Citations

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Background: Pyroptosis is a form of programmed cell death characterized by cleavage of gasdermin (GSDM) family proteins that form pores in the plasma membrane, cell rupture, and release of pro-inflammatory cytokines. Its pro-inflammatory potential has garnered significant research attention in recent years but has not been studied in lymphoma. Patients and Methods: We performed immunohistochemistry for cleaved GSDMD (n-terminal) in two large cohorts of diffuse large B-cell lymphoma (DLBCL) with multiplex fluorescent IHC (mfIHC) data, and analyzed for prognostic and immune impact. Results: Cleaved GSDMD was frequently expressed in DLBCL, and only the cytoplasmic (versus nuclear) form of expression correlated with significantly better patient survival in two DLBCL cohorts. To understand the expression at the single-cell level, we examined mfIHC results, which revealed that GSDMD+cells were tumor or immune cells, and varied in Ki-67, BCL2, and MYC expression. Cases with cytoplasmic cleaved GSDMD expression had higher mean and median levels of CD38+ (activated) M1 macrophages in two cohorts and lower PD-1/PD-L1 expression, as well as phenotypes of cognate interactions between live activated M1 macrophages, DLBCL cells, and T cells in spatially resolved immune landscape by mfIHC. In contrast, phagocytosis of pyroptotic lymphoma cells by M2 macrophages and cell death of T cells were observed in cases with predominantly nuclear form of cleaved GSDMD expression. Bulk gene expression profiling analysis identified a large proportion of RNA genes upregulated in DLBCLs with cytoplasmic cleaved GSDMD expression, and deconvolution analysis found decreased frequency of the “Inflammatory” lymphoma microenvironment (LME) subtype, increased frequency of cell states and ecotypes with favorable prognostic associations, and decreased frequency of T cell exhaustion states. Summary: Analysis of pyroptosis-inducing cleaved GSDMD (n-terminal) expression in a large number of DLBCL patients revealed dinstinctive cytoplasmic and nuclear expression patterns in live and pyroptotic cells associated with different prognostic and immune effects. Cytoplasmic cleaved GSDMD expression is a marker of cognate interactions between live cells (e.g., activated M1 macrophages) associated with adaptive responses and favorable prognostic effects, whereas nuclear cleaved GSDMD expression is associated with immunosuppressive mechanisms, including T cell death and phagocytosis by M2 macrophages.

Abstract Background: Recent advances in computer vision have given rise to image foundation models (FMs), large-scale neural networks pre-trained on millions of unlabeled histopathology patches, which can learn generalizable representations of tissue morphology. In many cancers, these FMs have predicted genetic mutations and prognoses directly from histology, suggesting that morphology encodes “hidden” molecular information. In lymphoma, this field is nascent but growing.General-purpose histopathology FMs have limited representation of lymphoid morphology. We developed and evaluated LymphoVision, a lymphoma-specialized image foundation model pretrained with self-supervised learning, to extract clinically meaningful features from routine hematoxylin and eosin (H&E) histology. Methods: LymphoVision was pre-trained using knowledge distillation and consistency (DINOv2) on over 37 million multi-resolution patches (5x, 10x, 20x, 40x) sampled using a density-aware strategy from 31,211 H&E whole slide images (WSIs) from 9,155 archival lymphoma and reactive lymphoproliferative cases. To ensure diverse, representative samples, our patch sampling method uses an efficient graph-based clustering algorithm and prioritizes morphologically informative regions by clustering patch embeddings and performing density sampling within each cluster. The pretrained encoder was evaluated using a clustering-constrained attention multiple instance learning (MIL) framework on three diagnostic tasks: (1) cell-of-origin (COO) classification in diffuse large B-cell lymphoma (DLBCL), distinguishing germinal center B-cell (GCB) from non-GCB subtypes based on the Hans immunohistochemistry algorithm; (2) follicular lymphoma (FL) grading, differentiating low-grade (grades 1–2) from high-grade (grade 3); and (3) multiclass lymphoma subtype classification across six categories: DLBCL, FL, marginal zone lymphoma (MZL), mantle cell lymphoma (MCL), classical Hodgkin lymphoma (CHL), and benign/reactive lymphoid conditions. WSI-level labels were derived from pathology reports, and none of the evaluation cases were included in pretraining. Models used a ViT-Giant LymphoVision backbone to generate 20x patch embeddings, followed by MIL using 50% training data and 2-fold cross-validation (25% validation, 25% test). Performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, recall (sensitivity) and F1 score. Results: For DLBCL COO classification (total n=358 cases), LymphoVision achieved a mean test AUC of 0.93 (range 0.92–0.94), accuracy of 84.5% and F1 of 0.85 across two independent test folds (n=91 per fold, 50% GCB). These results outperformed general-purpose FMs on the same task and cases, including Virchow-v2 (AUC 0.85) and UNI-v2 (AUC 0.88), despite those models being pretrained on 10–100 times more images. For FL grading (total n=660 cases), the test set in each fold included 166 cases (18% grade 3), yielding a mean test AUC of 0.88 (range 0.87–0.90), accuracy of 89.2% and F1 of 0.80. In multiclass lymphoma subtype classification (total n=1,176 cases), the test set included 296 cases per fold. LymphoVision achieved a mean test AUC of 0.98 (range 0.97-0.99), mean test accuracy of 87.1% and mean F1 of 0.84. Class-wise recall was 92.0% for DLBCL (n=112), 88.2% for FL (n=72), 77.2% for MZL (n=29), 70.8% for MCL (n=35), 87.5% for CHL (n=24), and 95.8% for benign/reactive lymphoid conditions (n=24). Conclusion: LymphoVision is a lymphoma-specialized FM trained using self-supervision that achieves high diagnostic accuracy across multiple clinically meaningful tasks using weak supervision and standard H&E images, suggesting the encoder has learned fine-grained lymphoma morphology. Based on historical benchmarks from the 1997 International Lymphoma Study Group classification project, expert hematopathologists achieved diagnostic agreement rates of 55% to 84% using H&E morphology alone for the non-Hodgkin lymphoma (NHL) entities included in our multiclass task. LymphoVision approached or exceeded these benchmarks. These findings underscore the value of our approach using disease-specialized pretraining and intelligent patch sampling for data-efficient histopathology learning. LymphoVision has the potential to augment diagnostics, enable scalable AI-powered classification in real-world workflows, and serve as a foundation for prognostic and theragnostic applications. External validation and multimodal integration are ongoing.

