Luke C. Swenson

Background: The clinical implications of emergent HIV drug resistance on samples with low-level viraemia (LLV P < 0.001). Numerous sensitivity analyses confirmed these findings. Conclusion: Our results demonstrate that emergent HIV drug resistance at LLV is strongly associated with subsequent virologic failure. Furthermore, we uncovered a ‘dose-dependent’ increase in the hazard ratio for virologic failure with decreasing GSS estimated at the time of LLV. On the basis of these findings, we propose that resistance genotyping be encouraged for HIV-infected individuals on antiretroviral therapy experiencing low-level viraemia.

Low-level viremia during antiretroviral therapy and its accurate measurement are increasingly relevant. Here, we present an international collaboration of 4,221 paired blood plasma viral load (pVL) results from four commercial assays, emphasizing the data with low pVL. The assays compared were the Abbott RealTime assay, the Roche Amplicor assay, and the Roche TaqMan version 1 and version 2 assays. The correlation between the assays was 0.90 to 0.97. However, at a low pVL, the correlation fell to 0.45 to 0.85. The observed interassay concordance was higher when detectability was defined as 200 copies/ml than when it was defined as 50 copies/ml. A pVL of ∼100 to 125 copies/ml by the TaqMan version 1 and version 2 assays corresponded best to a 50-copies/ml threshold with the Amplicor assay. Correlation and concordance between the viral load assays were lower at a low pVL. Clear guidelines are needed on the clinical significance of low-level viremia.

Background. Low-level viremia (LLV; human immunodeficiency virus [HIV-1] RNA 50–999 copies/mL) occurs frequently in patients receiving antiretroviral therapy (ART), but there are few or no data available demonstrating that HIV-1 drug resistance testing at a plasma viral load (pVL) Methods. Resistance testing by genotyping at LLV was attempted on 4915 plasma samples from 2492 patients. A subset of previously ART-naive patients was analyzed who achieved undetectable pVL and subsequently rebounded with LLV (n = 212). A genotypic sensitivity score (GSS) was calculated based on therapy and resistance testing results by genotyping, and stratified according to number of active drugs. Results. Eighty-eight percent of LLV resistance assays produced useable sequences, with higher success at higher pVL. Overall, 16 of 212 (8%) patients had pretherapy resistance. Thirty-eight of 196 (19%) patients without pretherapy resistance evolved resistance to 1 or more drug classes, primarily the nucleoside reverse transcriptase (14%) and/or nonnucleoside reverse transcriptase (9%) inhibitors. Patients with resistance at LLV (GSS P = .007). Progressively lower GSS scores at LLV were associated with a higher increase in pVL over time (P < .001). Acquisition of additional resistance mutations to a new class of antiretroviral drugs during LLV was not found in a subset of patients. Conclusions. Routine HIV-1 genotyping of LLV samples can be performed with a reasonably high success rate, and the results appear predictive of future virologic outcomes.

Changes in HIV tropism from R5 to non-R5 or development of drug resistance is often associated with virologic failure in patients treated with maraviroc, a CCR5 antagonist. We sought to examine changes in HIV envelope sequences and inferred tropism in patients who did not respond to maraviroc-based regimens. We selected 181 patients who experienced early virologic failure on maraviroc-containing therapy in the MOTIVATE trials. All patients had R5 HIV by the original Trofile assay before entry. We used population-based sequencing methods and the geno2pheno algorithm to examine changes in tropism and V3 sequences at the time of failure. Using deep sequencing, we assessed whether V3 sequences observed at failure emerged from preexisting subpopulations. From population genotyping data at failure, 90 patients had R5 results, and 91 had non-R5 results. Of the latter group, the geno2pheno false-positive rate (FPR) value fell from a median of 20 at screening to 1.1 at failure. By deep sequencing, the median percentage of non-R5 variants in these patients rose from 1.4% to 99.5% after a median of 4 weeks on maraviroc. In 70% of cases, deep sequencing could detect a pretreatment CXCR4-using subpopulation, which emerged at failure. Overall, there were two distinct patterns of failure of maraviroc. Patients failing with R5 generally had few V3 substitutions and low non-R5 prevalence by deep sequencing. Patients with non-R5 HIV who were failing developed very-high-prevalence non-R5 HIV (median, 99%) and had very low geno2pheno values.

