JAIDS Journal of Acquired Immune Deficiency Syndromes:
Association of HIV-1 Replication Capacity With Treatment Outcomes in Patients With Virologic Treatment Failure
De Luca, Andrea MD*; Weidler, Jodi PharmD†; Giambenedetto, Simona Di MD*; Coakley, Eoin MD†; Cingolani, Antonella MD*; Bates, Michael MD, PhD†; Lie, Yolanda BS†; Pesano, Rick MD, PhD†; Cauda, Roberto MD, PhD*; Schapiro, Jonathan MD‡
From the *Institute of Clinical Infectious Diseases, Catholic University, Rome, Italy; †Monogram Biosciences, Inc., South San Francisco, CA; and ‡Sheba Medical Center, Ramat Gan, Israel.
Received for publication November 17, 2006; accepted April 18, 2007.
Parts of this work were presented at the 12th Conference on Retroviruses and Opportunistic Infections, Boston, MA, February 22-25, 2005 [abstract 692].
Supported by V and VI Programma Nazionale AIDS, Istituto Superiore di Sanità, Ministero della Sanità, Rome, Italy (grants 30F.17, 30F.18, and 30G.8 to A. De Luca) and National Institute of Allergy and Infectious Diseases/National Institutes of Health Small Business Innovative Research (SBIR) grant R44 AI050321-03A1 (Monogram Biosciences).
A. De Luca has been a member of advisory boards or has received speaker's honoraria from GlaxoSmithKline, Abbott Virology, Boehringer Ingelheim, and Bayer Health Care Diagnostics. J. Schapiro has been a consultant or member of advisory boards or has received speaker's honoraria from GlaxoSmithKline, Abbott, Boehringer Ingelheim, Merck, Roche, Monogram Biosciences, Tibotec Virco, and Bayer. J. Weidler, E. Coakley, M. Bates, Y. Lie, and R. Pesano are employees of Monogram Biosciences.
Reprints: Andrea De Luca, MD, Istituto di Clinica delle Malattie Infettive, Policlinico Universitario “Agostino Gemelli,” Università Cattolica del Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italy (e-mail: firstname.lastname@example.org).
Background: The extent to which HIV-1 replication capacity (RC) influences the response to therapy remains to be established.
Methods: Phenotypic susceptibility and RC of baseline isolates (n = 139) from patients enrolled in the ARGENTA trial were measured and correlated to treatment outcomes over 36 months.
Results: RC in baseline isolates correlated with the number of phenotypically active drugs (R = 0.34, P < 0.001). Crude viral RC did not predict treatment outcomes. When viral RC was adjusted by baseline CD4 cell counts, HIV-1 RNA levels, and phenotypic susceptibility to the rescue regimen, it showed significant association with the immunologic outcome (per log10 RC higher, mean difference in 36 months' time-averaged change from baseline CD4 count = −68 cells/μL; P = 0.020). In the subgroup of patients with 3 or more phenotypically active drugs in the salvage regimen (n = 35, median RC = 65%), subjects carrying isolates with RC ≤65% as compared to those with RC >65% had better time-averaged HIV-1 RNA responses (mean: −1.04 vs. −0.32 log10 copies/mL; P = 0.012) and CD4 cell responses (mean: 132 vs. −7 cells/μL; P = 0.006). Among patients with HIV-1 RNA levels persistently >500 copies/mL (n = 61), RC, on a log10 basis, was inversely associated with time-averaged 36-month CD4 cell responses (β = −0.26; P = 0.046).
Conclusion: After normalizing for viral susceptibility to the employed regimen or in patient subsets with suboptimal virologic response, higher viral RC may predict worse subsequent treatment outcomes.
