Amino acid influx via LAT1 regulates iron demand and sensitivity to PPMX-T003 of aggressive natural killer cell leukemia.

Aggressive natural killer cell leukemia (ANKL) is a rare hematological malignancy with a fulminant clinical course. Our previous study revealed that ANKL cells proliferate predominantly in the liver sinusoids and strongly depend on transferrin supplementation. In addition, we demonstrated that liver-resident ANKL cells are sensitive to PPMX-T003, an anti-human transferrin receptor 1 inhibitory antibody, whereas spleen-resident ANKL cells are resistant to transferrin receptor 1 inhibition. However, the microenvironmental factors that regulate the iron dependency of ANKL cells remain unclear. In this study, we first revealed that the anti-neoplastic effect of PPMX-T003 was characterized by DNA double-strand breaks in a DNA replication-dependent manner, similar to conventional cytotoxic agents. We also found that the influx of extracellular amino acids via LAT1 stimulated sensitivity to PPMX-T003. Taken together, we discovered that the amount of extracellular amino acid influx through LAT1 was the key environmental factor determining the iron dependency of ANKL cells via adjustment of their mTOR/Myc activity, which provides a good explanation for the different sensitivity to PPMX-T003 between liver- and spleen-resident ANKL cells, as the liver sinusoid contains abundant amino acids absorbed from the gut.

Intrahepatic cholangiocarcinoma with arterial phase hyperenhancement and specialized tumor microenvironment associated with good prognosis after radical resection: A single-center retrospective study.

BACKGROUND: This single-center retrospective study aimed to clarify the clinical and pathologic background of mass-forming intrahepatic cholangiocarcinomas. METHODS: A total of 53 patients with mass-forming intrahepatic cholangiocarcinomas were selected from 2007 to 2021 and analyzed based on several parameters, including the preoperative computed tomography pattern (enhancement in the arterial phase of dynamic contrast-enhanced computed tomography), clinical data, and tumor microenvironment evaluated by immunohistochemistry. The hyperenhancement (n = 13) and hypoenhancement (n = 40) groups were defined using the 50% cutoff of tumors with higher attenuation than the liver parenchyma. RESULTS: The hyperenhancement group was characterized by a better overall survival than the hypoenhancement group (5-year survival: 86% vs 27%, respectively; P < .001) and by a higher infiltration of peritumoral (92% vs 58%; P = .020) and intratumoral CD3-positive T lymphocytes (85% vs 35%; P = .002). Conversely, the hypoenhancement group was characterized by a higher infiltration versus peritumoral CD163-positive tumor-associated macrophages (60% vs 8%; P = .001), peritumoral pentraxin 3-positive tumor-associated macrophages (50% vs 15%; P = .024), and intratumoral α-smooth muscle actin-positive cancer-associated fibroblasts (15% vs 68%; P = .001). A multiple regression analysis was performed to predict overall survival from the microenvironment, and the independent poor predictor factors were low intratumoral CD3-positive T lymphocytes (hazard ratio = 2.75), high peritumoral (hazard ratio = 2.38), and intratumoral CD163-positive tumor-associated macrophages (hazard ratio = 2.81) (all P values < 0.05). CONCLUSION: Compared with hypovascular, hypervascular mass-forming intrahepatic cholangiocarcinomas have better tumor immunity and prognosis.

Artificial intelligence and classification of mature lymphoid neoplasms.

Hematologists, geneticists, and clinicians came to a multidisciplinary agreement on the classification of lymphoid neoplasms that combines clinical features, histological characteristics, immunophenotype, and molecular pathology analyses. The current classification includes the World Health Organization (WHO) Classification of tumours of haematopoietic and lymphoid tissues revised 4th edition, the International Consensus Classification (ICC) of mature lymphoid neoplasms (report from the Clinical Advisory Committee 2022), and the 5th edition of the proposed WHO Classification of haematolymphoid tumours (lymphoid neoplasms, WHO-HAEM5). This article revises the recent advances in the classification of mature lymphoid neoplasms. Artificial intelligence (AI) has advanced rapidly recently, and its role in medicine is becoming more important as AI integrates computer science and datasets to make predictions or classifications based on complex input data. Summarizing previous research, it is described how several machine learning and neural networks can predict the prognosis of the patients, and classified mature B-cell neoplasms. In addition, new analysis predicted lymphoma subtypes using cell-of-origin markers that hematopathologists use in the clinical routine, including CD3, CD5, CD19, CD79A, MS4A1 (CD20), MME (CD10), BCL6, IRF4 (MUM-1), BCL2, SOX11, MNDA, and FCRL4 (IRTA1). In conclusion, although most categories are similar in both classifications, there are also conceptual differences and differences in the diagnostic criteria for some diseases. It is expected that AI will be incorporated into the lymphoma classification as another bioinformatics tool.

