Cargando…

Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients

INTRODUCTION: Use of deep sequencing is becoming a critical tool in clinical virology, with an important impact in the HIV field for routine diagnostic purposes. Here, we present the comparison of deep and Sanger sequencing in newly diagnosed HIV patients, and the use of DeepChek v1.3 & VisibleC...

Descripción completa

Detalles Bibliográficos
Autores principales: Fernández-Caballero, Jose-Angel, Chueca, Natalia, Alvarez, Marta, Gonzalez, Dimitri, García, Federico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International AIDS Society 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4225348/
https://www.ncbi.nlm.nih.gov/pubmed/25397493
http://dx.doi.org/10.7448/IAS.17.4.19748
_version_ 1782343485909630976
author Fernández-Caballero, Jose-Angel
Chueca, Natalia
Alvarez, Marta
Gonzalez, Dimitri
García, Federico
author_facet Fernández-Caballero, Jose-Angel
Chueca, Natalia
Alvarez, Marta
Gonzalez, Dimitri
García, Federico
author_sort Fernández-Caballero, Jose-Angel
collection PubMed
description INTRODUCTION: Use of deep sequencing is becoming a critical tool in clinical virology, with an important impact in the HIV field for routine diagnostic purposes. Here, we present the comparison of deep and Sanger sequencing in newly diagnosed HIV patients, and the use of DeepChek v1.3 & VisibleChek for their interpretation and integration with virological and clinical data. PATIENTS AND METHODS: Plasma samples from 88 newly diagnosed HIV-1-infected patients were included in the study. Median age (IQR) was 37 (27–47), median CD4 count (IQR) was 387 (220–554), and 85% were males. Median Viral Load (Log, IQR) was 5.03 (4.51–5.53). Deep sequencing was obtained using a GS-Junior (Roche). Sequences were preprocessed with the 454 AVA software; aligned reads were uploaded into the DeepChek v1.3 system (ABL SA). Sanger sequences (Trugene), were uploaded in parallel. Stanford algorithm (version 7.0) resistance interpretation to first line drugs and all the mutations (score≥5) were analyzed. For deep sequencing, 1%, 5% and 10% thresholds were chosen for resistance interpretation. RESULTS: Using VisibleChek for analysis, we were able to describe the detection of any mutation using Sanger in 37/88 patients, with a total number of 50 Stanford ≥5 mutations, K103N and E138A being the most prevalent (n=4). Using UDS-1%, we found 72/88 patients with at least one mutation (total of 206 Stanford ≥5 mutations). Using Sanger data, 9/88 patients (10.22%) showed any resistance to NNRTIs, while none showed resistance to NRTIs or PIs. Using UDS-10% increased resistance to NRTIs [3/88 (3.40%)], to NNRTIs 12/88 (13.63%), and to a lesser extent to PIs [1/88 (1.13%)]. Using UDS-5% increased resistance to NRTIs [4/88 (4.54%)] and to NNRTIs [12/88 (13.63%)], but not to PIs. Using UDS-1% increased resistance to all classes: NRTIs [14/88 (15.90%)], NNRTIs [26/88 (30.68%)], and PIs [6/88 (6.81]. CONCLUSIONS: DeepChek and VisibleChek allow for an easy, reliable and rapid analysis of UDS data from HIV-1. Compared to Sanger data, UDS detected a higher number of resistance mutations. UDS with a 5 &10% threshold resulted in an increase in the number of patients with any degree of resistance mainly to NRTI, NNRTIs. Going down as low as 1% increased resistance to all classes. A correct definition of clinically relevant thresholds for the interpretation of minor variant detection for different classes of ARVs is needed.
format Online
Article
Text
id pubmed-4225348
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher International AIDS Society
record_format MEDLINE/PubMed
