Abstract
Chronic Hepatitis B Virus infections affect 3.6% of the worldwide population and result in liver-related death in over
700000 people annually. Current standard of care, consisting of nucleos(t)ide analogues (NA), efficiently suppress
viral replication, prevent liver disease, but do not cure the infection, defined as Hepatitis B surface antigen (HBsAg)
loss. Lifelong NA treatment is therefore often required. However, in some patients with long term viral suppression,
treatment withdrawal may lead to persistent off-treatment viral control and an upto 40%
chance of HBsAg loss after 5 years. Obviously, identifying upfront which patients will benefit from a treatment
cessation is of utmost importance. We are currently coordinating the COIN-B study, a multicentric national study in
which 90 patients stop their antiviral treatment, and 50 continue. The design of this study allows for the evaluation
of baseline host factors, such as ethnicity, on off-treatment outcomes, but not the effects of viral flares or
retreatment. To this end, we will combine the data of the COIN-B study with several international prospective NA
withdrawal studies with intensive monitoring schedules and retreatment criteria. We will apply and refine advanced
statistical modelling to evaluate rare events, such as off-treatment HBsAg loss. This will enable a full picture of the
different viral and host factors associated with viral control or functional cure (HBsAg loss) after NA withdrawal.
This will further establish the optimal monitoring interval, retreatment indications and role of flares. Apart from
statistical modelling, we will apply bio-informatics approaches to investigate gene signatures in blood of COIN-B
patients. The goal of this substudy is to find a genetic biomarker able to select patients upfront for a NA treatment
cessation. In addition, this will learn how the blood transcriptome is modulated during off-treatment responses and
yield insight in the pathogenesis of flares and viral control. The combination of both approaches will ultimately lead
to the optimal predictive model of clinical parameters and gene signatures that will guide future treatment decisions
in the vast number of NA-treated patients.
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