Introduction: The introduction of all-oral direct-acting antiviral (DAA) treatment has revolutionized care of patients with chronic hepatitis C virus (HCV) infection. Regrettably, the high cost of DAA treatment is burdensome for healthcare systems and may be prohibitive for some patients who would otherwise benefit. Understanding how patient-related factors influence individual responses to DAA treatment may lead to more efficient prescribing.
Materials and Methods: In this observational study, patients with chronic HCV infection were comprehensively monitored by flow cytometry to identify pre-treatment immunological variables that predicted HCV RNA negativity within 4 weeks of commencing DAA treatment. Patients were classified as “fast responders” if HCV RNA <12 IU/ml by 4 weeks, or “slow responders” if HCV RNA ≥12 IU/ml at 4 weeks, irrespective of the later outcome. Twenty-three patients (genotype 1a (n=10), 1b (n=9) and 3 (n=4) were treated with daclatasvir plus sofosbuvir (n=15), ledipasvir plus sofosbuvir (n=4) or ritonavir-boosted paritaprevir, ombitasvir, and dasabuvir (n=4).
Results and Discussion: Univariate analyses were performed to determine which immunological parameters affected by DAA treatment were most significantly associated with fast or slow responder status. Frequencies of CD3+ T cells with respect to CD45+ leucocytes (Figure.1A) and CCR7+ CD45RA+ naïve CD8+ T cells with respect to CD8+ T cells (Figure.1B) were then entered into a binary logistic regression model as the two most significant and biologically non-redundant independent variables (Table 1A). Receiver operator characteristic (ROC) curve analysis (Area=0.909; S.E.=0.61) was used to determine a cut-off value of the predicted probabilities that maximized both sensitivity (75.0%) and specificity (91.0%) (Figure.1C). Using a cut-off value of 0.66, pre-treatment frequencies of CD3+ T cells and CCR7+ CD45RA+ naïve CD8+ T cells correctly classified 82.6 % of patients as fast or slow responders (compared to 52.2% with no model, i.e. classifying all patients as fast responders) in our study cohort (Table 1B). Notably, the 95% confidence intervals (CI) of the Odds Ratios were narrow and the model was a good fit (Nagelkerke R-squared = 0.623). In a prospective cohort, these same parameters correctly classified 90% of patients with an F-score of 0.67, which indicates reasonable test accuracy. Slow responders exhibited higher frequencies of CD3+ T cells, CD8+ TEM cells, and CD5high CD27- CD57+ CD8+ chronically activated T cells, which may be attributed to bystander hyper-activation of virus-nonspecific CD8+ T cells.
Conclusion: Non-specific, systemic CD8+ T cell activation predicted a longer time-to-viral clearance. This discovery enables pre-treatment identification of individuals who may not require a full course of DAA therapy; in turn, this could lead to individualized prescribing and more efficient resource allocation, especially in patients listed for liver transplantation.