Citations

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.

Abstract Primary central nervous system lymphoma (PCNSL) is confined to the brain, eyes, and cerebrospinal fluid without evidence of systemic spread. Rarely, PCNSL occurs in the context of immunosuppression (eg, posttransplant lymphoproliferative disorders or HIV [AIDS-related PCNSL]). These cases are poorly characterized, have dismal outcome, and are typically Epstein-Barr virus (EBV)-associated (ie, tissue-positive). We used targeted sequencing and digital multiplex gene expression to compare the genetic landscape and tumor microenvironment (TME) of 91 PCNSL tissues all with diffuse large B-cell lymphoma histology. Forty-seven were EBV tissue-negative: 45 EBV− HIV− PCNSL and 2 EBV− HIV+ PCNSL; and 44 were EBV tissue-positive: 23 EBV+ HIV+ PCNSL and 21 EBV+ HIV− PCNSL. As with prior studies, EBV− HIV− PCNSL had frequent MYD88, CD79B, and PIM1 mutations, and enrichment for the activated B-cell (ABC) cell-of-origin subtype. In contrast, these mutations were absent in all EBV tissue-positive cases and ABC frequency was low. Furthermore, copy number loss in HLA class I/II and antigen-presenting/processing genes were rarely observed, indicating retained antigen presentation. To counter this, EBV+ HIV− PCNSL had a tolerogenic TME with elevated macrophage and immune-checkpoint gene expression, whereas AIDS-related PCNSL had low CD4 gene counts. EBV-associated PCNSL in the immunosuppressed is immunobiologically distinct from EBV− HIV− PCNSL, and, despite expressing an immunogenic virus, retains the ability to present EBV antigens. Results provide a framework for targeted treatment.

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients 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. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p<0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p<0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p<0.01), and C2 for an INT phenotype related to a LZ signature (p<0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (>5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p<0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p<0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: 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; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.