Publication Details

AFRICAN RESEARCH NEXUS

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medicine

Patient-specific genetic factors predict treatment failure in sofosbuvir-treated patients with chronic hepatitis C

Liver International, Volume 42, No. 4, Year 2022

Background & Aims: According to pivotal clinical trials, cure rates for sofosbuvir-based antiviral therapy exceed 96%. Treatment failure is usually assumed to be because of virological resistance-associated substitutions or clinical risk factors, yet the role of patient-specific genetic factors has not been well explored. We determined if patient-specific genetic factors help predict patients likely to fail sofosbuvir treatment in real-world treatment situations. Methods: We recruited sofosbuvir-treated patients with chronic hepatitis C from five Canadian treatment sites, and performed a case-control pharmacogenomics study assessing both previously published and novel genetic polymorphisms. Specifically studied were variants predicted to impair CES1-dependent production of sofosbuvir’s active metabolite, interferon-λ signalling variants expected to impact a patient’s immune response to the virus and an HLA variant associated with increased spontaneous and treatment-induced viral clearance. Results: Three hundred and fifty-nine sofosbuvir-treated patients were available for analyses after exclusions, with 34 (9.5%) failing treatment. We identified CES1 variants as novel predictors for treatment failure in European patients (rs115629050 or rs4513095; odds ratio (OR): 5.43; 95% confidence interval (CI): 1.64-18.01; P =.0057), replicated associations with IFNL4 variants predicted to increase interferon-λ signalling (eg rs12979860; OR: 2.25; 95% CI: 1.25-4.06; P =.0071) and discovered a novel association with a coding variant predicted to enhance the activity of IFNL4's receptor (rs2834167 in IL10RB; OR: 1.81; 95% CI: 1.01-3.24; P =.047). Conclusions: Ultimately, this work demonstrates that patient-specific genetic factors could be used as a tool to identify patients at higher risk of treatment failure and allow for these patients to receive effective therapy sooner.

Statistics
Citations: 20
Authors: 20
Affiliations: 11
Identifiers
Research Areas
Genetics And Genomics
Health System And Policy
Infectious Diseases
Study Design
Case-Control Study