Abstract Marginal zone lymphoma (MZL) is an uncommon non-Hodgkin B-cell lymphoma accounting for 7-10% of lymphoma diagnoses. Extranodal MZL (EMZL) of mucosa-associated lymphoid tissue (MALT) is the most common subtype (61%) of MZL. While the mutational landscape, chromosomal aberrations and transcriptome of nodal and splenic MZL have been extensively evaluated, the mutational landscape of EMZL has only been evaluated in a small number of patients and mainly by targeted sequencing. Consequently, the comprehensive genomic landscape of EMZL remains largely uncharacterized. Herein, we performed whole exome sequencing followed by targeted sequencing of potential tumor driver genes, copy number and/or RNA transcriptome analyses in 165 patients with pretreatment EMZL tumors involving 16 anatomic locations. All samples were collected and underwent expert review as part of the Atlas of Blood Cancer Genomes consortium. The most common anatomic locations were ocular adnexa (OAMZL) (n=35), gastric (n=25, including 6 positive for Helicobacter pylori), salivary glands (n=25), and lungs (n=24). Using common criteria for variant mutation calling and significant focal copy number (CN), we identified 1218 variants (1030 missense and 188 truncation mutations) across 174 genes and 2837 CN focal alterations (1614 copy gains and 1223 copy number losses). Of these genes, 44 and 11 were mutated in >5% and >10% of specimens, respectively. We detected variants identified previously by targeted sequencing in EMZL from different locations (e.g. TNFAIP3 (A20) (12%), TBL1XR1 (13%), SPEN (10%), CARD11 (7%), TET2 (7%) and CREBBP (6%)). We detected mutations in KLF2 (10 mutations in 8 EMZL patients) and PTPRD (7 mutations in 7 EMZL patients) that were previously suggested to be specific for splenic and nodal MZL, respectively. We also detected variants not previously reported in EMZL, including a transcriptional factor ZEB2, LRP1B,and others. The most frequently mutated gene in our study (25% of tumors) was IGLL5. Some mutations were limited to specific locations (e.g. ZEB2 mutations seen exclusively in gastric and salivary gland EMZL), while most were observed across all anatomic locations. There was no association between specific mutations and clonal IGHV, in contrast to previous reports. Using Enrichr tool, we observed enrichment of mutations in genes belonging to the B cell receptor activation, NOTCH signaling, Wnt-beta catenin signaling, NF-kB signaling , PI3K AKT signaling, DNA repair and other pathways. Chromosomal and copy number changes were also commonly observed including previously reported trisomy of chromosomes 3 and 18. Combining DNA mutations and CN alterations, the number of genetic lesions per tumor sample in the 174 mutated genes ranged from 0 to 95, with an average load of 17 lesions per case. RNA sequencing was successful in 159 EMZL tumors from 16 distinct anatomic locations. Unsupervised clustering using all genes with a median log2-normalized gene expression value greater than 5 and a standard deviation greater than 1 (1323 genes) demonstrated that the majority of gastric EMZL with and without H. pylori infections clustered together on 2 dendrograms as did most of the samples of OAMZL. Since the clustering could be forced by organ-specific and not tumor-specific gene expression, we next focused on the expression of the 345 genes from the LM22 matrix that passed our transcriptomic row filtering. Unsupervised clustering resulted in clustering of most gastric EMZL irrespective of H. pylori positivity together in the same dendrogram branch, accompanied by few additional EMZL from other anatomic locations. Most of the EMZL samples from other locations did not show preferential co-clustering together. On comparison of gene expression between gastric and OAMZL, there was enrichment of the B-cell receptor signaling pathway, B-cell activation, B-cell naïve and B-cell memory cells, B-cell proliferation and NF-kB signaling in the latter. Immune microenvironment analysis showed a statistically significant increase in resting mast cells and decrease in M1 macrophages in gastric EMZL and increase in regulatory T cells in salivary EMZL. The broad genomic studies reported here underscore the biological similarity of EMZL across anatomical sites with the potential exceptions of gastric EMZL (regardless of H. Pylori status). These data provide valuable genomic and transcriptomic resources to inform future diagnostic and therapeutic strategies.

