Estimating the Causal Effects of T Cell Receptors

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

Abstract:A central question in human immunology is how a patient's T cell receptors impacts disease. Here, we introduce a method to infer the causal effects of T cell receptor (TCR) sequences on patient outcomes using observational TCR sequencing data and clinical outcomes data. Our approach corrects for unobserved confounders, such as a patient's environment and life history, using the patient's pre-selection TCR repertoire. This pre-selection repertoire is generated by a programmed stochastic recombination process, V(D)J recombination, which provides a natural experiment. We first derive a causal identification result that leverages biological theory to semiparametrically constrain a hierarchical causal model, enabling causal inference. We then develop a causal estimation strategy that uses permutation invariant neural networks and representation learning to scale to millions of sequences from hundreds of patients. Given sequence data, our method produces an estimate of the effect of interventions that add a specific TCR sequence to patient repertoires. On semisynthetic data, we demonstrate that our method can correct for unobserved confounding. 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.

Machine Learning (stat.ML); Machine Learning (cs.LG); Genomics (q-bio.GN)

arXiv:2410.14127 [stat.ML]

(or arXiv:2410.14127v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2410.14127 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.