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A novel human T-cell platform to define biological effects of genome editing

一种新型人类 T 细胞平台，用于定义基因组编辑的生物学效应

基本信息

批准号：
10016298
负责人：
Shengdar Tsai
金额：
$ 61.07万
依托单位：
ST. JUDE CHILDREN'S RESEARCH HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-12 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10016298
关键词：
Address Advanced Development Adverse effects Affect Artificial Intelligence Bar Codes Benign Biochemical Bioinformatics Biological Biological Assay Cell model Cell physiology Cells Chromatin Clonal Expansion Complex Coupled DNA Methylation Detection Engineering Epitopes Frequencies Gene Expression Genes Genetic Genetic Diseases Genetic Transcription Genetic Variation Genomic medicine Genomics Goals HIV Human Human Genetics Human Genome Immunologic Deficiency Syndromes In Vitro Inherited Malignant Neoplasms Maps Mature T-Lymphocyte Measures Methods Modeling Mutation Oncogenic Organizational Change Outcome Peptide Library Peripheral Blood Mononuclear Cell Phenotype Population Proto-Oncogenes Regulatory Element Retroviral Vector Ribonucleoproteins Safety Site Site-Directed Mutagenesis Standardization Streptococcus pyogenes T cell response T-Cell Proliferation T-Cell Receptor T-Lymphocyte T-cell receptor repertoire Technology Testing Therapeutic Training Variant adaptive immune response adverse outcome base comparative genomics cytokine deep learning effective therapy epigenomics functional genomics gene therapy genome editing genome-wide genotoxicity histone modification human disease immunogenic immunogenicity improved in vivo machine learning method next generation novel novel therapeutics off-target mutation off-target site response safety assessment safety testing side effect therapeutic development therapeutic gene therapeutic genome editing transcriptome sequencing

项目摘要

PROJECT SUMMARY Genome editing technologies have extraordinary potential as new genomic medicines that address underlying genetic causes of human disease; however, it remains challenging to predict their long-term safety, because we do not know the consequences of potential side effects of genome editing such as off-target mutations or immunogenicity. Our long-term goal is to understand and predict such unintended biological effects to advance the development of safe and effective therapies. T-cells are an ideal cellular model because: 1) they are highly relevant as the most widely used cells for development of therapeutic genome editing strategies (such as cell-based treatments for HIV and cancer) and 2) mature T-cells encode a diverse T-cell receptor repertoire that can be exploited as built-in cellular barcodes for quantifying clonal expansion or depletion in response to specific treatments. We, therefore, propose the following specific aims: 1) to predict which unintended editing sites have biological effects on human T-cells by integrating large-scale genome-wide activity and epigenomic profiles with state-of-the-art deep learning models and 2) to develop a human primary T-cell platform to detect functional effects of genome editing by measuring clonal representation, off-target mutation frequencies, immunogenicity, or gene expression. If successful, our experimental and predictive framework will profoundly increase confidence in the safety of the next generation of promising genome editing therapies.

项目总结