Background: Although combination antiretroviral therapy (cART) dramatically reduces rates of AIDS and death, a minority of patients experience clinical disease progression during treatment.
Objective: To investigate whether detection of CXCR4(X4)-specific strains or quantification of X4-specific HIV-1 load predict clinical outcome.
Methods: From the Swiss HIV Cohort Study, 96 participants who initiated cART yet subsequently progressed to AIDS or death were compared with 84 contemporaneous, treated nonprogressors. A sensitive heteroduplex tracking assay was developed to quantify plasma X4 and CCR5 variants and resolve HIV-1 load into coreceptor-specific components. Measurements were analyzed as cofactors of progression in multivariable Cox models adjusted for concurrent CD4 cell count and total viral load, applying inverse probability weights to adjust for sampling bias.
Results: Patients with X4 variants at baseline displayed reduced CD4 cell responses compared with those without X4 strains (40 versus 82 cells/μl; P = 0.012). The adjusted multivariable hazard ratio (HR) for clinical progression was 4.8 [95% confidence interval (CI) 2.3–10.0] for those demonstrating X4 strains at baseline. The X4-specific HIV-1 load was a similarly independent predictor, with HR values of 3.7 (95% CI, 1.2–11.3) and 5.9 (95% CI, 2.2–15.0) for baseline loads of 2.2–4.3 and > 4.3 log10 copies/ml, respectively, compared with < 2.2 log10 copies/ml.
Conclusions: HIV-1 coreceptor usage and X4-specific viral loads strongly predicted disease progression during cART, independent of and in addition to CD4 cell count or total viral load. Detection and quantification of X4 strains promise to be clinically useful biomarkers to guide patient management and study HIV-1 pathogenesis.
From the aDivision of Infectious Diseases, Wadsworth Center, New York State Department of Health
bDivision of HIV Medicine, Albany Medical College, Albany, New York, USA
cDepartment of Biostatistics, University of California, Los Angeles, USA
dInstitute of Medical Microbiology, University of Basel and InPheno AG, Basel
eDivision of Immunology and Allergy, University Hospital, Lausanne
fInstitute for Medical Microbiology, University of Bern, Bern
gDivision of Virology, University Hospital, Geneva
hCantonal Institute of Microbiology, Bellinzona, Switzerland
iDivision of Infectious Diseases and Hospital Epidemiology, University Hospital, Zurich.
*Current address: Global Campaign for Microbicides, PATH, Washington, DC, USA.
Received 26 October, 2006
Revised 7 October, 2007
Accepted 18 October, 2007
Correspondence to Dr B. Weiser, Wadsworth Center, New York State Department of Health, 120 New Scotland Avenue, Albany, NY 12208, USA. E-mail: firstname.lastname@example.org
Since its advent in 1996, combination antiretroviral therapy (cART) has led to a dramatic reduction in the rates of illness and death among HIV-1-infected individuals [1–3]. Nevertheless, a small proportion of patients experience disease progression despite cART, and questions remain regarding when to initiate and when to switch therapies [1–6]. Because cART can be toxic, costly and requires life-long adherence, the decision to start treatment in asymptomatic patients is complex and tailored to the individual . Furthermore, a substantial number of patients taking cART do not experience complete HIV-1 suppression [2,8–10]. Changing their therapy, particularly after drug resistance or intolerance has developed, is also a challenge.
CD4 cell count and plasma HIV-1 RNA level are currently the principal measurements guiding therapeutic decisions, for they predict disease progression and response to cART [6,7,10–13]. Debate continues, however, about optimal treatment strategies, highlighting the need for more data to guide clinical management [14,15]. In particular, new biomarkers are necessary to identify which patients are at highest risk of progressing to clinical disease and, therefore, most likely to benefit from immediate initiation or change of cART. These patients may be untreated, asymptomatic individuals or those with persistent viremia despite cART.
HIV-1 coreceptor usage could be a potential biomarker because it plays a critical role in pathogenesis and disease progression. HIV-1 requires contact with two cellular receptors to initiate infection; CD4 serves as primary receptor, with chemokine receptors CCR5 and CXCR4 serving as coreceptors [16–18]. Viruses transmitted between persons generally use CCR5 (R5 viruses) [17–22]. R5 viruses typically infect macrophages and primary CD4 lymphocytes and do not form syncytia in vitro .
After years of infection, CXCR4-using strains (X4 viruses) are detected in approximately 50% of infected individuals, with X4 and R5 viruses usually coexisting in the viral swarm [18–21,23–25]. X4 strains replicate in T cell lines as well as in primary T lymphocytes and induce syncytia in vitro . The emergence of X4 variants often heralds CD4 cell depletion and accelerated disease progression [18–21,23–27], and multiple properties of X4 variants may contribute to CD4 cell decline [25–27].
Use of cART has been demonstrated to suppress X4 strains preferentially during the first years of therapy [28–30]. Although the mechanism of this suppression is unexplained, it most likely stems from differences in cellular targets for R5 and X4 viruses and accessibility of such cells to therapeutic agents rather than to disparities in viral susceptibility . The preferential suppression of X4 variants suggests that treatment may lead to a change in the predominant phenotype of the viral population as well as the quantity of HIV-1 and thereby contribute to the clinical efficacy of cART [28–30].
