NRTI resistance mutations include those that inhibit NRTI incorporation into the HIV-1 primer DNA strand and those that promote the excision of chain-terminating NRTIs via ATP-mediated pyrophosphorolysis. K65R, K70E, L74V, F115Y, M184V/I, and Q151M plus the Q151M-associated mutations (A62V, V75I, F77L, and F116Y) inhibit NRTI incorporation; whereas M41L, D67N, K70R, L210W, T215Y/F, K219Q/E, and the amino acid T69ins promote NRTI excision. M41L, D67N, K70R, L210W, T215Y/F, and K219Q/E are called thymidine analog mutations (TAMs) because they are selected primarily by the thymidine analogs AZT and d4T. The TAMs have been subclassified into two overlapping clusters: type I (M41L, L210W, and T215Y) and type II (D67N, K70R, T215F, and K219Q/E) TAMs. The mechanisms of action of two additional mutations, T69D and V75T, which were reported in the 1990s to reduce susceptibility to ddC and d4T, respectively (6
), have been less well characterized.
With the analysis of increasingly large databases, many additional NRTI-selected mutations have been identified and in some cases have been shown to decrease NRTI susceptibility. Several of these mutations occur at known NRTI resistance positions: K65N, D67G/E/S/T, K70Q/N/G/S/T, L74I, V75M/A/S, and K219N/R/W/D/H (2
). Others are at novel positions in the 5′ polymerase coding domain: E40F, K43E/Q/N, E44D/A, V118I, E203K, H208Y, D218E, K223Q/E, and L228H/R (9
). Finally, several mutations 3′ to the polymerase coding domain facilitate nucleotide excision, presumably by slowing enzymatic translocation, allowing more time for nucleoside reverse transcriptase inhibitor (NRTI) excision (19
). The most important of these mutations, N348I (10
), was not evaluated in our study, because it lies outside the RT region that is tested by the PhenoSense assay.
Methodological innovations and prediction accuracy.
It has been difficult to determine the phenotypic impact of RT mutations on individual NRTIs, because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns. Moreover, the NRTIs have highly variable in vitro dynamic susceptibility ranges (i.e., the fold difference in EC50 between highly drug resistant and wild-type viruses). The EC50s of AZT, 3TC, and FTC for highly resistant viruses are usually more than 100 times higher than those for wild-type viruses. In contrast, the EC50s of d4T, ddI, and TDF for highly resistant viruses are rarely more than 5 times higher than those for wild-type viruses. Nonetheless, reductions in susceptibility with EC50s as low as 1.5 times higher than that of the wild type are clinically significant for d4T, ddI, and TDF. The dynamic range for ABC is slightly higher than that for d4T, ddI, and TDF.
To facilitate the comparability of a mutation's effect on different NRTIs despite their different dynamic ranges, we standardized the coefficients for each mutation by dividing the dependent variable (log fold change in HIV susceptibility) by its variance. This provides the ability to assess the relative influences of mutations on decreased susceptibility even for those NRTIs with narrow dynamic ranges. We also chose to study only those phenotypes performed by PhenoSense because of the greater reproducibility of this assay for NRTIs with narrow dynamic ranges (40
The overall classification accuracy for 3TC, ABC, AZT, d4T, ddI, and TDF was 81.5%, compared with 80.0% in our previous 2006 analysis (24
). The classification accuracy improved by ≥3.0% for 3TC and ABC and by about 1.0% for the remaining NRTIs. The standardized MSE for these six NRTIs also improved compared with that in our previous analysis, with a decrease from 0.24 to 0.20 over all NRTIs. The rather modest improvement in prediction accuracy despite the increase in the number of genotype-phenotype correlations in this study compared with our previous 2006 study most likely resulted from the ways in which the independent variables were selected in the two studies. In the 2006 study (24
), we used external knowledge to choose the independent variables by including nonpolymorphic mutations that had previously been shown to be selected by NRTI therapy. In this study, we made no prior assumptions about the mutations and used the LASSO algorithm—which is particularly useful for selecting a subset of predictors when the set of possible predictors is large—to analyze all 177 mutations that occurred in viruses from 10 or more individuals.