Unique lipid composition maintained by extracellular blockade leads to prooncogenicity.

Lipid-mediated inflammation is involved in the development and malignancy of cancer. We previously demonstrated the existence of a novel oncogenic mechanism utilizing membrane lipids of extracellular vesicles in Epstein-Barr virus (EBV)-positive lymphomas and found that the lipid composition of lymphoma cells is skewed toward ω-3 fatty acids, which are anti-inflammatory lipids, suggesting an alteration in systemic lipid composition. The results showed that arachidonic acid (AA), an inflammatory lipid, was significantly reduced in the infected cells but detected at high levels in the sera of EBV-positive patients lead to the finding of the blockade of extracellular AA influx by downregulating FATP2, a long-chain fatty acid transporter that mainly transports AA in EBV-infected lymphoma cells. Low AA levels in tumor cells induced by downregulation of FATP2 expression confer resistance to ferroptosis and support tumor growth. TCGA data analysis and xenograft models have demonstrated that the axis plays a critical role in several types of cancers, especially poor prognostic cancers, such as glioblastoma and melanoma. Overall, our in vitro, in vivo, in silico, and clinical data suggest that several cancers exert oncogenic activity by maintaining their special lipid composition via extracellular blockade.

Mutational, immune microenvironment, and clinicopathological profiles of diffuse large B-cell lymphoma and follicular lymphoma with BCL6 rearrangement.

BCL6-rearrangement (BCL6-R) is associated with a favorable prognosis of follicular lymphoma (FL), but the mechanism is unknown. We analyzed the clinicopathological, immune microenvironment (immune checkpoint, immuno-oncology markers), and mutational profiles of 10 BCL6-R-positive FL, and 19 BCL6-R-positive diffuse large B-cell lymphoma (DLBCL) cases (both BCL2-R and MYC-R negative). A custom-made panel included 168 genes related to aggressive B-cell lymphomas and FL. FL cases were nodal, histological grade 3A in 70%, low Ki67; and had a favorable overall and progression-free survival. DLBCL cases were extranodal in 60%, IPI high in 63%, non-GCB in 60%, EBER-negative; and had a progression-free survival comparable to that of DLBCL NOS. The microenvironment had variable infiltration of M2-like tumor-associated macrophages (TAMs) that were CD163, CSF1R, LAIR1, PD-L1, and CD85A (LILRB3) positive; but had low IL10 and PTX3 expression. In comparison to FL, DLBCL had higher TAMs, IL10, and PTX3 expression. Both lymphoma subtypes shared a common mutational profile with mutations in relevant pathogenic genes such as KMT2D, OSBPL10, CREBBP, and HLA-B (related to chromatin remodeling, metabolism, epigenetic modification, and antigen presentation). FL cases were characterized by a higher frequency of mutations of ARID1B, ATM, CD36, RHOA, PLOD2, and PRPRD (p < 0.05). DLBCL cases were characterized by mutations of BTG2, and PIM1; and mutations of HIST1H1E and MFHAS1 to disease progression (p < 0.05). Interestingly, mutations of genes usually associated with poor prognosis, such as NOTCH1/2 and CDKN2A, were infrequent in both lymphoma subtypes. Some high-confidence variant calls were likely oncogenic, loss-of-function. MYD88 L265P gain-of-function was found in 32% of DLBCL. In conclusion, both BCL6-R-positive FL and BCL6-R-positive DLBCL had a common mutational profile; but also, differences. DLBCL cases had a higher density of microenvironment markers.