Pioneering cohort studies of viral phenotype performed before cART was introduced measured a phenotypic characteristic of X4 viruses: syncytia induction in vitro . Although detection of syncytia-inducing virus strongly predicted disease progression, culture-based syncytia assays did not lend themselves to clinical use and remained as research tools. A more recent cohort study determined HIV-1 tropism by using a genotypic method: prediction of coreceptor usage based upon population-based sequences of the V3 region of the env gene . This study showed that X4 tropism independently predicted poor immunological response and increased mortality following the initiation of cART. A follow-up study of the same cohort, however, concluded that bulk sequencing paired with current V3 phenotyping algorithms is too insensitive for clinical use, particularly when X4 variants represent a minority species . To quantify HIV-1 coreceptor usage and determine X4-specific HIV-1 load, we have developed a highly sensitive, nucleic acid-based assay to determine the proportion of virus in a patient's plasma that uses each coreceptor.
The ultimate goal of cART is to improve the clinical outcome of infected individuals [2,7,10]. Such therapy has become so effective that relatively few treated patients experience disease progression, and clinical trials rely primarily on surrogate markers [2,3]. Previous studies, however, have revealed that patients can display clinical benefits from therapy independent of changes in CD4 cell count and HIV-1 load [3,10,33]. The present study, therefore, focused on the relationship of HIV-1 tropism to clinical endpoints, asking whether quantification of coreceptor usage identified patients at high risk for disease progression during cART. Patients in the Swiss HIV Cohort Study (SHCS) [2,34] were examined to assess the predictive value of the presence and plasma level of X4 viruses before the initiation of therapy and after 6 months of treatment.
The SHCS is a prospective, clinic-based, observational study of HIV-1-infected adults initiated in 1988, with follow-up documented every 6 months . The present project was based upon a subset of patients selected from 2674 who initiated cART between 1995 and 1998 and who were described in the previous report on clinical progression and persistent viremia . The Institutional Review Board at each site approved the study and each patient signed informed consent.
Selection of study subjects and samples
A group of 96 patients who progressed to HIV-1 disease despite cART were compared with 84 contemporaneous, treated nonprogressors. To be included, patients needed to meet clinical requirements and to have sufficient cryopreserved plasma for analysis; samples were analyzed for each patient who met these requirements. First, 170 patients who subsequently progressed to a new clinical AIDS-defining event or death while receiving cART were identified. To qualify for the present study, patients needed sufficient plasma available from the SHCS visit preceding the initiation of cART, called baseline, and an HIV-1 load ≥ 1000 copies/ml at that visit; the baseline visit was taken a median of 18 days [interquartile range (IQR), 0–64) before the initiation of cART to ensure that the estimate lies within the confidence interval (CI).
Follow-up samples were obtained after approximately 6 months of cART, with a median interval between the pre- and post-cART samples of 184 days (IQR, 135–212). Because an HIV-1 load ≥ 500 copies/ml was required for post-cART specimens, follow-up samples were analyzed only in patients with persistent viremia. Selection of all specimens allowed for at least one additional contemporaneous sample of plasma to remain in stock for future projects. A total of 115 baseline specimens were retrieved from progressors; 19 from one site could not be analyzed owing to a problem in shipping and handling. Coreceptor usage was quantified in the remaining 96 baseline samples. Follow-up specimens were available from 39 patients, with coreceptor results obtained from all 39.
To select nonprogressors, pre- and post-cART samples were identified from 91 patients who did not progress within the period of the original study (up to 31 December, 1998) and who were matched to progressors according to clinic site and the year cART was initiated. With the requirement for one plasma sample to remain in stock, four specimens lost to handling, and failure to amplify from seven, tropism was finally quantified in 84 baseline and 31 follow-up samples from nonprogressors. In total, 180 baseline and 70 follow-up samples were analyzed.
Markers of disease progression
CD4 lymphocyte counts were measured by using flow cytometry and HIV-1 RNA was measured using the Cobas Amplicor test, with a level of detection of 500 copies/ml (Roche Diagnostics, Rotkreuz, Switzerland) .
Quantification of HIV-1 coreceptor usage
To determine HIV-1 tropism, a quantitative, nonradioactive, DNA heteroduplex tracking assay (HTA) was developed based upon previous methods [35,36]. Because X4 and R5 variants ordinarily coexist in a viral swarm [18–21,23–25], it was necessary to quantify the proportion of viruses in plasma using each coreceptor. This proportion was expressed as a variable called the ‘quantity of X4 and R5′ (QXR), representing the fraction of virus in a specimen using the R5 coreceptor. If QXR = 1, almost all virions in a population use R5; if QXR = 0, almost all use X4. If a mixture of R5 and X4 virus are present, QXR is < 1 .
This HTA focused on the third variable domain of HIV-1 env (V3) because it encodes the key determinants of viral coreceptor usage [37–39]. HIV-1 RNA was extracted from plasma using a QIAamp viral RNA extraction kit (Qiagen, Valencia, California, USA), with samples from different patients processed separately to minimize possible cross-contamination or mislabeling. Reverse transcription and PCR amplification (reverse transcription PCR) of a 143 bp fragment spanning the V3 region was performed, as described, under conditions designed to optimize efficiency and variant sampling [28,40].
