Jessica M Fogel1, David Bonsall2, Vanessa Cummings1, Rory Bowden3, Tanya Golubchik2, Mariateresa de Cesare3, Ethan A Wilson4, Theresa Gamble5, Carlos Del Rio6,7, D Scott Batey8, Kenneth H Mayer9,10, Jason E Farley11, James P Hughes12, Robert H Remien13,14, Chris Beyrer15, Christophe Fraser2, Susan H Eshleman1. 1. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 2. Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK. 3. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. 4. Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 5. FHI 360, Durham, NC, USA. 6. Hubert Department of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA. 7. Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA. 8. Department of Social Work, University of Alabama at Birmingham, Birmingham, AL, USA. 9. Department of Medicine, Harvard Medical School, Boston, MA, USA. 10. Fenway Institute, Boston, MA, USA. 11. The REACH Initiative, Johns Hopkins University School of Nursing, Baltimore, MD, USA. 12. Department of Biostatistics, University of Washington, Seattle, WA, USA. 13. HIV Center for Clinical and Behavioral Studies, NY State Psychiatric Institute, New York, NY, USA. 14. Department of Psychiatry, Columbia University, New York, NY, USA. 15. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Abstract
OBJECTIVES: To evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load. METHODS: Plasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV). RESULTS: HIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with >5000 copies/mL; 50.0% for 26 samples with 1000-5000 copies/mL; 0% for 23 samples with <1000 copies/mL). For samples with results from both methods, drug resistance mutations (DRMs) were detected in 33 samples using ViroSeq and 42 samples using veSEQ-HIV (detection threshold: 5.0%). Overall, 146 major DRMs were detected; 107 were detected by both methods, 37 were detected by veSEQ-HIV only (frequency range: 5.0%-30.6%) and two were detected by ViroSeq only. HIV viral loads estimated by veSEQ-HIV strongly correlated with results from the Abbott RealTime Viral Load assay (R2 = 0.85; n = 142). CONCLUSIONS: The NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.
OBJECTIVES: To evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load. METHODS: Plasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV). RESULTS: HIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with >5000 copies/mL; 50.0% for 26 samples with 1000-5000 copies/mL; 0% for 23 samples with <1000 copies/mL). For samples with results from both methods, drug resistance mutations (DRMs) were detected in 33 samples using ViroSeq and 42 samples using veSEQ-HIV (detection threshold: 5.0%). Overall, 146 major DRMs were detected; 107 were detected by both methods, 37 were detected by veSEQ-HIV only (frequency range: 5.0%-30.6%) and two were detected by ViroSeq only. HIV viral loads estimated by veSEQ-HIV strongly correlated with results from the Abbott RealTime Viral Load assay (R2 = 0.85; n = 142). CONCLUSIONS: The NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.
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