Although the LASSO algorithm is parsimonious, 18 mutations—particularly those with the greatest regression coefficients—were significantly associated with decreased susceptibility to one or more NRTIs in the current and 2006 studies: M41L, K43E, K65R, D67N, T69ins, K70R, L74V, V75T, Y115F, Q151M, M184V/I, H208Y, L210W, T215F/Y, D218E, and K219R. In contrast, K43N/Q, V75M, F116Y, E203K, and L228H were significantly associated with decreased susceptibility only in the 2006 study, whereas E40F, K64H, F77L, A98G, V118I, I135T, and E203D were significantly associated with decreased susceptibility only in the current study.
The fact that regression models containing interaction terms did not significantly improve prediction accuracy suggests that most interactions among NRTI resistance mutations are additive rather than multiplicative. Although a small number of mutational effects may be multiplicative (e.g., T69 insertion and T215Y, F77L, and Q151M), we did not test models that used only preselected mutation pairs. Models that include interactions may not improve prediction accuracy for two additional reasons. Although highly correlated mutations may have multiplicative effects, the numbers of samples in which each of the two mutations occurs alone may be insufficient to demonstrate an interaction. Interactions may also be difficult to observe if some of the independent variables in a model are surrogates for a multiplicative interaction. For example, as noted in the following section, several additional mutations frequently occurred in combination with M41L, L210W, and T215Y (see Fig. S1 in the supplemental material). The inclusion of these additional mutations, therefore, may have made it difficult to identify multiplicative effects among the three type I TAMs.
New insights into NRTI mutations and reduced susceptibility. (i) Known NRTI resistance associations.
Our results are consistent with much of the published literature on NRTI susceptibility, including two large in vitro
), three intensification or salvage therapy trials that reported associations between preexisting NRTI mutations and the virological response to a new NRTI (15
), and numerous studies of individual NRTI resistance mutations. We showed that M184V decreases susceptibility (in descending order) to 3TC or FTC, ABC, and ddI and increases susceptibility (in descending order) to TDF, AZT, and d4T. We showed that D67N and K219Q/E are the TAMs with the least effect on NRTI susceptibility. Indeed, K219Q was not even selected by the LASSO algorithm, while K219E yielded small regression coefficient values. In contrast, the type II TAMs K70R and T215F were found to have statistically significant coefficients for TDF (K70R and T215F) and ABC (T215F).
Y115F, a mutation discovered for its contribution to ABC resistance, was also found to decrease susceptibility to TDF significantly—a finding that has been reported previously (32
) but has not garnered much attention. The original study that reported that V75T reduced susceptibility to d4T noted that V75T reduced susceptibility to ddI (14
). However, this association has not generally been cited. In contrast, our results indicate that V75T appears to contribute as much to reduced susceptibility to ddI as it does to reduced susceptibility to d4T.
Despite the finding that most mutations were associated with decreased susceptibility to multiple NRTIs, the correlations in the levels of resistance between AZT and 3TC, AZT and FTC, TDF and 3TC, and TDF and FTC were strikingly low. This observation, which was reported previously by Whitcomb et al. (36
), results from the fact that the most common NRTI resistance mutation, M184V, which causes reduced susceptibility to 3TC and FTC, increases susceptibility to AZT and TDF. This mutational interaction likely explains the clinical efficacy of NRTI backbones containing AZT or TDF in combination with a cytidine analog such as 3TC or FTC. However, not all efficacious dual NRTI backbones benefit from this interaction. The combination of ABC and 3TC is highly effective under most circumstances despite the fact that M184V decreases susceptibility to both NRTIs. The effectiveness of this combination may result from the fact that ABC has the greatest antiviral activity except for the cytidine analogs (27
). Nonetheless, the NRTI backbone of ABC and 3TC was found to be less effective than that of TDF and FTC for patients with high viral loads in a recent large clinical trial (28
(ii) Novel NRTI resistance associations.