DNA heteroduplex formation was carried out by annealing four CCR5-specific, fluoroscein isothiocyanate-labeled HIV-1 probes with unlabeled target DNA. Sequence differences between envelope variants using R5 or X4 coreceptors resulted in differential annealing and distinct heteroduplex electrophoretic mobilities, allowing rapid determination of whether R5, X4, or a mixture of both variants were detected (Fig. 1).
Those samples harboring X4 strains (QXR < 1) were subjected to additional analyses to quantify the proportion of variants using each coreceptor. V3 loops were cloned from each plasma sample and individually analyzed by HTA. After determining the tropism of each clone, the QXR was calculated for each plasma specimen by applying a mathematical model derived previously . X4-specific HIV-1 load was calculated by multiplying the total viral load by the proportion of the viral population using X4: X4-specific viral load = [(total HIV-1 load)(1 − QXR)].
Validation of the heteroduplex tracking assay
To validate the HTA, a panel of 50 viral isolates and clones obtained from independent sources were analyzed; no two strains originated from the same patient. Sixteen strains were obtained through the AIDS Research and Reference Reagent Program (NIAID, NIH; www.aidsreagent.org) and 34 from HIV-1-infected individuals participating in pathogenesis studies; 32 were isolated at the Wadsworth Center [28,39,41] and SPL3 and TYBE  were provided by Dr Ronald Collman (Table 1). There were 48 subtype B strains, one subtype A (UG92024), and one subtype D (UG92031). The HIV-1 coreceptor usage of each strain was determined by three different methods: cell-based phenotypic assay, HTA and V3 sequence prediction (Table 1). Phenotypic assays indicated that 34 strains comprised R5 viruses only, 12 comprised X4 viruses only, and four included a mixture of X4 and R5 quasispecies.
Computational analyses of the V3 nucleotide sequences (GenBank accession nos. EF688428-EF688458 for Wadsworth Center sequences), including a BLAST search and construction of a phylogenetic tree, indicated that the sequences were unique, and there was no evidence of contamination. V3 amino acid sequences were deduced and the HIV-1 coreceptor usage for each strain was predicted on the basis of the V3 loop's overall charge and the presence of basic residues at positions 11, 24 and 25 [39,46,47].
Concordance between the results of the phenotypic assays and the HTA was 98%. When the HTA's ability to detect X4 strains identified by phenotypic assay was examined, its sensitivity was 94%, specificity 100%, positive predictive value (PPV) 100% and negative predictive value (NPV) 97%. For R5 strains, the sensitivity of the HTA was 100%, specificity 94%, PPV 97% and NPV 100%.
V3 sequences in this panel were aligned and compared with the R5 reference strain SF162 (Table 1), revealing that X4 sequences often displayed amino acid insertions and disparities compared with most of the R5 sequences. This observation supports the idea that identification of tropism by HTA rests largely on the disparity between genomes coding for R5 versus X4 usage rather than upon differences among individual R5 or X4 sequences themselves. Because all four probes in this assay are R5 specific, annealing of an X4-specific V3 genome to these probes may result in DNA mismatches, unpaired loops of DNA and slower electrophoretic migration (Fig. 1).
The tropism indicated by the HTA agreed more often with the results of cell-based assays than did V3 sequence predictions. Concordance between results of cell-based assays and sequence predictions was 87%, within the range of previous studies [39,46,47] but significantly diminished compared with the 98% concordance achieved by using the HTA (P = 0.044, two-sided test for proportions). Statistical analyses of the sequence prediction method, based upon the 46 strains demonstrating sole usage of either R5 or X4 coreceptor, indicated that, for X4 strains, the sequence prediction method's sensitivity was 100%, specificity 85%, PPV 67% and NPV 100%. For R5 strains, the sensitivity of the sequence prediction was 85%, specificity 100%, PPV 100% and NPV 67%.
For the four samples that were made up of both R5 and X4 variants, which were omitted from the preceding statistical analyses, the coreceptor usage was predicted from population-based sequences and indicated the tropism of the predominant strains; it, therefore, did not predict mixed coreceptor usage. The HTA, by contrast, was capable of detecting both predominant and rare variants, reflecting the actual molecular quasispecies. Because population-based sequences identify the predominant base at each position, they may fail to reflect sequences of nucleotides that are linked on the same molecule of HIV-1 RNA; this phenomenon has been demonstrated by studies of drug resistance mutations in individual viral variants .
The HTA was used to analyse two additional sets of patient-derived variants. First, approximately 400 biological and molecular HIV-1 clones of known coreceptor specificity from 15 patients were characterized . The PPV of the HTA for detecting R5 and X4 strains was 100% and 98.1%, respectively. The HTA also confirmed the phenotypic results obtained by InPheno AG, Basel, Switzerland using the cellular system DeCIPhR to analyse seven clinical isolates .
