European guidelines on the clinical management of HIV-1 tropism testing

Infectious Diseases Unit, Ghent University Hospital, Gent, Belgium.
The Lancet Infectious Diseases (Impact Factor: 22.43). 03/2011; 11(5):394-407. DOI: 10.1016/S1473-3099(10)70319-4
Source: PubMed


Viral tropism is the ability of viruses to enter and infect specific host cells and is based on the ability of viruses to bind to receptors on those cells. Testing for HIV tropism is recommended before prescribing a chemokine receptor blocker. In most European countries, HIV tropism is identified with tropism phenotype testing. New data support genotype analysis of the HIV third hypervariable loop (V3) for the identification of tropism. The European Consensus Group on clinical management of tropism testing was established to make recommendations to clinicians and clinical virologists. The panel recommends HIV-tropism testing for the following groups: drug-naive patients in whom toxic effects are anticipated or for whom few treatment options are available; patients who have poor tolerability to or toxic effects from current treatment or who have CNS pathology; and patients for whom therapy has failed and a change in treatment is considered. In general, an enhanced sensitivity Trofile assay and V3 population genotyping are the recommended methods. Genotypic methods are anticipated to be used more frequently in the clinical setting because of their greater accessibility, lower cost, and faster turnaround time than other methods. For the interpretation of V3 loop genotyping, clinically validated systems should be used when possible. Laboratories doing HIV tropism tests should have adequate quality assurance measures. Similarly, close collaboration between HIV clinicians and virologists is needed to ensure adequate diagnostic and treatment decisions.

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    • "They assess tropism based on amino acids sequences from the V3 loop, which is known to be an important binding region on the gp120 envelope protein19,23. Because the use of phenotypic assays is still limited, the European Guidelines have encouraged the application of bioinformatics programs in coreceptor usage determination27. However, due to the intrinsic differences of each predictive system, divergent outputs are expected and remain a reason for concern in the wider application of this approach9,15,19,23–24. "
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    ABSTRACT: The clinical application of CCR5 antagonists involves first determining the coreceptor usage by the infecting viral strain. Bioinformatics programs that predict coreceptor usage could provide an alternative method to screen candidates for treatment with CCR5 antagonists, particularly in countries with limited financial resources. Thus, the present study aims to identify the best approach using bioinformatics tools for determining HIV-1 coreceptor usage in clinical practice. Proviral DNA sequences and Trofile results from 99 HIV-1-infected subjects under clinical monitoring were analyzed in this study. Based on the Trofile results, the viral variants present were 81.1% R5, 21.4% R5X4 and 1.8% X4. Determination of tropism using a Geno2pheno[coreceptor] analysis with a false positive rate of 10% gave the most suitable performance in this sampling: the R5 and X4 strains were found at frequencies of 78.5% and 28.4%, respectively, and there was 78.6% concordance between the phenotypic and genotypic results. Further studies are needed to clarify how genetic diversity amongst virus strains affects bioinformatics-driven approaches for determining tropism. Although this strategy could be useful for screening patients in developing countries, some limitations remain that restrict the wider application of coreceptor usage tests in clinical practice.
    Revista do Instituto de Medicina Tropical de São Paulo 07/2014; 56(4):287-90. DOI:10.1590/S0036-46652014000400003 · 1.01 Impact Factor
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    • "Current guidelines from Europe endorse the use of V3 loop sequencing of pvDNA to determine tropism, particularly for patients with low-level viremia or with viral suppression [9]. From a clinical standpoint, the ability to determine tropism from pvDNA allows testing to be extended to persons who are on effective antiretroviral therapy but who may be candidates for CCR5 antagonist therapy. "
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    ABSTRACT: Background HIV-1 coreceptor tropism testing is used to evaluate eligibility for CCR5 antagonist therapy. However, HIV-1 RNA-based tests are not suitable for virologically suppressed patients, therefore the use of proviral DNA tropism testing has been investigated. We describe a novel proviral DNA-based genotypic tropism assay and compare its performance to that of a sensitive HIV-1 RNA-based genotypic test. Methods Tropism was determined using HIV-1 plasma RNA and proviral DNA from 42 paired samples from patients with plasma viral loads ≥1000 HIV-1 RNA copies/mL. Proviral DNA sample types included whole blood, separated peripheral blood mononuclear cells resuspended in phosphate-buffered saline and peripheral blood mononuclear cells resuspended in spun plasma. The HIV-1 envelope V3 region was PCR-amplified, sequenced in triplicate, and analyzed for tropism with the geno2pheno algorithm using a 10% false-positive rate (FPR). Results Amplicons were obtained from proviral DNA and plasma RNA in 41/42 samples. Tropism predictions were highly concordant (93%–98%) between proviral DNA and plasma RNA, regardless of the proviral DNA isolation method. Non-R5 proviral DNA results were obtained for 100% of patients with detectable non-R5 plasma HIV-1 RNA results. Geno2pheno FPRs for proviral DNA and plasma RNA were highly correlated (Spearman rho = 0.86). Conclusions Our findings demonstrate that proviral DNA tropism determinations from whole blood or peripheral blood mononuclear cells were highly concordant with plasma HIV-1 RNA tropism determinations. This assay may be useful for screening virologically suppressed patients for CCR5-antagonist eligibility and for research purposes.
    AIDS Research and Therapy 05/2014; 11(1):14. DOI:10.1186/1742-6405-11-14 · 1.46 Impact Factor
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    • "CRT was inferred with the geno2pheno[co-receptor] algorithm (, setting the false positive rate (FPR) at 20%, according to the 2011 European Guidelines [17] since interpretation was based on a single DNA amplification and sequencing; isolates were classified as R5 (FPR >20%) or X4 (FPR ≤20%). Only clonal prediction was employed for classifying sequences. "
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    ABSTRACT: In HIV/HCV co-infected patients, HIV-1 gp120 activates human hepatic stellate cells (HSCs) which play a key role in fibrosis pathogenesis. It is still unclear whether pro-fibrogenic effects are more attributable to X4 or R5 strains in vivo. To assess if HIV-1 X4 or R5 variants are associated with a different progression of fibrosis. A total of 105 HIV/HCV co-infected patients were submitted to gp120 sequencing on proviral DNA and classified as X4 or R5 based on g2p (20% false positive rate). The fibrosis evolution was retrospectively determined by means of APRI and FIB-4 scores at 3-month intervals from the first anti-HCV-positive test. The association of co-receptor tropism with increased fibrosis scores was evaluated by linear mixed models. X4 variants were found in 41 patients (39%). The median observation period was similar in X4 and R5 patients (17 years). No difference was observed between the two groups of patients, except for nadir CD4 which was lower in X4 compared to R5 (percentage, p=0.005, and absolute number, p=0.005). X4 and R5 patients did not significantly differ for FIB-4 and APRI score over time (p=0.5, and p=0.1, respectively). No association between HCV-RNA levels over time and co-receptor tropism was noted (p=0.9). Conversely, a significant correlation of fibrosis scores with gamma-glutamyl transferase levels, lower current CD4 count, HIV viremia and use of antiretrovirals was observed. This retrospective analysis of fibrosis evolution did not support the evidence of a differing pro-fibrogenic activity for X4 and R5 HIV-1 variants in HIV/HCV co-infected patients.
    Journal of clinical virology: the official publication of the Pan American Society for Clinical Virology 01/2014; 59(3). DOI:10.1016/j.jcv.2013.12.009 · 3.02 Impact Factor
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