Baseline genotypic resistance analysis
Baseline sequence data were obtained from 234 of the 277 subjects enrolled in the trial (84%), with the sequences well distributed between the arms [N = 34 to 42 among the different arms with 34 in Arm A (SQV + RTV + DLV), 42 in Arm B (SQV + RTV + ADV), 37 in Arm C (SQV + RTV + DLV + ADV), 41 in Arm D (SQV + NFV + DLV), 41 in Arm E (SQV + NFV + ADV), and 39 in Arm F (SQV + NFV + DLV + ADV)]. Of note, the treatment-experienced ACTG 359 study subjects had a median prior IDV use of 13.8 months, median CD4 cell count at entry of 228 cells/mm3, and mean HIV-1 viral RNA load at entry of 32,297 copies/ml (4.51 log10 copies/ml). Overall, 30% (77 of 254) of subjects had HIV-1 RNA levels of ≤500 copies/ml at week 16 (virologic responders). In addition, sequence data were obtained for 141 of the 177 subjects (80%) who had experienced virologic failure with plasma viral RNA levels greater than 500 copies/ml at week 16 (virologic nonresponders). The drug regimens in this trial had the potential for strong selective pressure from the PIs and from a potent NNRTI. Thus the analysis of the impact and evolution of resistance using this trial represents an opportunity to examine relevant descriptive and statistical approaches that are generally applicable.
The NRTI resistance mutations and PI resistance mutations present at baseline are summarized in Tables and . Resistance mutations in RT to NRTIs were largely to lamivudine (M184V) and zidovudine (M41L, K70R, L210W, T215F/Y, and K219Q).20
The M184V mutation was present in 85% of subjects and a mutation at position 215 was present in 59% of subjects. These mutation patterns are consistent with high levels of resistance to lamivudine and a wide range of resistance to zidovudine seen in the phenotypic resistance analysis of a subset of these same subjects.18
The D67N mutation occurred in 40% of the subjects. In contrast, the 69 insertion complex, associated with multi-NRTI resistance,21
was not detected in any of the baseline sequences. Resistance mutations to NNRTIs were absent as would be expected given the NNRTI-naive status for trial entry.
Frequency of Wild-Type (wt) and RTI Resistance Mutations
Wild-Type (wt) and PI Resistance Mutations
The baseline resistance mutations in protease were dominated by primary resistance mutations M46I/L, I54V, V82A/F/T, and L90M (). Secondary or compensatory mutations that were most notable were L10I, L24I, M36I, L63P, and A71V/T. Positions 46 and 82 both had approximately 50% mutation prevalence in baseline samples. These observations are consistent with phenotypic studies, which reveal that a substitution at residue 82 is most frequently associated with IDV resistance,22
and that a sensitive predictor of IDV resistance can be obtained if substitutions at either residue 46 or 82 are considered. Havlir et al.
have previously shown that virologic failure to an IDV-containing regimen can occur in the absence of resistance mutations, again consistent with only 50% of subjects in this cohort having resistance mutations in the protease coding domain at entry.23
Correlation between GSS and therapeutic response
A series of logistic regression models were used to determine the relationship between baseline GSS and week 16 virologic response. With dGSS or cGSS as the only variable for week 16 outcome, we found that both scoring schemes strongly predicted virologic response at week 16 (p = 0.0022 or 0.0015, respectively). Each additional unit increase in the dGSS increased the odds of virologic response by about 2-fold, and 2.5-fold with the cGSS. However, entry viral RNA load was a strong determinant of virologic response and an effect modifier of drug resistance markers. Thus, when the dGSS and the baseline RNA were assessed as independent variables, both were found to be independent predictors of virologic response (p = 0.002 and 0.0007, respectively). Like-wise, a similar result was obtained when the cGSS and baseline viral RNA load were assessed as independent variables, with even smaller p values (p = 0.009 and 0.005). Therefore, both the dGSS and cGSS were significant predictors of virologic outcome at week 16, independent of entry viral RNA loads. The logistic regression models excluded the 18 subjects with missing week 16 HIV RNA outcomes (missing excluded). When identical models were generated treating the missing 18 subjects' week 16 outcomes as nonresponders, similar results were obtained, although the p values for the GSS increased 3- to 4-fold (data not shown).