Tumor heterogeneity and immune-evasive T follicular cell lymphoma phenotypes at single-cell resolution.

T follicular helper (TFH) cell lymphomas (TFHLs) are characterized by TFH-like properties and accompanied by substantial immune-cell infiltration into tumor tissues. Nevertheless, the comprehensive understanding of tumor-cell heterogeneity and immune profiles of TFHL remains elusive. To address this, we conducted single-cell transcriptomic analysis on 9 lymph node (LN) and 16 peripheral blood (PB) samples from TFHL patients. Tumor cells were divided into 5 distinct subclusters, with significant heterogeneity observed in the expression levels of TFH markers. Copy number variation (CNV) and trajectory analyses indicated that the accumulation of CNVs, together with gene mutations, may drive the clonal evolution of tumor cells towards TFH-like and cell proliferation phenotypes. Additionally, we identified a novel tumor-cell-specific marker, PLS3. Notably, we found a significant increase in exhausted CD8+ T cells with oligoclonal expansion in TFHL LNs and PB, along with distinctive immune evasion characteristics exhibited by infiltrating regulatory T, myeloid, B, and natural killer cells. Finally, in-silico and spatial cell-cell interaction analyses revealed complex networking between tumor and immune cells, driving the formation of an immunosuppressive microenvironment. These findings highlight the remarkable tumor-cell heterogeneity and immunoevasion in TFHL beyond previous expectations, suggesting potential roles in treatment resistance.

The pathobiology of follicular lymphoma.

Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6, the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

The hepatic niche leads to aggressive natural killer cell leukemia proliferation through the transferrin-transferrin receptor 1 axis.

Aggressive natural killer cell leukemia (ANKL) is a rare lymphoid neoplasm frequently associated with Epstein-Barr virus, with a disastrously poor prognosis. Owing to the lack of samples from patients with ANKL and relevant murine models, comprehensive investigation of its pathogenesis including the tumor microenvironment (TME) has been hindered. Here we established 3 xenograft mice derived from patients with ANKL (PDXs), which enabled extensive analysis of tumor cells and their TME. ANKL cells primarily engrafted and proliferated in the hepatic sinusoid. Hepatic ANKL cells were characterized by an enriched Myc-pathway and proliferated faster than those in other organs. Interactome analyses and in vivo CRISPR-Cas9 analyses revealed transferrin (Tf)-transferrin receptor 1 (TfR1) axis as a potential molecular interaction between the liver and ANKL. ANKL cells were rather vulnerable to iron deprivation. PPMX-T003, a humanized anti-TfR1 monoclonal antibody, showed remarkable therapeutic efficacy in a preclinical setting using ANKL-PDXs. These findings indicate that the liver, a noncanonical hematopoietic organ in adults, serves as a principal niche for ANKL and the inhibition of the Tf-TfR1 axis is a promising therapeutic strategy for ANKL.

Mutational Profile and Pathological Features of a Case of Interleukin-10 and RGS1-Positive Spindle Cell Variant Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma with spindle cell morphology is a rare variant. We present the case of a 74-year-old male who initially presented with a right supraclavicular (lymph) node enlargement. Histological analysis showed a proliferation of spindle-shaped cells with narrow cytoplasms. An immunohistochemical panel was used to exclude other tumors, such as melanoma, carcinoma, and sarcoma. The lymphoma was characterized by a cell-of-origin subtype of germinal center B-cell-like (GCB) based on Hans' classifier (CD10-negative, BCL6-positive, and MUM1-negative); EBER negativity, and the absence of BCL2, BCL6, and MYC rearrangements. Mutational profiling using a custom panel of 168 genes associated with aggressive B-cell lymphomas confirmed mutations in ACTB, ARID1B, DUSP2, DTX1, HLA-B, PTEN, and TNFRSF14. Based on the LymphGen 1.0 classification tool, this case had an ST2 subtype prediction. The immune microenvironment was characterized by moderate infiltration of M2-like tumor-associated macrophages (TMAs) with positivity of CD163, CSF1R, CD85A (LILRB3), and PD-L1; moderate PD-1 positive T cells, and low FOXP3 regulatory T lymphocytes (Tregs). Immunohistochemical expression of PTX3 and TNFRSF14 was absent. Interestingly, the lymphoma cells were positive for HLA-DP-DR, IL-10, and RGS1, which are markers associated with poor prognosis in DLBCL. The patient was treated with R-CHOP therapy, and achieved a metabolically complete response.

