, MAL, CD273/PDL2, and CD200 immunohistochemical stains (IHC) were performed. Whole exome sequencing (WES) and copy number analysis (CNA) to evaluate genes typically altered in FL and PMBL were performed. Results: None of the tFLs arose in the mediastinum or had a previous history of mediastinal disease. All cases showed typical centroblastic DLBCL cytology, with fine sclerosis typical of PMBL. 3/3 were GCB by the Hans IHC algorithm, 1/3 were MYC+, 3/3 BCL2+, 1/5 CD30+, 3/5 CD23+, 4/5 MAL+, 0/5 CD273/PDL2+, 1/5 CD200+, and 0/2 EBER+. Rearrangements of MYC, BCL2, or BCL6 were identified by FISH in 0/3, 1/3, and 2/3 cases, respectively. WES demonstrated sequence variants in genes associated with both FL (CREBBP [60%], KMT2D [40%], and TNFRSF14 [40%]) and PMBL (JAK-STAT pathway genes [80%], B2M [20%], and CD58 [20%]). 2 of the mutations identified in the tFLs have previously been shown to result in JAK-STAT activation (STAT6 p.E372K [PNAS 2016;113:13015-20] and SOCS1 p.F101L [Oncogene 2002;21:4351-62] identified in 1/5 cases each). CNA showed gains/amplification of REL in 3/5 cases, gains/amplification of STAT6 in 2/5, gains of large sections of chromosome 16, including IL4R, in 2/5, and both deletions and gains of 11q in 1/5. See Figure demonstrating the 5 cases on the Y-axis and the chromosomes on the X-axis. Conclusions: The tFLs in this small series seem to represent PMBL-like DLBCLs, rather than classic PMBLs, and have a blended pattern of immunophenotypic and genomic features between FL/DLBCL and PMBL. Although the cases express some PMBL-associated markers (CD23 and MAL), there is less frequent staining for others (CD30, CD273/PDL2, and CD200). The cases harbor both FL-associated and PMBL-associated sequence variants, including 40% with mutations known to activate the JAK-STAT pathway. This frequency of mutations in JAK-STAT pathway genes is higher than that seen in typical FL/DLBCL, but perhaps lower than in classic PMBL (Blood 2019;134:802-13). PMBL also frequently has gains/amplifications of 9p24.1, which was not seen in our cohort. However, gains/amplification of REL/2p, which is seen in approximately 50% of PMBL, was identified in 60% of the tFLs. The 11q aberration identified in 1 case would be unusual for PMBL, and is instead more commonly associated with a subset of aggressive lymphomas with Burkitt-like features (Haematologica 2019;104:1822-9). Recently, lymphomas with similar blended features between DLBCL and PMBL, which were not arising in the setting of tFL, have been reported (Duns G, et al. Blood 2021). Our study extends the types of biological transformations, in addition to more classic DLBCL, that can be seen in FL. These tFLs with blended PMBL-DLBCL biology may have implications for therapeutic decision making including targeted therapies used in PMBL. Figure 1 Figure 1. Disclosures Rimsza: NanoString Technologies: Other: Fee-for-service contract. Steidl: Curis Inc.: Consultancy; Trillium Therapeutics: Research Funding; Bayer: Consultancy; Epizyme: Research Funding; Seattle Genetics: Consultancy; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding.

Abstract Diffuse large B-cell lymphoma (DLBCL) with aberrant coexpression of CD10+BCL6+MUM1+ (DLBCL-AE), classified as germinal center B cell (GCB) type by the Hans algorithm (HA), was genetically characterized. To capture the complexity of DLBCL-AE, we used an integrated approach that included gene expression profiling (GEP), fluorescence in situ hybridization, targeted gene sequencing, and copy number (CN) arrays. According to GEP, 32/54 (59%) cases were classified as GCB-DLBCL, 16/54 (30%) as activated B-cell (ABC) DLBCL, and 6/54 (11%) as unclassifiable. The discrepancy between HA and GEP was 41%. Three genetic subgroups were identified. Group 1 included 13/50 (26%) cases without translocations and mainly showing and ABC/MCD molecular profile. Group 2 comprised 11/50 (22%) cases with IRF4 alterations (DLBCL-IRF4), frequent mutations in IRF4 (82%) and NF-κB pathway genes (MYD88, CARD11, and CD79B), and losses of 17p13.2. Five cases each were classified as GCB- or ABC-type. Group 3 included 26/50 (52%) cases with 1 or several translocations in BCL2/BCL6/MYC/IGH, and GCB/EZB molecular profile predominated. Two cases in this latter group showed complex BCL2/BCL6/IRF4 translocations. DLBCL-IRF4 in adults showed a similar copy number profile and shared recurrent CARD11 and CD79B mutations when compared with LBCL-IRF4 in the pediatric population. However, adult cases showed higher genetic complexity, higher mutational load with frequent MYD88 and KMT2D mutations, and more ABC GEP. IRF4 mutations were identified only in IRF4-rearranged cases, indicating its potential use in the diagnostic setting. In conclusion, DLBCL-AE is genetically heterogeneous and enriched in cases with IRF4 alterations. DLBCL-IRF4 in adults has many similarities to the pediatric counterpart.