There are a variety of clinically available genotypic drug-resistance interpretation systems, and the most commonly used are the Stanford HIV RT and Protease sequence data-base, Geno2pheno, Rega, ANRS, and virtual phenotype.24-26
These are based on genotype–phenotype correlations, known clinical outcomes, rule-based algorithms, and/or expert opinion. In the current study, we found that two different GSS based on the Stanford HIV RT and Protease sequence database, the dGSS and cGSS, were independent predictors of virologic response. However, there are some limitations in using the GSS to predict viral outcome. First, scores are scaled from 0 to 1 for all drugs, and do not adjust for individual or combined drug potency. Second, since resistance scores to ADV are unavailable in Stanford rules and ADV has been shown to be a much less potent drug in vivo
, a score for ADV susceptibility was set to zero. However, any ADV effect was likely small, as our data did not reveal any significant difference in the appearance or disappearance of NRTI mutations in the groups treated with or without ADV (see below). Third, since RTV was used as a PI booster, it was assigned an intermediate weight of 0.5 for both GSS models. Fourth, neither model can account for complex virus-host interactions or host differences in pharmacokinetics. Fifth, minor variants, i.e., those comprising less than 30% of the viral population, were not accounted for in the genotypic test used, and this is especially relevant as 50% of subjects taking IDV at entry showed no resistance mutations in the protease gene. Even with these limitations the GSS models were significantly correlated with outcome, confirming previous studies of the utility of genotypic information in assessing the likelihood of therapeutic response.11,12,27-29
Comparison of the predictive value of GSS versus PSS
Previously, we described a retrospective analysis of the utility of phenotypic characterization of drug resistance at entry as a predictor of therapy outcome;18
a phenotypic susceptibility score (PSS) was determined on a total of 142 subjects within the ACTG 359. The PSS was found to be a significant predictor of virologic response at week 16. There were 125 subjects with both baseline genotypic and phenotypic analyses, and week 16 HIV-1 RNA results available. The Spearman correlation between the value of the cPSS and the value of the cGSS was 0.79 (p
= 0.0001). Using logistic regression analysis and correcting for baseline viral RNA load, we found that both the cPSS and cGSS were significant predictors of virologic response in this subset of subjects, with the cGSS having a moderately smaller p
-value in this analysis (0.009 vs. 0.025) ().
Logistic Regression Relating Baseline GSS or PSS to Week 16 RNA Responses
The methodology of genotypic or phenotypic resistance testing has been a subject of previous study.18,25,30-38
Our analysis adds to several trials that have attempted to compare directly these two different methods. For the majority of these studies, the clinical outcomes have been similar regardless of the methodology: phenotype vs. virtual phenotype39,40
and genotype vs. virtual phenotype;33,41
however, in a subset of HIV-1-infected subjects with PI experience, a greater proportion of subjects achieved virologic suppression using genotypic testing vs. phenotypic testing42
to guide antiviral selection. Although the optimum interpretation system has yet to be determined, our data suggest that the genotypic susceptibility scores perform similarly to the phenotypic susceptibility score in being correlated with virologic outcome.
