Abstract
Chronic hepatitis B is now controllable when treated with nucleoside reverse transcriptase inhibitors (NRTIs), which inhibit hepatitis B virus (HBV) replication. However, once the NRTIs are ...discontinued, most patients relapse, necessitating lifelong NRTIs treatment. HBV infection relapse is assumed to be caused by the persistent existence of covalently closed circular DNA (cccDNA) in the nuclei of infected hepatocytes. The mechanism by which cccDNA-positive hepatocytes escape immune surveillance during NRTIs treatment remains elusive. Entecavir (ETV), a commonly used NRTI, post-transcriptionally up-regulates programmed cell death-ligand 1 (PD-L1), an immune checkpoint molecule, on the cell surface of hepatocytes regardless of HBV infection. Up-regulation by ETV depends on up-regulation of CKLF-like MARVEL transmembrane domain-containing 6, a newly identified potent regulator of PD-L1 expression on the cell surface. ETV-treated hepatic cells suppressed the activity of primary CD3 T cells and programmed cell death protein-1 (PD-1)-over-expressed Jurkat cells. Finally, ETV induces PD-L1 in primary hepatocytes infected by HBV. These results provide evidence that ETV considerably up-regulates PD-L1 on the cell surface of infected hepatocytes, which may be one of the mechanisms by which infected hepatocytes subvert immune surveillance.
A therapy for chronic HBV also induces hepatocyte PD-L1
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:
,
,
,
, and
that associated with a poor survival; and
,
,
,
, and
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,
= 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA,
= 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
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.
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.
This research focused on the analysis of several macrophage and regulatory T lymphocyte markers in diffuse large b‐cell lymphoma. We found that high PTX3 expression correlated with poor prognosis of the patients.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of malignant lymphoma. The incidence of Epstein–Barr virus (EBV)‐positive DLBCL in Asian and Latin American countries ranges from 8 to ...10%. The prognosis of patients with EBV‐positive DLBCL is controversial. To compare the clinical outcome of EBV‐positive and EBV‐negative patients with DLBCL in the rituximab era, we analyzed 239 patients with de novo DLBCL diagnosed between January 2007 and December 2011. The presence of EBV in lymphoma cells was detected using EBV‐encoded RNA in situ hybridization, and it was found that 18 (6.9%) of 260 patients with diagnosed DLBCL tested positive. Among the 260 cases, 216 cases were treated with rituximab plus chemotherapy, as were 8 EBV‐positive DLBCL patients. The median overall survival and progression‐free survival times in patients with EBV‐positive DLBCL were 8.7 months and 6.8 months, respectively. The median overall survival and progression‐free survival could not be determined in EBV‐negative DLBCL patients (P = 0.0002, P < 0.0001, respectively). The outcome of patients with EBV‐positive DLBCL remains poor, even in the rituximab era.
In comparison to EBV‐ NOS Diffuse Large B‐Cell Lymphoma, EBV‐positive DLBCL is significantly characterized by worsen and shorter overall survival and progression free survival.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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
,
,
,
(
),
(
),
,
(
),
,
,
, and
(
). 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.
Tumor microenvironment influences the behavior of follicular lymphoma (FL), although the specific cell subsets involved are not well known. The aim of this study was to determine the impact of ...programmed cell death 1 (PD-1) -positive inhibitory immunoregulatory lymphoid cells in the clinicobiologic features and outcome of patients with FL.
We examined samples from 100 patients (53 men and 47 women; median age, 54 years) at diagnosis, as well as in 32 patients at first relapse, with a recently generated monoclonal antibody against PD-1. The cells were quantified using computerized image analysis. Additional analysis consisted of double immunofluorescence and flow cytometry.
PD-1 expression was alternative to FOXP3 in lymphoid cells from both reactive tonsils and FL. At diagnosis, the median percentage of PD-1-positive cells was 14% (range, 0.1% to 74%). Patients with grade 3 FL, poor performance status, and high serum lactate dehydrogenase showed lower numbers of PD-1-positive cells. After a median follow-up of 6.2 years, patients with PD-1-positive cells <or= 5% (n = 25), 6% to 33% (n = 50), and more than 33% (n = 25) had a 5-year progression-free survival rate of 20%, 46%, and 48% (P = .038) and overall survival (OS) of 50%, 77%, and 95% (P = .004), respectively. PD-1 and FL International Prognostic Index maintained prognostic value for OS in multivariate analysis. Patients with PD-1-positive cells <or= 5% showed a higher risk of histologic transformation. At that time, transformed diffuse large B-cell lymphomas had lower percentage of PD-1-positive cells than FL.
A high content of PD-1-positive cells predicted favorable outcome of FL patients, whereas a marked reduction is observed in transformation.
Other iatrogenic immunodeficiency-associated lymphoproliferative disorders induced by immunosuppressive drugs, such as methotrexate (MTX-LPD), exhibit numerous pathological findings. We report the ...case of an 81-year-old Japanese woman diagnosed with MTX-LPD exhibiting two distinct pathological features from two different sites. Excisional biopsy of the left cervical lymph node revealed EBV-negative diffuse large B-cell lymphoma and biopsy of a pharyngeal ulcer revealed EBV-positive mucocutaneous ulcer. She was treated using an R-CHOP regimen and maintained complete remission for years. This case demonstrates the heterogeneous pathology of MTX-LPD and suggests the necessity of multiple biopsy.
