The pathobiology of follicular lymphoma Carreras, Joaquim
Journal of Clinical and Experimental Hematopathology,
01/2023, Volume:
63, Issue:
3
Journal Article
Peer reviewed
Open access
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.
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.
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.
Microenvironment contributes to follicular lymphoma (FL) pathogenesis and impacts survival with macrophages playing a controversial role. In the present study, using FL primary samples and HK ...follicular dendritic cells (FDC) to mimic the germinal center, together with mouse models, we have analyzed the three-way crosstalk of FL-FDC-macrophages and derived therapeutic opportunities. Ex vivo primary FL-FDC co-cultures (n = 19) and in vivo mouse co-xenografts demonstrated that FL-FDC crosstalk favors tumor growth and, via the secretion of CCL2 and CSF-1, promotes monocyte recruitment, differentiation, and polarization towards an M2-like protumoral phenotype. Moreover, FL-M2 co-cultures displayed enhanced angiogenesis, dissemination, and immunosuppression. Analysis of the CSF-1/CSF-1R pathway uncovered that CSF-1 was significantly higher in serum from grade 3A FL patients, and that high CSF-1R expression in FL biopsies correlated with grade 3A, reduced overall survival and risk of transformation. Furthermore, CSF-1R inhibition with pexidartinib (PLX3397) preferentially affected M2-macrophage viability and polarization program disrupting FL-M2 positive crosstalk. In vivo CSF1-R inhibition caused M2 reduction and repolarization towards M1 macrophages and antitumor effect cooperating with anti-CD20 rituximab. In summary, these results support the role of macrophages in FL pathogenesis and indicate that CSF-1R may be a relevant prognostic factor and a novel therapeutic target cooperating with anti-CD20 immunotherapy.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background: Diffuse large B-cell lymphoma (DLBCL) is one of the most frequent lymphomas. DLBCL is phenotypically, genetically, and clinically heterogeneous. Aim: We aim to identify new prognostic ...markers. Methods: We performed anomaly detection analysis, other artificial intelligence techniques, and conventional statistics using gene expression data of 414 patients from the Lymphoma/Leukemia Molecular Profiling Project (GSE10846), and immunohistochemistry in 10 reactive tonsils and 30 DLBCL cases. Results: First, an unsupervised anomaly detection analysis pinpointed outliers (anomalies) in the series, and 12 genes were identified: DPM2, TRAPPC1, HYAL2, TRIM35, NUDT18, TMEM219, CHCHD10, IGFBP7, LAMTOR2, ZNF688, UBL7, and RELB, which belonged to the apoptosis, MAPK, MTOR, and NF-kB pathways. Second, these 12 genes were used to predict overall survival using machine learning, artificial neural networks, and conventional statistics. In a multivariate Cox regression analysis, high expressions of HYAL2 and UBL7 were correlated with poor overall survival, whereas TRAPPC1, IGFBP7, and RELB were correlated with good overall survival (p < 0.01). As a single marker and only in RCHOP-like treated cases, the prognostic value of RELB was confirmed using GSEA analysis and Kaplan–Meier with log-rank test and validated in the TCGA and GSE57611 datasets. Anomaly detection analysis was successfully tested in the GSE31312 and GSE117556 datasets. Using immunohistochemistry, RELB was positive in B-lymphocytes and macrophage/dendritic-like cells, and correlation with HLA DP-DR, SIRPA, CD85A (LILRB3), PD-L1, MARCO, and TOX was explored. Conclusions: Anomaly detection and other bioinformatic techniques successfully predicted the prognosis of DLBCL, and high RELB was associated with a favorable prognosis.
Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease ...treatment, analyze medical images, evaluate clinical trials, and speed drug development. Methods: First, relevant aspects of AI are revised in a comprehensive manner, including the classification of hematopoietic neoplasms, types of AI, applications in medicine and hematological neoplasia, generative pre-trained transformers (GPTs), and the architecture and interpretation of feedforward neural net-works (multilayer perceptron). Second, a series of 233 diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab-CHOP from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) was analyzed. Results: Using conventional statistics, the high expression of MYC and BCL2 was associated with poor survival, but high BCL6 was associated with a favorable overall survival of the patients. Then, a neural network predicted MYC, BCL2, and BCL6 with high accuracy using a pan-cancer panel of 758 genes of immuno-oncology and translational research that includes clinically relevant actionable genes and pathways. A comparable analysis was performed using gene set enrichment analysis (GSEA). Conclusions: The mathematical way in which neural networks reach conclusions has been considered a black box, but a careful understanding and evaluation of the architectural design allows us to interpret the results logically. In diffuse large B-cell lymphoma, neural networks are a plausible data analysis approach.
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.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In classical Hodgkin lymphoma (cHL)-characterized by the presence of Hodgkin and Reed-Sternberg (HRS) cells-tumor-associated macrophages (TAMs) play a pivotal role in tumor formation. However, the ...significance of direct contact between HRS cells and TAMs has not been elucidated. HRS cells and TAMs are known to express PD-L1, which leads to PD-1
CD4
T cell exhaustion in cHL. Here, we found that PD-L1/L2 expression was elevated in monocytes co-cultured with HRS cells within 1 h, but not in monocytes cultured with supernatants of HRS cells. Immunofluorescence analysis of PD-L1/L2 revealed that their upregulation resulted in membrane transfer called "trogocytosis" from HRS cells to monocytes. PD-L1/L2 upregulation was not observed in monocytes co-cultured with PD-L1/L2-deficient HRS cells, validating the hypothesis that there is a direct transfer of PD-L1/L2 from HRS cells to monocytes. In the patients, both ligands (PD-L1/L2) were upregulated in TAMs in contact with HRS cells, but not in TAMs distant from HRS cells, suggesting that trogocytosis occurs in cHL patients. Taken together, trogocytosis may be one of the mechanisms that induces rapid upregulation of PD-L1/L2 in monocytes to evade antitumor immunity through the suppression of T cells as mediated by MHC antigen presentation.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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.
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•sPLA2-X is induced in EBV-induced B cell lymphoma in humanized mice•sPLA2-X hydrolyzes EV membranes to increase lipid mediator cargo•sPLA2-X alters the morphology and function of EVs•sPLA2-X facilitates EBV lymphomagenesis via a lipid-driven non-canonical mechanism
EVs act as intercellular communicators by transferring miRNAs and proteins. Kudo et al. find the importance of EV lipids in EBV lymphoma development. Hydrolysis of phospholipids in tumor-cell-derived EVs by sPLA2-X increases vesicle aggregation, produces immunoregulatory lipid mediators, and facilitates EV uptake by recipient macrophages, thereby exacerbating lymphomagenesis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases ...of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.
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