Estimating the Causal Effects of T Cell Receptors

• URL: http://arxiv.org/abs/2410.14127v1
• Date: Fri, 18 Oct 2024 02:45:14 GMT
• Title: Estimating the Causal Effects of T Cell Receptors
• Authors: Eli N. Weinstein, Elizabeth B. Wood, David M. Blei,
• Abstract summary: We introduce a method to infer the causal effects of T cell receptor sequences on patient outcomes. Our approach corrects for unobserved confounders, such as a patient's environment and life history. As a demonstration, we use it to analyze the effects of TCRs on COVID-19 severity.
• Score: 20.01390828400336
• License:
• Abstract: 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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