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

基本信息

  • 批准号:
    10446946
  • 负责人:
  • 金额:
    $ 35.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

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, 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.
项目总结

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Li Zhang其他文献

Ramanujan-type congruences for overpartitions modulo 3
模 3 过度划分的拉马努金型同余

Li Zhang的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Li Zhang', 18)}}的其他基金

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:
    10651683
  • 财政年份:
    2022
  • 资助金额:
    $ 35.24万
  • 项目类别:
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:
    10490312
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
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
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
CAMPO Data Management and Statistical Core
CAMPO 数据管理和统计核心
  • 批准号:
    10226226
  • 财政年份:
    2019
  • 资助金额:
    $ 35.24万
  • 项目类别:
CAMPO Data Management and Statistical Core
CAMPO 数据管理和统计核心
  • 批准号:
    10017232
  • 财政年份:
    2019
  • 资助金额:
    $ 35.24万
  • 项目类别:
CAMPO Data Management and Statistical Core
CAMPO 数据管理和统计核心
  • 批准号:
    10469359
  • 财政年份:
    2019
  • 资助金额:
    $ 35.24万
  • 项目类别:
Molecular Mechanism Governing Oxygen Signaling and Heme Regulation by Gis1
Gis1 控制氧信号传导和血红素调节的分子机制
  • 批准号:
    8770294
  • 财政年份:
    2014
  • 资助金额:
    $ 35.24万
  • 项目类别:
Molecular Mechanism Governing Oxygen Signaling and Heme Regulation by Gis1
Gis1 控制氧信号传导和血红素调节的分子机制
  • 批准号:
    9059941
  • 财政年份:
    2014
  • 资助金额:
    $ 35.24万
  • 项目类别:
Molecular Mechanism Governing Oxygen Signaling and Heme Regulation by Gis1
Gis1 控制氧信号传导和血红素调节的分子机制
  • 批准号:
    9072488
  • 财政年份:
    2014
  • 资助金额:
    $ 35.24万
  • 项目类别:
An Oxygen-Sensing Network Involving Heme and Chaperones
涉及血红素和伴侣的氧传感网络
  • 批准号:
    7901855
  • 财政年份:
    2009
  • 资助金额:
    $ 35.24万
  • 项目类别:

相似海外基金

Single-cell analysis of adaptive immune system cells in IBD patients
IBD 患者适应性免疫系统细胞的单细胞分析
  • 批准号:
    22KJ2212
  • 财政年份:
    2023
  • 资助金额:
    $ 35.24万
  • 项目类别:
    Grant-in-Aid for JSPS Fellows
Antigen presentation to the adaptive immune system in the choroid contributes to ocular autoimmune disease
脉络膜中的适应性免疫系统的抗原呈递导致眼部自身免疫性疾病
  • 批准号:
    10740465
  • 财政年份:
    2023
  • 资助金额:
    $ 35.24万
  • 项目类别:
Elucidation of the adaptive immune system in teleost fish
阐明硬骨鱼的适应性免疫系统
  • 批准号:
    22K05824
  • 财政年份:
    2022
  • 资助金额:
    $ 35.24万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Interaction of Galectin-9 and Pregnancy-Specific Glycoprotein 1 in the Regulation of Cells of the Innate and Adaptive Immune System
Galectin-9 和妊娠特异性糖蛋白 1 在先天性和适应性免疫系统细胞调节中的相互作用
  • 批准号:
    10434937
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
Peripheral Adaptive Immune System Changes Associated with Alzhiemer's Disease
与阿尔茨海默病相关的外周适应性免疫系统变化
  • 批准号:
    10194864
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
Interaction of Galectin-9 and Pregnancy-Specific Glycoprotein 1 in the Regulation of Cells of the Innate and Adaptive Immune System
Galectin-9 和妊娠特异性糖蛋白 1 在先天性和适应性免疫系统细胞调节中的相互作用
  • 批准号:
    10302501
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
Learning a molecular shape space for the adaptive immune system
学习适应性免疫系统的分子形状空间
  • 批准号:
    10275426
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
CAREER: Emergence of Functional Organization in the Adaptive Immune System
职业:适应性免疫系统中功能组织的出现
  • 批准号:
    2045054
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
    Continuing Grant
Learning a molecular shape space for the adaptive immune system
学习适应性免疫系统的分子形状空间
  • 批准号:
    10669709
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
Learning a molecular shape space for the adaptive immune system
学习适应性免疫系统的分子形状空间
  • 批准号:
    10467050
  • 财政年份:
    2021
  • 资助金额:
    $ 35.24万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了