Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2024 Sep 6;16(9):1422.
doi: 10.3390/v16091422.

Comparison of Different HIV-1 Resistance Interpretation Tools for Next-Generation Sequencing in Italy

Daniele Armenia  1 , Luca Carioti  2 , Valeria Micheli  3 , Isabella Bon  4 , Tiziano Allice  5 , Celestino Bonura  6 , Bianca Bruzzone  7 , Fiorenza Bracchitta  3 , Francesco Cerutti  5 , Giovanni Maurizio Giammanco  6 , Federica Stefanelli  7 , Maria Addolorata Bonifacio  8 , Ada Bertoli  9 , Marialinda Vatteroni  10 , Gabriele Ibba  11 , Federica Novazzi  12 , Maria Rosaria Lipsi  13 , Nunzia Cuomo  14 , Ilaria Vicenti  15 , Francesca Ceccherini-Silberstein  2 , Barbara Rossetti  16 , Antonia Bezenchek  17   18 , Francesco Saladini  15 , Maurizio Zazzi  15 , Maria Mercedes Santoro  2 ; Italian HIV NGS network and the ARCA Cohort
Affiliations
Comparative Study

Comparison of Different HIV-1 Resistance Interpretation Tools for Next-Generation Sequencing in Italy

Daniele Armenia et al. Viruses. .

Abstract

Background: Next-generation sequencing (NGS) is gradually replacing Sanger sequencing for HIV genotypic drug resistance testing (GRT). This work evaluated the concordance among different NGS-GRT interpretation tools in a real-life setting.

Methods: Routine NGS-GRT data were generated from viral RNA at 11 Italian laboratories with the AD4SEQ HIV-1 Solution v2 commercial kit. NGS results were interpreted by the SmartVir system provided by the kit and by two online tools (HyDRA Web and Stanford HIVdb). NGS-GRT was considered valid when the coverage was >100 reads (100×) at each PR/RT/IN resistance-associated position listed in the HIVdb 9.5.1 algorithm.

Results: Among 629 NGS-GRT, 75.2%, 74.2%, and 70.9% were valid according to SmartVir, HyDRA Web, and HIVdb. Considering at least two interpretation tools, 463 (73.6%) NGS-GRT had a valid coverage for resistance analyses. The proportion of valid samples was affected by viremia <10,000-1000 copies/mL and non-B subtypes. Mutations at an NGS frequency >10% showed fair concordance among different interpretation tools.

Conclusion: This Italian survey on NGS resistance testing suggests that viremia levels and HIV subtype affect NGS-GRT coverage. Within the current routine method for NGS-GRT, only mutations with frequency >10% seem reliably detected across different interpretation tools.

Keywords: HIV drug resistance; HIV-1 subtype; bioinformatic interpretation tools; minority variants; next-generation sequencing; viremia.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
Flow chart for the evaluation of concordance among tools for HIV-1 NGS data interpretation in resistance assessment.
Figure 2
Figure 2
Proportion of samples with valid coverage for resistance evaluation obtained through different NGS data interpretation tools, according to subtypes and contextual viremia levels. Bars represent the proportion of samples with 100× coverage among all PR/RT/IN positions associated with resistance according to the Stanford drug resistance algorithm (HIVdb 9.5.0). Black and white bars represent proportions according to subtypes and viremia levels, respectively. (A,B) SmartVir standalone tool; (C,D) free web tool HIVdb; (E,F) free web tool HyDRA Web; (G,H) at least 2 tools among SmartVir, HyDRA Web, and HIVdb.
Figure 3
Figure 3
Concordance in detecting mutations between different NGS resistance interpretation tools. (A) Comparison between SmartVir and HIVdb; (B) comparison between SmartVir and HyDRA Web; (C) comparison between HyDRA Web and HIVdb. Each symbol represents a substitution detected with a valid coverage per each tool. Blue and green symbols represent mutations concordantly detected as majority (frequency >20%) and minority variants (frequency 1–5%), respectively. Red crosses represent mutations discordantly detected as low-level minority mutations (frequency 1–5%) or minority mutations (5–20%). Orange crosses represent mutations discordantly detected as minority mutations (frequency 5–20%) or majority mutations (5–20%). In the table on the right of scatter plots, the distribution of maximum frequency of discordant mutations detected between tools is reported; the third quartile of the distribution is highlighted in bold. Dotted lines represent 10% of frequency cut-off.
Figure 4
Figure 4
Concordance in detecting mutations among at least two NGS resistance interpretation tools at each position associated with resistance spanning PR/RT/IN. (A) Protease positions associated with resistance to PI. (B) Reverse transcriptase positions associated with resistance to NRTI. (C) Reverse transcriptase positions associated with resistance to NNRTI. (D) Integrase positions associated with resistance to INSTI. X axis represents the positions associated with drug resistance according to the Stanford drug resistance algorithm (HIVdb 9.5.0); Y axis represents the maximum frequency detected in the reports retrieved from the three interpretation tools. Each symbol represents a substitution detected with a valid coverage in at least two tools. Blue and green symbols represent mutations concordantly detected as majority (frequency > 20%) and minority variants (frequency 1–5%), respectively. Red crosses represent mutations discordantly detected as low-level minority mutations (frequency 1–5%) or minority mutations (5–20%). Orange crosses represent mutations discordantly detected as minority mutations (frequency 5–20%) or majority mutations (5–20%).
See this image and copyright information in PMC

References

    1. European AIDS Clinical Society Guidelines—Version 12.0. 2023. [(accessed on 31 July 2024)]. Available online: https://www.eacsociety.org/media/guidelines-12.0.pdf.
    1. Panel on Antiretroviral Guidelines for Adults and Adolescents, Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents with HIV, Department of Health and Human Services. [(accessed on 31 July 2024)];2024 Available online: https://clinicalinfo.hiv.gov/sites/default/files/guidelines/documents/ad....
    1. Parkin N.T., Avila-Rios S., Bibby D.F., Brumme C.J., Eshleman S.H., Harrigan P.R., Howison M., Hunt G., Ji H., Kantor R., et al. Multi-Laboratory Comparison of Next-Generation to Sanger-Based Sequencing for HIV-1 Drug Resistance Genotyping. Viruses. 2020;12:694. doi: 10.3390/v12070694. - DOI - PMC - PubMed
    1. Chen N.Y., Kao S.W., Liu Z.H., Wu T.S., Tsai C.L., Lin H.H., Wong W.W., Chang Y.Y., Chen S.S., Ku S.W.W. Shall I Trust the Report? Variable Performance of Sanger Sequencing Revealed by Deep Sequencing on HIV Drug Resistance Mutation Detection. Int. J. Infect. Dis. 2020;93:182–191. doi: 10.1016/j.ijid.2020.02.004. - DOI - PubMed
    1. Labate L., Bruzzone B., Spagnuolo V., Zazzi M., Santoro M., di Biagio A., Castagna A. PRESTIGIO RING: “A 59-year-old HIV-1 positive, highly treatment-experienced woman failing darunavir/ritonavir plus raltegravir”. New Microbiol. 2023;46:226–230. doi: 10.1093/cid/ciu287. - DOI - PubMed

Publication types

LinkOut - more resources