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.

项目摘要 适应性免疫系统负责特异性识别和消除来自免疫系统的抗原。 免受感染和疾病它通过大量的抗原结合抗体（B细胞 受体（BCR）和T细胞受体（TCR），即免疫库。由于其巨大的广度， 由于免疫库识别的表位不同，免疫库是极其多样和动态的。进展 在免疫受体测序（Rep-seq），如下一代测序，已经推动了定量的 和免疫库的分子水平分析，从而揭示了免疫系统的高维复杂性。 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， 库在连续时间点的动态性质或与临床结果相关。我们建议 使用网络分析，并制定了一种新的集成特征选择方法，沿着与其他 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），以纵向询问和测量免疫库结构 and in a clinical临床context上下文.网络分析是一种强有力的方法，可以帮助我们识别共享抗原的TCR 特异性和高度可变的BCR，这可以帮助开发或改善现有的免疫治疗剂， 免疫诊断学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 应用多表混合隶属度方法构建网络，以提高T的分辨率， B细胞簇。此外，我们通过引入贝叶斯因子来评估共享聚类的重要性， 结合克隆产生概率和真实的数据丰度。B和T细胞反应平行发展 并相互影响，因此我们将进一步研究BCR/TCR网络特性如何相互作用，除了 分别评估他们的个人反应。我们将在多项研究中实施拟议的方法， 更好地说明数据的多样性和丰富性，以展示所提出的 工具.这些研究是独特的和可推广的，因为它们包括三种癌症类型， 免疫原性到非免疫原性在转移性和局部环境中具有不同的 免疫模式。此外，所提出的方法可用于研究免疫反应， 癌症以外的疾病，包括呼吸道冠状病毒，如SARS-CoV-2。因此，首先， 研究前列腺癌患者在连续时间点的批量Rep-seq变化情况， 接受了Sipuleucel-T和COVID-19患者。我们将开发基于网络的预测模型 Durvalumab治疗肺癌患者的临床结局/特征，以阐明 该网络的临床预后特征也将SARS-CoV-2感染患者与健康供体分类。 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 食管癌和胃食管癌免疫治疗（CD 40激动剂抗体）对T和B细胞反应 结癌患者。此外，我们将开发生物信息学软件， 以翻译方式处理免疫测序数据的复杂性的方法和技术， 提供了一个全面的平台，具有用户友好的可视化工具。