用未成熟T细胞受体数据推断特定受体对疾病的影响。
Estimating the Causal Effects of T Cell Receptors
- 利用未成熟TCR作为自然实验，校正环境等混杂因素。
- 发现某些TCR能显著降低新冠重症风险且体外结合病毒抗原。
- 适合免疫学研究者和精准治疗开发者参考。
人类免疫学的核心问题之一是患者T细胞谱系如何影响疾病进程。本文提出一种方法，基于观测的TCR谱系测序数据与临床结果，推断TCR序列对患者预后的因果效应。该方法通过患者未成熟、未选择前的TCR谱系校正未观测混杂因素（如环境与生活史），而该谱系可从广泛存在的非功能性TCR数据中估算。由于V(D)J重组为随机突变过程，构成天然实验。我们形式化推导出因果推断方法，并开发了可扩展的神经网络估计器以实现识别公式。该方法可估计向患者谱系引入特定TCR序列的干预效应。以新冠重症为例，识别出三类特征：(1) 在患者中被观察到；(2) 体外结合SARS-CoV-2抗原；(3) 对临床结局具有强正向效应的潜在治疗性TCR。
原文摘要 · Abstract (English)
A central question in human immunology is how a patient's repertoire of T cells impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR repertoire sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient's environment and life history, by using the patient's immature, pre-selection TCR repertoire. The pre-selection repertoire can be estimated from nonproductive TCR data, which is widely available. It is generated by a randomized mutational process, V(D)J recombination, which provides a natural experiment. We show formally how to use the pre-selection repertoire to draw causal inferences, and develop a scalable neural-network estimator for our identification formula. Our method produces an estimate of the effect of interventions that add a specific TCR sequence to patient repertoires. As a demonstration, we use it to analyze the effects of TCRs on COVID-19 severity, uncovering potentially therapeutic TCRs that are (1) observed in patients, (2) bind SARS-CoV-2 antigens in vitro and (3) have strong positive effects on clinical outcomes.
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