Chiu, April

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and fusions. Detection of gene rearrangements using fluorescence in situ hybridization (FISH) is a standard practice for DLBCL diagnosis and guides treatment decisions. High-throughput chromosome conformation capture (Hi-C) DNA sequencing is a next-generation sequencing-based technology to identify genome-wide rearrangements using a single assay. In this study, Hi-C sequencing was performed using FFPE tissues from 159 patients with DLBCL, and identified 746 cancer genes at or proximal to the rearrangement breakpoints with a total of 1903 occurrences. Focusing on clinically important rearrangements, Hi-C detected 102 rearrangements of MYC, BCL2, and/or BCL6 in 92 patients and revealed the fusion partners, including 25 rearrangements missed by FISH. Causes of FISH negative results included FISH-cryptic breakpoints, complex or faint FISH signals, low percentage of FISH positivity, and issues related to tissue fixation. Moreover, in 20 patients (12.6%), Hi-C sequencing detected 22 rearrangements characteristic of other lymphoma types, including CCND1 rearrangements that could lead to reclassification as mantle cell lymphoma. Survival analysis for genome-wide rearrangements using machine learning models identified MYC, PD-L1, CCND1, BCL2, NTRK1/PRCC, RRAS, and FANCE rearrangements with significant prognostic effects in the DLBCL cohort. In conclusion, Hi-C sequencing detects gene rearrangements crucial for diagnosis in an unbiased and molecular manner and showed high sensitivity and specificity in our study. These advantages of Hi-C sequencing offer help to improve the workflow of clinical pathology laboratories, diagnostic precision, and treatment of large B-cell lymphoma.

Abstract Pyroptosis is a form of programmed cell death characterized by the cleavage of the gasdermin (GSDM) family proteins that form pores in the plasma membrane, cell rupture, and the release of proinflammatory cytokines. In this study, we performed immunohistochemistry for cleaved gasdermin D (GSDMD), gasdermin E (GSDME) N-terminus, and gasdermin C (GSDMC) in 2 different cohorts of diffuse large B-cell lymphoma (DLBCL) and analyzed the impact of GSDM expression on prognosis and immunity. The results showed frequent cleaved GSDMD (N-terminal) expression. Only cytoplasmic GSDMD N-terminal expression correlated with significantly better patient survival in the 2 cohorts. In contrast, GSDME was mainly expressed in the vascular endothelium, and correlated with significantly adverse prognostic effect. Correlating with the multiplex fluorescent immunohistochemistry results, we found that cytoplasmic GSDMD N-terminal expression was associated with increased CD38+ (activated) M1 macrophages in both cohorts, cognate interactions between live DLBCL cells and activated M1 macrophages (and T cells), and lower PD-1/PD-L1 expression in the analyzed cases. In contrast, T-cell pyroptosis, lymphoma cell-resistance to cell death, and phagocytosis by M2 macrophages were observed in tissues with predominantly nuclear GSDMD N-terminal expression. Bulk gene expression profiling and deconvolution analysis revealed associations of cytoplasmic GSDMD N-terminal expression with downregulation of “Don’t eat me”–signaling genes, upregulation of many RNA genes, decreased frequency of “inflammatory” lymphoma microenvironment subtype, increased frequencies of prognostically favorable cell states and ecotypes, and decreased frequency of T-cell exhaustion state. In summary, this study showed distinct cellular and subcellular patterns of 3 GSDM proteins and their associated immune response phenotypes and prognostic effects, with implications for novel therapeutic strategies for B-cell lymphoma.

Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL), accounting for approximately 30% of all NHL cases. DLBCL diagnosis relies on histologic examination, immunophenotyping, molecular and fluorescence in situ hybridization (FISH) analysis to differentiate it from similar NHL subtypes with overlapping morphological features, including more high-grade lymphomas like double-hit lymphoma. Because FISH is performed in a panel of single- or dual-target test format and analyzed through microscopy and manual image analysis, assessment of multiple target biomarkers can be cumbersome and expensive. As additional biomarkers for diagnosis, subtyping, prognosis, and therapeutic decision-making become available, target panels will become larger, posing additional challenges to clinical laboratories. Patients and Methods In this study, Hi-C sequencing of FFPE samples was performed to identify all genomic rearrangements by a single sequencing analysis for an unselected DLBCL case series of 159 patients, and the results were compared to FISH for the detection of clinically important variants. Results Hi-C sequencing showed superior performance compared to FISH: standard FISH for DLBCL includes testing for MYC, BCL2, and BCL6. Between FISH and Hi-C, these biomarkers were detected 105 times, of which, FISH detected 80/105 (76%) and Hi-C detected 102/105 (97%). The cohort included 4 double-hit lymphomas by FISH but Hi-C sequencing detected five. Besides DLBCL diagnostic biomarkers (MYC, BCL2, BCL6), many additional biomarkers were detected by Hi-C sequencing including variants not tested by FISH because the clinical suspicion was ambiguous and the relevant FISH probes were not used, and those not tested because the biomarkers were not thought to be common and, hence, left out of the FISH panel. In total, Hi-C sequencing detected additional biomarkers in 42 (26%) cases, including (1) variants targeted by FISH (i.e. MYC, BCL2, BCL6) but negative, (2) classification biomarkers for other lymphomas (CCND1, MALT, IRF4), and (3) those not tested because the biomarkers are not common and not included in the FISH panel. These additional biomarkers were supportive of suspected diagnosis of DLBCL versus double-hit lymphoma (24 cases), prompted reconsideration of a different diagnosis that may have led to different treatments (6 cases, e.g. mantle cell lymphoma), or biomarkers such as PDL1, ALK, and ATM which have implications for targeted therapies and/or inclusion in clinical trials (5 cases). Conclusion The Hi-C FFPE sequencing technology has the potential to improve the workflow of pathology labs for detection of a wide range of structural variants informing precision diagnosis and treatment for lymphomas.

Abstract Background: Flow cytometry (FC) is widely utilized for the identification of measurable residual disease (MRD) in multiple myeloma (MM), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files currently limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect MM MRD using the 2-tube Euroflow Consortium Panel (Cytognos). Raw FC files corresponding to 150 MRD-positive bone marrow samples (MRD < 5%, median = 0.0022%), and 39 MRD-negative samples were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline, deployed on a custom clinical-grade cloud environment (Biolegend). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), cluster-informed and density-based downsampling with preservation of low-event subsets, dimensionality reduction (UMAP), and cluster-based anomaly detection compared to negative controls (29 bone marrow aspirates). In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general-purpose flow cytometry analysis software (Kaluza v 2.3, Beckman Coulter), and %MRD estimates were compared to reported results based on traditional expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells to be manually analyzed from 4.1 million to 196,761 cells per tube, on average (95% cellularity reduction); resulting in a smaller FC data file (from 203.2 MB to 20.5 MB; 90% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 88% and 77% retention, for 0.01-0.001% MRD and < 0.001% MRD, respectively). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. In 96% of positive cases, MRD was rapidly identified as immunoglobulin light chain-restricted plasma cells on a single pre-calculated UMAP plot (tube 2), after dual coloring for kappa and lambda. On the remainder 4% of positive cases, MRD was easily segregated from benign plasma cells using the AI-generated “aberrancy scale” parameter or other strategies. The use of AI-enhanced files reduced manual analysis time from 15.05 minutes (SD = 6.84) to 1.29 minutes (SD = 0.90) per case, on average (p < 0.0001) (91% reduction of manual analysis time). MM MRD was detected in all positive cases above limit of detection for our assay (≥0.0002%), with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 0.9841, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw MM MRD FC data from the Euroflow Consortium panel into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of MM MRD in clinical diagnostics, reducing the number of cells analyzed by 95% and manual analysis time by 91%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed MM MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform, and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of MM MRD FC analysis by more clinical laboratories.

