Computational approaches to unravel immune receptor sequencing for cancer immunotherapy

项目总结 适应性免疫系统负责识别和消除抗原。 源于感染和疾病。它通过大量的抗原结合抗体识别抗原。 (B细胞受体，BCR)和T细胞受体(TCR)，这是免疫系统。因为它的广度很大 在免疫谱系识别的表位中，免疫谱系极其多样和动态。 免疫受体测序(Rep-seq)的进步，如下一代测序，推动了 免疫谱系的定量和分子水平的分析，从而揭示了高维 免疫受体序列格局的复杂性。然而，当前的分析工具缺乏跟踪能力 并检查连续时间点上曲目的动态性质或确定共同特征 彻底而高效地跨越曲目。我们将开发计算效率高的方法和先进的 机器学习技术，包括网络分析、特征选择和分类，以及高级 统计方法，纵向询问和测量免疫谱系体系结构，以识别 所有曲目的共同特征，并评估其临床相关性。网络分析是一种强大的 一种可以识别具有抗原特异性和高度突变的bcr的TCR的方法，有助于开发 或者改进现有的免疫疗法和免疫诊断学。然而，网络建设是 因此，我们将开发一种自适应亚采样策略来减少计算量 负担。我们将在两项研究中实施建议的方法，以更好地说明 这些数据证明了拟议工具的灵活性和威力。此外，我们还将发展 生物信息学软件，通过结合所建议的方法和技术来处理复杂的 REP-SEQ数据以翻译方式提供，并提供具有用户友好的可视化的综合平台 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.