ABSTRACT The World Health Organization (WHO) 5th Edition and International Consensus Classification (ICC) have continued to delineate more genetically defined acute leukemias. Both recognize genetic aberrations associated with myelodysplastic syndromes/neoplasms (MDS) as defining MDS‐related AML (AML‐MR). Mutations in RUNX1 are included in this category in the ICC, but not WHO. The current classifications are unclear on how acute leukemias with ambiguous lineage (ALAL), including those of mixed lineage (MPAL) and those without a defined lineage (AUL), should be categorized in the presence of such mutations. Our multi‐institutional study aims to determine the significance of RUNX1 mutation in ALAL compared to de novo AML. Newly diagnosed MPAL, AUL, and AML with RUNX1 mutations were collected. ALAL with MDS‐related cytogenetics, BCR::ABL1 , KMT2A rearrangements, or ZNF384 or BCL11b abnormalities were excluded. We analyzed 17 cases of ALAL and 71 cases of AML that met inclusion criteria. ALAL‐ RUNX1 and AML‐ RUNX1 showed overlapping mutational landscapes with frequent comutations in MDS‐associated genes. No ALAL‐ RUNX1 or AML‐ RUNX1 cases showed concomitant TP53 mutation. ALAL‐ RUNX1 had more bone marrow blasts and more karyotypic abnormalities; however, neither impacted survival, nor did RUNX1 variant allele frequency (VAF). Survival analysis revealed similarly poor outcomes between them. AML‐directed therapies and allogeneic hematopoietic stem cell transplant trended toward improved survival in ALAL‐ RUNX1; however, it did not reach significance. Our results suggest ALAL‐ RUNX1 is associated with younger age, higher blasts, and more karyotypic abnormalities, but has similar clinical and genetic features and outcomes to AML‐ RUNX1 . Our findings suggest, like other MR mutations, RUNX1 ‐mutated ALALs should be included within AML‐MR.

Abstract Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal fluid and plasma; however, the labor-intensive nature of conventional proteomics limits their investigation in histology-specific contexts. We describe the findings of an artificial intelligence-guided laser capture microdissection (LCM) pipeline for harvesting neurofibrillary tangles (NFTs) by maturation level into pretangles (pre-NFTs), mature tangles (iNFTs), and ghost tangles (eNFTs) using 38 cases obtained from 2 independent biobanks. We evaluated the performance characteristics of proprietary machine learning algorithms for the subclassification of these NFT categories in anti-pTau217-stained whole slide images of entorhinal cortex, hippocampus, frontal, and parietal cortex sections. Overall precision/recall/F1 scores were highest for Classifier A (0.6/0.46/0.5). The best performing algorithm was used to guide LCM capture and inform NFT enrichment. Targeted proteomics on tau signature peptides (pTau181, pTau217, and TauMTBR) was performed on approximately 1250 NFT collections. The results demonstrated that their abundance increased from pretangles to mature tangles (∼2-11-fold increase), and that this was followed by a sharp reduction in ghost tangles (∼3-116-fold decrease), with pTau217 showing the most drastic change. Pathologist-trained NFT classifiers represent an objective albeit imperfect means to enrich specific morphologic forms permitting coupled LCM-MS (mass spectrometry) to investigate AD-associated PTM.

