ABSTRACT
BACKGROUND: The association between low-frequency human immunodeficiency virus type 1 (HIV-1) drug resistance mutations (DRMs) and treatment failure (TF) is controversial. We explore this association using next-generation sequencing (NGS) methods that accurately sample low-frequency DRMs. METHODS: We enrolled women with HIV-1 in Malawi who were either antiretroviral therapy (ART) naive (cohort A), had ART failure (cohort B), or had discontinued ART (cohort C). At entry, cohorts A and C began a nonnucleoside reverse transcriptase inhibitor-based regimen and cohort B started a protease inhibitor-based regimen. We used Primer ID MiSeq to identify regimen-relevant DRMs in entry and TF plasma samples, and a Cox proportional hazards model to calculate hazard ratios (HRs) for entry DRMs. Low-frequency DRMs were defined as ≤20%. RESULTS: We sequenced 360 participants. Cohort B and C participants were more likely to have TF than cohort A participants. The presence of K103N at entry significantly increased TF risk among A and C participants at both high and low frequency, with HRs of 3.12 (95% confidence interval [CI], 1.58-6.18) and 2.38 (95% CI, 1.00-5.67), respectively. At TF, 45% of participants showed selection of DRMs while in the remaining participants there was an apparent lack of selective pressure from ART. CONCLUSIONS: Using accurate NGS for DRM detection may benefit an additional 10% of patients by identifying low-frequency K103N mutations.
Subject(s)
Drug Resistance, Viral , HIV Infections , HIV-1 , Mutation , Treatment Failure , Humans , HIV-1/genetics , HIV-1/drug effects , Female , HIV Infections/drug therapy , HIV Infections/virology , Drug Resistance, Viral/genetics , Adult , Malawi , Anti-HIV Agents/therapeutic use , High-Throughput Nucleotide Sequencing , Cohort Studies , Young Adult , Treatment OutcomeABSTRACT
BACKGROUND: The association between low-frequency HIV-1 drug resistance mutations (DRMs) and treatment failure (TF) is controversial. We explore this association using NGS methods that accurately sample low-frequency DRMs. METHODS: We enrolled women with HIV-1 in Malawi who were either ART naïve (A), had ART failure (B), or had discontinued ART (C). At entry, A and C began an NNRTI-based regimen and B started a PI-based regimen. We used Primer ID MiSeq to identify regimen-relevant DRMs in entry and TF plasma samples, and a Cox proportional hazards model to calculate hazard ratios (HRs) for entry DRMs. Low-frequency DRMs were defined as ≤ 20%. RESULTS: We sequenced 360 participants. Cohort B and C participants were more likely to have TF than Cohort A participants. The presence of K103N at entry significantly increased TF risk among A and C participants at both high and low frequency, with HR of 3.12 [1.58-6.18, 95% CI] and 2.38 [1.00-5.67, 95% CI] respectively. At TF, 45% of participants showed selection of DRMs while in the remaining participants there was an apparent lack of selective pressure from ART. CONCLUSIONS: Using accurate NGS for DRM detection may benefit an additional 10% of the patients by identifying low-frequency K103N mutations.
