Statistical Methods for T Cell Receptor (TCR) Analysis

T 细胞受体 (TCR) 分析的统计方法

• 批准号: 10620574
• 负责人: Wei Sun
• 金额: $ 33万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-23 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10620574
• 关键词: 2019-nCoV; Accounting; Affect; Affinity; Alleles; Alzheimer' s Disease; Antibodies; Antigens; Area; Autoimmune Diseases; Biological Markers; Blood; Books; COVID-19; Case-Control Studies; Cell surface; Cells; Characteristics; Clinical; Collection; Communicable Diseases; Computer software; Cytomegalovirus; Cytomegalovirus Infections; Data; Disease; Ethical Issues; Exposure to; Genes; Genotype; HLA Antigens; Human; Immune; Immune response; Immune system; Infection; Malignant Neoplasms; Measures; Mediating; Methods; Modeling; Monitor; Nomenclature; Outcome; Pattern; Population; Population Heterogeneity; Process; Proteins; Pythons; ROC Curve; Recording of previous events; Records; Resources; SARS-CoV-2 infection; Sample Size; Sampling; Screening procedure; Sensitivity and Specificity; Source; Statistical Methods; Statistical Models; T cell response; T memory cell; T-Cell Receptor; T-Lymphocyte; T-cell receptor repertoire; Techniques; Testing; Training; Virus; Virus Diseases; Weight; Work; base; burden of illness; clinically relevant; computerized tools; conditioning; diagnostic tool; disorder risk; health record; improved; neural network; open source; pathogen; prognostic; rare variant; screening; tool

Project Summary/Abstract The T cell receptor (TCR) repertoire of a subject resembles a huge book with millions of records, with each record being a TCR. Some of these records are generated by a random process and do not contain any clinically relevant information. Some other records, however, encode current or past history of immune-related diseases or exposure to pathogens. Accurate decoding of a TCR book gives us immensely valuable information on the health record of the corresponding subject. Such information can be collected through a single blood draw, which is much more convenient than conducting separate tests for different diseases or exposures, and thus is ideal as a screening or monitoring tool for a large population. In addition, T cell is very sensitive to detect even a very small amount of antigen and T cell memory lasts many years after the initial immune response. These features make the TCR repertoire an attractive source for constructing biomarkers for many immune-related diseases. An important factor that affects the TCRs in a TCR repertoire is the Human Leukocyte Antigen (HLA) of the corresponding subject. Each human has up to 16 unique HLA alleles that are part of thousands of HLA alleles in the human population. Ignoring HLA information leads to reduced accuracy of TCR-based biomarkers, particularly for the subjects with relatively rare HLAs. However, previous works have ignored the HLAs because they are highly polymorphic. We propose a statistical framework that combines powerful computational tools such as neural network with rigorous statistical models to fill this critical unmet need. Our methods deliver HLA- specific associations between TCRs and disease status and use TCRs to predict the disease status of a subject while conditioning on her/his HLA alleles. We will evaluate our methods by making prediction on the infection by cytomegalovirus or SARS-COV-2, though our methods are general, and they can be applied to study many other diseases/conditions that induce T cell response.

项目摘要/摘要 主题的T细胞受体（TCR）曲目类似于一本具有数百万张记录的巨大书籍 记录是TCR。其中一些记录是由随机过程生成的，不包含任何临床 相关信息。但是，其他一些记录编码了与免疫相关疾病的当前或过去史 或暴露于病原体。 TCR书的准确解码为我们提供了有关该书的宝贵信息 相应主题的健康记录。这些信息可以通过一次抽血来收集 比对不同疾病或暴露的单独测试更方便，因此是理想的 作为大量人群的筛选或监视工具。另外，T细胞甚至非常敏感 初始免疫反应后，少量的抗原和T细胞记忆持续多年。这些功能 使TCR曲目成为为许多免疫相关疾病构建生物标志物的有吸引力的来源。 影响TCR库中TCR的一个重要因素是人类白细胞抗原（HLA） 相应的主题。每个人都有多达16个独特的HLA等位基因，这些等位基因是成千上万的HLA等位基因的一部分 在人口中。忽略HLA信息会导致基于TCR的生物标志物的准确性降低， 特别是对于患有相对罕见的HLA的受试者。但是，以前的作品忽略了HLA，因为 它们是高度多态的。我们提出了一个结合强大的计算工具的统计框架 例如具有严格统计模型的神经网络，以满足这种关键的未满足需求。我们的方法提供了HLA- TCR和疾病状况之间的具体关联，并使用TCR来预测受试者的疾病状态 在她/他的HLA等位基因的情况下进行调节。我们将通过对感染进行预测来评估我们的方法 巨细胞病毒或SARS-COV-2，尽管我们的方法是一般的，并且可以应用许多其他 诱导T细胞反应的疾病/疾病。