Abstract Objective We hypothesized that a set of immunohistochemistry (IHC) stains could be used to distinguish Burkitt lymphoma (BL), the quintessential B-cell lymphoma with a germinal center B-cell (GCB) dark zone (DZ) expression signature, from diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS). This might also be applicable to high-grade B-cell lymphomas (HGBCLs) with MYC and BCL2 rearrangements (double-hit lymphomas [DHLs]) and triple-hit lymphomas (THLs). Methods A 5-marker IHC algorithm was designed from gene lists that distinguish physiologic DZ from light zone GCBs. Results In training and validation cohorts, we distinguished BL from DLBCL, NOS with high sensitivity and specificity. Because DHLs/THLs are enriched for the gene expression DZ signature (DZsig), we evaluated 19 DHLs/THLs and 4 HGBCLs, NOS. Most (83%) cases were IHC DZ. The NanoString DLBCL90 assay was performed on 34 cases to correlate IHC DZ results with the molecular DZsig. The IHC DZ call was significantly associated with the DZsig (P = .0011). The sensitivity and specificity of IHC to recognize DZsig+ cases among DLBCL, NOS and DHLs with BCL2 rearrangements/THLs were 91% and 100%, respectively. Conclusions The IHC DZ algorithm can support a diagnosis of BL and identifies MYC-BCL2 DHLs/THLs with a molecular DZsig.

Abstract Anaplastic large cell lymphoma (ALCL) cases were discussed at the 2023 Society of Hematopathology/European Association for Haematopathology workshop held on November 9-11, 2023, in Houston, Texas. This session focused on the clinical and pathologic spectrum of 3 types of ALCL: anaplastic lymphoma kinase (ALK)-positive, ALK-negative, and breast implant associated (BIA). Cases of primary cutaneous ALCL were excluded because they were included in another session. Although the diagnosis of ALCL is often straightforward, the 42 submitted cases encompassed unusual clinical presentations, morphologic variants, and atypical immunophenotypes, exemplifying potential difficulties and challenges in establishing the diagnosis of ALCL. Distinguishing ALCL from CD30-positive peripheral T-cell lymphoma, not otherwise specified, or classic Hodgkin lymphoma was discussed. In patients with a previous history of mycosis fungoides and other T-cell lymphoma/leukemia, the differential diagnosis of a CD30-positive T-cell lymphoma mimicking ALCL (mycosis fungoides transformation vs de novo ALCL) was also discussed. In patients with suspected BIA-ALCL, it is critical to properly handle the periprosthetic fluid when the disease first presents and the capsule at the time of initial capsulectomy to make a correct diagnosis and pathologic staging because a missed diagnosis may lead to disease progression. Comprehensive immunohistochemical analysis; fluorescence in situ hybridization for DUSP22, TP63, or JAK2 rearrangement; assessment of clonality of the T-cell receptor and immunoglobulin genes; and sequencing for mutations were performed as part of the workup of the submitted cases, particularly on ALK-negative ALCL cases, emphasizing the importance of ancillary testing in establishing the diagnosis.

Abstract Objective The successful diagnosis and classification of lymphoid neoplasms in blood and bone marrow is the responsibility of the practicing pathologist. This guide provides a general “roadmap” for this process, from initial case recognition to final classification. Methods The integration of hematologic, morphologic, immunophenotypic, and genetic features for the full spectrum of precursor and mature B-cell, T-cell, and natural killer–cell neoplasms that typically manifest in blood and bone marrow is included. Results Classification systems for lymphoid neoplasms provide criteria for pathologists to render a diagnosis that is optimal for patient care, treatment, and outcome prediction. Conclusions This guide provides diagnostic strategies for lymphoid neoplasms encountered in blood and bone marrow specimens using both the International Consensus Classification and the World Health Organization fifth edition classification systems. Key tips are provided for each entity along with testing requirements, differential diagnosis, nonneoplastic mimics, and other unique features based on the experience of the Bone Marrow Pathology Group members.