Diffuse large B‐cell lymphoma with MYC rearrangement is defined as double/triple‐hit lymphoma (DHL/THL) or single‐hit lymphoma (SHL) by the inclusion of the BCL2 and BCL6 rearrangements status. ...DHL/THL is called as “high‐grade B‐cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements” in the World Health Organization 2017 Classification of Tumors of Hematopoietic and Lymphoid Tissues. To find a prognostic biomarker of DHL/THL, we firstly examined 19 cases (molecular analysis series;10 cases of DHL/THL and 9 cases of SHL) with gene expression profile analysis. The gene expression profile analysis showed that the high expression of AICDA was associated with an adverse prognosis in DHL/THL, but not in SHL. Then, we evaluated immunohistochemical expression of AID, the protein product of AICDA, in 50 cases (molecular analysis series of 19 cases and additional immunohistochemistry series of 31 cases; 12 cases of DHL/THL and 19 cases of SHL) and confirmed that its expression was also associated with an adverse prognosis in DHL/THL. Therefore, AICDA and AID can be a predictor of an adverse clinical outcome in DHL/THL and immunohistochemistry of AID is useful to find DHL/THL‐adverse prognosis group.
Full text
Available for:
BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Rheumatoid arthritis patients often develop the diffuse large B-cell lymphoma subtype of methotrexate-associated lymphoproliferative disorder (DLBCL). We characterized the genomic profile and ...pathologic characteristics of 20 biopsies using an integrative approach. DLBCL was associated with extranodal involvement, a high/high-intermediate international prognostic index in 53% of cases, and responded to MTX withdrawal. The phenotype was nongerminal center B-cell in 85% of samples and Epstein-Barr encoding region positive (EBER) in 65%, with a high proliferation index and intermediate MYC expression levels. The immune microenvironment showed high numbers of CD8 cytotoxic T lymphocytes and CD163 M2 macrophages with an (CD163/CD68) M2 ratio of 3.6. Its genomic profile was characterized by 3p12.1-q25.31, 6p25.3, 8q23.1-q24.3, and 12p13.33-q24.33 gains, 6q22.31-q24.1 and 13q21.33-q34 losses, and 1p36.11-p35.3 copy neutral loss-of-heterozygosity. This profile was closer to nongerminal center B-cell DLBCL not-otherwise-specified, but with characteristic 3q, 12q, and 20p gains and lower 9p losses (P<0.05). We successfully verified array results using fluorescent DNA in situ hybridization on PLOD2, MYC, WNT1, and BCL2. Protein immunohistochemistry revealed that DLBCL expressed high IRF4 (6p25.3) and SELPLG (12q24.11) levels, intermediate TNFRSF14 (1p36.32; the exons 1 to 3 were unmutated), BTLA (3q13.2), PLOD2 (3q24), KLHL6 (3q27.1), and MYC (8q24.21) levels, and low AICDA (12p13.31) and EFNB2 (13q33.3) levels. The correlation between the DNA copy number and protein immunohistochemistry was confirmed for BTLA, PLOD2, and EFNB2. The characteristics of EBER versus EBER cases were similar, with the exception of specific changesEBER cases had higher numbers of CD163 M2 macrophages and FOXP3 regulatory T lymphocytes, high programmed cell death 1 ligand 1 expression levels, slightly fewer genomic changes, and 3q and 4p focal gains. In conclusion, DLBCL has a characteristic genomic profile with 3q and 12 gains, 13q loss, different expression levels of relevant pathogenic biomarkers, and a microenvironment with high numbers of cytotoxic T lymphocytes and M2 macrophages.
Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent subtypes of non-Hodgkin lymphomas. We used artificial neural networks (multilayer perceptron and radial basis function), machine ...learning, and conventional bioinformatics to predict the overall survival and molecular subtypes of DLBCL. The series included 106 cases and 730 genes of a pancancer immune-oncology panel (nCounter) as predictors. The multilayer perceptron predicted the outcome with high accuracy, with an area under the curve (AUC) of 0.98, and ranked all the genes according to their importance. In a multivariate analysis,
,
,
, and
correlated with favorable survival (hazard risks: 0.3-0.5), and
,
, and
, with poor survival (hazard risks = 1.0-2.1). Gene set enrichment analysis (GSEA) showed enrichment toward poor prognosis. These high-risk genes were also associated with the gene expression of M2-like tumor-associated macrophages (
), and
expression. The prognostic relevance of this set of 7 genes was also confirmed within the IPI and
translocation strata, the EBER-negative cases, the DLBCL not-otherwise specified (NOS) (High-grade B-cell lymphoma with
and
and/or
rearrangements excluded), and an independent series of 414 cases of DLBCL in Europe and North America (GSE10846). The perceptron analysis also predicted molecular subtypes (based on the Lymph2Cx assay) with high accuracy (AUC = 1).
,
, and
were associated with the germinal center B-cell (GCB) subtype, and
,
,
, and
were associated with the activated B-cell (ABC)/unspecified subtype. The GSEA had a sinusoidal-like plot with association to both molecular subtypes, and immunohistochemistry analysis confirmed the correlation of
with the GCB subtype in another series of 96 cases (notably, MAPK3 also correlated with LMO2, but not with M2-like tumor-associated macrophage markers CD163, CSF1R, TNFAIP8, CASP8, PD-L1, PTX3, and IL-10). Finally, survival and molecular subtypes were successfully modeled using other machine learning techniques including logistic regression, discriminant analysis, SVM, CHAID, C5, C&R trees, KNN algorithm, and Bayesian network. In conclusion, prognoses and molecular subtypes were predicted with high accuracy using neural networks, and relevant genes were highlighted.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
PDF