spelling pubmed-42253482014-11-13 Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients Fernández-Caballero, Jose-Angel Chueca, Natalia Alvarez, Marta Gonzalez, Dimitri García, Federico J Int AIDS Soc Poster Sessions – Abstract P216 INTRODUCTION: Use of deep sequencing is becoming a critical tool in clinical virology, with an important impact in the HIV field for routine diagnostic purposes. Here, we present the comparison of deep and Sanger sequencing in newly diagnosed HIV patients, and the use of DeepChek v1.3 & VisibleChek for their interpretation and integration with virological and clinical data. PATIENTS AND METHODS: Plasma samples from 88 newly diagnosed HIV-1-infected patients were included in the study. Median age (IQR) was 37 (27–47), median CD4 count (IQR) was 387 (220–554), and 85% were males. Median Viral Load (Log, IQR) was 5.03 (4.51–5.53). Deep sequencing was obtained using a GS-Junior (Roche). Sequences were preprocessed with the 454 AVA software; aligned reads were uploaded into the DeepChek v1.3 system (ABL SA). Sanger sequences (Trugene), were uploaded in parallel. Stanford algorithm (version 7.0) resistance interpretation to first line drugs and all the mutations (score≥5) were analyzed. For deep sequencing, 1%, 5% and 10% thresholds were chosen for resistance interpretation. RESULTS: Using VisibleChek for analysis, we were able to describe the detection of any mutation using Sanger in 37/88 patients, with a total number of 50 Stanford ≥5 mutations, K103N and E138A being the most prevalent (n=4). Using UDS-1%, we found 72/88 patients with at least one mutation (total of 206 Stanford ≥5 mutations). Using Sanger data, 9/88 patients (10.22%) showed any resistance to NNRTIs, while none showed resistance to NRTIs or PIs. Using UDS-10% increased resistance to NRTIs [3/88 (3.40%)], to NNRTIs 12/88 (13.63%), and to a lesser extent to PIs [1/88 (1.13%)]. Using UDS-5% increased resistance to NRTIs [4/88 (4.54%)] and to NNRTIs [12/88 (13.63%)], but not to PIs. Using UDS-1% increased resistance to all classes: NRTIs [14/88 (15.90%)], NNRTIs [26/88 (30.68%)], and PIs [6/88 (6.81]. CONCLUSIONS: DeepChek and VisibleChek allow for an easy, reliable and rapid analysis of UDS data from HIV-1. Compared to Sanger data, UDS detected a higher number of resistance mutations. UDS with a 5 &10% threshold resulted in an increase in the number of patients with any degree of resistance mainly to NRTI, NNRTIs. Going down as low as 1% increased resistance to all classes. A correct definition of clinically relevant thresholds for the interpretation of minor variant detection for different classes of ARVs is needed. International AIDS Society 2014-11-02 /pmc/articles/PMC4225348/ /pubmed/25397493 http://dx.doi.org/10.7448/IAS.17.4.19748 Text en © 2014 Fernández-Caballero J-A et al; licensee International AIDS Society http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Sessions – Abstract P216
Fernández-Caballero, Jose-Angel
Chueca, Natalia
Alvarez, Marta
Gonzalez, Dimitri
García, Federico
Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title_full Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title_fullStr Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title_full_unstemmed Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title_short Use of deep sequencing data for routine analysis of HIV resistance in newly diagnosed patients
title_sort use of deep sequencing data for routine analysis of hiv resistance in newly diagnosed patients
topic Poster Sessions – Abstract P216
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4225348/
https://www.ncbi.nlm.nih.gov/pubmed/25397493
http://dx.doi.org/10.7448/IAS.17.4.19748
work_keys_str_mv AT fernandezcaballerojoseangel useofdeepsequencingdataforroutineanalysisofhivresistanceinnewlydiagnosedpatients
AT chuecanatalia useofdeepsequencingdataforroutineanalysisofhivresistanceinnewlydiagnosedpatients
AT alvarezmarta useofdeepsequencingdataforroutineanalysisofhivresistanceinnewlydiagnosedpatients
AT gonzalezdimitri useofdeepsequencingdataforroutineanalysisofhivresistanceinnewlydiagnosedpatients
AT garciafederico useofdeepsequencingdataforroutineanalysisofhivresistanceinnewlydiagnosedpatients