Statistical Methods for T Cell Receptor (TCR) Analysis

项目摘要/摘要 受试者的T细胞受体(TCR)谱就像一本有数百万条记录的巨书，每条记录 作为TCR的记录。其中一些记录是由随机过程生成的，不包含任何临床上的 相关信息。然而，其他一些记录编码了免疫相关疾病的当前或过去的病史 或接触病原体。对一本TCR书籍的准确解码为我们提供了关于 相应受试者的健康记录。这种信息可以通过一次抽血收集， 比对不同疾病或暴露进行单独测试要方便得多，因此是理想的 作为对大量人口的筛查或监测工具。此外，T细胞对检测甚至非常敏感 少量的抗原和T细胞记忆在最初的免疫反应后持续多年。这些功能 使TCR曲目成为构建许多免疫相关疾病生物标记物的有吸引力的来源。 影响TCR曲目中TCR的一个重要因素是人类白细胞抗原(人类白细胞抗原) 相应的主题。每个人最多有16个独特的HLA等位基因，这些等位基因是数千个HLA等位基因的一部分 在人类人口中。忽视人类白细胞抗原信息会导致基于TCR的生物标记物的准确性降低， 尤其是对于具有相对罕见的HLA的受试者。然而，以前的工作忽略了HLA，因为 它们是高度多态的。我们提出了一个结合了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，以满足这一关键的未得到满足的需求。我们的方法提供人类白细胞抗原- TCR和疾病状态之间的特定关联，并使用TCR预测受试者的疾病状态 同时条件作用于她/他的人类白细胞抗原等位基因。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，尽管我们的方法是通用的，它们可以应用于许多其他研究 引起T细胞反应的疾病/状况。

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.