title: Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype.
abstract: Drug resistance testing has been shown to be beneficial for clinical management of HIV type 1 infected patients. Whereas phenotypic assays directly measure drug resistance, the commonly used genotypic assays provide only indirect evidence of drug resistance, the major challenge being the interpretation of the sequence information. We analyzed the significance of sequence variations in the protease and reverse transcriptase genes for drug resistance and derived models that predict phenotypic resistance from genotypes. For 14 antiretroviral drugs, both genotypic and phenotypic resistance data from 471 clinical isolates were analyzed with a machine learning approach. Information profiles were obtained that quantify the statistical significance of each sequence position for drug resistance. For the different drugs, patterns of varying complexity were observed, including between one and nine sequence positions with substantial information content. Based on these information profiles, decision tree classifiers were generated to identify genotypic patterns characteristic of resistance or susceptibility to the different drugs. We obtained concise and easily interpretable models to predict drug resistance from sequence information. The prediction quality of the models was assessed in leave-one-out experiments in terms of the prediction error. We found prediction errors of 9.6-15.5% for all drugs except for zalcitabine, didanosine, and stavudine, with prediction errors between 25.4% and 32.0%. A prediction service is freely available at http://cartan.gmd.de/geno2pheno.html.
authors.affiliation: GMD-German National Research Center for Information Technology, Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany.
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.forename: Barbara
authors.initials: B
authors.lastname: Schmidt
authors.forename: Hauke
authors.initials: H
authors.lastname: Walter
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
authors.forename: Daniel
authors.initials: D
authors.lastname: Hoffmann
authors.forename: Klaus
authors.initials: K
authors.lastname: Korn
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D019295:Computational Biology / Q000379:methods; D003663:Decision Trees; D004198:Disease Susceptibility; D024882:Drug Resistance, Viral / Q000502:physiology*; D014644:Genetic Variation; D005838:Genotype; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics*; D006801:Humans; D007113:Immunity, Innate; D008969:Molecular Sequence Data; D010641:Phenotype