Discrepant results in the interpretation of HIV‐1 drug‐resistance genotypic data among widely used algorithms

Kijak, GH; Rubio, A. García; Se, Pampuro; Zala, Carlos; Cahn, Pedro; Galli, Rick; Js, Montaner; Salomón, Horacio

doi:10.1046/j.1468-1293.2003.00131.x

Cited by 56 publications

(38 citation statements)

References 32 publications

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“…As new drugs are developed for the care of HIV-positive patients, novel mutations are recognized, which prompt the International AIDS Society of USA [Johnson et al, 2005], whose authority is recognized worldwide, to update regularly the spectrum of HIV-1 resistance-associated mutations. However, even though an exhaustive IAS mutation list is accessible to clinicians and many free-of-charge websites [Beerenwinkel et al, 2003;Kuiken et al, 2003;Rhee et al, 2003] furnish a computer-assisted interpretation of mutational profiles, some discrepancies continue to exist between these resistance profiles and response to therapy [Kijak et al, 2003;Ravela et al, 2003;Sturmer et al, 2003;Torti et al, 2003;De Luca et al, 2004]. Although good compliance with treatment regimens [Paterson et al, 2000;Cingolani et al, 2002], optimal antiviral potency [Daar, 2003;Gathe, 2003], and adequate drug concentrations [Yasuda et al, 2004] are major concerns for obtaining a sustained control of viral replication, the major obstacle to realizing a successful regimen capable of providing a sustained control of viral replication regards some unresolved questions related to HIV-1 resistance.…”

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confidence: 99%

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Saracino

Monno

Scudeller

et al. 2005

Journal of Medical Virology

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An extended spectrum of HIV-1 reverse-transcriptase (RT) mutations in HAART-treated patients has been recently described. To verify the possible association of previously unreported RT mutations with a decrease of phenotypic susceptibility to nucleoside (NRTIs) and non-nucleoside (NNRTIs) RT inhibitors, the RT sequence of 328 HIV-1-positive patients (102 naïve and 226 treated with HAART participating in either the PhenGen or Genpherex study) was analyzed. All treated patients were tested at the time of therapeutic failure with both phenotypic (Antivirogram 1 , Virco) and genotypic analyses (VircoGen TM ); the frequency of RT substitutions (positions 1-240) with respect to consensus B was compared to that of naïve patients using a Chi-square test. Amino acid changes at 13 positions not included in the IAS list of resistance-associated mutations were detected more frequently in treated than in naïve subjects. The mutations involving 10 of these positions were associated with a reduced susceptibility to antiretroviral drugs; K20R, T39A, K43EQN, E203KD, H208Y, and D218E were correlated with NRTI resistance while mutations K101EQP, H221Y, K223EQ, L228HR were associated to NNRTI resistance. A correlation was found between K20R and lamivudine resistance (P ¼ 0.006) while T39A (P ¼ 0.005), K43EQN (<0.001), E203KD (P ¼ 0.010), and H208Y (P ¼ < 0.001) seemed to be associated with a previous use of zidovudine and stavudine and with the development of thymidine analog resistance. For H208Y, an association with use/resistance to abacavir (P ¼ 0.004) was also noted. D218E showed a weak association to didanosine resistance (P ¼ 0.013). The data confirm that previously unreported mutations are associated with antiretroviral drug experience and, more importantly, with a reduced susceptibility to NRTIs and NNRTIs.

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Section: Introductionmentioning

confidence: 99%

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Saracino

Monno

Scudeller

et al. 2005

Journal of Medical Virology

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“…Over 20 genotypic interpretation systems (GISs) have been developed to interpret the complex patterns of amino acid substitutions seen with PI-associated resistance (14,15,20), but their performance characteristics have rarely been com-pared. Given the complexity of PI-associated resistance, a phenotype can be determined to better characterize resistance, although clinical data in support of such a strategy are limited (17).…”

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confidence: 99%

Predicting Tipranavir and Darunavir Resistance Using Genotypic, Phenotypic, and Virtual Phenotypic Resistance Patterns: an Independent Cohort Analysis of Clinical Isolates Highly Resistant to All Other Protease Inhibitors

