Machine learning approaches for clinical diagnosis of autoimmune diseases

一些自身免疫性疾病的遗传风险因素与T细胞在疾病机制中的作用尚不完全清楚。T细胞受体（TCR）是由T细胞细胞核中基因片段重组而成的基因编码的。从个体T细胞中产生的各种各样的TCR库进化到与各种各样的威胁结合。T细胞活化是由TCR结合启动的，这导致克隆扩增。表达相同TCR的T细胞谱系包括参与主动免疫反应的细胞，以及一些持续激活免疫记忆的细胞。在自身免疫性疾病中，T细胞可能参与针对宿主自身组织或微生物群的免疫反应。下一代测序使大量的tcr库得以测序，这为更好地了解自身免疫性疾病提供了独特的机会。从一组TCR曲目样本中，可以通过解释机器学习分类模型来识别与条件相关的模式。然而，具有相同疾病的个体之间相同TCR的有限共享，以及独特TCR序列的大量样本，导致难以识别预测自身免疫性疾病状态的TCR库的特征。有前途的TCR库分类方法考虑了不相同TCR序列之间的关系。将TCR序列拆分为kmers的方法显示出与深度学习相当的高效性能。这项工作致力于研究增强基于kmer的TCR表表示的方法的效用。在整个过程中，使用真实的TCR曲目数据集对方法进行评估，这些数据集包括来自乳糜泻和炎症性肠病患者以及巨细胞病毒感染患者的样本。还模拟了TCR曲目，以指导方法开发。为了评估在TCR曲目表示中捕获kmers的相似性将提高通用性的假设，采用减少氨基酸字母表的新方法对替代方法进行基准测试，以揭示单独的属性通知kmers的有限效用。然而，根据乳糜泻状态对小肠中一种罕见的T细胞亚群的TCR谱进行分类时存在一个例外，这表明该方法可能存在合适的用例。接下来，一些kmer可能比其他kmer提供更多信息的概念导致了基于偏差的kmer过滤器的探索，这表明充分的正则化排除了过滤的需要。此外，发现基于似然的kmer计数归一化对真实TCR曲目数据中可能出现的不准确性很敏感。本文提出的方法可以提高某些TCR库分类模型的通用性，尽管这不能得出普遍结论。虽然结果表明有可能确定可能与自身免疫性疾病相关的TCR全库模式，但TCR全库分类方法的进一步发展需要与更先进的TCR全库测序技术相协调。了解自身免疫性疾病的潜在机制的能力也将依赖于实验验证。

Genetic risk factors for some autoimmune conditions implicate T cells in disease mechanisms that are incompletely understood. The T-cell receptor (TCR) is encoded by genes that are recombined from an assortment of gene segments in the nuclei of T cells. The vastly diverse TCR repertoire arising from an individual's T cells evolved to bind to a wide variety of threats. T cell activation is initiated by TCR binding, which leads to clonal expansion. A lineage of T cells expressing the same TCR includes cells that participate in an active immune response, and some that persist to enable immunological memory. In autoimmune disease, T cells may be involved in an immune response directed against the host's own tissues or microbiome. Next generation sequencing has enabled vast libraries of TCRs to be sequenced, which presents a unique opportunity to better understand autoimmune disease. From a set of TCR repertoire samples, patterns associated with a condition might be identifiable through interpretation of a machine learning classification model. However, the limited sharing of identical TCRs between individuals with the same condition, as well as the vast outnumbering of samples by unique TCR sequences, leads to difficulty identifying signatures of TCR repertoires that are predictive of autoimmune disease status. Promising TCR repertoire classification approaches consider relationships between non-identical TCR sequences. Methods that split TCR sequences into kmers demonstrate efficient performance that is comparable to deep learning. This work is dedicated to investigating the utility of methods that augment kmer-based representations of the TCR repertoire. Throughout, methodology is evaluated using real TCR repertoire datasets including samples from patients with coeliac disease and inflammatory bowel disease, as well as participants with cytomegalovirus infection. TCR repertoires are also simulated to guide methodological development. To assess the hypothesis that capturing similarity of kmers in a TCR repertoire representation will improve generalisability, a novel approach employing a reduced amino acid alphabet is benchmarked against alternatives to reveal the limited utility of property-informed kmers alone. However, one exception when classifying TCR repertoires from a rarer subset of T cells in the small intestine by coeliac disease status suggests that appropriate use cases may exist for the approach. Next, the notion that some kmers may be more informative than others leads to exploration of a deviation-based kmer filter, which indicates that adequate regularisation precludes the need for filtering. Further, a likelihood-based normalisation of kmer counts is found to be sensitive to inaccuracies that one might expect in real TCR repertoire data. Methodology presented in this thesis may improve generalisability of certain TCR repertoire classification models, though this cannot be concluded universally. While results demonstrate the potential to identify TCR repertoire patterns that might be associated with autoimmune disease, further development of TCR repertoire classification approaches is warranted in coordination with more advanced TCR repertoire sequencing techniques. The ability to gain insights into the underlying mechanisms of autoimmune disease will also rely on experimental validation.