Reconstruction experiments demonstrated that this HTA could identify X4 subpopulations representing as little as 1% of the total quasispecies. These findings are consistent with published analyses demonstrating that rare variants could be detected and quantified by HTA . Analyses of plasma samples from this study and others demonstrated that this system was capable of determining HIV-1 tropism in 97% of samples with HIV-1 RNA loads ≥ 1000 copies/ml and 85% of those with viral loads < 1000 copies/ml.
Virological responses were measured in terms of the percentage of patients with HIV-1 RNA < 500 copies/ml 6 months after initiating cART. For immunological responses, the change in CD4 cell counts was determined between that at baseline and that at the visit closest to 6 months. QXR, the proportion of plasma HIV-1 using CCR5, was stratified into two categories: QXR = 1 if all virus identified used CCR5, and QXR< 1 if X4 virus was detected. The association between virological responses and baseline QXR was assessed by comparing the percentages of patients with undetectable HIV-1 RNA load across the different strata using Fisher's exact test. Immunological responses across two strata were compared by Wilcoxon rank-sum tests.
Kaplan–Meier curves and Cox proportional hazard regression models were applied to quantify the association of baseline or follow-up QXR (1 versus < 1) with subsequent clinical progression, defined as a new clinical AIDS-defining event or death. In addition to the two QXR strata, an additional model was included that analyzed the relationship of X4 viral load to HIV-1 disease progression by stratifying X4-specific viral load into three strata: no detectable X4-specific viral load (i.e., QXR = 1), detectable X4 viremia below the median value of X4-specific viral loads, and detectable X4 viremia above the median value. To compare the predictive capacity with the established progression markers CD4 cell count and HIV-1 RNA load, concurrent log2-transformed CD4 cell count values and log10-transformed HIV-1 loads were included in the univariable and multivariable Cox models. Inverse probability weights were applied to adjust for sampling bias. Analyses were carried out with STATA version 9.1 (StataCorp, College Station, Texas, USA).
To examine whether QXR can predict the response to cART, a subset of SHCS patients who initiated treatment in 1995–1998 were studied, comparing 96 patients who progressed to a clinical AIDS-defining event or death with 84 contemporaneous nonprogressors. Baseline demographic characteristics showed that progressing and nonprogressing patients were comparable in age, sex and risk for HIV-1 acquisition (P > 0.1; Table 2). As expected, however, the progressors exhibited evidence of more advanced HIV-1 infection. Not only did they display lower CD4 cell counts and higher HIV-1 loads than nonprogressors, but they also were more likely to harbor X4-specific HIV-1 variants (P < 0.001 for all three comparisons). HIV-1 coreceptor usage was expressed here as a QXR value, with QXR < 1 signifying a mixture of R5 and X4 variants and QXR = 1 signifying a viral population comprising R5 strains exclusively. A significantly larger proportion of progressors exhibited QXR < 1 than nonprogressors, and the mean X4-specific HIV-1 load was, therefore, higher in progressors as well (P < 0.001).
Patients whose samples were analyzed in this study were comparable to the entire SHCS population with respect to gender, age and mode of HIV-1 acquisition (all P > 0.1). Among SHCS nonprogressors, however, individuals whose samples were analyzed for QXR exhibited more advanced immunosuppression than patients whose samples were not analyzed; 40% versus 24% had Centers for Disease Control and Prevention (CDC) stage C disease, with a median baseline CD4 cell count of 119 versus 207 cells/μl (both P < 0.01). Among progressors, 42% of patients with QXR results had reached CDC stage C and the median baseline CD4 cell count was 50 cells/μl. There was no obvious explanation for this imbalance, but because it diminished the difference between baseline predictors observed in progressors and nonprogressors, it resulted in an underestimation of the true effect of QXR.
Association of QXR with immunological and virological responses
QXR values before and during treatment were examined to see if they were associated with immunological responses to cART (Table 3). Patients with baseline QXR < 1 displayed significantly reduced CD4 cell responses to cART compared with those with QXR = 1 (40 versus 82 cells/μl; P = 0.012). This finding was also observed in patients with persistent viremia and QXR < 1 after 6 months of cART (11 versus 65 cells/μl; P = 0.04). The virological response to cART, defined here as suppression of HIV-1 RNA load to < 500 copies/ml after 6 months of treatment, was not associated with QXR at baseline (P = 0.33).
Predictors of clinical progression
Kaplan–Meier estimates of the proportion of subjects who progressed to a new AIDS-defining illness or death, stratified according to QXR, revealed that QXR values strongly predicted the probability of disease progression when measured before the initiation of cART (P = 0.0002; Fig. 2) or, to a lesser extent, after 6 months of therapy in those with HIV-1 loads ≥ 500 copies/ml (P = 0.04).
