Ondrejka, Sarah L.

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.

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<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<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<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<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<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: HIV-associated diffuse large B-cell lymphoma (HIV-DLBCL) is a significant complication in HIV-positive individuals, presenting unique clinical challenges and a poorer prognosis compared to patients with de novo DLBCL. Epstein-Barr Virus (EBV) status is a critical factor influencing the pathogenesis, clinical behavior, and therapeutic responses in these lymphomas. Plasmablastic lymphomas (PBL) have shared some overlapping genomic abnormalities with HIV-DLBCL including MYC rearrangements. This study aims to elucidate the differences in the genetic profiles and pathway activations between EBV(+) and EBV(-) HIV-DLBCL through comprehensive genomic profiling. Methods: Cases of DLBCL were identified in patients living with HIV from a multi-institute cooperative group. Cases of Burkitt lymphoma and PBL were excluded. We performed whole exome sequencing (WES) on 106 tumors of HIV-DLBCL and RNA sequencing on 39 tumors and mapped the reads to hg38 reference genome using BWA and STAR, respectively. EBV status of cases was determined by in situ hybridization and/or reads mapped on EBV genome in sequencing data. Somatic mutations were identified using variant callers (Strelka2, Mutect2) with WES data. For those mutated genes in COSMIC, allele frequency cutoff was set at 0.03. Mutations with an artifact score larger than 0.7 in SOBDetector and those located in segmental duplications were further removed. They were annotated with Ensembl Variant Effect Predictor, manually checked, and plotted with maftools. RNAseq read counts aligned to each gene were obtained by featureCounts and DEseq2 was used for differential expression (DE) analysis between EBV positive and negative groups. Pathway analysis with the DE genes was performed using GSEA. FISH analysis was performed using MYC, BCL2, and BCL6 break-apart probes. Results: There were 41 cases of EBV(+) HIV-DLBCL and 65 cases of EBV(-) HIV-DLBCL. FISH of EBV(+) HIV-DLBCL identified 42% (3/7) with MYC rearrangement (MYCr), none with BCL2 rearrangements (BCL2r) (0/6), and 33% (2/6) positive for BCL6 rearrangement (BCL6r) with 1 tumor having a MYCr/BCL6r. For EBV(-) HIV-DLBCL, there were 35% with a MYCr (6/17), 18% with BCL2r (2/11), and 27% with a BCL6r (3/11) with 2 tumors with MYCr/BCL6r, and 1 tumor with MYCr/BCL2r/BCL6r. The most frequent mutations seen in EBV(+) HIV-DLBCL were STAT3 (32%), DDX3X (22%), and TP53 (15%). For EBV(-) HIV-DLBCL, the most frequent mutations were KMT2D (25%), TP53 (23%), GNA13 (22%), and TET2 (22%). Pathways that were most commonly associated with EBV(+) HIV-DLBCL were JAK/STAT (42%) (STAT3, JAK1) and NF-kB (39%)(DDX3X, TNFAIP3) while EBV(-) HIV-DLBCL was associated with epigenetic modifiers (62%)(KMT2D, TET2, EP300), JAK/STAT (45%)(SOCS1, JAK3) and PI3K/mTOR (40%)(GNA13, MTOR). Significant disparities between EBV(+) and EBV(-) were KMT2D (24% vs 50%, P=0.0025), TET2 (10% vs 26%, P=0.008), SOCS1 (29% vs 39%, P=0.04), DDX3X (15% vs 5%, P=0.03), and GNA13 (5% vs 24%, P=0.001). RAS mutations (KRAS, NRAS) were infrequent in HIV-DLBCL (EBV(+), 12%; EBV(-), 6%), in contrast to prior studies of PBL. Gene expression profiling showed enrichment of MYC targets, oxidative phosphorylation, and the mTORC pathway in EBV(+) as compared to EBV(-) HIV-DLBCL. Conclusion: EBV(+) HIV-DLBCL show a distinct mutational profile compared to EBV(-) HIV-DLBCL, with EBV(+) cases showing frequent mutations with STAT3 and DDX3X in contrast to EBV(-) cases with frequent mutations associated with epigenetic modification pathways (KMT2D, TET2). These genetic profiles underscore the need for tailored therapeutic approaches depending on EBV status. Although the analysis is limited and requires confirmation in a larger cohort, this is the largest study at present evaluating these tumors.

