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.

Figure 1

Frequency distribution of resistance factors of a subset of 271 samples for which data are available for all 14 antiretroviral drugs. Resistance factors (RF) have been rounded to integers and grouped into equidistant bins on a logarithmic scale. RF values smaller than one are reported as equal to one.

Figure 2

Mutual information profiles for ZDV (a), ddC (b), ddI (c), d4T (d), 3TC (e), ABC (f), NVP (g), DLV (h), EFV (i), SQV (j), IDV (k), RTV (l), NFV (m), and APV (n). In a–i, position 0 denotes the insertion flag, 1–250 represent the first 250 positions of the HIV-1 reverse transcriptase; in j–n, positions 1–99 of the HIV-1 protease are displayed. Peaks above 0.06 bits are annotated for ddC, ddI, and d4T, and peaks above 0.1 bits are annotated for all other drugs.

Figure 3

Decision trees for ZDV (a), ddC (b), ddI (c), d4T (d), 3TC (e), ABC (f), NVP (g), DLV (h), EFV (i), SQV (j), IDV (k), RTV (l), NFV (m), and APV (n). Numbers N(E) at the leaves denote the number of samples (N) and the estimated error (E). Branches leading to the same leaf are summarized. Capital letters annotating the edges denote amino acids in one-letter code.