The primary goal of antiretroviral treatment is to suppress viral replication durably so as to facilitate immune reconstitution and reduce AIDS-related morbidity and mortality. In many HIV-infected patients, however, plasma HIV RNA levels rebound after initial suppression or are not suppressed at all, which is attributable, in part, to the selection of drug-resistant mutants. Nonetheless, despite ongoing viral replication, immunologic stability is often observed when antiretroviral drug pressure selects for a less fit drug-resistant virus population.1,2 In fact, when HIV-1 develops resistance to antiretroviral drugs through the acquisition of mutations in the pol gene, it often pays a price in terms of reduced replication capacity (RC) and reduced pathogenicity.3,4 The deleterious effects on viral fitness associated with acquisition of drug resistance presumably result from the alterations in natural substrate binding and catalytic activity that occur as a consequence of mutation-induced structural changes in the reverse transcriptase and protease enzymes as well as in the C terminus of Gag.5-7 Additional data also suggest that viral isolates from drug-naive individuals show variable fitness because of natural genetic polymorphisms that are present in each strain.8 The prognostic value of viral fitness on subsequent disease progression or therapeutic response has not been fully investigated. It has been shown that viral fitness, as measured by competition assays and recombinant virus assays, is a predictor of disease progression in antiretroviral drug-naive patients or those with limited prior drug exposure.9-11 Data correlating RC with treatment outcomes in patients experiencing antiretroviral treatment failure under potent antiretroviral therapy are currently limited, however. To analyze the correlation between viral RC and treatment outcomes, we analyzed patients failing antiretroviral therapy enrolled in the Antiretroviral Genotypic Resistance and Patient-reported Adherence (ARGENTA) trial who were followed prospectively for up to 3 years.
PATIENTS AND METHODS
Patients and Study Design
The ARGENTA trial was designed as a randomized, open, controlled, single-center study to establish the virologic benefit of genotype-guided treatment decisions over the use of empiric therapy in patients failing highly active antiretroviral therapy (HAART). Details on inclusion criteria, randomization procedures, and interventions are reported in the original study.12 Briefly, treatment decisions in both arms were taken by the same panel: for patients randomly assigned to the active study arm, decisions were made with the support of results from genotypic resistance testing; for patients in the control “standard of care” arm, decisions were made empirically, based on treatment and clinical history. After 6 months of follow-up, patients from both arms with a plasma HIV-1 RNA level >1000 copies/mL received treatment decisions based on access to resistance genotyping and expert advice. HIV-1 RNA levels and CD4 cell counts were monitored approximately every 3 months (range: 8 to 16 weeks). Details of the results of the extended follow-up of the ARGENTA trial are published elsewhere.13 The study was approved by the local ethics committee. All patients gave written informed consent.
Virologic and Immunologic Assays
Baseline patient plasma samples stored at −70°C were retrospectively analyzed for viral phenotypic drug susceptibility and RC using a single-cycle recombinant virus assay (PhenoSense HIV; Monogram Biosciences Clinical Reference Laboratory, South San Francisco, CA).14 The assays are run using a recombinant vector containing a portion of the patient's virus, including the C-terminal end of Gag (the last 83 amino acids), all protease, and the N-terminal 305 amino acids of reverse transcriptase. Phenotypic susceptibility to 17 drugs was reported as fold-change in the median inhibitory concentration (IC50) compared with a wild-type reference strain (NL4-3); resistance cutoffs (biologic and clinical) were determined by the reference laboratory on the basis of evidence available at the time of testing (September 2004). Viral RC was assessed by modifying the phenotypic drug susceptibility assay to compare luciferase activity in infected cells in the absence of drugs with that of the NL4-3 reference virus, after normalization based on luciferase activity in the transfected cells.15,16 RC values were expressed as a percentage of NL4-3 reference and adjusted so that the median value of wild-type viruses approximated 100%. As of December 2006, among 18,210 samples lacking genotypic evidence of drug resistance, the mean RC was 98.4% (Monogram Biosciences, data on file).
Viral genotypic drug resistance was examined using a commercial assay (Trugene HIV; Bayer Health Care Diagnostics, Milan, Italy), as previously reported.12
Plasma HIV-1-RNA concentrations were measured using a signal amplification branched DNA assay with a detection limit of 50 copies/mL (Versant 3.0; Bayer Health Care Diagnostics).
Peripheral blood CD4+ lymphocyte counts were measured by standard flow cytometry.
HIV-1 RNA copy numbers were log-transformed before calculations. The Student t test was used to compare continuous variables, and the χ2 test was used to compare categoric variables. The proportion of patients with HIV-1 RNA levels <500 copies/mL was analyzed up to month 36 by an intention-to-treat method considering dropouts as failures. The changes from baseline HIV-1 RNA levels and CD4 cell counts over 36 months were analyzed by intention-to-treat using the last observation carried forward method and ignoring treatment changes. As cumulative measures of virologic and immunologic responses over the observation period, we also used the time-averaged difference of plasma HIV-1 RNA levels and CD4 cell counts (area under the concentration-time curve of the plasma HIV-1 RNA levels on a log10 basis and of the CD4 cell counts, divided by time on treatment). Linear regression models were used to assess the association of continuous baseline variables with cumulative measures of virologic and immunologic responses.
