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  Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance

Altmann, A., Beerenwinkel, N., Sing, T., Savenkov, I., Däumer, M., Kaiser, R., et al. (2007). Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance. Antiviral Therapy, 12(2), 169-178.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-1F8E-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-1F8F-A
Genre: Journal Article

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 Creators:
Altmann, Andre1, Author              
Beerenwinkel, Niko1, Author              
Sing, Tobias1, Author              
Savenkov, Igor1, Author              
Däumer, Martin, Author
Kaiser, Rolf, Author
Rhee, Soo-Yon, Author
Fessel, W Jeffrey, Author
Shafer, Robert W, Author
Lengauer, Thomas1, Author              
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, escidoc:40046              

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 Abstract: Background: The outcome of antiretroviral combination therapy depends on many factors involving host, virus, and drugs. We investigate prediction of treatment response from the applied drug combination and the genetic constellation of the virus population at baseline. The virus’s evolutionary potential for escaping from drug pressure is explored as an additional predictor. Methods: We compare different encodings of the viral genotype and antiretroviral regimen including phenotypic and evolutionary information, namely predicted phenotypic drug resistance, activity of the regimen estimated from sequence space search, the genetic barrier to drug resistance, and the genetic progression score. These features were evaluated in the context of different statistical learning procedures applied to the binary classification task of predicting virological response. Classifier performance was evaluated using cross-validation and receiver operating characteristic curves on 6,337 observed treatment change episodes from the Stanford HIV Drug Resistance Database and a large US clinic-based patient population. Results: We find that the choice of appropriate features affects predictive performance more profoundly than the choice of the statistical learning method. Application of the genetic barrier to drug resistance, which combines phenotypic and evolutionary information, outperformed the genetic progression score, which uses exclusively evolutionary knowledge. The benefit of phenotypic information in predicting virological response was confirmed by using predicted fold changes in drug susceptibility. Moreover, genetic barrier and predicted phenotypic drug resistance were found to be the best encodings across all datasets and statistical learning methods examined. Availability: THEO (THErapy Optimizer), a prototypical implementation of the best performing approach, is freely available for research purposes at http://www.geno2pheno.org.

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Language(s): eng - English
 Dates: 2008-02-282007
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 356656
Other: Local-ID: C12573CC004A8E26-6640B953DA2CDA2DC1257288005464A4-Altmann2007
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Title: Antiviral Therapy
Source Genre: Journal
 Creator(s):
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Publ. Info: -
Pages: - Volume / Issue: 12 (2) Sequence Number: - Start / End Page: 169 - 178 Identifier: -