Introduction Peripheral T-cell lymphoma (PTCL) encompasses a heterogeneous group of clinically aggressive entities. ~30% of cases cannot be further classified and are designated as PTCL - not otherwise specified (NOS). We delineated two novel molecular subgroups among PTCL-NOS with distinct clinical and biological features: PTCL-GATA3 which is characterized by high expression of GATA3, a master regulator of T-helper-2-cell (TH2) differentiation, and its target genes (i.e., CCR4, IL4, IL13) and PTCL-TBX21 which is characterized by high expression of TBX21, a master regulator of TH1 differentiation, and its target genes (i.e., CXCR3, IFNG), with the GATA3 subtype showing significantly worse clinical outcomes (overall survival). Nonsense mutations in CCR4 are found predominately in PTCL-GATA3, often within the cytoplasmic domain and gain-of-function in nature. Methods Mutation and copy number data were generated from whole exome sequencing (TFH phenotype excluded following LLMPP pathology review; n = 153). Single nucleotide variants were determined using Mutect2 and Varsvan2 using in-house filtering. Copy number (CN) profiles were generated using CNVkit. Four CCR4 cytoplasmic domain mutants and controls (cytoplasmic domain knockout [CDKO], wild-type CCR4 overexpression (CCR4-WT), and empty vector [EV]) were transduced into healthy-donor CD4+ T cells (n = 2). Polarization studies were generated using TH1- or TH2-associated cytokine exposure. Surface plasmon resonance was used to determine the binding affinity of CCR4 proteins to CCL17 and CCL22. In silico docking studies were done through Schrödinger's BioLuminate. Antibody-dependent cell-mediated cytotoxicity (ADCC) assay was performed through co-incubation of NK92 (CD16 and CD56) and CCR4 edited primary CD4+ T-cells (stained with calcein AM) for 2 hours at a 5:1 NK:T cell ratio by 7-AAD flow cytometry. Results PTCL-GATA3 had a higher frequency of CN aberrations (e.g. TP53, PTEN CN loss) and a more aberrant genome (p < 0.001) than PTCL-TBX21, including mutations with genes associated with DNA repair and damage (TP53, ATM) and T-cell differentiation (CCR4). CCR4 mutations were virtually mutually exclusive with TP53 mutations, and these cases showed higher CCR4 mRNA and protein expression than CCR4-WT cases (p < 0.05). Primary CD4+ T-cells with ectopic expression of the CCR4 variants, especially CCR4-Q330X, did not require CD3/CD28 for proliferation, bypassing CD3/CD28 ligation for T-cell activation. In addition, proliferation rates were similar between TH2-conditional media and normal media, but proliferation and TBX21 expression in TH1-conditional media were inhibited, implying a novel role of CCR4 in T-cell receptor (TCR) activation and TH-differentiation. CCR4 variant proteins showed higher binding affinity for their natural ligands, CCL22 and CCL17, with mutants exhibiting tighter binding to both ligands (1.5- to 3-fold decrease in KD) by surface plasmon resonance and in silico modeling studies. The in silico models demonstrated mutant CCR4 bound mogamulizumab tighter than CCR4-WT, consistent with a higher ADCC-induced apoptosis rate, highest with CCR4-Q330X mutation. CCR4-Y331X exhibited a worse docking score compared to CCR4-WT and a lower ADCC-induced apoptosis rate. Conclusions Our findings reveal distinct genetic characterizations including aberrant TP53 and PI3K signaling and CCR4 mutations in PTCL-GATA3. These CCR4 mutations are shown to have a key role in PTCL-GATA3 pathogenesis by dysregulating TH-differentiation and aberrantly activating the TCR. CCR4 mutation promotes the TH2 phenotype by blocking TH1 differentiation. CCR4 mutant proteins demonstrated an increased binding affinity towards its known ligands, CCL17 and CCL22, and mogamulizumab, consistent with previous studies exhibiting mogamulizumab efficacy against CCR4 mutations. However, the most frequent CCR4 mutant, Y331X, showed worse binding compared to other CCR4 mutants or CCR4-WT, a mutation that showed Mogamulizumab-resistance in a prior CTCL study as well as in an in vitro ADCC assay. The results emphasize aberrant pathways implicated by genomic and clinical evaluation of two novel PTCL subtypes, as well as support the novel role of mutant CCR4 in enhancing T-cell lymphomagenesis and T-helper differentiation, highlighting multiple therapeutic targets.

The mature splenic B‐cell lymphomas are an enigmatic group of lymphoid neoplasms that have long caused significant difficulty for the practicing pathologist due to overlapping diagnostic features among entities and the decreasing availability of splenic tissue for assessment. While some entities have highly characteristic and specific clinicopathological features (e.g. hairy cell leukaemia), others are substantially more difficult to recognise (e.g. splenic diffuse red pulp lymphoma). At the same time, classification systems have been evolving, resulting in multiple changes to the boundaries among these entities and even the existence of some entities in their own right. Moreover, unbiased multi‐omic interrogation (whole genome/transcriptome sequencing, methylome) of the splenic B‐cell lymphomas over the past decade has given us significant insights into the underling biology of these neoplasms. We present a clinicopathological perspective on the historical, current and future state of the diagnosis and classification of splenic B‐cell lymphomas integrating multi‐omic data and highlighting areas of focus for the field in order to continue to strive to improve patient outcomes through accurate diagnosis.

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<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<0.0001) and related pathways, such as TNFα-NFκB signaling (NES, 2.21; FDR<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<0.0001); H3K27me3 H-scores were 170±81 and 76±63, respectively (P<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<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 >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.