To analyze the time to clinical progression (a new AIDS-defining event or death), survival analysis was performed using the Cox model with hazard ratios and 95% confidence intervals. In patients not experiencing clinical events, follow-up was right-censored at the last observation or at month 36 (156 weeks), whichever came first. The phenotypic susceptibility score (PSS) of the first salvage regimen was calculated by summing the number of drugs in the patients' regimen to which the virus was fully susceptible, according to the results of the phenotypic resistance assay. All analyses were carried out using the SPSS software package (version 13.0; SPSS, Chicago, IL).
Characteristics of Study Patients
From April 1999 to February 2000, 174 patients were randomized; the main characteristics of patients at baseline according to randomization group have been described previously in detail.12 Of these patients, 139 had stored baseline plasma samples available for the RC assay; all were successfully analyzed and comprise the case record for this study. Baseline characteristics did not differ from those of the entire group of the original trial (data not shown). Overall, 28% of patients were female, and the median age was 37 years (interquartile range [IQR]: 33 to 42 years). Intravenous drug use was the HIV transmission route in 33%, whereas heterosexual contacts and male homosexual contacts accounted for 30% and 31% of the transmission routes, respectively. The median CD4+ cell count was 264 cells/μL (IQR: 146 to 389 cells/μL), median plasma HIV-1 RNA level was 4.28 log10 copies/mL (IQR: 3.79 to 4.70 log10 copies/mL), and 37% had a prior AIDS-defining event. Median time on HAART was 18 months (quartiles 12 to 24), with a median of 2 regimens experienced (range: 1 to 5 regimens); in particular, 36% of individuals were failing the first HAART regimen, 35% the second regimen, and 29% the third regimen or higher. Seventy-one patients (51%) were from the genotypic resistance testing study arm, whereas 68 (49%) were from the comparator, the standard-of-care arm. After randomization, 47% of patients received an unboosted protease inhibitor (PI)-based regimen, 13% a ritonavir-boosted PI-based regimen, 9% a nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimen, and 31% a regimen based on an NNRTI + PI combination (26% unboosted, 5% boosted). Over the 36 months of observation, the most frequently used nucleoside reverse transcriptase inhibitors (NRTIs) were stavudine (76% of patients), lamivudine (66%), didanosine (40%), abacavir (35%), and zidovudine (27%); the NNRTIs nevirapine and efavirenz were given to 42% and 30% of patients, respectively. The most frequently used PIs were nelfinavir (50%), indinavir (48%), lopinavir/ritonavir (36%), amprenavir (22%), and saquinavir (15%); overall, 61% of patients received ritonavir-boosted PI-based regimens.
Viral Replication Capacity and Phenotypic Susceptibility
The median RC value for patients' baseline viral isolates was 59% (range: 1% to 185%). Baseline virus was susceptible to a median of 10 of 17 drugs tested (IQR: 7 to 13). The median PSS of the first salvage regimen after switching baseline therapy was 2 (range: 0 to 4). The RC of viral isolates was directly proportional to the number of drugs to which they were susceptible (R = 0.34; P < 0.001); that is, the more the isolate was scored susceptible to the tested drugs, the higher was its RC. Significantly lower RC was found in isolates showing phenotypic resistance as compared to those shown to be susceptible for each of the tested PI drugs (P < 0.01 for each comparison; Fig. 1). There was a trend toward lower RC in isolates resistant to emtricitabine and lamivudine as compared to isolates susceptible to these NRTI drugs (P = 0.055 and P = 0.096, respectively; see Fig. 1). Isolates resistant to other individual NRTI or NNRTI drugs did not show significant mean differences in RC compared with susceptible isolates. When the linear correlation between RC (in a log10 scale) and the continuum of fold-change in susceptibility to NRTIs was analyzed, significant inverse associations were found with lamivudine (R = −0.23, P = 0.007), emtricitabine (R = −0.20, P = 0.018), and abacavir (R = −0.21, P = 0.012). Furthermore, there was a significant inverse linear correlation between log10 increase of the RC values and log10 increase in the fold-change of viral IC50 for all tested PIs (saquinavir: R = −0.27, P = 0.02; nelfinavir: R = −0.29, P < 0.0001; lopinavir: R = −0.27, P < 0.001; ritonavir: R = −0.35, P < 0.0001; indinavir: R = −0.32, P < 0.0001; and atazanavir: R = −0.27, P = 0.002) with the exception of amprenavir (R = −0.11, P = 0.19).
