Literature DB >> 20950432

A comparative analysis of HIV drug resistance interpretation based on short reverse transcriptase sequences versus full sequences.

Kim Steegen1, Michelle Bronze, Elke Van Craenenbroeck, Bart Winters, Koen Van der Borght, Carole L Wallis, Wendy Stevens, Tobias F Rinke de Wit, Lieven J Stuyver.   

Abstract

BACKGROUND: As second-line antiretroviral treatment (ART) becomes more accessible in resource-limited settings (RLS), the need for more affordable monitoring tools such as point-of-care viral load assays and simplified genotypic HIV drug resistance (HIVDR) tests increases substantially. The prohibitive expenses of genotypic HIVDR assays could partly be addressed by focusing on a smaller region of the HIV reverse transcriptase gene (RT) that encompasses the majority of HIVDR mutations for people on ART in RLS. In this study, an in silico analysis of 125,329 RT sequences was performed to investigate the effect of submitting short RT sequences (codon 41 to 238) to the commonly used virco®TYPE and Stanford genotype interpretation tools.
RESULTS: Pair-wise comparisons between full-length and short RT sequences were performed. Additionally, a non-inferiority approach with a concordance limit of 95% and two-sided 95% confidence intervals was used to demonstrate concordance between HIVDR calls based on full-length and short RT sequences.The results of this analysis showed that HIVDR interpretations based on full-length versus short RT sequences, using the Stanford algorithms, had concordance significantly above 95%. When using the virco®TYPE algorithm, similar concordance was demonstrated (>95%), but some differences were observed for d4T, AZT and TDF, where predictions were affected in more than 5% of the sequences. Most differences in interpretation, however, were due to shifts from fully susceptible to reduced susceptibility (d4T) or from reduced response to minimal response (AZT, TDF) or vice versa, as compared to the predicted full RT sequence. The virco®TYPE prediction uses many more mutations outside the RT 41-238 amino acid domain, which significantly contribute to the HIVDR prediction for these 3 antiretroviral agents.
CONCLUSIONS: This study illustrates the acceptability of using a shortened RT sequences (codon 41-238) to obtain reliable genotype interpretations by virco®TYPE and Stanford algorithms. Implementation of this simplified protocol could significantly reduce the cost of both resistance testing and ARV treatment monitoring in RLS.

Entities:  

Year:  2010        PMID: 20950432      PMCID: PMC2984380          DOI: 10.1186/1742-6405-7-38

Source DB:  PubMed          Journal:  AIDS Res Ther        ISSN: 1742-6405            Impact factor:   2.250


  14 in total

1.  Prediction of HIV-1 drug susceptibility phenotype from the viral genotype using linear regression modeling.

Authors:  H Vermeiren; E Van Craenenbroeck; P Alen; L Bacheler; G Picchio; P Lecocq
Journal:  J Virol Methods       Date:  2007-06-15       Impact factor: 2.014

Review 2.  Update of the drug resistance mutations in HIV-1: December 2009.

Authors:  Victoria A Johnson; Francoise Brun-Vezinet; Bonaventura Clotet; Huldrych F Gunthard; Daniel R Kuritzkes; Deenan Pillay; Jonathan M Schapiro; Douglas D Richman
Journal:  Top HIV Med       Date:  2009-12

3.  Clinical cut-offs for HIV-1 phenotypic resistance estimates: update based on recent pivotal clinical trial data and a revised approach to viral mixtures.

Authors:  Bart Winters; Elke Van Craenenbroeck; Koen Van der Borght; Pierre Lecocq; Jorge Villacian; Lee Bacheler
Journal:  J Virol Methods       Date:  2009-08-03       Impact factor: 2.014

4.  Sensitivity and specificity of using CD4+ measurement and clinical evaluation to determine antiretroviral treatment failure in Thailand.

Authors:  Romanee Chaiwarith; Charussri Wachirakaphan; Wilai Kotarathititum; Jutharat Praparatanaphan; Thira Sirisanthana; Khuanchai Supparatpinyo
Journal:  Int J Infect Dis       Date:  2007-02-28       Impact factor: 3.623

5.  CD4+ T-cell count monitoring does not accurately identify HIV-infected adults with virologic failure receiving antiretroviral therapy.

Authors:  David M Moore; Anna Awor; Robert Downing; Jonathan Kaplan; Julio S G Montaner; John Hancock; Willy Were; Jonathan Mermin
Journal:  J Acquir Immune Defic Syndr       Date:  2008-12-15       Impact factor: 3.731

6.  HIV type-1 clade C resistance genotypes in treatment-naive patients and after first virological failure in a large community antiretroviral therapy programme.

