h Determining the phenotypic impacts of reverse transcriptase (RT) mutations on individual nucleoside RT inhibitors (NRTIs) has remained a statistical challenge because clinical NRTI-resistant HIV-1 isolates usually contain multiple mutations, often in complex patterns, complicating the task of determining the relative contribution of each mutation to HIV drug resistance. Furthermore, the NRTIs have highly variable dynamic susceptibility ranges, making it difficult to determine the relative effect of an RT mutation on susceptibility to different NRTIs. In this study, we analyzed 1,273 genotyped HIV-1 isolates for which phenotypic results were obtained using the PhenoSense assay (Monogram, South San Francisco, CA). We used a parsimonious feature selection algorithm, LASSO, to assess the possible contributions of 177 mutations that occurred in 10 or more isolates in our data set. We then used least-squares regression to quantify the impact of each LASSO-selected mutation on each NRTI. Our study provides a comprehensive view of the most common NRTI resistance mutations. Because our results were standardized, the study provides the first analysis that quantifies the relative phenotypic effects of NRTI resistance mutations on each of the NRTIs. In addition, the study contains new findings on the relative impacts of thymidine analog mutations (TAMs) on susceptibility to abacavir and tenofovir; the impacts of several known but incompletely characterized mutations, including E40F, V75T, Y115F, and K219R; and a tentative role in reduced NRTI susceptibility for K64H, a novel NRTI resistance mutation. In a previous study, we applied several data-mining approaches to quantify associations between NRTI-associated HIV-1 drug resistance mutations and in vitro susceptibility data (24). About 630 susceptibility test results were available for abacavir (ABC), didanosine (ddI), lamivudine (3TC), stavudine (d4T), and zidovudine (AZT), and 350 were available for tenofovir (TDF). In that study, we used a predefined list of nonpolymorphic NRTI-selected mutations to reduce the number of independent variables influencing NRTI susceptibility. Here we analyze a data set that is about twice as large and uses two regression methods in tandem: one to identify genotypic predictors of NRTI susceptibility from the many RT mutations present in the data set (rather than relying on a predefined list of mutations, as we did previously) and one to quantify the impact of RT mutations on NRTI susceptibility. In addition, we used several approaches to determine whether models that included statistical interactions among NRTI resistance mutations improved the prediction of reductions in NRTI susceptibility.
MATERIALS AND METHODSHIV-1 isolates. We analyzed HIV-1 isolates in the HIV Drug Resistance Database (HIVDB) (22) for which in vitro NRTI susceptibility testing had been performed by the PhenoSense (Monogram, South San Francisco, CA) assay (20). About 35% of the test results were from studies published previously by other laboratories; 65% were from s...