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 Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

Abstract Objective Gastrointestinal (GI) symptoms are common in systemic mastocytosis (SM). We hereby report the clinicopathologic and molecular features of GI mastocytosis. Methods The study includes 104 patients with suspected SM. Of 258 GI biopsy ­specimens, 33 (20 patients, 19%) were mastocytosis-positive, with 19 (16 patients) initially missed. Results Tryptase (n = 29) was weakly positive/negative in two-thirds of the mastocytosis-positive GI biopsy specimens. Additional immunohistochemistry (n = 23) showed positive mast cell expression for CD30 (n = 2), CD123 (n = 7), and PD-L1 (n = 12), as well as increased PD-1–positive cells (n = 4). Sixty patients had positive bone marrow (BM) for SM (SM-BM+), including 19 with positive GI biopsy specimens (SM-GI+). Almost all (43/44) SM-BM– patients were SM-GI–. The SM-GI vs SM-BM status showed high positive predictive value (95%) and specificity (98%). Patients with SM with mastocytosis-positive vs mastocytosis-negative GI biopsy specimens had higher serum tryptase levels (P = .04). KIT D816V mutation was detected in 7 of 13 and 12 of 13 mastocytosis-positive GI biopsy specimens using allele-specific polymerase chain reaction and droplet digital polymerase chain reaction (ddPCR), respectively. Conclusions GI mastocytosis is highly predictive of SM in BM; however, most SM-BM+ patients are SM-GI–. The SM-BM– patients are very unlikely to be SM-GI+. Because GI neoplastic mast cells are often tryptase negative, diagnostic evaluation should include CD117 and CD25 immunohistochemistry, as well as KIT D816V mutation analysis by ddPCR as needed.

Abstract Objective The aim of this study was to evaluate a large cohort of gallbladder amyloid cases to determine clinical and morphologic features. Methods Cholecystectomy specimens (N = 118) typed using proteomics-based techniques between 2008 and 2023 were identified. Clinical and morphologic features were reviewed. Results Six amyloid types were identified: ATTR (n = 63, 53.4%), AL (n = 46, 39.0%), AA (n = 4, 3.4%), AApoA1 (n = 2, 1.7%), ALECT2 (n = 2, 1.7%), and AEFEMP1 (n = 1, 0.8%). Amyloidogenic mutations were detected in 3 ATTR cases and 2 AApoA1 cases. Morphologic review (n = 26) revealed perimuscular vessel involvement in all cases. Amyloidosis was an unexpected diagnosis first made on the cholecystectomy specimen in half of the patients with clinical information (n = 10). All 9 patients with follow-up had evidence of systemic disease. In 2 patients, cholecystic involvement was initially missed and only retrospectively identified after the diagnosis of cardiac amyloidosis. Conclusions In patients with clinical data, amyloidosis was often unexpected, the gallbladder was commonly the first tissue sampled with amyloidosis, and all patients had systemic disease. Thorough review of cholecystectomy specimens with careful inspection of perimuscular vessels, coupled with a low threshold for ordering Congo red stain in elderly individuals and amyloid typing using a robust method such as proteomics, can prevent a delay in amyloid diagnosis and management.

Abstract Primary testicular diffuse large B-cell lymphoma (PT-DLBCL) is a rare and aggressive lymphoma with molecular heterogeneity not well characterize. In this study, we performed next-generation sequencing analysis for a large number of DNA and RNA samples from patients with PT-DLBCL. DNA sequencing analysis identified ≥ 3 chromosomes with copy number variations (CNVs) and microsatellite instability as prognostic biomarkers, rather than MYD88 mutations and genetic subtypes. Remarkably, targeted RNA-seq analysis in 195 patients revealed that TP53 mutations with a ≥ 40% variant allele frequency had significantly adverse prognostic impact, and that a 150-gene expression signature subdivided PT-DLBCL into two distinct clusters, termed as testicular lymphoma tumor (TLT) and microenvironment (ME) subtypes. The TLT subtype featured upregulation of genes involved in B-cell receptor signaling, cell cycle, DNA damage and repair, higher frequencies of CNVs and MYD88 mutations, elder ages, larger tumor sizes, and significantly poorer survival. Genomic microRNA profiling analysis identified significantly differentially expressed microRNAs between 113 PT-DLBCL and 180 systemic DLBCL patients, and further subdivided the PT-DLBCL cohort by microRNA signatures. The subcohort with upregulation of 16 microRNAs associated with PT-DLBCL and testicular tissue expression had significantly better survival. This study revealed characteristic genetic, gene expression, and microRNA profiles and heterogeneity in PT-DLBCL.

