Investigation of the landscape of immunosequencing and its clinical relevance through novel immunoinformatic approaches

通过新型免疫信息学方法研究免疫测序的前景及其临床相关性

• 批准号: 10446946
• 负责人: Li Zhang
• 金额: $ 35.24万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10446946
• 关键词: 2019-nCoV; Adaptive Immune System; Agonist; Antibodies; Antigens; Architecture; B cell repertoire; B-Cell Antigen Receptor; B-Lymphocytes; Bar Codes; Bioinformatics; Blood; COVID-19 patient; Cancer Patient; Cells; Characteristics; Clinical; Computational Technique; Computer Analysis; Computer software; Coronavirus; Custom; Data; Development; Disease; Environment; Epitopes; Esophagogastric Junction; Esophagus; Evaluation; Evolution; Future; Gene Expression; Generations; Genetic Heterogeneity; Goals; Growth; Immune; Immune response; Immunodiagnostics; Immunoglobulins; Immunologic Receptors; Immunotherapeutic agent; Immunotherapy; Infection; Investigation; Joints; Lead; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Measures; Methods; Modality; Modeling; Molecular; Nature; Outcome; Pathway Analysis; Pattern; Probability; Process; Property; Provenge; Pythons; Resolution; Role; Specificity; Specimen; Statistical Methods; T cell response; T-Cell Receptor; T-Lymphocyte; TNFRSF5 gene; Techniques; Time; Tumor Immunity; Virus Diseases; Visualization; Visualization software; adaptive immune response; analysis pipeline; analytical tool; antigen antibody binding; base; bioinformatics tool; biomarker discovery; cancer immunotherapy; cancer type; cell type; clinical prognostic; clinically relevant; feature selection; flexibility; genetic signature; high dimensionality; immunogenic; improved; individual response; network architecture; next generation sequencing; novel; open source; predictive modeling; prognostic; public repository; receptor; respiratory; responders and non-responders; response; single-cell RNA sequencing; tool; transcriptome; tumor; user-friendly; vaccine discovery

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, the current analysis tools lack the ability to track and examine the dynamic nature of the repertoire across serial time points or correlate with clinical outcomes. We propose to use network analysis and formulate a novel ensemble feature selection approach, along with other advanced machine learning techniques and statistical approaches (e.g., Bayesian nonparametric approach and shrinkage estimation method), to interrogate and measure immune repertoire architecture longitudinally and in a clinical context. Network analysis is a powerful approach that can help us identify TCRs sharing antigen specificity and highly mutable BCR, which can help to develop or improve existing immunotherapeutics and immunodiagnostics. To integrate gene expression data and scRep-seq data in single-cell setting, we propose to apply the multitable mixed-membership approach to construct a network to increase the resolution of T and B cell clusters. In addition, we assess the importance of shared clusters by introducing Bayes factor to incorporate clonal generation probability and real data abundance. B and T cell responses develop in parallel and influence one another, thus we will further study how BCR/TCR network properties interact, in addition to assessing their individual response separately. We will implement the proposed methods on multiple studies to better illustrate the diversity and richness of the data to demonstrate the flexibility and power of the proposed tools. These studies are unique and generalizable, because they include three cancer types spanning from immunogenic to non-immunogenic in both metastatic and localized settings with different immunotherapeutic modalities. In addition, the proposed methods can be used to study immune response to diseases besides cancer, including respiratory coronaviruses, such as SARS-CoV-2. Therefore, first, we will investigate the landscape of bulk Rep-seq changes over serial timepoints for prostate cancer patients who received Sipuleucel-T and COVID-19 patients. We will develop prognostic/prediction model based on network properties with clinical outcome/characteristics for durvalumab-treated lung cancer patients to elucidate the clinically prognostic features of the network as well classify SARS-CoV-2 infected patients from healthy donors. Moreover, based on unique features of single-cell RNA sequencing, we will classify the immune cells and study the T and B cell responses to immunotherapy (CD40 agonist antibody) for esophageal and gastroesophageal junction cancer patients. Furthermore, we will develop bioinformatics software by incorporating the proposed methods and techniques to tackle the complexity of the immunosequencing 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源自抗原的抗原。 来自感染和疾病。它通过大量抗原结合抗体（B 细胞 受体（BCR）和 T 细胞受体（TCR），免疫库。由于其巨大的广度 免疫组库识别的表位，免疫组库极其多样化且动态。进展 免疫受体测序（Rep-seq），例如下一代测序，推动了定量研究 和免疫库的分子水平分析，从而揭示免疫组库的高维复杂性 免疫受体序列景观。然而，目前的分析工具缺乏跟踪和检查的能力 一系列时间点上的曲目的动态性质或与临床结果相关。我们建议 使用网络分析并制定一种新颖的集成特征选择方法以及其他方法 先进的机器学习技术和统计方法（例如贝叶斯非参数方法 和收缩估计方法），纵向询问和测量免疫库结构 并在临床背景下。网络分析是一种强大的方法，可以帮助我们识别共享抗原的 TCR 特异性和高度可变的 BCR，有助于开发或改进现有的免疫疗法和 免疫诊断。为了在单细胞环境中整合基因表达数据和 scRep-seq 数据，我们建议 应用多表混合成员方法构建网络以提高 T 和 B 细胞簇。此外，我们通过引入贝叶斯因子来评估共享集群的重要性 结合克隆生成概率和真实数据丰度。 B 细胞和 T 细胞反应并行发展 并相互影响，因此我们将进一步研究BCR/TCR网络属性如何相互作用，除了 分别评估他们的个人反应。我们将在多项研究中实施所提出的方法，以 更好地说明数据的多样性和丰富性，以展示所提出的灵活性和力量 工具。这些研究是独特且具有普遍意义的，因为它们包括三种癌症类型： 在转移性和局部环境中具有不同的免疫原性和非免疫原性 免疫治疗方式。此外，所提出的方法可用于研究免疫反应 除癌症以外的疾病，包括呼吸道冠状病毒，例如 SARS-CoV-2。因此，首先，我们将 研究前列腺癌患者在连续时间点上的批量 Rep-seq 变化情况 接收了 Sipuleucel-T 和 COVID-19 患者。我们将开发基于网络的预测/预测模型 与 durvalumab 治疗的肺癌患者的临床结果/特征相关的特性，以阐明 该网络的临床预后特征还可将 SARS-CoV-2 感染患者与健康捐赠者进行分类。 此外，基于单细胞RNA测序的独特特征，我们将对免疫细胞进行分类并研究 T 细胞和 B 细胞对食管和胃食管免疫治疗（CD40 激动剂抗体）的反应 交界癌患者。此外，我们将通过整合拟议的生物信息学软件来开发生物信息学软件 以翻译方式解决免疫测序数据复杂性的方法和技术 提供具有用户友好的可视化工具的综合平台。