Molecular Pathogenesis of MALT lymphoma

The original dissertation included a number or articles which were excluded from the digital file for copyright reasons. This is a list of the articles:1) Hamoudi RA, Appert A, Ye H, Ruskone-Fourmestraux A, Streubel B, Chott A, Raderer M, Gong L, Wlodarska I, De Wolf-Peeters C, MacLennan KA, de Leval L, Isaacson PG, & Du MQ. Differential expression of NF-kappaB target genes in MALT lymphoma with and without chromosome translocation: insights into molecular mechanism. Leukemia. 2010 Aug;24(8):1487-1497 http://www.ncbi.nlm.nih.gov/pubmed/205206402) Ye H, Gong L, Liu H, Hamoudi RA, Shirali S, Ho L, Chott A, Streubel B, Siebert R, Gesk S, Martin-Subero JI, Radford JA, Banerjee S, Nicholson AG, Ranaldi R, Remstein ED, Gao Z, Zheng J, Isaacson PG, Dogan A & Du MQ. MALT lymphoma with t(14;18)(q32;q21)/IGH-MALT1 is characterized by strong cytoplasmic MALT1 and BCL10 expression. J Pathol. 2005 Feb;205(3):293-301. http://www.ncbi.nlm.nih.gov/pubmed/156824433) Liu H, Hamoudi RA, Ye H, Ruskone-Fourmestraux A, Dogan A, Isaacson PG & Du MQ. t(11;18)(q21;q21) of mucosa-associated lymphoid tissue lymphoma results from illegitimate non-homologous end joining following double strand breaks. Br J Haematol. 2004 May;125(3):318-329. http://www.ncbi.nlm.nih.gov/pubmed/150864124) Liu H, Ye H, Ruskone-Fourmestraux A, De Jong D, Pileri S, Thiede C, Lavergne A, Boot H, Caletti G, Wündisch T, Molina T, Taal BG, Elena S, Thomas T, Zinzani PL, Neubauer A, Stolte M, Hamoudi RA, Dogan A, Isaacson PG & Du MQ. T(11;18) is a marker for all stage gastric MALT lymphomas that will not respond to H. pylori eradication. Gastroenterology. 2002 May;122(5):1286-1294. http://www.ncbi.nlm.nih.gov/pubmed?term=119845155) Liu H, Ye H, Dogan A, Ranaldi R, Hamoudi RA, Bearzi I, Isaacson PG & Du MQ. T(11;18)(q21;q21) is associated with advanced mucosa-associated lymphoid tissue lymphoma that expresses nuclear BCL10. Blood. 2001 Aug 15;98(4):1182-1187. http://www.ncbi.nlm.nih.gov/pubmed?term=114934686) Liu H, Ruskon-Fourmestraux A, Lavergne-Slove A, Ye H, Molina T, Bouhnik Y, Hamoudi RA, Diss TC, Dogan A, Megraud F, Rambaud JC, Du MQ & Isaacson PG. Resistance of t(11;18) positive gastric mucosa-associated lymphoid tissue lymphoma to Helicobacter pylori eradication therapy. Lancet. 2001 Jan 6;357(9249):39-40. http://www.ncbi.nlm.nih.gov/pubmed?term=11197361Mucosa associated lymphoid tissue (MALT) lymphoma is characterized by t(11;18)(q21;q21)/API2-MALT1, t(1;14)(p22;q32)/BCL10-IGH and t(14;18)(q32;q21)/IGH-MALT1, which commonly activate the NF-κB pathway. Gastric MALT lymphomas harbouring such translocation do not respond to Helicobacter pylori eradication, while those without translocation can be cured by antibiotics. To understand the molecular mechanism of MALT lymphoma with and without chromosome translocation, 24 cases (15 translocation-positive and 9 translocation-negative) of MALT lymphomas together with 7 follicular lymphomas and 7 mantle cell lymphomas were analysed by Affymetrix gene expression microarray platform. Unsupervised clustering showed that cases of MALT lymphoma were clustered as a single branch. However, within the MALT lymphoma group, translocation-positive cases were intermingled with translocation-negative cases. Gene set enrichment analysis (GSEA) of the NF-κB target genes and 4394 additional gene sets covering various cellular pathways, biological processes and molecular functions showed that translocation-positive MALT lymphomas were characterized by an enhanced expression of NF-κB target genes, particularly TLR6, CCR2, CD69 and BCL2, while translocation-negative cases were featured by active inflammatory and immune responses, such as IL8, CD86, CD28 and ICOS. Separate analyses of the genes differentially expressed between translocation-positive and negative cases and measurement of gene ontology term in these differentially expressed genes by hypergeometric test reinforced the above findings by GSEA. The differential expression of these NF-κB target genes between MALT lymphoma with and without translocation was confirmed by quantitative RT-PCR and immunohistochemistry or Western blot. Expression of TLR6, in the presence of TLR2, enhanced both API2-MALT1 and BCL10 mediated NF-κB activation in vitro. In addition, there was cooperation between expression of BCL10, MALT1 or API2-MALT1, and stimulation of the antigen receptor or CD40 or TLR in NF-κB activation as shown by both reporter assay and IκBα degradation. Interestingly, expression of BCL10 but not API2-MALT1 and MALT1, in the presence of LPS stimulation, also triggered IκBβ degradation, suggesting activation of different NF-κB dimers between these oncogenic products. Study by co-immunoprecipitation showed that BCL10 directly interacts with MALT1. Sub-cellular localisation experiments in BJAB B-cells, showed that BCL10 localisation was affected by MALT1. When BCL10 was over-expressed, the protein was predominantly expressed in the nuclei, but when MALT1 was over-expressed, BCL10 was mainly localised in the cytoplasm. When both BCL10 and MALT1 were over-expressed, BCL10 was expressed in the cytoplasm in the early hours when the protein level was low, but in both the cytoplasm and nuclei after 9 hours when the protein level was high. Over-expression of API2-MALT1 did not shown any apparent effect on BCL10 sub-cellular localisation in vitro. Finally, comparison of MALT lymphoma expression microarray with other lymphomas showed lactoferrin to be highly expressed in MALT lymphoma. This was confirmed by qRT-PCR, showing lactoferrin to be significantly over-expressed in MALT lymphoma compared to FL and MCL. Thus lactoferrin may be a potential marker for MALT lymphoma

