Extensive diversity and impact of drug-resistant HIV-1 variants in individuals with prior virologic failure

Abstract

HIV-1 drug resistance remains a major challenge to treatment and control efforts, particularly in sub-Saharan Africa (sSA). However, standard resistance genotyping does not adequately capture linked drug resistance mutations within the viral quasispecies that may influence virologic failure (VF). We used a next-generation sequencing primer ID (NGS-Primer ID) assay to characterize linked HIV-1 drug resistance mutations in plasma from participants in the Resistance Testing to Improve Management of Virologic Failure (REVAMP) study who had detectable viremia on first-line non-nucleoside reverse transcriptase inhibitor (NNRTI)-based antiretroviral therapy (ART) and were maintained on NNRTI-based regimens. For each participant, we calculated a weighted genotypic susceptibility score (wGSS) based on the GSS of each reported pattern and its sequence-supported frequency within the sample. Plasma specimens from 108 participants were sequenced. Sanger sequencing showed a median GSS of 1.0 (IQR, 1.0-2.0), whereas NGS-Primer ID identified a median of 10 distinct resistance patterns per participant (IQR, 5-17), with 67% (IQR, 8-92) of reported DRM-pattern frequency within participants corresponding to linked dual-class mutations. A broad spectrum of drug-susceptible and resistant- DRM patterns were observed, with the median difference between the highest and lowest pattern-specific GSS being 2.0 (2.0-2.75). Within each participant, a median of 10% of reported patterns showed less resistance, while 19% showed more resistance compared to that predicted by Sanger sequencing. Individuals with persistent VF had significantly lower wGSS at study entry than those who later achieved virologic re-suppression (median, 1.3 vs 2.1; p < 0.001). NGS-Primer ID revealed substantial intra-host diversity in linked HIV-1 drug resistance patterns that was not captured by conventional Sanger sequencing. Incorporating linked resistance patterns into susceptibility assessment may improve prediction of subsequent virologic failure.