Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.
Cuypers, L., Libin, P., Schrooten, Y., Theys, K., DI MAIO, V.c., Cento, V., et al. (2017). Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning. INFECTION GENETICS AND EVOLUTION, 53, 15-23 [10.1016/j.meegid.2017.05.007].
|Tipologia:||Articolo su rivista|
|Citazione:||Cuypers, L., Libin, P., Schrooten, Y., Theys, K., DI MAIO, V.c., Cento, V., et al. (2017). Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning. INFECTION GENETICS AND EVOLUTION, 53, 15-23 [10.1016/j.meegid.2017.05.007].|
|Altre informazioni significative:||Lize Cuypers and Pieter Libin were supported by a PhD grant of the FWO (Fonds Wetenschappelijk Onderzoek – Vlaanderen, respectively Asp/12 and Asp/15), and Kristof Theys by a postdoctoral grant of the FWO (PDO/11). The computational resources and services used in this work were provided by the Hercules Foundation and the Flemish Government – department EWI-FWO Krediet aan Navorsers (Theys, KAN2012 220.127.116.11.). The authors declare no conflict of interest, other than the financial disclosures described above.|
|IF:||Con Impact Factor ISI|
|Settore Scientifico Disciplinare:||Settore MED/07 - Microbiologia e Microbiologia Clinica|
|Revisione (peer review):||Sì, ma tipo non specificato|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.meegid.2017.05.007|
|Stato di pubblicazione:||Pubblicato|
|Data di pubblicazione:||9-mag-2017|
|Titolo:||Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning|
|Autori:||Cuypers, L; Libin, P; Schrooten, Y; Theys, K; DI MAIO, Vc; Cento, V; Lunar, M; Nevens, F; Poljak, M; CECCHERINI SILBERSTEIN, F; Nowé, A; Van Laethem, K; Vandamme, A|
|Appare nelle tipologie:||01 - Articolo su rivista|
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