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

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

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

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

In silico Analysis of Publicly Available Transcriptomics Data Identifies Putative Prognostic and Therapeutic Molecular Targets for Papillary Thyroid Carcinoma

Background: Thyroid cancer is the most common endocrine malignancy. However, the molecular mechanism involved in its pathogenesis is not well characterized. Purpose: The objective of this study is to identify key cellular pathways and differentially expressed genes along the thyroid cancer pathogenesis sequence as well as to identify potential prognostic and therapeutic targets. Methods: Publicly available transcriptomics data comprising a total of 95 samples consisting of 41 normal, 28 non-aggressive and 26 metastatic papillary thyroid carcinoma (PTC) cases were used. Transcriptomics data were normalized and filtered identifying 9394 differentially expressed genes. The genes identified were subjected to pathway analysis using absGSEA identifying PTC related pathways. Three of the genes identified were validated on 508 thyroid cancer biopsies using RNAseq and TNMplot. Results: Pathway analysis revealed a total of 2193 differential pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. Pathways for non-aggressive PTC include calcium and potassium ion transport, hormone signaling, protein tyrosine phosphatase activity and protein tyrosine kinase activity. Metastatic pathways include growth, apoptosis, activation of MAPK and regulation of serine threonine kinase activity. Genes for non-aggressive are KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B and metastatic PTC are EGFR, PTK2B, KCNN4 and BCL2. Three of the genes identified were validated using clinical biopsies showing significant overexpression in aggressive compared to non-aggressive PTC; EGFR (p < 0.05), KCNN4 (p < 0.001) and PTK2B (p < 0.001). DrugBank database search identified several FDA approved drug targets including anti-EGFR Vandetanib used to treat thyroid cancer in addition to others that may prove useful in treating PTC. Conclusion: Transcriptomics analysis identified putative prognostic targets including EGFR, PTK2B, BCL2, KCNQ1, KCNN4 and CACNA1D. EGFR, PTK2B and KCN44 were validated using thyroid cancer clinical biopsies. The drug search identified FDA approved drugs including Vandetanib in addition to others that may prove useful in treating the disease

