Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors

Deforche, Koen; Camacho, Ricardo Jorge; Grossman, Zehava; Silander, Tomi; Soares, Marcelo A.; Moreau, Yves; Shafer, Robert W.; Laethem, Kristel Van; Carvalho, Ana Patrícia; Wynhoven, Brian; Cane, P; Snoeck, Joke; Clarke, John R.; Sirivichayakul, Sunee; Ariyoshi, K; Holguín, África; Rudich, H; Rodrigues, Rosangela; Bouzas, María Belén; Cahn, Pedro; Brígido, Luis Fernando de Macedo; Soriano, Vincent; Sugiura, Wataru; Phanuphak, Praphan; Morris, Lynn; Weber, Jonathan; Pillay, Daisy; Tanuri, Amílcar; Harrigan, P. Richard; Shapiro, Jesse; Katzenstein, David; Kantor, Rami; Vandamme, Anne‐Mieke

doi:10.1016/j.meegid.2006.09.004

Cited by 33 publications

(38 citation statements)

References 21 publications

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“…Bayesian network learning (BNL) has previously been shown to be useful to map epistatic interactions important for antiviral resistance development (Deforche et al, 2006(Deforche et al, , 2007a. A Bayesian network (BN) is a probabilistic model that describes statistical independencies between variables (Pearl, 1988).…”

Section: Introductionmentioning

confidence: 99%

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

Theys

Deforche

Libin

et al. 2010

Journal of General Virology

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A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an in vivo fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with in vitro resistance phenotype in genotypephenotype pairs (R 2 50.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

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Section: Introductionmentioning

confidence: 99%

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

Theys

Deforche

Libin

et al. 2010

Journal of General Virology

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“…(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) identified to be associated with PI-exposure using Bayesian network learning, in comparison with what is generally seen in for example HIV drug resistance analyses (Deforche et al, 2006(Deforche et al, , 2007(Deforche et al, , 2008 for which a larger number of positions was identified to be under positive selective pressure (Snoeck et al, 2011).…”

Section: Absence Of Positive Selective Pressurementioning

confidence: 97%

“…During such a search, the benefit of adding an arc is evaluated against the additional cost of that arc (Deforche et al, 2006). In the context of HIV drug resistance (Deforche et al, 2006(Deforche et al, , 2007(Deforche et al, , 2008Theys et al, 2010), resistance variants and epistatic interactions of resistance mutations and natural polymorphisms have been mapped, increasing the understanding of resistance pathways and prediction systems. Therefore, applying BN learning on HCV sequence data of patients treated with DAAs can increase the knowledge about epistatic interactions between amino acid variants, and their relation to exposure/response to treatment.…”

Section: Introductionmentioning

confidence: 99%

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Cuypers

Libin

Schrooten

et al. 2017

Infection, Genetics and Evolution

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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 PIexperienced 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 PInaïve 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.

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“…None of the clinical variables considered were HIV mutations. Some other works using probabilistic models have studied the development of resistance to PIs, mainly Nelfinavir, Indinavir and Saquinavir, through learned Baysian networks [12,13]. However, none of these learned models yielded any temporal information.…”

Section: Related Workmentioning

confidence: 99%

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Hernández-Leal

Rios-Flores

Ávila‐Ríos

et al. 2013

Artificial Intelligence in Medicine

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Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors

Cited by 33 publications

References 21 publications

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

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