Resistance mutations present at entry that are overrepresented, lost, or newly evolved in the subjects who experienced virologic failure at week 16
Among the 216 subjects with available baseline genotypic data and week 16 HIV-1 RNA data, 67 of 216 (31%) had a favorable virologic response (week 16 HIV RNA ≤500 copies/ml). To determine the relationship between resistance mutations present at entry and virologic response, we compared the frequencies of wild-type or mutant codons at week 16 for those subjects with and without a virologic response (Tables and ). For all comparisons, a two-sided Fisher's exact test was used, and missing data were excluded. Consistent with the overall week 16 response rate of 30% in the trial, the baseline wild-type sequences partitioned at a 2:1 ratio between the groups that experienced virologic failure vs. response (except as outlined below). Of note, there were no NNRTI resistance mutations present at entry to include in the analysis, and all subjects discontinued NRTI therapy with the exception of subjects who received the minimally potent ADV.
NRTI resistance mutations
At baseline, there were several positions in the RT coding domain that were overrepresented in the subjects who were virologic nonresponders at week 16 (Tables and ). Subjects with mutations at position 67 (D to E/G/N), 69 (T to A/D/N/S), 70 (K to R/G/E), and 118 (V to I) were significantly enriched in those who were virologic nonresponders (p
= 0.005, 0.005, 0.02, and 0.013, respectively). In addition, although at positions 215 and 219, there were similar rates of wild-type vs. mutant codons present in the virologic responders and nonresponders, there was a statistically significant overrepresentation of the thymidine analog resistance mutations T215F (16% vs. 1.5%) and K219Q (17.5% vs. 4.5%) in the virologic nonresponders vs. the virologic responders (p
= 0.002 and 0.015, respectively). However, the more common T215Y mutation partitioned equally among the responders and nonresponders. The enrichment of T215F and K219Q in the subjects who were virologic nonresponders correlates with the differential evolution of the TAM-1 cluster (M41L, L210W, and T215Y) vs. TAM-2 cluster (K70R, T215F, and K219Q),43-45
and is possibly due to steric constraints on the ATP site as assessed by molecular modeling approaches and known ATP-protein interactions or fitness differences (see below).46
Partitioning of Mutant and Wild-Type Positions
In comparing the week 16 sequences of the virologic non-responders to their entry sequences there were three codons associated with NRTI resistance that showed changes not involving a reversion to wild type. The most frequent position with new mutations was at position 215, where changes were detected in 18 sequences (13% of subjects). However, the substitutions were not the common TAMs (215 T to F or Y). Instead, the 215 position was enriched for the phenotypic reversion substitutions S/Z/I/D/C/V. In addition, two new polymorphisms were detected in the TAM position 219. At position 184, there were two subjects who gained the M184I substitution. Like M184V, this substitution is associated with high level resistance to 3TC and FTC. The only other new NRTI resistance mutation was a L74V substitution (associated with resistance to DDI and ABC) in one subject in the DLV arm. The appearance of these latter substitutions (M184I and L74V) likely represents the fortuitous outgrowth of viral subpopulations that were initially present at baseline and evolved linked resistance mutations for which there was selective pressure. Although ADV is known to have minimal NRTI activity, as expected we did not detect any significant difference in the gain or loss of any TAM at week 16 when comparing groups with or without ADV; p-values at baseline and 16 weeks for these NRTI resistance mutations in the DLV or ADV arms are all >0.16.
NNRTI resistance mutations
Multiple NNRTI mutations have been associated with DLV resistance: K103N, V106M, Y181C, Y188L, and P236L.20
Although none of these was present at entry, a total of 53 NNRTI resistance mutations were detected in the 141 virologic nonresponders in the DLV-containing arms (Tables and ). As expected, none of the subjects treated with ADV, but not DLV, developed any NNRTI-associated mutations. The most frequently observed mutation was K103N, which was detected in 34 subjects. Y181C arose in 17 subjects, and P236L was seen in two subjects.