Peripheral T-cell lymphoma (PTCL) encompasses a diverse group of post-thymic lymphomas, with ~40% of cases not further classifiable and designated as PTCL-not otherwise specified (PTCL-NOS). Two molecular prognostic subtypes of PTCL-NOS, PTCL-TBX21 and PTCL-GATA3, were identified through gene expression profiling (GEP). A subset of PTCL-NOS with T-follicular helper (TFH) differentiation was subsequently categorized as nodal PTCL with TFH phenotype (PTCL-TFH), also known in current classifications as nodal PTCL-TFH, NOS, or TFH lymphoma, NOS. However, the boundary between PTCL-NOS and PTCL-TFH remains poorly defined. To refine these subtypes, 101 PTCL-NOS with centralized pathology review and extensive immunophenotyping by immunohistochemistry (IHC) using 22 antibodies, digital GEP (nCounter, NanoString Inc) (n= 70), and RNA sequencing (n= 73) were included in this study. IHC was used to identify PTCL-TFH cases (those with strong expression of more than two TFH markers); the remaining patients were classified as PTCL-GATA3 and PTCL-TBX21 using pre-defined GEP signatures. Cases were reclassified into PTCL-NOS (n= 63) and PTCL-TFH (n= 38), with PTCL-NOS subclassified into PTCL-GATA3 (n= 22; 34%) and PTCL-TBX21 (n= 41; 66%) and showing significant differences in OS (p<0.001). PTCL-GATA3 was characterized by medium to large transformed cells (average= 90%, 70-100%) and had minimal tumor microenvironment (TME) (TME-poor: 100%). In contrast, PTCL-TBX21 was more heterogeneous, associated with pleomorphic cells in a polymorphous background (TME-rich: 78%), including Lennert lymphoma-like cases (22%). mRNA and CIBERSORT analysis substantiated the findings and identified a subset enriched in cytotoxic features in the PTCL-TBX21 subtype. IHC indicated that PTCL-GATA3 cases were CD4+/CD8- (83%) or CD4-/CD8- (17%) and lacked expression of CD8 or cytotoxic markers compared to PTCL-TBX21 (p<0.01). PTCL-GATA3 showed significantly higher expression of LEF1 (average= 80%), Ki67 (80%), and MYC (25%) than PTCL-TBX21 (25%, 30%, <5%, respectively, p<0.01). mRNA and protein expression of these biomarkers showed a significant positive correlation (r=0.5, p<0.001), and expectedly higher mRNA expression of LEF1 and MYC was observed in the PTCL-GATA3 versus PTCL-TBX21 (p<0.05). Strong expression of CD30 (>50% of cells) was only seen in PTCL-GATA3 cases. EBER positivity, found only in rare background cells, was not significantly different (18% of PTCL-GATA3 vs. 12% of PTCL-TBX21). Within PTCL-TBX21, we identified cytotoxic and non-cytotoxic subsets with divergent morphological, phenotypic, and clinical findings. The cytotoxic PTCL-TBX21 exhibited an activated cytotoxic phenotype (23/25, 96%), denoted by TIA1 and granzyme-B and/or perforin expression. Extranodal involvement and single-cell apoptosis were observed in 17% and 45% of the cytotoxic cases, respectively, but absent in non-cytotoxic PTCL-TBX21 cases. A trend towards higher Ki67 expression (average= 40% vs. 20%, p= 0.08) was seen in the cytotoxic subgroup. In contrast, the non-cytotoxic PTCL-TBX21 was associated with a CD4+/CD8- phenotype and higher ICOS expression (average= 30%) and CCR4 (60%) compared to the cytotoxic PTCL-TBX21 (p= 0.001). PTCL-TFH cases showed a CD4+/CD8- phenotype (90%) and rarely a CD4-/CD8- phenotype (10%). While a subset of PTCL-TFH cases had AITL-like features such as numerous clear cells and/or prominent vasculature (31% of cases), which were not seen in the other subgroups, the remaining cases exhibited morphology that was indistinguishable from other PTLC-NOS cases including sheets of transformed cells or pleomorphic cells in a polymorphous background. Morphologically, PTCL-TFH cases, like PTCL-TBX21 were associated with a rich TME (TME-rich: 75%). CIBERSORT analysis showed enrichment of plasma cells (p<0.01) in PTCL-TFH, compared to PTCL-TBX21, and validated by morphology (30% of cases vs. 5%). Expression of one TFH marker was frequent in some PTCL-NOS cases (PTCL-GATA3= 41% of cases, PTCL-TBX21-non cytotoxic= 47%, PTCL-TBX21-cytotoxic= 5%). Conclusion: Our comprehensive evaluation underscores the importance of integrating morphology, immunophenotyping, and GEP in achieving an accurate diagnosis, potentially leading to more tailored treatment strategies for PTCL. Correlation with pending whole-exome sequencing studies will be provided at the time of presentation.