To examine the independent effect of QXR < 1 and X4-specific viral load on disease progression, Cox univariable and multivariable regression models were applied (Table 4). The adjusted multivariable HR for clinical progression was 4.8 (95% CI, 2.3–10.0) for QXR < 1 at baseline. For QXR < 1 at follow-up, the univariable HR was 3.7 (95% CI, 1.1–13.0), and reached borderline significance in the CD4 cell- and HIV-1 RNA-adjusted multivariable model (HR 2.9; 95% CI, 0.95–8.7; P = 0.06). X4-specific HIV-1 load was a similarly independent predictor, with HR values of 3.7 (95% CI, 1.2–11.3) for baseline X4-specific viral loads of 2.2–4.3 log10 copies/ml and 5.9 (95% CI, 2.2–15.0) for X4 loads > 4.3 log10 copies/ml. Although total HIV-1 load and CD4 cell count were associated with clinical disease, QXR and X4-specific viral load strongly predicted disease progression during cART, independent of and in addition to CD4 cell count or total viral load.
This report identified HIV-1 coreceptor usage as a powerful predictor of the response to cART. By using a highly sensitive assay to detect X4 strains, the study demonstrated that patients harboring X4 variants not only displayed a diminished CD4 cell response, compared with those without such strains, but also displayed a markedly increased risk of progressing to AIDS or death despite treatment. The increased probability of clinical progression was observed in patients with detectable X4 variants before initiating cART and in those with persistent viremia and X4 strains after 6 months of therapy. Furthermore, pretreatment X4 loads as low as 2.2 log10 copies/ml (≈160 copies/ml) predicted a significantly increased probability of clinical progression.
Because QXR and X4-specific viral load identified a subset of individuals at increased risk for disease progression, they promise to be clinically useful biomarkers for patient management. First, these biomarkers may inform the decision to begin cART in untreated patients. It would be of interest to consider a clinical trial evaluating the initiation of cART in asymptomatic individuals with QXR < 1, including those with CD4 cell counts > 350 cells/μl. One goal of treatment in such patients would be to preferentially suppress X4 variants [28–30] as well as to reduce HIV-1 levels and thereby slow disease progression [1–3]. Among SHCS patients with persistent viremia at follow-up despite cART, those harboring X4 strains were also more likely to experience disease progression, suggesting that they too might benefit from an immediate change in therapy. Serial measurements of X4 load would permit quantitative monitoring.
An HTA was used to quantify HIV-1 tropism in this study. Its ability to determine coreceptor usage was validated by analyzing 50 well-characterized independent viral isolates of known tropism, with a 98% concordance between the results of the HTA and cell-based assays. This HTA detected X4 strains making up as little as 1% of the viral quasispecies, permitting us to link disease progression to the presence and level of X4 strains even when these strains were present as a minority. Greater than 50% of the samples with detectable X4 variants in this study displayed QXR > 0.75, indicating that X4 variants composed < 25% of the viral quasispecies. These data confirm the observation that X4 strains often exist in infected individuals as minority species  and they underscore the benefit of using a highly sensitive assay. Additional studies in other patient populations are needed to confirm the findings presented here and to characterize further the clinical utility of measuring tropism and X4-specific load.
The response to cART depends upon both biological and behavioral variables, and multiple phenomena besides viral tropism are likely to have contributed to the outcomes observed here. In addition to baseline HIV-1 load and CD4 cell count [6,7,10–13], the success of cART is associated with plasma drug levels and adherence to therapy . Drug concentrations themselves may vary significantly between patients administered the same agent. These variations occur for numerous reasons, including discrepancies in drug metabolism, especially related to the cytochrome P450 system, underlying disease, concomitant medications or genetic traits [50,51]. Future studies of the determinants of response to cART will be necessary to evaluate these interactions.
This report helps to elucidate the tremendous clinical success of cART. Although studies have documented that many individuals who initiated cART did not experience suppression of plasma viremia [2,8–10], most of these patients derived significant immunological and clinical benefits. Furthermore, cART has provided clinical advantages to patients with advanced HIV-1 infection beyond those indicated by the CD4 cell count and HIV-1 load [3,10,33]. Previous analyses of patients exhibiting CD4 cell increases despite persistent viremia have suggested that the benefits of cART in these patients may stem from the diminished replicative capacity of many drug-resistant viruses . This report supports the role of preferential suppression of X4 variants as another means by which cART may lead to CD4 cell reconstitution without complete viral suppression. A prior study of patients with advanced HIV-1 infection suggested that both replicative capacity and viral tropism may be involved .
In the setting of sustained suppression of plasma viremia, a different picture may be seen. HIV-1 variants obtained from highly purified blood cell types from patients with prolonged viral suppression were found to be compartmentalized between cellular reservoirs, with certain cell types, including naive CD4 T cells, displaying an accumulation of X4 strains over time . These HIV-1 populations may serve as potential sources of virus reseeding the body in the event that plasma viremia rebounds.
Because this investigation focused on a subset of SHCS participants who initiated cART in 1995–1998 , our selection of patients relied on the availability of cryopreserved plasma samples. Although the patients we studied were demographically comparable to the entire SHCS population, the nonprogressors described in this report displayed more advanced immunosuppression than nonprogressors whose samples were unavailable. There is no obvious explanation for this unintentional imbalance. Because it diminishes the difference between progressors and nonprogressors, we are confident, however, that the findings of this study remain valid. In addition, fewer samples were available for QXR analysis at follow-up than at baseline, owing primarily to the effectiveness of cART in suppressing HIV-1 load to < 500 copies/ml. Additional studies including controlled trials and analyses of larger numbers of patients will be necessary to evaluate the role of QXR and X4 viral load in therapeutic research and clinical management.
