Use of machine learning to identify a T cell response to SARS-CoV-2

Shoukat, M. S.; Foers, A. D.; Woodmansey, S.; Evans, S. C.; Fowler, A.; Soilleux, E.

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyse publicly available data from SARS-CoV-2-recovered patients who had low severity disease (n=17) and SARS-CoV-2 infection-naïve (control) individuals (n=39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/ control samples with a training sensitivity, specificity and accuracy of 88.2%, 100%, and 96.4%, and a testing sensitivity, specificity and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer-lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.