Model for Assessment of Proficiency of Human Immunodeficiency Virus Type 1 Sequencing-Based Genotypic Antiretroviral Assays

Huang, De-Bin; Bremer, James W.; Brambilla, Donald; Palumbo, Paul

doi:10.1128/jcm.43.8.3963-3970.2005

Cited by 11 publications

(14 citation statements)

References 8 publications

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“…Panel complexity, detection of mixtures and level of sequence agreement were found to predict HIVDR genotyping quality, confirming previous reports [5, 20, 25, 28, 30, 34, 35]. …”

Section: Discussionsupporting

confidence: 88%

Capacity building and predictors of success for HIV‐1 drug resistance testing in the Asia‐Pacific region and Africa

Land¹,

Zhou

Cunningham

et al. 2013

Journal of the International AIDS Society

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BackgroundThe TREAT Asia Quality Assessment Scheme (TAQAS) was developed as a quality assessment programme through expert education and training, for laboratories in the Asia-Pacific and Africa that perform HIV drug-resistance (HIVDR) genotyping. We evaluated the programme performance and factors associated with high-quality HIVDR genotyping.MethodsLaboratories used their standard protocols to test panels of human immunodeficiency virus (HIV)-positive plasma samples or electropherograms. Protocols were documented and performance was evaluated according to a newly developed scoring system, agreement with panel-specific consensus sequence, and detection of drug-resistance mutations (DRMs) and mixtures of wild-type and resistant virus (mixtures). High-quality performance was defined as detection of ≥95% DRMs.ResultsOver 4.5 years, 23 participating laboratories in 13 countries tested 45 samples (30 HIV-1 subtype B; 15 non-B subtypes) in nine panels. Median detection of DRMs was 88–98% in plasma panels and 90–97% in electropherogram panels. Laboratories were supported to amend and improve their test outcomes as appropriate. Three laboratories that detected <80% DRMs in early panels demonstrated subsequent improvement. Sample complexity factors – number of DRMs (p<0.001) and number of DRMs as mixtures (p<0.001); and laboratory performance factors – detection of mixtures (p<0.001) and agreement with consensus sequence (p<0.001), were associated with high performance; sample format (plasma or electropherogram), subtype and genotyping protocol were not.ConclusionHigh-quality HIVDR genotyping was achieved in the TAQAS collaborative laboratory network. Sample complexity and detection of mixtures were associated with performance quality. Laboratories conducting HIVDR genotyping are encouraged to participate in quality assessment programmes.

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“…Panel complexity, detection of mixtures and level of sequence agreement were found to predict HIVDR genotyping quality, confirming previous reports [5, 20, 25, 28, 30, 34, 35]. …”

Section: Discussionsupporting

confidence: 88%

Capacity building and predictors of success for HIV‐1 drug resistance testing in the Asia‐Pacific region and Africa

Land¹,

Zhou

Cunningham

et al. 2013

Journal of the International AIDS Society

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“…Sequences were assembled and edited using Sequencher, version 4.10.1 (GeneCodes Corporation, Ann Arbor, MI) and aligned using ClustalW in Bio-Edit Sequence Alignment Editor v7.0.9.0 [21] , yielding a percentage identity for NIMR versus UoM results. We determined the proportion achieving at least 98% [22] identity, and the proportion with matching subtypes.…”

Section: Methodsmentioning

confidence: 99%

Deciphering the Complex Distribution of Human Immunodeficiency Virus Type 1 Subtypes among Different Cohorts in Northern Tanzania

et al. 2013

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BackgroundIncreased understanding of the genetic diversity of HIV-1 is challenging but important in the development of an effective vaccine. We aimed to describe the distribution of HIV-1 subtypes in northern Tanzania among women enrolled in studies preparing for HIV-1 prevention trials (hospitality facility-worker cohorts), and among men and women in an open cohort demographic surveillance system (Kisesa cohort).MethodsThe polymerase encompassing partial reverse transcriptase was sequenced and phylogenetic analysis performed and subtype determined. Questionnaires documented demographic data. We examined factors associated with subtype using multinomial logistic regression, adjusted for study, age, and sex.ResultsAmong 140 individuals (125 women and 15 men), subtype A1 predominated (54, 39%), followed by C (46, 33%), D (25, 18%) and unique recombinant forms (URFs) (15, 11%). There was weak evidence to suggest different subtype frequencies by study (for example, 18% URFs in the Kisesa cohort versus 5–9% in the hospitality facility-worker cohorts; adjusted relative-risk ratio (aRR) = 2.35 [95% CI 0.59,9.32]; global p = 0.09). Compared to men, women were less likely to have subtype D versus A (aRR = 0.12 [95% CI 0.02,0.76]; global p = 0.05). There was a trend to suggest lower relative risk of subtype D compared to A with older age (aRR = 0.44 [95% CI 0.23,0.85] per 10 years; global p = 0.05).ConclusionsWe observed multiple subtypes, confirming the complex genetic diversity of HIV-1 strains circulating in northern Tanzania, and found some differences between cohorts and by age and sex. This has important implications for vaccine design and development, providing opportunity to determine vaccine efficacy in diverse HIV-1 strains.

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“…The National Institute of Allergy and Infectious Diseases (NIAID) Virology Quality Assurance (VQA) program provides a comprehensive quality assessment program for virologic assays for HIV, including drug resistance genotyping [ 26 ]. A crucial function of the VQA program is to ensure the validity and inter- and intra-laboratory comparability of virologic laboratory data generated for NIAID-supported clinical trials and research by the provision and analysis of proficiency testing panels.…”

Section: Introductionmentioning

confidence: 99%

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

Parkin

Ávila‐Ríos

Bibby

et al. 2020

Viruses

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Next-generation sequencing (NGS) is increasingly used for HIV-1 drug resistance genotyping. NGS methods have the potential for a more sensitive detection of low-abundance variants (LAV) compared to standard Sanger sequencing (SS) methods. A standardized threshold for reporting LAV that generates data comparable to those derived from SS is needed to allow for the comparability of data from laboratories using NGS and SS. Ten HIV-1 specimens were tested in ten laboratories using Illumina MiSeq-based methods. The consensus sequences for each specimen using LAV thresholds of 5%, 10%, 15%, and 20% were compared to each other and to the consensus of the SS sequences (protease 4–99; reverse transcriptase 38–247). The concordance among laboratories’ sequences at different thresholds was evaluated by pairwise sequence comparisons. NGS sequences generated using the 20% threshold were the most similar to the SS consensus (average 99.6% identity, range 96.1–100%), compared to 15% (99.4%, 88.5–100%), 10% (99.2%, 87.4–100%), or 5% (98.5%, 86.4–100%). The average sequence identity between laboratories using thresholds of 20%, 15%, 10%, and 5% was 99.1%, 98.7%, 98.3%, and 97.3%, respectively. Using the 20% threshold, we observed an excellent agreement between NGS and SS, but significant differences at lower thresholds. Understanding how variation in NGS methods influences sequence quality is essential for NGS-based HIV-1 drug resistance genotyping.

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Model for Assessment of Proficiency of Human Immunodeficiency Virus Type 1 Sequencing-Based Genotypic Antiretroviral Assays

Cited by 11 publications

References 8 publications

Capacity building and predictors of success for HIV‐1 drug resistance testing in the Asia‐Pacific region and Africa

Capacity building and predictors of success for HIV‐1 drug resistance testing in the Asia‐Pacific region and Africa

Deciphering the Complex Distribution of Human Immunodeficiency Virus Type 1 Subtypes among Different Cohorts in Northern Tanzania

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

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