SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database.
Fig: Associations of TCR diversity and abundance metrics to disease severity. (a) TCR-Seq diversity and abundance metrics were collected and stratified by disease severity in both the COVID-19-ISB and COVID-19-NIH/NIAID cohorts. (Mann–Whitney rank test: ***p val < 0.001, **p val < 0.01, *p val < 0.05, with multiple hypothesis testing with Benjamini/Hochberg correction, αα = 0.05). (b) Uni-variate logistic regression models were fit on all TCR-Seq sample-level measures and performance was assessed via receiving operating characteristic (ROC) curves and calculating area under the curve (AUC) with 2-fold cross-validation with 100 iterations, averaging predictions across all iterations and folds. (c) Multi-variate logistic regression models were fit on all TCR-Seq sample-level measures and performance was assessed in the same manner as previously described in (b).
We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.
Sidhom, JW., Baras, A.S. Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires. Sci Rep 11, 14275 (2021). https://doi.org/10.1038/s41598-021-93608-8