We thank all the patients, physicians, study nurses, data managers and technicians participating in the SHCS for providing invaluable data; Tamara Schroeder and Alissa Gormley for their contributions to the development and performance of the HTA; Binshan Shi, Kimdar Kemal, and Cheryl Brunner for performing validation studies of this assay; and the Wadsworth Center Molecular Genetics Core for DNA sequencing.
Members of the SHCS: C. Aebi, M. Battegay, E. Bernasconi, J. Böni, H. Bucher, Ph. Bürgisser, A. Calmy, S. Cattacin, M. Cavassini, J. Cheseaux, R. Dubs, M. Egger, L. Elzi, P. Erb, M. Fischer, M. Flepp, A. Fontana, P. Francioli (President of the SHCS, Centre Hospitalier Universitaire Vaudois, Lausanne), H. Furrer (Chairman of the Clinical and Laboratory Committee), A. Gayet-Ageron, S. Gerber, M. Gorgievski, C. Grawe, H. Günthard, (Chairman of the Scientific Board), T. Gyr, H. Hirsch, B. Hirschel, I. Hösli, Ch. Kahlert, L. Kaiser, U. Karrer, O. Keiser, C. Kind, Th. Klimkait, B. Ledergerber, G. Martinetti, B. Martinez de Tejada, N. Müller, D. Nadal, M. Opravil, F. Paccaud, G. Pantaleo, L. Perrin, S. Regenass, M. Rickenbach (Head of Coordination and Data Centre), C. Rudin (Chairman of the Mother and Child Substudy), P. Schmid, D. Schultze, J. Schüpbach, R. Speck, P. Taffé, P. Tarr, A. Telenti, A. Trkola, Y. Vallet, P. Vernazza, A. Wechsler, R. Weber, D. Wunder, C. Wyler, and S. Yerly (Chairperson of the Clinical and Laboratory Committee).
Funding: This study was financed in part in the framework of the Swiss HIV Cohort Study, supported by the Swiss National Science Foundation, and by grants R01-AI52015 and U01-AI34004 from the US National Institutes of Health.
1. Egger M, Hirschel B, Francioli P, Sudre P, Wirz M, Flepp M, et al
. Impact of new antiretroviral combination therapies in HIV infected patients in Switzerland: prospective multicentre study. Swiss HIV Cohort Study. BMJ 1997; 315:1194–1199.
2. Ledergerber B, Egger M, Opravil M, Telenti A, Hirschel B, Battegay M, et al
. Clinical progression and virological failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Swiss HIV Cohort Study. Lancet 1999; 353:863–868.
3. Mocroft A, Ledergerber B, Katlama C, Kirk O, Reiss P, d'Arminio Monforte A, et al
. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003; 362:22–29.
4. Opravil M, Ledergerber B, Furrer H, Hirschel B, Imhof A, Gallant S, et al
. Clinical efficacy of early initiation of HAART in patients with asymptomatic HIV infection and CD4 cell count > 350 × 106
/l. AIDS 2002; 16:1371–1381.
5. Sterling TR, Chaisson RE, Keruly J, Moore RD. Improved outcomes with earlier initiation of highly active antiretroviral therapy among human immunodeficiency virus-infected patients who achieve durable virologic suppression: longer follow-up of an observational cohort study. J Infect Dis 2003; 188:1659–1665.
6. Anastos K, Barron Y, Cohen MH, Greenblatt RM, Minkoff H, Levine A, et al
. The prognostic importance of changes in CD4+ cell count and HIV-1 RNA level in women after initiating highly active antiretroviral therapy. Ann Intern Med 2004; 140:256–264.
7. Hammer SM, Saag MS, Schechter M, Montaner JS, Schooley RT, Jacobsen DM, et al
. Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society-USA Panel. JAMA 2006; 297:827–843.
8. Mezzaroma I, Carlesimo M, Pinter E, Muratori DS, Di Sora F, Chiarotti F, et al
. Clinical and immunologic response without decrease in virus load in patients with AIDS after 24 months of highly active antiretroviral therapy. Clin Infect Dis 1999; 29:1423–1428.
9. Deeks SG, Barbour JD, Martin JN, Swanson MS, Grant RM. Sustained CD4+ T cell response after virologic failure of protease inhibitor-based regimens in patients with human immunodeficiency virus infection. J Infect Dis 2000; 181:946–953.
10. Ledergerber B, Lundgren JD, Walker AS, Sabin C, Justice A, Reiss P, et al
. Predictors of trend in CD4-positive T-cell count and mortality among HIV-1-infected individuals with virological failure to all three antiretroviral-drug classes. Lancet 2004; 364:51–61.
11. Kitchen CM, Kitchen SG, Dubin JA, Gottlieb MS. Initial virological and immunologic response to highly active antiretroviral therapy predicts long-term clinical outcome. Clin Infect Dis 2001; 33:466–472.
12. Egger M, May M, Chene G, Phillips AN, Ledergerber B, Dabis F, et al
. Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet 2002; 360:119–129.