Authors:  Catherine Orrell; Rochelle P Walensky; Elena Losina; Jennifer Pitt; Kenneth A Freedberg; Robin Wood
Journal:  Antivir Ther       Date:  2009

7.  Prevalence of HIV-1 drug resistance after failure of a first highly active antiretroviral therapy regimen in KwaZulu Natal, South Africa.

Authors:  Vincent C Marconi; Henry Sunpath; Zhigang Lu; Michelle Gordon; Kofi Koranteng-Apeagyei; Jane Hampton; Steve Carpenter; Janet Giddy; Douglas Ross; Helga Holst; Elena Losina; Bruce D Walker; Daniel R Kuritzkes
Journal:  Clin Infect Dis       Date:  2008-05-15       Impact factor: 9.079

8.  Failure of immunologic criteria to appropriately identify antiretroviral treatment failure in Uganda.

Authors:  Steven J Reynolds; Gertrude Nakigozi; Kevin Newell; Anthony Ndyanabo; Ronald Galiwongo; Iga Boaz; Thomas C Quinn; Ron Gray; Maria Wawer; David Serwadda
Journal:  AIDS       Date:  2009-03-27       Impact factor: 4.177

9.  Varied patterns of HIV-1 drug resistance on failing first-line antiretroviral therapy in South Africa.

Authors:  Carole L Wallis; John W Mellors; Willem D F Venter; Ian Sanne; Wendy Stevens
Journal:  J Acquir Immune Defic Syndr       Date:  2010-04-01       Impact factor: 3.731

10.  The public health approach to identify antiretroviral therapy failure: high-level nucleoside reverse transcriptase inhibitor resistance among Malawians failing first-line antiretroviral therapy.

Authors:  Mina C Hosseinipour; Joep J G van Oosterhout; Ralf Weigel; Sam Phiri; Debbie Kamwendo; Neil Parkin; Susan A Fiscus; Julie A E Nelson; Joseph J Eron; Johnstone Kumwenda
Journal:  AIDS       Date:  2009-06-01       Impact factor: 4.177

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  5 in total

1.  Comparison of genotypic and virtual phenotypic drug resistance interpretations with laboratory-based phenotypes among CRF01_AE and subtype B HIV-infected individuals.

Authors:  Awachana Jiamsakul; Romanee Chaiwarith; Nicolas Durier; Sunee Sirivichayakul; Sasisopin Kiertiburanakul; Peter Van Den Eede; Rossana Ditangco; Adeeba Kamarulzaman; Patrick C K Li; Winai Ratanasuwan; Thira Sirisanthana
Journal:  J Med Virol       Date:  2015-07-17       Impact factor: 2.327

2.  A pragmatic approach to HIV-1 drug resistance determination in resource-limited settings by use of a novel genotyping assay targeting the reverse transcriptase-encoding region only.

Authors:  Susan C Aitken; Michelle Bronze; Carole L Wallis; Lieven Stuyver; Kim Steegen; Sheila Balinda; Cissy Kityo; Wendy Stevens; Tobias F Rinke de Wit; Rob Schuurman
Journal:  J Clin Microbiol       Date:  2013-03-27       Impact factor: 5.948

3.  Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations.

Authors:  Koen Van der Borght; Elke Van Craenenbroeck; Pierre Lecocq; Margriet Van Houtte; Barbara Van Kerckhove; Lee Bacheler; Geert Verbeke; Herman van Vlijmen
Journal:  BMC Bioinformatics       Date:  2011-10-03       Impact factor: 3.169

4.  HIV-1 phenotypic reverse transcriptase inhibitor drug resistance test interpretation is not dependent on the subtype of the virus backbone.

Authors:  Michelle Bronze; Kim Steegen; Carole L Wallis; Hans De Wolf; Maria A Papathanasopoulos; Margriet Van Houtte; Wendy S Stevens; Tobias Rinke de Wit; Lieven J Stuyver
Journal:  PLoS One       Date:  2012-04-09       Impact factor: 3.240

5.  Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIV-infected patients in Asia: TASER-M cohort analysis.

Authors:  Awachana Jiamsakul; Rami Kantor; Patrick C K Li; Sunee Sirivichayakul; Thira Sirisanthana; Pacharee Kantipong; Christopher K C Lee; Adeeba Kamarulzaman; Winai Ratanasuwan; Rossana Ditangco; Thida Singtoroj; Somnuek Sungkanuparph
Journal:  BMC Res Notes       Date:  2012-10-24
  5 in total

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