Copy Number Alteration and Mutational Profile of High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements, Diffuse Large B-Cell Lymphoma with MYC-Rearrangement, and Diffuse Large B-Cell Lymphoma with MYC-Cluster Amplification.

Diffuse large B-cell lymphoma (DLBCL) with MYC alteration is classified as high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (double/triple-hit lymphoma; DHL/THL), DLBCL with MYC rearrangement (single-hit lymphoma; SHL) and DLBCL with MYC-cluster amplification (MCAD). To elucidate the genetic features of DHL/THL, SHL, and MCAD, 23 lymphoma cases from Tokai University Hospital were analyzed. The series included 10 cases of DHL/THL, 10 cases of SHL and 3 cases of MCAD. The analysis used whole-genome copy number microarray analysis (OncoScan) and a custom-made next-generation sequencing (NGS) panel of 115 genes associated with aggressive B-cell lymphomas. The copy number alteration (CNA) profiles were similar between DHL/THL and SHL. MCAD had fewer CNAs than those of DHL/THL and SHL, except for +8q24. The NGS profile characterized DHL/THL with a higher "mutation burden" than SHL (17 vs. 10, p = 0.010), and the most relevant genes for DHL/THL were BCL2 and SOCS1, and for SHL was DTX1. MCAD was characterized by mutations of DDX3X, TCF3, HLA-A, and TP53, whereas MYC was unmutated. In conclusion, DHL/THL, SHL, and MCAD have different profiles.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels.

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

The multilobated morphology is still a better prognosis factor of diffuse large B-cell lymphoma in the R-CHOP era.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of B-cell lymphoma. Although the multilobated subtype of DLBCL has been observed since the 1970s, little is known about the clinical significance of this unique variant in the era of rituximab, cyclophosphamide, hydroxydaunorubicin, oncovin, prednisone/prednisolone (R-CHOP) therapy. In this study, the retrospective clinicopathological analysis of 312 patients diagnosed with DLBCL showed that the multilobated DLBCL group comprised 11% of the cases and was predominantly male (p = 0.027), achieved complete remission in the first therapy (p = 0.023), and exhibited germinal center B-cell phenotypes in the Hans algorithm (p = 0.025). The multilobated DLBCL groups had a better prognosis in overall survival (OS) and progression-free survival (PFS) than the non-multilobated DLBCL group (OS, p = 0.006; PFS, p = 0.010). In the multivariate Cox regression analyses for OS, independent prognosis factors were high soluble IL-2 receptor (p = 0.025), high risk of International Prognostic Index, and multilobated morphology (p = 0.031). The most characteristic copy number gains found in more than 50% of the cases were located at 1q, 3p, 10q, 12q, and 14q. Overall, the multilobated morphology in DLBCL exhibits a good outcome in the R-CHOP era.

Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel.

Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95-100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (PD-L1, CD274, PDCD1LG1, B7-H1) was validated in a series from the Tokai University Hospital by immunohistochemistry. In conclusion, artificial intelligence analysis of transcriptomic data of ulcerative colitis is a feasible analytical strategy.

Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA.

Celiac disease is a common immune-related inflammatory disease of the small intestine caused by gluten in genetically predisposed individuals. This research is a proof-of-concept exercise focused on using Artificial Intelligence (AI) and an autoimmune discovery gene panel to predict and model celiac disease. Conventional bioinformatics, gene set enrichment analysis (GSEA), and several machine learning and neural network techniques were used on a publicly available dataset (GSE164883). Machine learning and deep learning included C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network (multilayer perceptron). As a result, the gene panel predicted celiac disease with high accuracy (95-100%). Several pathogenic genes were identified, some of the immune checkpoint and immuno-oncology pathways. They included CASP3, CD86, CTLA4, FASLG, GZMB, IFNG, IL15RA, ITGAX, LAG3, MMP3, MUC1, MYD88, PRDM1, RGS1, etc. Among them, B and T lymphocyte associated (BTLA, CD272) was highlighted and validated at the protein level by immunohistochemistry in an independent series of cases. Celiac disease was characterized by high BTLA, expressed by inflammatory cells of the lamina propria. In conclusion, artificial intelligence predicted celiac disease using an autoimmune discovery gene panel.

