Machine learning approaches for clinical diagnosis of autoimmune diseases

一些自身免疫疾病的遗传风险因素使T细胞与疾病机制有关，但目前尚不完全清楚。T细胞受体(TCR)是由T细胞细胞核中各种基因片段重组而成的基因编码。从个体的T细胞产生的极其多样化的TCR谱系进化成与各种各样的威胁相结合。T细胞的活化是由TCR结合启动的，从而导致克隆性增殖。表达相同TCR的T细胞谱系包括参与主动免疫反应的细胞，以及一些持续支持免疫记忆的细胞。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组的免疫反应。下一代测序已经使大量的TCR文库得以测序，这为更好地了解自身免疫性疾病提供了一个独特的机会。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联的模式。然而，具有相同疾病的个体之间相同的TCR的共享有限，以及独特的TCR序列的样本数量远远多于样本，导致很难识别预测自身免疫性疾病状态的TCR谱系的签名。有希望的TCR曲目分类方法考虑了不同TCR序列之间的关系。将TCR序列分割成Kmer的方法展示了与深度学习相媲美的高效性能。这项工作致力于调查增强TCR曲目的基于Kmer的表示的方法的实用性。在整个过程中，使用真实的TCR曲目数据集对方法学进行评估，包括来自腹部疾病和炎症性肠病患者的样本，以及巨细胞病毒感染的参与者的样本。还模拟了TCR曲目，以指导方法学的发展。为了评估在TCR曲目表示中捕获Kmer的相似性将提高通用性的假设，将一种使用简化氨基酸字母表的新方法与备选方案进行比较，以揭示仅有属性信息的Kmer的有限效用。然而，当从小肠中较罕见的T细胞亚群中根据乳糜泻状态对TCR谱系进行分类时，一个例外情况表明，该方法可能存在适当的用例。其次，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，这表明适当的正则化排除了过滤的需要。此外，Kmer计数的基于可能性的归一化被发现对实际TCR曲目数据中可能预期的不准确很敏感。本文提出的方法可能会提高某些TCR曲目分类模型的泛化能力，尽管这不是普遍的结论。虽然结果表明有可能识别可能与自身免疫性疾病相关的TCR谱系模式，但进一步开发TCR谱系分类方法与更先进的TCR谱系测序技术是必要的。洞察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.