Abstract Purpose: Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of non–Hodgkin lymphomas with aggressive clinical behavior. We performed comprehensive miRNA profiling in PTCLs and corresponding normal CD4+ Th1/2 and TFH-like polarized subsets to elucidate the role of miRNAs in T-cell lymphomagenesis. Experimental Design: We used nCounter (NanoString Inc) for miRNA profiling and validated using Taqman qRT-PCR (Applied Biosystems, Inc). Normal CD4+ T cells were polarized into effector Th subsets using signature cytokines, and miRNA significance was revealed using functional experiments. Results: Effector Th subsets showed distinct miRNA expression with corresponding transcription factor expression (e.g., BCL6/miR-19b, -106, -30d, -26b, in IL21-polarized; GATA3/miR-155, miR-337 in Th2-polarized; and TBX21/miR-181a, -331-3p in Th1-polarized cells). Integration of miRNA signatures suggested activation of TCR and PI3K signaling in IL21-polarized cells, ERK signaling in Th1-polarized cells, and AKT–mTOR signaling in Th2-polarized cells, validated at protein level. In neoplastic counterparts, distinctive miRNAs were identified and confirmed in an independent cohort. Integrative miRNA–mRNA analysis identified a decrease in target transcript abundance leading to deregulation of sphingolipid and Wnt signaling and epigenetic dysregulation in angioimmunoblastic T-cell lymphoma (AITL), while ERK, MAPK, and cell cycle were identified in PTCL subsets, and decreased target transcript abundance was validated in an independent cohort. Elevated expression of miRNAs (miR-126-3p, miR-145-5p) in AITL was associated with poor clinical outcome. In silico and experimental validation suggest two targets (miR-126→ SIPR2 and miR-145 → ROCK1) resulting in reduced RhoA-GTPase activity and T–B-cell interaction. Conclusions: Unique miRNAs and deregulated oncogenic pathways are associated with PTCL subtypes. Upregulated miRNA-126-3p and miR-145-5p expression regulate RhoA-GTPase and inhibit T-cell migration, crucial for AITL pathobiology.

Summary Even in the era of highly active combination antiretroviral therapy (cART), patients with HIV have a disproportionate risk of developing aggressive lymphomas that are frequently Epstein‐Barr virus (EBV)‐related. Here, we investigate HIV‐associated diffuse large B‐cell lymphoma (HIV‐DLBCL) and compare EBV‐positive and EBV‐negative cases. HIV‐DLBCL were identified from two academic medical centres and characterised by immunohistochemistry, EBV status, fluorescence in situ hybridisation, cell of origin determination by gene expression profiling, and targeted deep sequencing using a custom mutation panel of 334 genes. We also applied the Lymphgen tool to determine the genetic subtype of each case. Thirty HIV‐DLBCL were identified, with a median patient age of 46 years and male predominance (5:1). Thirteen cases (48%) were EBV‐positive and 14 (52%) EBV‐negative. Nine of the 16 tested cases (56%) had MYC rearrangement, three (19%) had BCL6 (two of which were double hit MYC / BCL6 ) and none had BCL2 rearrangements. Using the Lymphgen tool, half of the cases (15) were classified as other. All HIV‐DLBCL showed mutational abnormalities, the most frequent being TP53 (37%), MYC (30%), STAT3 (27%), HIST1H1E (23%), EP300 (20%), TET2 (20%), SOCS1 (17%) and SGK1 (17%). EBV‐negative cases were mostly of germinal centre B‐cell (GCB) origin (62%), showed more frequent mutations per case (a median of 13·5/case) and significant enrichment of TP53 (57% vs. 15%; P = 0·046), SGK1 (36% vs. 0%; P = 0·04), EP300 (43% vs. 0%; P = 0·02) and histone‐modifying gene (e.g. HIST1H1E , HIST1H1D , 79% vs. 31%; P = 0·02) mutations. EBV‐positive cases were mostly of non‐GCB origin (70%), with fewer mutations per case (median 8/case; P = 0·007), and these tumours were enriched for STAT3 mutations ( P = 0·10). EBV‐positive cases had a higher frequency of MYC mutations but the difference was not significant (36% vs. 15%; P = 0·38). EBV‐association was more frequent in HIV‐DLBCLs, arising in patients with lower CD4 counts at diagnosis (median 46·5 vs. 101, P = 0·018). In the era of cART, approximately half of HIV‐DLBCL are EBV‐related. HIV‐DLBCL are enriched for MYC rearrangements, MYC mutations and generally lack BCL2 rearrangements, regardless of EBV status. Among HIV‐DLBCL, tumours that are EBV‐negative and EBV‐positive appear to have important differences, the latter arising in context of lower CD4 count, showing frequent non‐GCB origin, lower mutation burden and recurrent STAT3 mutations.