9p24.1 Genetic Alteration and PD-L1 Expression Are Characteristic of De Novo and Methotrexate-associated Epstein-Barr Virus-positive Hodgkin Lymphoma, But Not Methotrexate-associated Hodgkin-like Lesions.

Although the alteration of the 9p24.1 chromosome locus and PD-L1 overexpression is found in nodular sclerosis classic Hodgkin lymphoma, whether these aberrations occur in CHL and Hodgkin-like lesion (HLL) of methotrexate-associated lymphoproliferative disorder (MTX-CHL and MTX-HLL) is unknown. We compared the clinicopathologic features, the genomic status of the 9p24.1 locus and PD-L1 expression in a series of 34 patients including 17 with Epstein-Barr virus-positive de novo CHL, 7 with MTX-CHL, 10 with MTX-HLL using an immunofluorescence in situ hybridization method and immunohistochemistry. The proportions of cells with 9p24.1 genetic alteration in CD30-positive Hodgkin/Reed-Sternberg cells of de novo CHL, MTX-CHL and MTX-HLL were 55%, 68%, and 24%, respectively. The positive rates of PD-L1 measured by immunohistochemical H-scores of de novo CHL, MTX-CHL and MTX-HLL were 142±38, 157±75, and 70±42, respectively. Alteration of the 9p24.1 gene and expression of PD-L1 protein were correlated with all 3 diseases (correlation coefficient, 0.731). Both alteration of the 9p24.1 gene and overexpression of PD-L1 protein were observed in Epstein-Barr virus-positive de novo CHL and MTX-CHL but not in MTX-HLL. In conclusion, MTX-CHL has similar pathogenesis-like de novo CHL, but MTX-HLL seems to be a different disease from de novo CHL and MTX-CHL.

Clinicopathological analysis of follicular lymphoma with BCL2, BCL6, and MYC rearrangements.

Most follicular lymphomas (FL) show t(14;18)/IGH-BCL2 translocation, but rearrangement (R) negative cases exist. A series of 140 FL patients with a BCL2, BCL6, and MYC gene status examined by fluorescence in situ hybridization (FISH) were classified into five groups: (a) BCL2-R group (BCL2-R/BCL6-G/MYC-G) (G, germline), 77 cases; (b) BCL2/BCL6 double-R group (BCL2-R/BCL6-R/MYC-G), 16 cases; (c) BCL6-R group (BCL2-G/BCL6-R/MYC-G), 16 cases; (d) MYC-R group (BCL2-R or G/BCL6-R or G/MYC-R), three cases; (e) Triple-G group (BCL2-G/BCL6-G/MYC-G), 28 cases. The BCL6-R group had different clinicopathological characteristics. It showed lower rates of an advanced clinical stage and bone marrow invasion, less disease progression (p = 0.036), and a 'trend' toward a favorable progression-free survival (PFS) (p = 0.06). It also showed higher rates of grade 3A and MUM1-expression, and when analyzing the interfollicular spread pattern of CD20-positive cells, had fewer cases showing the IF3+ pattern (high interfollicular spread). Moreover, cases with BCL6-R and/or BCL6 gain (with cases of BCL2 rearrangement and/or of copy number gain excluded) correlated with favorable PFS (p = 0.014) and less IF3+ pattern (p = 0.007). We demonstrated that BCL6-R FLs showed unique clinicopathological findings, and FISH of BCL2, BCL6, and MYC is useful for FL diagnosis and clinical management.

Secreted phospholipase A2 modifies extracellular vesicles and accelerates B cell lymphoma.

