Integrative network modeling of regulatory modules in Large Granular Lymphocyte Leukemia

大颗粒淋巴细胞白血病调节模块的综合网络建模

• 批准号: 10163135
• 负责人: Jeffrey Chunlong Xing
• 金额: $ 4.91万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10163135
• 关键词: Address; Affect; Antibodies; Autoimmune; Autoimmune Diseases; Binding; Binding Sites; Biocompatible Materials; Biological; Bone Marrow; Bone Marrow Diseases; Cells; Chronic; Clinical; Complex; Computer Models; Computing Methodologies; Cytotoxic T-Lymphocytes; DNA; DNA Methylation; DNA Modification Methylases; DNA Sequence Alteration; Data; Development; Disease; Disease Progression; Drug Targeting; Ensure; Epigenetic Process; Etiology; Feedback; Follow-Up Studies; Gene Expression; Gene Expression Regulation; Gene Silencing; Genes; Genome; Goals; HDAC1 gene; HDAC4 gene; Hematological Disease; Hematopoietic; Hematopoietic Neoplasms; Hepatomegaly; Histone-Lysine N-Methyltransferase; Hypermethylation; Immune response; In Vitro; Inflammation; Inflammatory; Intervention; Invaded; Joints; Knowledge; Laboratory Research; Large granular lymphocyte; Lead; Leukemic Cell; Leukocytes; Literature; Liver; Malignant Neoplasms; Mathematics; Measures; Mediating; MicroRNAs; Modeling; Modification; Mutation; Natural Killer Cells; Network-based; Pathogenesis; Pathologic; Pathway interactions; Patients; Pharmacology; Play; Prevalence; RNA; Regulator Genes; Research Personnel; Role; STAT3 gene; Sampling; Scientist; Signal Transduction; Spleen; Splenomegaly; Testing; Therapeutic; Therapeutic immunosuppression; base; biological systems; chronic T-cell leukemia; clinical practice; computational network modeling; cytokine; design; effective therapy; epigenetic therapy; epigenome; experience; extracellular; genome sequencing; insight; leukemia; malignant breast neoplasm; mathematical model; mutant; network models; patient response; predictive modeling; protein expression; success; transcription factor; treatment strategy; tumor; whole genome

The broad long term objectives of this project are to elucidate the regulatory mechanisms contributing to the pathogenesis of cancer. By understanding how and why cancers are dysregulated, we hope to develop better therapeutics and design more effective treatment strategies. Our lab studies a blood cancer called large granular lymphocyte (LGL) leukemia. Currently it is thought that leukemic LGL cells accumulate activating genetic mutations in the context of pathologically persistent inflammation. These activating mutations, acting in concert with inflammatory signaling, lead to chronic proliferation and expansion of these LGL cells. This then contributes to clinical burden in the form of decreased white blood cells, enlarged spleen or liver, bone marrow disorders, and/or autoimmune manifestations. There is currently no reliable cure and most patients are managed on lifelong immunosuppressive therapy. Therefore, we hope to pursue mechanistic studies that address this unmet need. Often, cancers have inappropriate activation of master gene regulators such as transcription factors (TF). Like many other cancers, LGL Leukemia cells have hyper-activation of a TF called STAT3, with 30-40% of patients carrying an activating mutation in this gene. Because many patients do not have a STAT3 mutation, and because STAT3 activation can be part of a healthy immune response, we reasoned that there may be other factors leading to inappropriate leukemic proliferation. Therefore, we propose to dissect the regulatory mechanisms at play in LGL Leukemia. In this study, we will characterize changes that occur in the genome at multiple regulatory levels and correlate those changes with gene expression programming (Aim 1). This will help us identify regulatory modules important in this disease, as well as contrast functional differences between patient samples in a personalized way. In addition, we will integrate a computational model of the salient gene signaling network based on our experimental findings and existing scientific knowledge (Aim 2). By leveraging this network model, we expect to identify key nodes or motifs in the regulatory network that are important for disease progression, and therefore, identify critical targets for drug intervention. If successful, the findings from this project are expected to contribute to new treatment strategies in LGL leukemia and more broadly, to our understanding of cancer dysregulation.

该项目的长期目标是阐明监管机制