13. Chene G, Sterne JAC, May M, Costagliola D, Ledergerber B, Phillips AN, et al
. Prognostic importance of initial response in HIV-1 infected patients starting potent anitretroviral therapy: analysis of prospective studies. Lancet 2003; 362:679–686.
14. Holmberg SD, Palella FJ Jr, Lichtenstein KA, Havlir DV. The case for earlier treatment of HIV infection. Clin Infect Dis 2004; 39:1699–1704.
15. Phillips AN, Lepri AC, Lampe F, Johnson M, Sabin C. When should antiretroviral therapy be started for HIV infection? Interpreting the evidence from observational studies. AIDS 2003; 17:1863–1869.
16. Feng Y, Broder CC, Kennedy PE, Berger EA. HIV-1 entry cofactor: functional cDNA cloning of a seven-transmembrane G-protein coupled receptor. Science 1996; 272:872–877.
17. Samson M, Libert F, Doranz BJ, Rucker J, Liesnard D, Farber CM, et al
. Resistance to HIV-1 infection in Caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 1996; 382:722–725.
18. Ray N, Doms RW. HIV-1 coreceptors and their inhibitors. Curr Topics Microbiol Immunol 2006; 303:97–120.
19. Bjorndal A, Deng H, Jansson M, Fiore JR, Colognesi C, Karlsson A, et al
. Coreceptor usage of primary human immunodeficiency virus type 1 isolates varies according to biological phenotype. J Virol 1997; 71:7478–7487.
20. Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, Frazadegan H, et al
. Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. J Virol 1999; 73:10489–10502.
21. Scarlatti G, Tresoldi E, Bjorndal A, Fredriksson R, Colognese C, Deng HK, et al
. In vivo evolution of HIV-1 co-receptor usage and sensitivity to chemokine-mediated suppression. Nat Med 1997; 3:1259–1265.
22. Freel SA, Fiscus SA, Pilcher CD, Menezes P, Giner J, Patrick D, et al
. Envelope diversity, coreceptor usage and syncytium-inducing phenotype of HIV-1 variants in saliva and blood during primary infections. AIDS 2003; 17:2025–2033.
23. Koot M, Keet IP, Vos AH, de Goede RE, Roos MT, Coutinho RA, et al
. Prognostic value of HIV-1 syncytium-inducing phenotype for rate of CD4+ cell depletion and progression to AIDS. Ann Intern Med 1993; 118:681–688.
24. Connor RI, Sheridan KE, Ceradini D, Choe S, Landau NR. Change in coreceptor use correlates with disease progression in HIV-1-infected individuals. J Exp Med 1997; 185:621–628.
25. Blaak H, van't Wout AB, Brouwer M, Hooibrink B, Hovenkamp E, Schuitemaker H. In vivo HIV-1 infection of CD45RA(+) CD4(+) T cells is established primarily by syncytium-inducing variants and correlates with the rate of CD4(+) T cell decline. Proc Natl Acad Sci USA 2000; 97:1269–1274.
26. Kreisberg JF, Kwa D, Schramm B, Trautner V, Connor R, Schuitemaker H, et al
. Cytopathicity of human immunodeficiency virus type 1 primary isolates depends on coreceptor usage and not patient disease status. J Virol 2001; 75:8842–8847.
27. Jekle A, Keppler OT, De Clercq E, Schols D, Weinstein M, Goldsmith MA. In vivo evolution of human immunodeficiency virus type 1 toward increased pathogenicity through CXCR4-mediated killing of uninfected CD4 T cells. J Virol 2003; 77:5846–5854.
28. Philpott S, Weiser B, Anastos K, Kitchen CM, Robison E, Meyer WA III, et al
. Preferential suppression of CXCR4-specific strains of HIV-1 by antiviral therapy. J Clin Invest 2001; 107:431–437.
29. Equils O, Garratty E, Wei LS, Plaeger S, Tapia M, Deville J, et al
. Recovery of replication-competent virus from CD4 T cell reservoirs and change in coreceptor use in human immunodeficiency virus type 1-infected children responding to highly active antiretroviral therapy. J Infect Dis 2000; 182:751–757.
30. Skrabal K, Trouplin V, Labrosse B, Obry V, Damond F, Hance AJ, et al
. Impact of antiretroviral treatment on the tropism of HIV-1 plasma virus populations. AIDS 2003; 17:809–814.
31. Brumme ZL, Dong W, Yip B, Wynhoven B, Hoffman N, Swanstrom R, et al
. Clinical and immunological impact of HIV envelope V3 sequence variation after starting initial triple antiretroviral therapy. AIDS 2004; 18:F1–F9.
32. Low AJ, Dong W, Chan D, Sing T, Swanstrom R, Jensen M, et al
. Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates. AIDS 2007; 21:F17–F24.
33. Miller V, Phillips AN, Clotet B, Mocroft A, Ledergerber B, Kirk O, et al
. Association of virus load, CD4 cell count, and treatment with clinical progression in human immunodeficiency virus-infected patients with very low CD4 cell counts. J Infect Dis 2002; 186:189–197.
