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Analysis reveals an immune metric for chronic hepatitis B, suggesting potential predictive capacity post-treatment.
Description Chronic hepatitis B (CHB) infection is a global public health challenge that affects nearly 300 million persons worldwide, with a functional cure rate less than 7%. Although studies have shown that immune response played a crucial role in achieving the functional cure of CHB, a unified immune metric is lacking. Here, using scRNA-seq and T cell and B cell receptor sequencing on peripheral immune cells from matched CHB patients before and longitudinally during 48-week peg-IFN treatment, we were powered within each individual to discover immune metric that vary by time and cell subset. Beyond previously obscured individual- and cell-type specific inflammatory-related gene upregulation, we describe temporally aligned interferon-stimulated related subsets across different immune cell types using hdWGCNA. Unsupervised or supervised machine learning identified a score that distinguished CHB patients with functional cure and those without functional cure, thus suggesting a quantitative immune metric of functional cure for patients with CHB. This discriminatory power goes beyond that of the classical biomarkers HBsAg. We further identify that interferon-stimulated immune metric arising in the first quarter of therapy, which may potentially early predict HBsAg loss. Overall, our study provides a high-resolution single-cell transcriptomic atlas revealing the dynamics of immune cells in achieving CHB functional cure, which could improve clinical management of CHB.
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Liu et al. (2025) studied this question.
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