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

项目摘要 基因组编辑技术作为新的基因组药物具有非凡的潜力， 人类疾病的遗传原因;然而，预测其长期安全性仍然具有挑战性，因为我们 不知道基因组编辑的潜在副作用的后果，如脱靶突变，或 免疫原性我们的长期目标是了解和预测这种意想不到的生物效应， 开发安全有效的治疗方法。T细胞是理想的细胞模型，因为：1）它们高度 作为开发治疗性基因组编辑策略（例如基于细胞的HIV和癌症治疗）的最广泛使用的细胞，2）成熟T细胞编码不同的T细胞受体库， 可用作内置细胞条形码，用于量化响应特定病毒的克隆扩增或耗尽 治疗。因此，我们提出了以下具体目标：1）预测哪些非预期编辑网站具有 通过整合大规模全基因组活性和表观基因组谱， 最先进的深度学习模型; 2）开发人类初级T细胞平台，以检测功能性 通过测量克隆代表性，脱靶突变频率，免疫原性， 或基因表达。如果成功，我们的实验和预测框架将大大增加信心 下一代有前途的基因组编辑疗法的安全性。

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.