34. Ledergerber B, von Overbeck J, Egger M, Luthy R. The Swiss HIV Cohort Study: rationale, organization and selected baseline characteristics. Soz Praventivmed 1994; 39:387–394.
35. Delwart EL, Gordon CJ. Tracking changes in HIV-1 envelope quasispecies using DNA heteroduplex analysis. Methods 1997; 12:348–354.
36. Nelson JA, Fiscus SA, Swanstrom R. Evolutionary variants of the human immunodeficiency virus type 1 V3 region characterized by using a heteroduplex tracking assay. J Virol 1997; 71:8750–8758.
37. Ho SH, Shek L, Gettie A, Blanchard J, Cheng-Mayer C. V3 loop-determined coreceptor preference dictates the dynamics of CD4+-T-cell loss in simian-human immunodeficiency virus-infected macaques. J Virol 2005; 79:12296–13283.
38. Pastore C, Nedellec R, Ramos A, Pontow S, Ratner L, Mosier DE. Human immunodeficiency virus type 1 coreceptor switching: V1/V2 gain-of-fitness mutations compensate for V3 loss-of-fitness mutations. J Virol 2006; 80:750–758.
39. Cardozo T, Kimura T, Philpott S, Weiser B, Burger H, Zolla-Pazner S. Structural basis for coreceptor selectivity by the HIV-1 V3 loop. AIDS Res Hum Retroviruses 2007; 23:415–426.
40. Fang G, Weiser B, Kuiken C, Philpott SM, Rowland-Jones S, Plummer J, et al
. Recombination following superinfection by HIV-1. AIDS 2003; 18:153–159.
41. Kemal KS, Foley B, Burger H, Anastos K, Minkoff H, Kitchen C, et al
. HIV-1 in genital tract and plasma of women: compartmentalization of viral sequences, coreceptor usage, and glycosylation. Proc Natl Acad Sci USA 2003; 100:12972–12977.
42. Yi Y, Chen W, Frank I, Cuttilli J, Singh A, Starr-Spires L, et al
. An unusual syncytia-inducing human immunodeficiency virus type 1 primary isolate from the central nervous system that is restricted to CXCR4, replicates efficiently in macrophages and induces neuronal apoptosis. Neurovirology 2003; 9:432–442.
43. Vodicka MA, Goh WC, Wu LI, Rogel ME, Bartz S, Schweickart VL, et al
. Indicator cell lines for detection of primary strains of human and simian immunodeficiency viruses. Virology 1997; 233:193–198.
44. Felber BK, Pavlakis GN. A quantitative bioassay to HIV-1 based on trans-activation. Science 1988; 239:184–187.
45. Morner A, Bjorndal A, Albert J, Kewalramani VN, Littman DR, Inoue R, et al
. Primary human immunodeficiency type 2 isolates like HIV-1 isolates frequently use CCR5 but show promiscuity in coreceptor usage. J Virol 1999; 73:2343–2349.
46. Bhattacharyya D, Brooks BR, Callahan L. Positioning of positively charged residues in the V3 loop correlates with HIV type 1 syncytium-inducing phenotype. AIDS Res Hum Retroviruses 1996; 12:83–90.
47. Hung C-S, van der Heyden N, Ratner L. Analysis of the critical domain in the V3 loop of human immunodeficiency virus type 1 gp120 involved in CCR5 utilization. J Virol 1999; 73:8216–8226.
48. Kemal KS, Burger H, Mayers D, Anastos K, Foley B, Kitchen C, et al
. HIV-1 drug resistance in variants from the female genital tract and plasma. J Infect Dis 2007; 195:535–545.
49. DeMarco S, Henze H, Lederer A, Moehle K, Mukherjee R, Romagnoli B, et al
. Discovery of novel, highly potent and selective β-hairpin mimetic CXCR4 inhibitors with excellent anti-HIV activity and pharmacokinetic profiles. Bioorg Med Chem 2006; 14:8396–8404.
50. Oette M, Kroidl A, Gobels K, Stabbert A, Menge M, Sagir A, et al
. Predictors of short-term success of antiretroviral therapy in HIV infection. J Antimicrob Chemother 2006; 58:147–153.
51. Corbett AH, Patterson KB, Tien H-C, Kalvass LA, Eron JJ, Ngo LT, et al
. Dose separation does not overcome the pharmacokinetic interaction between fosamprenavir and lopinavir/ritonavir. Antimicrob Agents Chemother 2006; 50:2756–2761.
52. Solomon A, Lane N, Wightman F, Gorry PR, Lewin SR. Enhanced replicative capacity and pathogenicity of HIV-1 isolated from individuals infected with drug-resistant virus and declining CD4+ T-cell counts. J Acquir Immune Defic Syndr 2005; 40:140–148.
53. Delobel P, Sandres-Sauné K, Cazabat M, L'Faqihi F-E, Aquilina C, Obadia M, et al
. Persistence of HIV-1 populations in blood monocytes and naive and memory CD4 T cells during prolonged suppressive HAART. AIDS 2005; 19:1739–1750.
Keywords:© 2008 Lippincott Williams & Wilkins, Inc.
AIDS; antiretroviral therapy; CCR5; CXCR4; heteroduplex tracking assay; HIV-1 tropism