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

• 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
• 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

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.

项目概要 适应性免疫系统负责特异性识别和消除抗原 源于感染和疾病。它通过大量抗原结合抗体识别抗原 （B 细胞受体，BCR）和 T 细胞受体（TCR），免疫库。由于广度巨大 免疫组库识别的表位极其多样且动态。 免疫受体测序 (Rep-seq) 的进步，例如下一代测序，推动了 对免疫库进行定量和分子水平分析，从而揭示高维 免疫受体序列景观的复杂性。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识别共同特征 彻底有效地跨越剧目。我们将开发具有先进计算效率的方法 机器学习技术，包括网络分析、特征选择和分类以及高级 统计方法，纵向询问和测量免疫库结构，以确定 跨曲目的共同特征并评估其临床相关性。网络分析很强大 可以识别具有抗原特异性和高度可变的 BCR 的 TCR 的方法，这有助于开发 或改进现有的免疫治疗和免疫诊断。但网络建设 计算量大，因此，我们将开发一种自适应子采样策略来减轻计算量 负担。我们将在两项研究中实施所提出的方法，以更好地说明研究的多样性和丰富性 数据来证明所提议工具的灵活性和强大功能。此外，我们还将开发 生物信息学软件通过结合所提出的方法和技术来解决复杂性 以翻译方式进行 Rep-seq 数据，并提供具有用户友好可视化的综合平台 工具。