The Maraviroc Switch collaborative study (MARCH) is a study in aviremic patients on stable antiretroviral therapy and utilizes population-based sequencing of proviral DNA to determine HIV tropism and susceptibility to maraviroc. An external quality assessment (EQA) program was implemented to ensure competency in assessing the tropism of clinical samples conducted by MARCH laboratories (n = 14). The MARCH EQA has three prestudy phases assessing V3 loop sequencing and tropism determination using the bioinformatic algorithm geno2pheno, which generates a false-positive rate (FPR). DNA sequences with low FPRs are more likely to be from CXCR4-using (X4) viruses. Phase 1 of the EQA involved chromatogram interpretation. Phases 2, 2/3, and 3 involved patient and clonal samples. Clinical samples used in these phases were from treatment-experienced HIV-infected volunteers; 18/20 had viral loads of <50 copies/ml, and 10/15 were CXCR4-tropic on prior phenotyping. All samples were tested in triplicate, and any replicate with a geno2pheno FPR of <10% was designated X4. Performance was deemed adequate if ≤2 R5 and ≤1 X4 specimens were miscalled. For several clinical samples in the EQA, triplicate testing revealed marked DNA variability (FPR range, 0 to 96.7%). Therefore, a consensus-based approach was employed for each sample, i.e., a median FPR across laboratories was used to define sample tropism. Further sequencing analysis showed mixed viral populations in the clinical samples, explaining the differences in tropism predictions. All laboratories passed the EQA after achieving predefined competence thresholds in either of the phase 2 rounds. The use of clinical samples from patients resembling those who were likely to be screened in the MARCH, coupled with triplicate testing, revealed inherent DNA variability that might have been missed if single or duplicate testing and/or clonal samples alone were used. These data highlight the importance of intensive EQA of tropism laboratories before embarking on clinical studies. (This study has been registered at ClinicalTrials.gov under registration no. NCT01384682 [http://www.clinicaltrials.gov/ct2/show/study/NCT01384682?term=NCT01384682&rank=1].)

Insight concerning the switch in HIV-1 coreceptor use will lead to a better understanding of HIV-1 pathogenesis and host-virus dynamics. Predicting CXCR4 utilization by analyzing HIV-1 envelope consensus sequences is highly specific, but minority variants in the viral population are often missed resulting in low sensitivity. Commercial phenotypic assays are costly, and the development of sensitive in-house phenotypic assays to detect CXCR4-using HIV may not be feasible for some laboratories. A sensitive, inexpensive genotyping assay was developed to detect viral sequences associated with CXCR4-utilizing virus (X4). Codon-specific primer pairs were used to detect X4-associated codons at five positions in the HIV-1 envelope V3 loop (11, 13, 24, 25, and 32). Sixty plasma samples from HIV-1-infected individuals were analyzed by consensus sequencing and codon-specific PCR (CS-PCR). Forty-six of these were also phenotyped by Trofile or Enhanced Sensitivity Trofile (ESTA). CS-PCR detected X4 variants 17% more often than 11/24/25 consensus sequencing alone (n=60), 30% more often than Trofile (n=27), and in a limited data set, 16% more often than ESTA (n=19). CS-PCR combined with consensus sequencing had approximately 80% concordance with ESTA.

Background. A tropism test is required before administration of the antiretroviral drug maraviroc. However, plasma RNA testing is not possible in patients with undetectable plasma viral loads. Here we assess genotypic testing of cellular human immunodeficiency virus (HIV) DNA from peripheral blood mononuclear cells (PBMCs) to predict virologic responses in treatment-experienced patients beginning maraviroc-containing regimens. Methods. PBMC samples from 181 maraviroc recipients at study entry in MOTIVATE or A4001029 (51% R5 by original Trofile). The V3 loop was amplified in triplicate from cellular HIV DNA, and matching plasma RNA (n = 156). Sequencing was performed using standard population-based methods and next-generation deep sequencing, with tropism assessment as previously defined. Results. Genotypic DNA-based tropism testing from the cellular compartment had 78%–81% sensitivity relative to RNA-based Trofile at the same time point. Cell-based genotypic tropism methods and plasma-based phenotypic and genotypic methods were predictive of virologic response. However, when classifications were discordant, the outcomes favored the plasma predictions over the DNA ones. Conclusions. Genotypic determination of HIV tropism can be performed using cell-derived viral DNA, and is a predictor of virologic success on maraviroc in therapy-experienced patients. However, the PBMC compartment appears to be a suboptimal predictor compared to plasma.