Introduction: Primary diffuse large B-cell lymphoma (DLBCL) of the central nervous system (CNS), traditionally referred to as primary CNS lymphoma (PCNSL), is a rare type of primary large B-cell lymphoma of immune-privileged sites with significantly poorer prognosis than systemic DLBCL. Genetic profiles of PCNSL were widely studied, whereas the tumor microenvironment has not been well characterized. Patients and Methods: A series of molecular analyses were performed using DLBCL FFPE tissues collected by the International Lymphoma Consortium. First, multiplex fluorescent immunohistochemistry (mfIHC) using antibodies for CD20, PAX5, CD3, CD4, CD8, FOXP3, CD45RO, CD68, CD56, PD-1, PD-L1, PD-L2, and CTLA-4 was performed on PCNSL tissues from 121 immunocompetent patients and de novo systemic DLBCL tissues from 405 patients. Digital quantification results for 405 patients with EBV-negative systemic DLBCL or primary testicular (PT) DLBCL have been reported previously. The preliminary mfIHC results of PCNSL will be presented. Second, mfIHC for multiple B cell biomarkers, CD3, CD4, CD8, CD68, CD163, CD11c, and CD31 were performed for 10 PCNSL validation cases, 24 PT-DLBCL and 451 systemic DLBCL tissues (unpublished). Third, RNA samples were extracted and sequenced for the targeted 1408 cancer-associated genes, successfully in 142 PCNSL cases, including 102 cases evaluated by either of these two mfIHC panels and additional 40 PCNSL cases, Results: First, in the first large mfIHC cohort, PCNSLs compared with de novo systemic nodal or extranodal DLBCL had significantly higher mean/median CD56+ cell densities whereas lower T cell densities (consistent with the immune privilege), as well as lower CD45RO+, PD-1+, and FOXP3+ percentages in T cells whereas higher CD4¯CD8¯ double-negative percentages in T cells. Prognostic analysis in the PCNSL cohort showed that high CD56+ cell densities and high CD45RO+CD8+ T cell densities were associated with significantly poorer and better patient survival, respectively, which are opposite to their prognostic effects in systemic DLBCL as previously reported. Twelve patients had very high PD-L1 expression in B cells and significantly poorer survival in the PCNSL cohort. Second, as validation, the 10 PCNSL cases in the second mfIHC study showed significantly less T cells than systemic DLBCL and PT-DLBCL cases, and low CD8 T cell densities and having low percentage of CD8 T cells adjacent to tumor cells were associated with significantly poorer survival. Immunofluorescence landscapeand spatial analysisrevealed significant differences in immune privilege between PCNSL and PT-DLBCL. Third, gene expression profiling analysis by targeted RNA-seq identified 282 significantly differentially expressed genes between PCNSL and systemic DLBCL, referred to as the PCNSL signature. Unsupervised clustering analysis divided the PCNSL cohort into two clusters, which had no significant difference in MYD88 mutation frequencies analyzed at the mRNA level. Importantly, the cluster with higher expression of many neural glial genes in the PCNSL signature had significantly poorer survival, which remained significant in multivariate analysis adjusting for clinical factors and/or treatments, two PCNSL subcohorts (as training and validation cohorts), and methotrexate-treated patients. NCAM1 (Neural Cell Adhesion Molecule 1, encoding CD56) was among the upregulated PCNSL signature genes, explaining the poorer prognosis associated with higher CD56+ cell densities in PCNSL by the mfIHC analysis. Summary: Our studies represent the largest immunophenotypic and gene expression profiling studies currently in PCNSL, a rare type of primary DLBCL of immune-privileged sites. Multiplex immunofluorescence revealed the immune landscape in PCNSL in comparison with systemic and testicular DLBCL, and identified prognostic immune biomarkers. Targeted RNA-seq identified a PCNSL gene signature enriched with neuronal-glial genes and showing significant prognostic effect in PCNSL. These novel findings in the PCNSL microenvironment warrant further studies.