We also wanted to determine whether potent NNRTI drug selection had an effect on reversion of TAMs or loss of M184V in the absence of ongoing NRTI drug selection. To address this question, we analyzed resistance mutations at baseline and week 16 in the subgroup of patients that received only DLV, but not ADV (in addition to the PIs) and fixed an NNRTI-resistance mutation (19 subjects). In the subjects that developed either K103N or Y181C in the DLV arm, 12 of 17 subjects (71%) maintained all of their baseline TAM mutations. In addition, the rate of M184V retention in the DLV arm was relatively similar (65% retained M184V). These data indicate that in the absence of potent NRTI drug selection, there is no strong tendency for unselected NRTI resistance mutations to revert back to or be replaced by the wild-type pol
sequence while fixing a strongly selected NNRTI mutation. However, when NRTI resistance mutations were lost, this was more likely to occur in arms that contained DLV compared to the arms without DLV; 24% of the week 16 sequences in the DLV-containing arms lost one or more TAMs compared to only 8% in the DLV-negative arms (p
= 0.01). M41L was the most common TAM that was lost (either alone or in combination, at a frequency of 20%). The persistence of NNRTI mutations has been well documented despite the absence of drug selection for up to 12 months, indicating minimal effect of NNRTI mutations on viral fitness.47
Thus, while NNRTI resistance mutations were easily added to the background NRTI mutations in many cases, a presumed cumulative effect of fitness loss was also suggested with the more pronounced loss of NRTI mutations in the DLV arms. A potential factor leading to the relative enrichment of various NRTI mutations in the subjects who were virologic nonresponders is NNRTI hypersusceptibility.48,49
The presence of mutations at three RT codons (215, 208, and 118) is independently associated with hypersusceptibility to NNRTIs.50,51
Of note, T215Y was highly predictive of NNRTI hypersusceptibility, whereas T215F was not predictive. Our observation of the overrepresentation of T215F but not T215Y in the subjects who were virologic nonresponders is consistent with the T215Y mutation conferring NNRTI hypersusceptibility.
PI resistance mutations
At baseline, there were several mutations within pro that were overrepresented in the virologic nonresponders (Tables and ). Mutations at position 10 (L to F/I/R/V), 24 (L to I), 71 (A to T/V/I), 73 (G to C/S/T), and 88 (N to D/S/T) were enriched in the nonresponder samples relative to the wild type (all p values < 0.05). There were relatively few examples of I47V, but these were overrepresented in the virologic responders (p = 0.038) indicating a protective effect. The M46I mutation approached statistical significance (p = 0.057) for being overrepresented in the nonresponders, while the M46L mutation had the same partitioning as the wild type. As expected, the position most frequently substituted among this group of IDV-experienced subjects was position 82 (present in approximately 50% of subjects); however, mutations at this position were partitioned equally among the virologic responders and nonresponders. The I84V and L90M mutations would be expected to contribute to resistance to regimens containing SQV and NFV, and both of these primary resistance mutations showed a trend in the nonresponders.
The differential partitioning of baseline PI resistance-associated mutations in the viral strains of nonresponders largely paralleled increases seen in the frequency of protease mutations in the week 16 sequence of these subjects, especially for primary resistance mutations (). The following mutations were significantly increased at week 16 compared to their paired entry sample: L10I, M36I, F53L, I54V, A71V, G73S, I84V, and L90M (all p
values <0.02). In general, there was a trend for higher incidence of PI resistance mutations at week 16, with two exceptions. There was a significant decrease in I93L from 52% to 43% (p
< 0.004) from 73 of 141 subjects at baseline to 61 of 141 subjects at week 16. In addition, there was a trend for the loss of V82A (p
= 0.08) even though I54V, which is frequently seen as a compensatory mutation to V82A,52,53
increased. There was no statistically significant increase in L24I, M46I, L63P, or N88S, suggesting that although they could contribute to resistance as evidenced by their enrichment in the nonresponder subset, they were not selected for resistance pathways when more mutations appeared. These data suggest that the V82A pathway commonly seen with IDV (and RTV) did not participate in the evolution of resistance to the SQV and NFV-based PIs. Consistent with these data, mutations at position 82 were associated with greater reductions in viral RNA load, while mutations at positions 24 and 90 were associated with smaller reductions in viral RNA load (see below).