Human immunodeficiency virus type 1 (HIV-1) V3 loop sequence can be used to infer viral coreceptor use. The effect of input copy number on population-based sequencing of the V3 loop of HIV-1 was examined through replicate deep and population-based sequencing of samples with known tropism, a heterogeneous clinical sample (624 population-based sequences and 47 deep-sequencing replicates), and a large cohort of clinical samples from phase III clinical trials of maraviroc including the MOTIVATE/A4001029 studies (n= 1,521). Proviral DNA from two independent samples from each of 101 patients from the MOTIVATE/A4001029 studies was also analyzed. Cumulative technical error occurred at a rate of 3 × 10⁻⁴mismatches/bp, without observed effect on inferred tropism. Increasing PCR replication increased minority species detection with an ∼10% minority population detected in 18% of cases using a single replicate at a viral load of 1,072 copies/ml and in 44% of cases using three replicates. The nucleotide prevalence detected by population-based and deep sequencing were highly correlated (Spearman's ρ, 0.73), and the accuracy increased with increasing input copy number (P< 0.001). Triplicate sequencing was able to predict tropism changes in the MOTIVATE/A4001029 studies for both low (P= 0.05) and high (P= 0.02) viral loads. Sequences derived from independently extracted and processed samples of proviral DNA for the same patient were equivalent to replicates from the same extraction (P= 0.45) and had correlated position-specific scoring matrix scores (Spearman's ρ, 0.75;P≪ 0.001); however, concordance in tropism inference was only 83%. Input copy number and PCR replication are important factors in minority species detection in samples with significant heterogeneity.

At the early stage of infection, human immunodeficiency virus (HIV)-1 predominantly uses the CCR5 coreceptor for host cell entry. The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS. The presence of a ‘fitness valley’ separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs. Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process. We have developed a phylogenetic pipeline for the molecular clock analysis, ancestral reconstruction, and visualization of deep sequence data. These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples (median 7 time points) from 8 untreated subjects with chronic HIV infections (Amsterdam Cohort Studies on HIV-1 infection and AIDS). We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days. HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. We determined that the first mutations contributing to CXCR4 use emerged about 16 (per subject range 4 to 30) months before the earliest predicted CXCR4-using ancestor, which preceded the first positive cell-based assay of CXCR4 usage by 10 (range 5 to 25) months. CXCR4 usage arose in multiple lineages within 5 of 8 subjects, and ancestral lineages following alternate mutational pathways before going extinct were common. We observed highly patient-specific distributions and time-scales of mutation accumulation, implying that the role of a fitness valley is contingent on the genotype of the transmitted variant. At the start of infection, human immunodeficiency virus (HIV) generally requires a specific protein receptor (CCR5) on the cell surface to bind and enter the cell. In roughly half of all HIV infections, the virus population eventually switches to using a different receptor (CXCR4). This ‘HIV coreceptor switch’ is associated with an accelerated rate of progression to AIDS. Although it is not known why this switch occurs in some infections and not others, it is thought to be shaped by constraints on how HIV can evolve from one mode to another. In this study, we test this hypothesis by reconstructing the evolutionary histories of HIV within 8 patients known to have undergone an HIV coreceptor switch. Each history is recreated from samples of HIV genetic sequences that were derived from repeated blood samples by next-generation sequencing, an emerging technology that is rapidly becoming an essential tool in the study of rapidly-evolving populations such as viruses or cancerous cells. Because we have samples from different points in time, we can use models of evolution to extrapolate back in time to the ancestors of each infection. Our analysis reveals patient-specific dynamics in HIV evolution that sheds new light on the determinants of the coreceptor switch.