In an intention-to-treat analysis using the last observation carried forward method, the mean time-averaged change from baseline HIV-1 RNA over 36 months was −0.59 log10 copies/mL. An intention-to-treat analysis considering dropouts as failures showed that the proportion of patients with an HIV-1 RNA level <500 copies/mL was 22%, 19%, 20%, 19%, 22%, 22%, and 32% at 3, 6, 9, 12, 18, 24, and 36 months, respectively. After 36 months, the mean time-averaged change from baseline CD4 count was 38 cells/μL.
During an overall follow-up of 330 patient-years, 21 patients experienced clinical progression: 7 progressed to new or recurrent AIDS-defining events, and 14 died. Kaplan-Meier analysis showed that after 36 months, the estimated cumulative proportion of patients without clinical progression was 0.82.
Phenotypic Susceptibility of Baseline Virus and Treatment Outcomes
The PSS of the first salvage regimen was significantly predictive of the virologic response. Intention-to-treat analysis showed that patients with a PSS ≥2 exhibited a significantly greater drop in viral load than those with a PSS <2 during the 36-month observation period (mean time-averaged change from baseline HIV-1 RNA level: −0.76 vs. −0.41 log10 copies/mL; P = 0.014; Fig. 2A). Patients with a higher PSS also showed a better CD4 cell recovery. Nonetheless, although the 12-month CD4 cell response was significantly better in the group with a PSS ≥2 as compared to the group with a PSS <2 (mean change from baseline: 74 vs. 14 cells/μL; P = 0.025), the cumulative 36-month measure showed only a trend in difference (mean time-averaged change from baseline CD4 count: 59 vs. 18 cells/μL; P = 0.061; see Fig. 2B). There was no correlation between phenotypic susceptibility and clinical progression during the follow-up.
Replication Capacity of Baseline Virus and Treatment Outcomes
Overall, baseline RC, unadjusted for other baseline variables, was not predictive of treatment outcomes. Higher viral RC showed only a trend toward worse CD4 cell reconstitution over 36 months (per log10 RC higher, mean difference in time-averaged change from baseline CD4 count = −49 cells/μL; P = 0.098), although there was no association with virologic outcome at all.
Because of the inverse relation between RC and phenotypic resistance, we analyzed the predictive value of RC after adjusting for drug susceptibility and in subgroups with uniform susceptibility or activity of the first salvage regimen. In a multiple linear model adjusting for baseline CD4 cell count, viral load, and PSS of the first salvage regimen (as a continuous variable), RC was predictive of the cumulative 36-month CD4 count outcome (per log10 RC higher, mean difference in time-averaged change from baseline CD4 count = −68 cells/μL; P = 0.020).
In the subgroup of 35 patients with a fully active rescue regimen (PSS ≥3), the median RC was 65%; 21 (60%) of these patients reached an HIV-1 RNA level <500 copies/mL during the follow-up. Patients with a lower than median RC exhibited better cumulative virologic responses (mean 36-month time-averaged change from baseline HIV-1 RNA level: −1.04 vs. −0.32 log10 copies/mL; P = 0.012; Fig. 3A). In this subset, lower than median RC was also associated with better CD4 count recovery (mean time-averaged change from baseline CD4 count: 132 vs. −7 cells/μL; P = 0.006; see Fig. 3B). In the same subset, we also explored the predictive value of resistance-associated viral genotypic mutations in predicting treatment outcomes. The presence of selected PI-resistance mutations (those at protease codons 10 and 82; both P values = 0.03) was associated with worse cumulative CD4 cell responses, whereas M184V/I predicted better CD4 cell responses (P = 0.027). In a multiple linear model, after adjusting for the presence of M184V/I, RC independently predicted CD4 cell responses (per log10 higher mean difference in time-averaged change from baseline CD4 count = −80 cells/μL, P = 0.049; dichotomized above the median value mean difference in time-averaged change from baseline CD4 count = −112 cells/μL, P = 0.032). We could not detect independent associations between genotypic resistance mutations and virologic outcomes in this subset.