E40F and K219R, two previously reported but poorly characterized NRTI-associated mutations, were associated with significantly decreased susceptibility to six and seven NRTIs, respectively. This association appears to be the result of each mutation's strong correlation with type I TAMs. Among the 13 patients with viruses containing E40F, 11 (84%) also had M41L, L210W, and T215Y. Among the 49 patients with viruses containing K219R, 41 (84%) also had the same three type I TAMs. In contrast, 26% of all viruses in the study had each of the three type I TAMs.
K64H, K64N, and K64Y are nonpolymorphic mutations that are strongly selected by NRTI therapy (22
). Each of these K64 variants was recently reported to occur in <0.1% of 12,730 ARV-naïve patients compared with 0.5% to 1.1% of 4,598 patients with a history of receiving NRTIs but not NNRTIs (30
). In the current study, K64H was significantly associated with decreased susceptibility to d4T (16 patients; regression coefficient, 0.63) and TDF (13 patients; regression coefficient, 1.2). K64H occurred in combination with ≥3 type II TAMs in 12 patients and in combination with M41L, L210W, and T215Y in 4 patients. To further define the effect of mutations at position 64, we performed site-directed mutagenesis to back mutate clones with K64H from four isolates and clones with the less frequently detected mutations K64N and K64Y from one isolate each. Susceptibility testing of the six isogenic pairs of clones showed that K64H induced a median 1.4-fold (range, 1.3- to 1.6-fold) and 1.3-fold (range, 1.2- to 1.8-fold) decreased susceptibility to d4T and TDF, respectively. K64N induced 2.4-fold and 1.4-fold decreased susceptibility to d4T and TDF, respectively. K64Y induced 2.1-fold and 1.8-fold decreased susceptibility to d4T and TDF, respectively. Further studies of viruses with these mutations are ongoing.
A98G was first reported to reduce susceptibility to several NNRTIs in the early 1990s (3
). However, we recently reported that A98G was selected by NRTIs as well as NNRTIs, because it occurred in 25 (0.2%) of 12,370 ARV-naïve patients, 97 (2.1%) of 4,598 patients treated with NRTIs but not NNRTIs, and 711 (8.5%) of 8,367 patients treated with NNRTIs (usually in combination with NRTIs) (30
). The most likely explanation for the association with slightly decreased susceptibility to AZT and TDF was that 42/68 (62%) of viruses with A98G also had M41L, L210W, and T215Y. The only other NNRTI mutation shown to influence NRTI susceptibility was Y181C, which, as previously reported, modestly increased susceptibility to AZT and TDF (16
Initial and salvage ARV therapies have become increasingly effective in well-resourced countries. Potent ARVs from five mechanistic classes are now routinely used in combination with NRTIs. It has therefore become increasingly difficult to assess the impact of baseline NRTI resistance mutations on the response to an NRTI used as part of a salvage therapy regimen. Therefore, correlations between RT mutations and in vitro NRTI susceptibility are increasingly important for quantifying the effects of NRTI mutations on susceptibility to NRTIs.
Our study provides a comprehensive yet fine-grained view of the most common NRTI resistance mutations. Because our results were standardized by the variance in the log fold resistance levels for each NRTI, we provide the first analysis that quantifies the relative phenotypic effect of each mutation across each of the NRTIs. Despite the use of a feature selection approach designed to assess the potential roles of many different RT mutations, the NRTI resistance mutations we identified with the greatest effect on NRTI susceptibility were for the most part known nonpolymorphic treatment-selected mutations. Although one of these mutations, K64H, was not previously reported to decrease susceptibility to NRTIs, it was recently reported to be under strong NRTI selection pressure (30
), and site-directed mutagenesis experiments were consistent with our regression model. For several other mutations, novel associations with decreased susceptibility to specific NRTIs were identified and in some cases explained by their association with other, more common NRTI resistance mutations.