权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Computational approaches to unravel immune receptor sequencing for cancer immunotherapy

揭示癌症免疫治疗免疫受体测序的计算方法

基本信息

批准号：
10490312
负责人：
Li Zhang
金额：
$ 18.32万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-17 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10490312
关键词：
Adaptive Immune System Algorithms Amino Acid Motifs Antigens Architecture B cell repertoire B-Cell Antigen Receptor B-Lymphocytes Bioinformatics Cells Classification Clinical Computational Technique Computer software Custom Data Development Disease Environment Epitopes Future Genetic Heterogeneity Goals Graph Immune Immune response Immunodiagnostics Immunoglobulins Immunologic Receptors Immunotherapeutic agent Immunotherapy Infection Lead Machine Learning Malignant Neoplasms Measures Metadata Methods Modeling Molecular Nature Network-based Outcome Pathway Analysis Pattern Probability Procedures Process Role Sampling Specificity Statistical Methods T-Cell Receptor T-Lymphocyte Techniques Time Tumor Immunity Visualization Visualization software adaptive immune response analysis pipeline antigen antibody binding base bioinformatics tool biomarker discovery cancer immunotherapy clinical prognostic clinically relevant computational pipelines feature selection flexibility high dimensionality improved network architecture next generation sequencing novel open source performance tests receptor responders and non-responders response single-cell RNA sequencing statistics tool transcriptome user-friendly

项目摘要

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.

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.