Literature DB >> 25397497

Analysis of transmitted HIV-1 drug resistance using 454 ultra-deep-sequencing and the DeepChek(®)-HIV system.

Ana Garcia-Diaz1, Adele McCormick1, Clare Booth1, Dimitri Gonzalez2, Chalom Sayada3, Tanzina Haque1, Margaret Johnson1, Daniel Webster1.   

Abstract

INTRODUCTION: Next-generation sequencing (NGS) is capable of detecting resistance-associated mutations (RAMs) present at frequencies of 1% or below. Several studies have found that baseline low-frequency RAMs are associated with failure to first-line HAART. One major limitation to the expansion of this technology in routine diagnostics is the complexity and laboriousness integral to bioinformatics analysis. DeepChek (ABL, TherapyEdge) is a CE-marked software that allows automated analysis and resistance interpretation of NGS data.
OBJECTIVE: To evaluate the use of 454 ultra-deep-sequencing (Roche(®) 454, Life Sciences; 454-UDS) and DeepChek for routine baseline resistance testing in a clinical diagnostic laboratory.
METHODS: 107 newly diagnosed HIV-1-infected patients (subtypes: A, n=9; B, n=52; C, n=21; D, n=2; F, n=3; G, n=1; CRF01, n=7; CRF02, n=7; CRF06, n=1; CRF07, n=1; CRF10, n=1 and unassigned complex, n=2) with a median plasma viral load of 88,727 copies/mL (range: 1380-2,143,543) were tested by 454-UDS and Sanger sequencing for the detection of protease and reverse transcriptase RAMs. In addition, integrase RAMs were investigated in 57 of them. Sequence analysis and resistance interpretation were performed using DeepChek applying 1% and 20% thresholds for variant detections; filters applied were comparison between Sanger and 454-UDS, and Stanford and IAS list for resistance interpretation.
RESULTS: The time elapsed from generation of raw 454 data (between 2,000-5,000 sequences/sample) to elaboration of a resistance report was approximately 10 minutes per sample, equivalent to the time required for the same process using Sanger sequencing. Four patients (3.7%) showed baseline resistance by Sanger and 454-UDS at frequencies above 20%, which affected both NRTIs (n=2) and NNRTIs (n=2). In addition, 12 patients (11.2%) showed transmitted drug resistance (TDR) by 454-UDS at frequencies below 20% affecting NRTIs (n=9), NNRTIs (n=7) and PIs (n=2). Integrase resistance was not detected at baseline by 454-UDS or Sanger sequencing.
CONCLUSIONS: DeepChek allowed easy and rapid analysis and interpretation of NGS data, thus facilitating the incorporation of this technology in routine diagnostics. The use of NGS considerably increased the detection rates of TDR to NRTI, NNRTIs and PIs. No transmitted resistance to integrase inhibitors was found in our population by Sanger sequencing or UDS.

Entities:  

Year:  2014        PMID: 25397497      PMCID: PMC4225408          DOI: 10.7448/IAS.17.4.19752

Source DB:  PubMed          Journal:  J Int AIDS Soc        ISSN: 1758-2652            Impact factor:   5.396


  9 in total

1.  A Pan-HIV Strategy for Complete Genome Sequencing.

Authors:  Michael G Berg; Julie Yamaguchi; Elodie Alessandri-Gradt; Robert W Tell; Jean-Christophe Plantier; Catherine A Brennan
Journal:  J Clin Microbiol       Date:  2015-12-23       Impact factor: 5.948

2.  Next-Generation Human Immunodeficiency Virus Sequencing for Patient Management and Drug Resistance Surveillance.

Authors:  Marc Noguera-Julian; Dianna Edgil; P Richard Harrigan; Paul Sandstrom; Catherine Godfrey; Roger Paredes
Journal:  J Infect Dis       Date:  2017-12-01       Impact factor: 5.226

3.  Recent trends and patterns in HIV-1 transmitted drug resistance in the United Kingdom.

Authors:  A Tostevin; E White; D Dunn; S Croxford; V Delpech; I Williams; D Asboe; A Pozniak; D Churchill; A M Geretti; D Pillay; C Sabin; A Leigh-Brown; E Smit
Journal:  HIV Med       Date:  2016-08-01       Impact factor: 3.180

4.  Comparison between next-generation and Sanger-based sequencing for the detection of transmitted drug-resistance mutations among recently infected HIV-1 patients in Israel, 2000-2014.

Authors:  Roy Moscona; Daniela Ram; Marina Wax; Efrat Bucris; Itzchak Levy; Ella Mendelson; Orna Mor
Journal:  J Int AIDS Soc       Date:  2017-08-10       Impact factor: 5.396

5.  Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants.

Authors:  Marine Perrier; Nathalie Désiré; Alexandre Storto; Eve Todesco; Christophe Rodriguez; Mélanie Bertine; Quentin Le Hingrat; Benoit Visseaux; Vincent Calvez; Diane Descamps; Anne-Geneviève Marcelin; Charlotte Charpentier
Journal:  PLoS One       Date:  2018-06-01       Impact factor: 3.240

6.  Multi-Laboratory Comparison of Next-Generation to Sanger-Based Sequencing for HIV-1 Drug Resistance Genotyping.

Authors:  Neil T Parkin; Santiago Avila-Rios; David F Bibby; Chanson J Brumme; Susan H Eshleman; P Richard Harrigan; Mark Howison; Gillian Hunt; Hezhao Ji; Rami Kantor; Johanna Ledwaba; Emma R Lee; Margarita Matías-Florentino; Jean L Mbisa; Marc Noguera-Julian; Roger Paredes; Vanessa Rivera-Amill; Ronald Swanstrom; Daniel J Zaccaro; Yinfeng Zhang; Shuntai Zhou; Cheryl Jennings
Journal:  Viruses       Date:  2020-06-27       Impact factor: 5.048

Review 7.  Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework.

Authors:  Rupert Capina; Katherine Li; Levon Kearney; Anne-Mieke Vandamme; P Richard Harrigan; Kristel Van Laethem
Journal:  Viruses       Date:  2020-06-14       Impact factor: 5.048

8.  Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing.

Authors:  Marc Noguera-Julian; Emma R Lee; Robert W Shafer; Rami Kantor; Hezhao Ji
Journal:  Viruses       Date:  2020-06-20       Impact factor: 5.818

9.  Bioinformatic data processing pipelines in support of next-generation sequencing-based HIV drug resistance testing: the Winnipeg Consensus.

Authors:  Hezhao Ji; Eric Enns; Chanson J Brumme; Neil Parkin; Mark Howison; Emma R Lee; Rupert Capina; Eric Marinier; Santiago Avila-Rios; Paul Sandstrom; Gary Van Domselaar; Richard Harrigan; Roger Paredes; Rami Kantor; Marc Noguera-Julian
Journal:  J Int AIDS Soc       Date:  2018-10       Impact factor: 5.396

  9 in total

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