Extracellular vesicles (EVs) including exosomes act as intercellular communicators by transferring protein and microRNA cargoes, yet the role of EV lipids remains unclear. Here, we show that the pro-tumorigenic action of lymphoma-derived EVs is augmented via secreted phospholipase A2 (sPLA2)-driven lipid metabolism. Hydrolysis of EV phospholipids by group X sPLA2, which was induced in macrophages of Epstein-Barr virus (EBV) lymphoma, increased the production of fatty acids, lysophospholipids, and their metabolites. sPLA2-treated EVs were smaller and self-aggregated, showed better uptake, and increased cytokine expression and lipid mediator signaling in tumor-associated macrophages. Pharmacological inhibition of endogenous sPLA2 suppressed lymphoma growth in EBV-infected humanized mice, while treatment with sPLA2-modified EVs reversed this phenotype. Furthermore, sPLA2 expression in human large B cell lymphomas inversely correlated with patient survival. Overall, the sPLA2-mediated EV modification promotes tumor development, highlighting a non-canonical mechanistic action of EVs as an extracellular hydrolytic platform of sPLA2.

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series.

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients' overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

Anti-HBV drug entecavir ameliorates DSS-induced colitis through PD-L1 induction.

PD-L1-mediated signaling is one of the major processes that regulate local inflammatory responses in the gut. To date, protective effects against colitis through direct Fc-fused PD-L1 administration or indirect PD-L1 induction by probiotics have been reported. We have previously shown that the anti-HBV drug entecavir (ETV) induces PD-L1 expression in human hepatocytes. In the present study, we investigated whether ETV induces PD-L1 expression in intestinal cells and provides a protective effect against DSS-induced colitis. ETV induced PD-L1 expression in epithelial cells, rather than T and B cells, improving the symptoms of colitis. In the mechanistic analysis, Th17 cell differentiation was inhibited and B cell infiltration into the lamina propria was reduced. In addition, PD-L1 expression was positively correlated with Foxp3 or CSF1-R. In conclusion, ETV upregulated PD-L1 expression in epithelial cells and ameliorated inflammation in DSS-induced colitis. These results suggest that ETV may be a potential therapeutic agent as a PD-L1 enhancer for the treatment of human IBD.

High PTX3 expression is associated with a poor prognosis in diffuse large B-cell lymphoma.

Tumor-associated macrophages (TAMs) are associated with a poor prognosis of diffuse large B-cell lymphoma (DLBCL). As macrophages are heterogeneous, the immune polarization and their pathological role warrant further study. We characterized the microenvironment of DLBCL by immunohistochemistry in a training set of 132 cases, which included 10 Epstein-Barr virus-encoded small RNA (EBER)-positive and five high-grade B-cell lymphomas, with gene expression profiling in a representative subset of 37 cases. Diffuse large B-cell lymphoma had a differential infiltration of TAMs. The high infiltration of CD68 (pan-macrophages), CD16 (M1-like), CD163, pentraxin 3 (PTX3), and interleukin (IL)-10-positive macrophages (M2c-like) and low infiltration of FOXP3-positive regulatory T lymphocytes (Tregs) correlated with poor survival. Activated B cell-like DLBCL was associated with high CD16, CD163, PTX3, and IL-10, and EBER-positive DLBCL with high CD163 and PTX3. Programmed cell death-ligand 1 positively correlated with CD16, CD163, IL-10, and RGS1. In a multivariate analysis of overall survival, PTX3 and International Prognostic Index were identified as the most relevant variables. The gene expression analysis showed upregulation of genes involved in innate and adaptive immune responses and macrophage and Toll-like receptor pathways in high PTX3 cases. The prognostic relevance of PTX3 was confirmed in a validation set of 159 cases. Finally, in a series from Europe and North America (GSE10846, R-CHOP-like treatment, n = 233) high gene expression of PTX3 correlated with poor survival, and moderately with CSF1R, CD16, MITF, CD163, MYC, and RGS1. Therefore, the high infiltration of M2c-like immune regulatory macrophages and low infiltration of FOXP3-positive Tregs is associated with a poor prognosis in DLBCL, for which PTX3 is a new prognostic biomarker.