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Design: Promiscuous guanine (G) to adenine (A) substitutions catalysed by apolipoprotein B RNA-editing catalytic component (APOBEC) enzymes are observed in a proportion of HIV-1 sequences in vivo and can introduce artifacts into some genetic analyses. The potential impact of undetected lethal editing on genotypic estimation of transmitted drug resistance was assessed., Methods: Classifiers of lethal, APOBEC-mediated editing were developed by analysis of lentiviral pol gene sequence variation and evaluated using control sets of HIV-1 sequences. The potential impact of sequence editing on genotypic estimation of drug resistance was assessed in sets of sequences obtained from 77 studies of 25 or more therapy-naive individuals, using mixture modelling approaches to determine the maximum likelihood classification of sequences as lethally edited as opposed to viable., Results: Analysis of 6437 protease and reverse transcriptase sequences from therapy-naive individuals using a novel classifier of lethal, APOBEC3G-mediated sequence editing, the polypeptide-like 3G (APOBEC3G)-mediated defectives (A3GD) index', detected lethal editing in association with spurious 'transmitted drug resistance' in nearly 3% of proviral sequences obtained from whole blood and 0.2% of samples obtained from plasma., Conclusion: Screening for lethally edited sequences in datasets containing a proportion of proviral DNA, such as those likely to be obtained for epidemiological surveillance of transmitted drug resistance in the developing world, can eliminate rare but potentially significant errors in genotypic estimation of transmitted drug resistance.

Eleven protease mutations have been associated with reduced susceptibility to darunavir (DRV). We examined the prevalence and covariates of these mutations in 2 populations. Thirty percent of 1175 Northern California patients and 24% of 2744 non-California patients in the Stanford HIV Drug Resistance Database had viruses with 1 or more mutations associated with resistance to DRV. In multivariate analyses, the number of DRV resistance-associated mutations depended on the number of previous protease inhibitors (PIs) administered and on amprenavir/fosamprenavir treatment. Most PI-treated patients should respond favorably to DRV-based salvage therapy.

The Stanford HIVdb is implementing three applications to address the issue of HIV drug resistance. Knowledge integration of the applications is desirable. How best to integrate the software applications is the main question. We have pursued two possibilities for accomplishing knowledge integration. The most feasible method appears to use mapping ontologies. Future work is focused on defining a set of mapping relations to aid in the knowledge integration.

Understanding the genetic basis of HIV-1 drug resistance is essential to developing new antiretroviral drugs and optimizing the use of existing drugs. This understanding, however, is hampered by the large numbers of mutation patterns associated with cross-resistance within each antiretroviral drug class. We used five statistical learning methods (decision trees, neural networks, support vector regression, least-squares regression, and least angle regression) to relate HIV-1 protease and reverse transcriptase mutations to in vitro susceptibility to 16 antiretroviral drugs. Learning methods were trained and tested on a public data set of genotype-phenotype correlations by 5-fold cross-validation. For each learning method, four mutation sets were used as input features: a complete set of all mutations in > or =2 sequences in the data set, the 30 most common data set mutations, an expert panel mutation set, and a set of nonpolymorphic treatment-selected mutations from a public database linking protease and reverse transcriptase sequences to antiretroviral drug exposure. The nonpolymorphic treatment-selected mutations led to the best predictions: 80.1% accuracy at classifying sequences as susceptible, low/intermediate resistant, or highly resistant. Least angle regression predicted susceptibility significantly better than other methods when using the complete set of mutations. The three regression methods provided consistent estimates of the quantitative effect of mutations on drug susceptibility, identifying nearly all previously reported genotype-phenotype associations and providing strong statistical support for many new associations. Mutation regression coefficients showed that, within a drug class, cross-resistance patterns differ for different mutation subsets and that cross-resistance has been underestimated.

HIV-1 genotypic resistance test results were obtained on clinical samples from 116 patients with plasma HIV-1 RNA levels of less than 75 copies/mL. Genotype validity was confirmed in 49 of 50 patients with a previous or follow-up genotype. The belief that genotypic resistance testing is unreliable in samples with low-level viremia should be reassessed.

Few studies have investigated sequential HIV-1 mutation changes in the HIV gene in patients changing antiretroviral drugs. We analyze such data from the HIV Drug Resistance Database at Stanford University using three data mining methods: association rule analysis, logistic regression, and classification trees. Although the AUC measures of the overall prediction is not high, these methods can effectively identify strong predictors of mutation change and focus further analysis by domain experts.

Background. It is important, for drug-resistance surveillance, to identify human immunodeficiency virus type 1 (HIV-1) strains that have undergone antiretroviral drug selection.Methods. We compared the prevalence of protease and reverse-transcriptase (RT) mutations in HIV-1 sequences from persons with and without previous treatment with protease inhibitors (PIs), nucleoside RT inhibitors (NRTIs), and nonnucleoside RT inhibitors (NNRTIs). Treatment-associated mutations in protease isolates from 5867 persons and RT isolates from 6247 persons were categorized by whether they were polymorphic (prevalence, >0.5%) in untreated individuals and whether they were established drug-resistance mutations. New methods were introduced to minimize misclassification from transmitted resistance, population stratification, sequencing artifacts, and multiple hypothesis testing.Results. Some 36 established and 24 additional nonpolymorphic protease mutations at 34 positions were related to PI treatment, 21 established and 22 additional nonpolymorphic RT mutations at 24 positions with NRTI treatment, and 15 established and 11 additional nonpolymorphic RT mutations at 15 positions with NNRTI treatment. In addition, 11 PI-associated and 1 NRTI-associated established mutations were polymorphic in viruses from untreated persons.Conclusions. Established drug-resistance mutations encompass only a subset of treatment-associated mutations; some of these are polymorphic in untreated persons. In contrast, nonpolymorphic treatment-associated mutations may be more sensitive and specific markers of transmitted HIV-1 drug resistance.

Objective: Although 2 widely used susceptibility assays have been developed, their precision and sensitivity have not been assessed., Design and Methods: To assess the precision of the Antivirogram and PhenoSense assays, we examined susceptibility results of HIV-1 isolates lacking drug resistance mutations and containing matching patterns of drug resistance mutations. To assess sensitivity, we determined for each assay the proportion of isolates with common patterns of matching drug resistance mutations having reductions in susceptibility greater than those in isolates without drug resistance mutations., Results: We analyzed protease inhibitor (PI) susceptibility results obtained by the Antivirogram assay for 293 isolates and by the PhenoSense assay for 300 isolates. We analyzed reverse transcriptase (RT) inhibitor susceptibility results obtained by the Antivirogram assay for 202 isolates and by the PhenoSense assay for 126 isolates. For wild-type and mutant isolates, the median absolute deviance of the fold resistance of nucleoside RT inhibitor susceptibility results was significantly lower for the PhenoSense assay than for the Antivirogram assay. The PhenoSense assay was also significantly more likely than the Antivirogram assay to detect resistance to abacavir, didanosine, and stavudine in isolates with the common drug resistance mutations M41L, M184V, and T215Y (+/-L210W). We found no significant differences between the 2 assays for detecting PI and nonnucleoside RT inhibitor resistance., Conclusion: The PhenoSense assay is more precise than the Antivirogram assay and superior at detecting resistance to abacavir, didanosine, and stavudine.