Talbot

Grant

Taylor

et al. 2010

Antimicrob Agents Chemother

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Genotypic interpretation systems (GISs) for darunavir and tipranavir susceptibility are rarely tested by the use of independent data sets. The virtual phenotype (the phenotype determined by Virco [the "Vircotype"]) was used to interpret all genotypes in Québec, Canada, and phenotypes were determined for isolates predicted to be resistant to all protease inhibitors other than darunavir and tipranavir. We used multivariate analyses to predict relative phenotypic susceptibility to darunavir and tipranavir. We compared the performance characteristics of the Agence Nationale de Recherche sur le Sida scoring algorithm, the Stanford HIV database scoring algorithm (with separate analyses of the discrete and numerical scores), the Vircotype, and the darunavir and tipranavir manufacturers' scores for prediction of the phenotype. Of the 100 isolates whose phenotypes were determined, 89 and 72 were susceptible to darunavir and tipranavir, respectively. In multivariate analyses, the presence of I84V and V82T and the lack of L10F predicted that the isolates would be more susceptible to darunavir than tipranavir. The presence of I54L, V32I, and I47V predicted that the isolates would be more susceptible to tipranavir. All GISs except the system that provided the Stanford HIV database discrete score performed well in predicting the darunavir resistance phenotype (R 2 ‫؍‬ 0.61 to 0.69); the R 2 value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R 2 ‫؍‬ 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R 2 ‫؍‬ 0.00 to 0.31). In this independent cohort harboring highly protease inhibitor-resistant HIV isolates, reduced phenotypic susceptibility to darunavir and tipranavir was rare. Generally, GISs predict susceptibility to darunavir substantially better than they predict susceptibility to tipranavir.

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“…Studies were performed mainly on subtype B viruses, and even within this subtype, differences have been detected (6,21,29,34,35,36).…”

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“…Comparisons between these interpretation systems have already been made for subtype B strains; however, the subtype dependency of resistance assessment by these interpretations systems has not yet been determined (6,21,29,34,35,36). In this study, we investigated four frequently used interpretation systems across a large number of non-B sequences to determine whether discordance between the systems was dependent on the viral subtype.…”

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confidence: 99%

Discordances between Interpretation Algorithms for Genotypic Resistance to Protease and Reverse Transcriptase Inhibitors of Human Immunodeficiency Virus Are Subtype Dependent

Snoeck

Kantor

Shafer

et al. 2006

Antimicrob Agents Chemother

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The major limitation of drug resistance genotyping for human immunodeficiency virus remains the interpretation of the results. We evaluated the concordance in predicting therapy response between four different interpretation algorithms (Rega 6.3, HIVDB-08/04, ANRS [07/04], and VGI 8.0). Sequences were gathered through a worldwide effort to establish a database of non-B subtype sequences, and demographic and clinical information about the patients was gathered. The most concordant results were found for nonnucleoside reverse transcriptase (RT) inhibitors (93%), followed by protease inhibitors (84%) and nucleoside RT inhibitor (NRTIs) (76%). For therapy-naive patients, for nelfinavir, especially for subtypes C and G, the discordances were driven mainly by the protease (PRO) mutational pattern 82I/V ؉ 63P ؉ 36I/V for subtype C and 82I ؉ 63P ؉ 36I ؉ 20I for subtype G. Subtype F displayed more discordances for ritonavir in untreated patients due to the combined presence of PRO 20R and 10I/V. In therapy-experienced patients, subtype G displayed a lot of discordances for saquinavir and indinavir due to mutational patterns involving PRO 90 M and 82I. Subtype F had more discordance for nelfinavir attributable to the presence of PRO 88S and 82A ؉ 54V. For the NRTIs lamivudine and emtricitabine, CRF01_AE had more discordances than subtype B due to the presence of RT mutational patterns 65R ؉ 115 M and 118I ؉ 215Y, respectively. Overall, the different algorithms agreed well on the level of resistance scored, but some of the discordances could be attributed to specific (subtypedependent) combinations of mutations. It is not yet known whether therapy response is subtype dependent, but the advice given to clinicians based on a genotypic interpretation algorithm differs according to the subtype.Genotyping for the assessment of anti-human immunodeficiency virus (HIV) drug resistance is often used in the management of individual patient therapy. Currently, it is recommended in European as well as American guidelines (17,38). In several retrospective and prospective studies, genotyping proved beneficial in optimizing treatment for individual patients (5,10,16,23,25,31,37).Although genotyping is commonly used, there are still many uncertainties with respect to the value of genotype in the assignment of a new regimen. The current genotypic assays are not always able to report all drug resistance mutations among non-B subtypes (11,18,19,24). Regardless of subtype, genotyping is not sensitive to mutations that are present as a minor variant in the population (22,40). Genotyping results also differ depending on the laboratory where they are performed. Quality control studies indicate that mutations, even present as a pure variant, are often underestimated (32).However, separate from the quality and sensitivity issues, the interpretation of genotypic results is still not standardized.

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Discrepant results in the interpretation of HIV‐1 drug‐resistance genotypic data among widely used algorithms

Cited by 56 publications

References 32 publications

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Predicting Tipranavir and Darunavir Resistance Using Genotypic, Phenotypic, and Virtual Phenotypic Resistance Patterns: an Independent Cohort Analysis of Clinical Isolates Highly Resistant to All Other Protease Inhibitors

Discordances between Interpretation Algorithms for Genotypic Resistance to Protease and Reverse Transcriptase Inhibitors of Human Immunodeficiency Virus Are Subtype Dependent

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