score $ 5 in the Stanford HIV Drug Resistance Database were included in the analysis. Viral susceptibility to each drug was classified as ‘‘Sensitive’’, ‘‘Intermediate’’ or ‘‘Resistant’’. Sixty- three paired drug susceptibility predictions (each pair including population and deep sequencing genotypic data) were evaluated per subject and timepoint, 20 for ANRS, 21 for HIVdb v6.0.8 and 22 for Rega v8.0.2. Results Subjects The study included 7 individuals, 2 of them women, with previous exposure to a median of 15 antiretrovirals during a median of 13 years. Their median (interquartilic range, IQR) age was 44 (39; 49) years. The median (IQR) year of HIV diagnosis was 1988 (1985; 1994). Median (IQR) HIV-1 RNA levels and CD4 counts were, respectively, 110,600 (46,550; 370,000) copies/ mL and 50 (12; 126) cells/mm3 at baseline; and 36,700 (2,895; 205,000) copies/mL and 150 (27; 247) cells/mm3 at virological failure. Median (IQR) nadir CD4 counts were 14 (1; 152) cells/ mm3. Deep salvage therapy included darunavir, tipranavir, etravirine or raltegravir in 4, 2, 2 and 5 subjects, respectively. (Table S1) Treatment adherence was adequate in 4 individuals (Subjects 1 to 4) (Table S1) and partial in 2 (Subjects 5 and 6), one individual (Subject 7) (Table S1) interrupted antiretroviral therapy during follow-up. Phenotypic susceptibility predictions by population and deep genotyping The median (IQR) deep sequencing coverage per base was 3938 (3130 – 6945) sequences at baseline and 3965 (3688 – 7091) sequences at virological failure. Deep sequencing detected all mutations found by population sequencing in all subjects, and found additional mutations in 6 out of 7 individuals. (Table S1) Additional mutations were congruent with the treatment history and modified 5.2% of phenotypic susceptibility predictions overall (Table S2), with no significant differences between interpretation algorithms. Deep sequencing provided limited additional genotypic infor- mation at baseline relative to PS. Baseline DS changed the ANRS predicted susceptibility of lopinavir/r and saquinavir/r from sensitive to intermediate in subject 1 and the ANRS susceptibility of darunavir/r from intermediate to resistant in subject 2 (Table S1). In subject 7 (Table S1), however, the detection of minority protease I54L and V82A mutants by deep sequencing in addition to the I54I/V, T74S and L90M detected by population sequencing, consistently decreased the virus susceptibility to indinavir/r across the three algorithms, and led to decreases in predicted susceptibility to saquinavir/r and darunavir/r and to lopinavir/r by the HIVdb and Rega algorithms. The additional detection of the reverse transcriptase M41L and T215Y mutants in this individual led to decreases in susceptibility to different NRTIs by the three algorithms. Most additional resistance mutations leading to changes in susceptibility predictions were found after virological failure to deep salvage therapy (Table S1). Consistent decreases in etravirine susceptibility were observed in the two subjects treated with etravirine in this study (subjects 1 and 2). Detection of additional mutations in protease in these two individuals also led to decreased susceptibility to saquinavir/r and lopinavir/r in the ANRS algorithm, respectively. Low-level K103N mutants were detected at virological failure in subject 5. This individual had received efavirenz in the past and was receiving nevirapine at baseline, when he was switched from tenofovir, stavudine, lamivudine and 454 in Heavily Pre-Treated HIV+ Subjects nevirapine, to tenofovir/emtricitabine, darunavir/r and enfuvir- tide. The withdrawal of stavudine-mediated pressure over pre- existing K103N mutants might have enabled their emergence and subsequent detection at virological failure. Finally, the detection of minority K219Q and T215Y mutants in subjects 6 and 7, respectively, led to consistent decreases in predicted viral susceptibility to different NRTIs. Most baseline resistance mutations were lost in subject 7 during treatment interruption becoming undetectable even by deep sequencing at the time of virological failure. No changes in predicted susceptibility were observed for tipranavir or raltegravir, although detection of minority G140S in subject 3 suggested improved fitness of the Q148R mutants detected by population sequencing at virological failure (Table S1). Discussion While being technically non-inferior to population genotyping, deep sequencing enabled the detection of additional resistance mutations with potential clinical significance in 6 out of 7 individuals included in this study. Additional mutations, however, only modified about 5% of antiretroviral susceptibility predictions, including decreased etravirine efficacy in the 2 subjects developing virological failure to this drug, and decreased NRTI, efavirenz and indinavir/r efficacy in 2, 1, and 1 subject, respectively. Changes in darunavir susceptibility observed in 2 individuals were not consistent across algorithms. Deep sequencing had no impact on susceptibility predictions for tipranavir or raltegravir. Deep sequencing could modify patient clinical management by avoiding drugs whose resistance may have been underestimated by population sequencing. Indeed, baseline low-frequency PI-resis- tant mutants were selected during treatment exposure in two subjects: I54T in subject 1, and V32I, Q58E and L89V in subject 2. I54T is a PI-related mutation that appears to be associated with decreased susceptibility to each of the PIs, although its effect has not been well studied. Q58E is a non-polymorphic PI-related mutation associated with reduced susceptibility tipranavir [21,22,23] and possibly to several other PIs. Selection of I54T and Q58E mutations during darunavir therapy may be explained due to residual phenotypic or compensatory effect of such mutations on darunavir susceptibility, genome colinearity with other darunavir-associated mutations, or simple stochastic effects. Interestingly, emergence of I54T in subject 1 was associated with decay in I54A, indicating a possible fitness advantage of I54T over I54A in this particular context. Conversely, substitutions V32I and L89V are nonpolymorphic PI-selected accessory mutations which emerge during treatment with darunavir/ritonavir. Both muta- tions were associated with decreased response to darunavir/ ritonavir in the POWER trials [24], suggesting that they were selected in subject 2 because they conferred additional resistance to darunavir. Most additional mutants in our study, however, were detected at virological failure. Varghese et al. also detected additional minority variants with major etravirine mutations only in patients failing an NNRTI-containing regimen [14]. Taken together, these results suggest that deep sequencing might be more useful to assess loss of antiviral efficacy after virological failure than to screen for pre-existing resistance in subjects with extensive treatment exposure. As with population sequencing, deep sequencing should also be performed immediately or shortly after virological failure. The increased sensitivity of deep sequencing could also reassure clinicians about the absence of additional genotypic resistance when making clinical management decisions. This is particularly important for the management of subjects with suboptimal PLoS ONE | www.plosone.org 3 May 2011 | Volume 6 | Issue 5 | e19461