In patients not achieving a plasma HIV-1 RNA level <500 copies/mL at each time point, after adjusting for the number of phenotypically active drugs, higher RC was associated with a weaker CD4 cell recovery for up to 24 months, with mean differences in CD4 cell changes between 37 and 147 cells/μL per log10 RC increases (Table 1). Moreover, in the subset of persistently viremic patients (HIV-1 RNA >500 copies/mL at all time points during the 36 months of follow-up, n = 61, median RC = 49%), those with an RC lower than the median value showed a better 24-month CD4 cell recovery (mean: 48 vs. −29 cells/μL; P = 0.047) and a trend toward better time-averaged 36-month CD4 cell responses (mean: 32 vs. −21 cells/μL; P = 0.062) (see Fig. 4). There was an inverse linear correlation between baseline RC, on a log10 basis, and time-averaged 36-month CD4 cell responses (β = −0.26; P = 0.046). In this subset, we also explored the viral genotypic resistance predictors of treatment outcomes. The presence of NRTI resistance mutations D67N (P = 0.04), M184V/I (P = 0.02), and T215Y/F (P = 0.04) was significantly associated with better cumulative CD4 cell responses over 36 months, whereas PI resistance mutations showed no effect. In multiple linear models, including RC and the 3 mentioned NRTI resistance mutations, only M184V/I remained independently predictive of the CD4 cell outcome (mean difference in time-averaged change from baseline CD4 count = 60 cells/μL; P = 0.040).
Baseline viral RC was not associated with clinical progression over the duration of follow-up.
HIV-1 fitness, as measured by RC, is an intrinsic viral characteristic that seems to have a number of clinical and virologic correlates. The development of antiretroviral resistance frequently places a constraint on HIV-1 in the form of reduced viral fitness. This likely contributes to the sustained immunologic benefit that is often observed despite HAART failure and virologic breakthrough.
In this study, we retrospectively analyzed patients enrolled in the ARGENTA trial, a randomized study designed to assess the clinical utility of genotypic resistance testing for guiding therapy in HAART-failing patients. Prospective follow-up of the trial has been extended for up to 3 years.13 For the purpose of this study, we assessed viral RC and phenotypic susceptibility in a subgroup of patients with a baseline sample available.
As expected, we found that viral RC was inversely correlated with decreased susceptibility to PIs. Also, the magnitude of viral resistance to lamivudine, emtricitabine, and abacavir was associated with lower RC, whereas NNRTI-resistant strains had a similar fitness as compared to the susceptible counterparts. This is in agreement with several previous observations and suggests that NRTI-based and PI-based regimens might confer a partial virologic benefit even after the selection of drug-resistant strains.17-27
In the overall population studied, viral RC was not associated with treatment outcomes. This was not unexpected, given the fact that viral drug resistance was inversely correlated to RC and that phenotypic susceptibility results were predictive of virologic and immunologic outcomes in this study, as in others. To deal with this fact, we adjusted viral RC by the phenotypic susceptibility of the virus to the salvage regimen used and found that, indeed, the higher viral fitness was associated with less CD4 cell reconstitution. To analyze this in depth, we analyzed the predictive value of RC in subsets of patients with homogeneous PSSs of their salvage regimens or in subsets of patients unable to reach undetectable viremia with subsequent therapies. In the subset of subjects with a fully active regimen (PSS = 3 or 4), higher HIV-1 RC correlated with weaker virologic and immunologic responses. In patients starting rescue therapy and not achieving virologic suppression, higher RC predicted inferior CD4 cell recovery.
This is the first study to show the predictive value of RC, as measured by the PhenoSense RC assay, over subsequent viral load and CD4 cell count changes with salvage therapy in patients failing their current HAART regimen.