Identification of p53-target genes in human papillomavirus-associated head and neck cancer by integrative bioinformatics analysis

IntroductionHead and neck cancer (HNC) is a highly prevalent and heterogeneous malignancy. Although extensive efforts have been made to advance its treatment, the prognosis remained poor with increased mortality. Human papillomaviruses (HPV) have been associated with high risk in HNC. TP53, a tumor suppressor, is the most frequently altered gene in HNC, therefore, investigating its target genes for the identification of novel biomarkers or therapeutic targets in HPV-related HNC progression is highly recommended.MethodsTranscriptomic profiles from three independent gene expression omnibus (GEO) datasets, including 44 HPV+ and 70 HPV- HNC patients, were subjected to integrative statistical and Bioinformatics analyses. For the top-selected marker, further in-silico validation in TCGA and GTEx databases and experimental validation in 65 (51 HPV- and 14 HPV+) subjects with histologically confirmed head and neck squamous cell carcinoma (HNSCC) have been performed.ResultsA total of 498 differentially expressed genes (DEGs) were identified including 291 up-regulated genes and 207 down-regulated genes in HPV+ compared to HPV- HNSCC patients. Functional annotations and gene set enrichment analysis (GSEA) showed that the up-regulated genes were significantly involved in p53-related pathways. The integrative analysis between the Hub-genes identified in the complex protein-protein network and the top frequent genes resulting from GSEA showed an intriguing correlation with five biomarkers which are EZH2, MDM2, PCNA, STAT5A and TYMS. Importantly, the MDM2 gene showed the highest gene expression difference between HPV+ and HPV- HNSCC (Average log2FC = 1.89). Further in-silico validation in a large HNSCC cohort from TCGA and GTEx databases confirmed the over-expression of MDM2 in HPV+ compared to HPV- HNSCC patients (p = 2.39E-05). IHC scoring showed that MDM2 protein expression was significantly higher in HPV+ compared to HPV- HNSCC patients (p = 0.031).DiscussionOur findings showed evidence that over-expression of MDM2, proto-oncogene, may affect the occurrence and proliferation of HPV-associated HNSCC by disturbing the p53-target genes and consequently the p53-related pathways

Loss of miR-101-3p Promotes Transmigration of Metastatic Breast Cancer Cells through the Brain Endothelium by Inducing COX-2/MMP1 Signaling

Brain metastases represent one of the incurable end stages in breast cancer (BC). Developing effective or preventive treatments is hampered by a lack of knowledge on the molecular mechanisms driving brain metastasis. Transmigration of BC cells through the brain endothelium is a key event in the pathogenesis of brain metastasis. In this study, we identified miR-101-3p as a critical micro-RNA able to reduce transmigration of BC cells through the brain endothelium. Our results revealed that miR-101-3p expression is downregulated in brain metastatic BC cells compared to less invasive variants, and varies inversely compared to the brain metastatic propensity of BC cells. Using a loss-and-gain of function approach, we found that miR-101-3p downregulation increased transmigration of BC cells through the brain endothelium in vitro by inducing COX-2 expression in cancer cells, whereas ectopic restoration of miR-101-3p exerted a metastasis-reducing effect. In regulatory experiments, we found that miR-101-3p mediated its effect by modulating COX-2-MMP1 signaling capable of degrading the inter-endothelial junctions (claudin-5 and VE-cadherin), key components of the brain endothelium. These findings suggest that miR-101-3p plays a critical role in the transmigration of breast cancer cells through the brain endothelium by modulating the COX-2-MMP1 signaling and thus may serve as a therapeutic target that can be exploited to prevent or suppress brain metastasis in human breast cancer

miR-623 Targets Metalloproteinase-1 and Attenuates Extravasation of Brain Metastatic Triple-Negative Breast Cancer Cells

