Computational approaches to unravel immune receptor sequencing for cancer immunotherapy

项目摘要 适应性免疫系统负责抗原的特异性识别和消除 源于感染和疾病。它通过大量的抗原结合抗体识别抗原 细胞受体（B细胞受体，BCR）和T细胞受体（TCR），即免疫库。由于其巨大的宽度 在免疫库识别的表位中，免疫库是极其多样和动态的。 免疫受体测序（Rep-seq）的进展，如下一代测序，已经推动了免疫系统的发展。 免疫库的定量和分子水平分析，从而揭示了高维 免疫受体序列景观的复杂性。然而，目前的分析工具缺乏跟踪能力， 并检查跨序列时间点的剧目的动态性质，或者识别共同特征， 彻底而有效地进行交流我们将开发计算效率高的方法， 机器学习技术，包括网络分析，特征选择和分类，以及高级 统计学方法，纵向询问和测量免疫库结构， 所有库的共同特征，并评估其临床相关性。网络分析是一种强大的 这种方法可以识别具有抗原特异性的TCR和高度可变的BCR，这有助于开发 或改进现有的免疫治疗和免疫诊断。网络建设是 计算昂贵，因此，我们将开发一种自适应子采样策略，以减轻计算 负担我们将在两项研究中实施所提出的方法，以更好地说明 数据来证明所提出的工具的灵活性和功能。此外，我们将开发 生物信息学软件，结合提出的方法和技术，以解决复杂的 Rep-seq数据以一种平移的方式，并提供一个全面的平台，用户友好的可视化 工具.

PROJECT SUMMARY The adaptive immune system is responsible for the specific recognition and elimination of antigens originating from infection and disease. It recognizes antigens via an immense array of antigen-binding antibodies (B-cell receptors, BCRs) and T-cell receptors (TCRs), the immune repertoire. Because of the enormous breadth of epitopes recognized by immune repertoires, immune repertoires are extremely diverse and dynamic. Advances in immune receptor sequencing (Rep-seq), such as next generation sequencing, have driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. However, current analysis tools lack the ability to track and examine the dynamic nature of the repertoire across serial time points or to identify the common features across repertoires thoroughly and efficiently. We will develop computationally efficient methods with advanced machine learning techniques, including network analysis, feature selection and classification, and advanced statistical approaches, to interrogate and measure immune repertoire architecture longitudinally, to identify common features across repertoires and to assess their clinical relevance. Network analysis is a powerful approach that can identify TCRs sharing antigen specificity and highly mutable BCR, which can help to develop or improve existing immunotherapeutics and immunodiagnostics. However, network construction is computationally expensive, therefore, we will develop an adaptive subsampling strategy to relieve computation burden. We will implement the proposed methods on two studies to better illustrate the diversity and richness of the data to demonstrate the flexibility and power of the proposed tools. Furthermore, we will develop bioinformatics software by incorporating the proposed methods and techniques to tackle the complexity of the Rep-seq data in a translational fashion and provide a comprehensive platform with user-friendly visualization tools.