In a previous study10 of HIV-1-infected hemophiliacs with limited antiretroviral treatment experience, RC measurements were shown to correlate with baseline HIV-1 RNA levels and CD4 T-cell counts. RC also independently predicted CD4 T-cell decline over time and progression to AIDS. Barbour et al11 studied drug-naive subjects recently diagnosed with HIV infection. They found that CD4 cell counts were significantly greater at study entry and over time, before and after treatment initiation, in those with decreased RC. In patients with virologic failure, an inverse relation between RC and prior CD4 cell count course has been demonstrated.28
Similarly, preliminary analysis of patients from the California Collaborative Treatment Group (CCTG) 575 trial29 showed that lower RC at baseline was the strongest predictor of a greater CD4 cell increase from prestudy nadir to study baseline.30 In agreement with our findings, an association with RC was also noted for the change in CD4 cell count from nadir to months 6 and 12. Also, similar to our study, among the patients with virologic failure at month 6, the HIV-1 RNA reduction from baseline to month 6 was less profound in those whose baseline isolates had an RC less than 35% as compared to those with RC values >35%. Notably, RC testing in the CCTG 575 trial was performed before June 2002, when the Monogram Biosciences RC assay was validated for routine patient testing. To compare different versions of the assay before and after June 2002, an adjustment factor of 1.8 is required. Therefore, an RC value of 35% in the CCTG 575 study would be multiplied by 1.8 to equal a value of 63% in this study (Monogram Biosciences, data on file).
Finally, a recent study indicates that in patients failing HAART with a virus fully resistant to lamivudine, keeping lamivudine monotherapy as compared to interrupting all antiretroviral drugs is associated with maintenance of lower viral RC, together with better virologic and immunologic outcomes over 48 weeks.31 All the previously mentioned studies show that drug resistance to PIs and NRTIs is associated with different degrees of loss of RC, and therefore with the continued clinical benefit of keeping selective antiretroviral drug pressure despite drug resistance. Nonetheless, it should be noted that the best clinical benefit is obtained by maximizing virologic suppression, thereby avoiding drug resistance accumulation. Indeed, continued viral replication under antiretroviral drug pressure leads to gradual accumulation of resistance mutations with increased cross-resistance to other drugs of the same classes and with the accumulation of compensatory mutations that eventually lead to the selection of resistant strains with higher RC. Therefore, the partial benefit of the lower RC of some drug-resistant strains is lost over time.32-34
Limitations of this study must be recognized. First, RC, as measured by the PhenoSense assay, is a measure of viral fitness but does not measure the fitness of the whole viral isolate. Indeed, other regions outside the viral pol or the C-terminal Gag region used in this assay play a role in determining viral fitness.35,36 Nonetheless, this assay is one of the few standardized highly reproducible measures of viral RC and has shown clinical validity in several settings. Second, the effect of viral RC on subsequent treatment outcomes could be shown only after adjusting for other confounders or in specific subsets of patients only. This was attributable to the inverse correlation of RC with drug resistance, particularly resistance to PIs, which was the major determinant of subsequent therapy outcomes. Third, part of the predictive value of RC was explained by the presence of the reverse transcriptase substitution M184V/I. The influence of this viral mutation selected by lamivudine and emtricitabine on viral fitness and treatment outcomes is clearly established.31 Here, we found that its predictive value was stronger than the measured RC in the subset with a sustained HIV-1 RNA level >500 copies/mL; however, in the whole patient set and in the subset with a PSS ≥3, RC maintained an independent prediction of virologic and immunologic outcomes, showing that the correlation between viral genotype and fitness is much more complex and goes beyond the association with M184V.37 Fourth, the regimens used at the time when the ARGENTA trial was performed are outdated. Only a minor proportion of patients used boosted PIs in their salvage regimen, although, nowadays, these potent drugs are included in most, if not all, rescue therapy regimens in patients previously failing other therapies. This is reflected in the lower than optimal responses observed in the studied patients, even in those with a phenotypically “fully active” regimen. This fact might have given a different weight to RC as compared to drug susceptibility in this series; nonetheless, this study provides a proof of concept that HIV-1 RC might influence treatment responses. Finally, this is a retrospective assessment of the prognostic value of RC in a prospective study. Clinical usefulness of RC testing can only be proven by studies with a prospective design assessing the clinical benefit of using the RC assay to guide therapeutic decisions.
In HAART-failing patients, drug susceptibility testing should be the primary tool to guide treatment decisions. In patients for whom viral suppression cannot be achieved, RC may prove to be a useful tool to help guide treatment decisions. It should be reasonable to hypothesize that therapy-failing patients who harbor a low-RC virus and for whom there are insufficient susceptible drug options available might opt to continue the failing therapy, whereas patients failing therapy with a virus of high RC may benefit from treatment change, even if there are few available options, to attempt to select for a less fit virus.
The authors are grateful to Alessandro Cozzi-Lepri for statistical advice and to Neil Parkin for technical advice on resistance testing.
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