BACKGROUND: Most breast cancer-related deaths result from metastasis. Understanding the molecular basis of metastasis is needed for the development of effective targeted and preventive strategies. Matrix metalloproteinase-1 (MMP1) plays an important role in brain metastasis (BM) of triple-negative breast cancer (TNBC) by promoting extravasation of cancer cells across the brain endothelium (BE). MMP1 expression is controlled by endogenous microRNAs. Preliminary bioinformatics analysis has revealed that miR-623, known to target the 3ʹUTR of MMP1, is significantly downregulated in brain metastatic tumors compared to primary BC tumors. However, the involvement of miR-623 in MMP1 upregulation in breast cancer brain metastatic cells (BCBMC) remains unexplored. Here, we investigated the role of miR-623 in MMP1 regulation and its impact on the extravasation of TNBC cells through the BE in vitro. MATERIALS AND METHODS: A loss-and-gain of function method was employed to address the effect of miR-623 modulation on MMP1 expression. MMP1 regulation by miR-623 was investigated by real-time PCR, western blot, luciferase and transwell migration assays using an in vitro human BE model. RESULTS: Our results confirmed that brain metastatic TNBC cells express lower levels of miR-623 compared with cells having low propensity to spread toward the brain. miR-623 binds to the 3′-untranslated region of MMP1 transcript and downregulates its expression. Restoring miR-623 expression significantly decreased MMP1 expression, preserved the endothelial barrier integrity, and attenuated transmigration of BCBMC through the BE. CONCLUSION: Our study elucidates, for the first time, the crucial role of miR-623 as MMP1 direct regulator in BCBMC and sheds light on miR-623 as a novel therapeutic target that can be exploited to predict and prevent brain metastasis in TNBC. Importantly, the presents study helps in unraveling a brain metastasis-specific microRNA signature in TNBC that can be used as a guide to personalized metastasis prediction and preventive approach with better therapeutic outcome

Transcriptomic Changes Associated with ERBB2 Overexpression in Colorectal Cancer Implicate a Potential Role of the Wnt Signaling Pathway in Tumorigenesis

Colorectal cancer (CRC) remains the third most common cause of cancer mortality worldwide. Precision medicine using OMICs guided by transcriptomic profiling has improved disease diagnosis and prognosis by identifying many CRC targets. One such target that has been actively pursued is an erbb2 receptor tyrosine kinase 2 (ERBB2) (Human Epidermal Growth Factor Receptor 2 (HER2)), which is overexpressed in around 3–5% of patients with CRC worldwide. Despite targeted therapies against HER2 showing significant improvement in disease outcomes in multiple clinical trials, to date, no HER2-based treatment has been clinically approved for CRC. In this study we performed whole transcriptome ribonucleic acid (RNA) sequencing on 11 HER2+ and 3 HER2− CRC patients with advanced stages II, III and IV of the disease. In addition, transcriptomic profiling was carried out on CRC cell lines (HCT116 and HT29) and normal colon cell lines (CCD841 and CCD33), ectopically overexpressing ERBB2. Our analysis revealed transcriptomic changes involving many genes in both CRC cell lines overexpressing ERBB2 and in HER2+ patients, compared to normal colon cell lines and HER2− patients, respectively. Gene Set Enrichment Analysis indicated a role for HER2 in regulating CRC pathogenesis, with Wnt/β-catenin signaling being mediated via a HER2-dependent regulatory pathway impacting expression of the homeobox gene NK2 homeobox 5 (NKX2-5). Results from this study thus identified putative targets that are co-expressed with HER2 in CRC warranting further investigation into their role in CRC pathogenesis