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：
10305538
负责人：
Li Zhang
金额：
$ 20.13万
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 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10305538
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 clinically relevant computational pipelines feature selection flexibility high dimensionality improved network architecture next generation sequencing novel open source performance tests prognostic receptor responders and non-responders response single-cell RNA sequencing statistics tool transcriptome user-friendly

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 clinically relevant computational pipelines feature selection flexibility high dimensionality improved network architecture next generation sequencing novel open source performance tests prognostic receptor responders and non-responders response single-cell RNA sequencing statistics tool transcriptome user-friendly

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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.

项目摘要自适应免疫系统负责抗原的特定识别和消除起源于感染和疾病。它通过巨大的抗原结合抗体识别抗原（B细胞受体，BCR）和T细胞受体（TCRS），免疫曲目。由于宽度在免疫曲目识别的表位中，免疫曲目非常多样化和动态。免疫受体测序的进步（REP-SEQ），例如下一代测序，已驱动免疫表的定量和分子级分析，从而揭示了高维的免疫受体序列景观的复杂性。但是，当前的分析工具缺乏跟踪的能力并在串行时间点检查曲目的动态性质或确定共同特征在整个曲目中彻底有效。我们将开发具有高级的计算高效方法机器学习技术，包括网络分析，功能选择和分类以及高级统计方法，以纵向审问和测量免疫曲目建筑，以识别跨曲目的共同特征并评估其临床相关性。网络分析是一个强大的可以识别具有共享抗原特异性和高度可变BCR的TCR的方法，这可以帮助发展或改善现有的免疫疗法和免疫诊断。但是，网络构建是因此，计算上昂贵的，我们将制定一种自适应子采样策略来缓解计算负担。我们将对两项研究实施拟议的方法，以更好地说明数据以证明所提出的工具的灵活性和功能。此外，我们将发展生物信息学软件通过合并提出的方法和技术来应对的复杂性以转化方式rep-seq数据，并提供一个具有用户友好可视化的综合平台工具。