Francesca Incardona

Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time

Background Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanfords HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Methodology/Principal Findings Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8–16) weeks (2152 TCEs), 24 (16–32) weeks (2570 TCEs), and 48 (44–52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratios were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5–1.7] for HIVdb, 1.7 [1.5–1.8] for ANRS, and 1.7 [1.9–1.6] for Rega. Odds ratios increased over time, but remained comparable (odds ratios ranging between 1.9–2.1 at 24 weeks and 1.9–2.2 at 48 weeks). The Area under the curve of the ROC did not differ between the systems at all time points; p = 0.60 at week 12, p = 0.71 at week 24, and p = 0.97 at week 48. Conclusions/Significance Three commonly used HIV drug resistance interpretation systems ANRS, Rega and HIVdb predict virological response at 12, 24, and 48 weeks, after change of treatment to the same extent.

Selecting anti-HIV therapies based on a variety of genomic and clinical factors

Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome. Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org Contact: rosen@il.ibm.com

Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy.

Background Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. Principal Findings The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. Conclusion The combined EuResist prediction engine is freely available at http://engine.euresist.org.

Prediction of Response to Antiretroviral Therapy by Human Experts and by the EuResist Data-Driven Expert System (the EVE study)

The EuResist expert system is a novel data‐driven online system for computing the probability of 8‐week success for any given pair of HIV‐1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment.

Predicting response to antiretroviral treatment by machine learning: The euresist project

For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools.

Efficacy of etravirine combined with darunavir or other ritonavir-boosted protease inhibitors in HIV-1-infected patients: an observational study using pooled European cohort data†

This observational study in antiretroviral treatment‐experienced, HIV‐1‐infected adults explored the efficacy of etravirine plus darunavir/ritonavir (DRV group; n = 999) vs. etravirine plus an alternative boosted protease inhibitor (other PI group; n = 116) using pooled European cohort data.

PhyloGeoTool: interactively exploring large phylogenies in an epidemiological context

Motivation: Clinicians, health officials and researchers are interested in the epidemic spread of pathogens in both space and time to support the optimization of intervention measures and public health policies. Large sequence databases of virus sequences provide an interesting opportunity to study this spread through phylogenetic analysis. To infer knowledge from large phylogenetic trees, potentially encompassing tens of thousands of virus strains, an efficient method for data exploration is required. The clades that are visited during this exploration should be annotated with strain characteristics (e.g. transmission risk group, tropism, drug resistance profile) and their geographic context. Results: PhyloGeoTool implements a visual method to explore large phylogenetic trees and to depict characteristics of strains and clades, including their geographic context, in an interactive way. PhyloGeoTool also provides the possibility to position new virus strains relative to the existing phylogenetic tree, allowing users to gain insight in the placement of such new strains without the need to perform a de novo reconstruction of the phylogeny. Availability and implementation: https://github.com/rega‐cev/phylogeotool (Freely available: open source software project). Contact: phylogeotool@kuleuven.be Supplementary information: Supplementary data are available at Bioinformatics online.

Standardized representation, visualization and searchable repository of antiretroviral treatment-change episodes

BackgroundTo identify the determinants of successful antiretroviral (ARV) therapy, researchers study the virological responses to treatment-change episodes (TCEs) accompanied by baseline plasma HIV-1 RNA levels, CD4+ T lymphocyte counts, and genotypic resistance data. Such studies, however, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. Moreover, the absence of a standard method for representing the data comprising a TCE makes it difficult to apply uniform criteria in the analysis of published studies of TCEs.ResultsTo facilitate data sharing for TCE analyses, we developed an XML (Extensible Markup Language) Schema that represents the temporal relationship between plasma HIV-1 RNA levels, CD4 counts and genotypic drug resistance data surrounding an ARV treatment change. To demonstrate the adaptability of the TCE XML Schema to different clinical environments, we collaborate with four clinics to create a public repository of about 1,500 TCEs. Despite the nascent state of this TCE XML Repository, we were able to perform an analysis that generated a novel hypothesis pertaining to the optimal use of second-line therapies in resource-limited settings. We also developed an online program (TCE Finder) for searching the TCE XML Repository and another program (TCE Viewer) for generating a graphical depiction of a TCE from a TCE XML Schema document.ConclusionsThe TCE Suite of applications – the XML Schema, Viewer, Finder, and Repository – addresses several major needs in the analysis of the predictors of virological response to ARV therapy. The TCE XML Schema and Viewer facilitate sharing data comprising a TCE. The TCE Repository, the only publicly available collection of TCEs, and the TCE Finder can be used for testing the predictive value of genotypic resistance interpretation systems and potentially for generating and testing novel hypotheses pertaining to the optimal use of salvage ARV therapy.

Drug resistance testing through remote genotyping and predicted treatment options in human immunodeficiency virus type 1 infected Tanzanian subjects failing first or second line antiretroviral therapy

Introduction Antiretroviral therapy (ART) has been successfully introduced in low-middle income countries. However an increasing rate of ART failure with resistant virus is reported. We therefore described the pattern of drug resistance mutations at antiretroviral treatment (ART) failure in a real-life Tanzanian setting using the remote genotyping procedure and thereafter predicted future treatment options using rule-based algorithm and the EuResist bioinformatics predictive engine. According to national guidelines, the default first-line regimen is tenofovir + lamivudine + efavirenz, but variations including nevirapine, stavudine or emtricitabine can be considered. If failure on first-line ART occurs, a combination of two nucleoside reverse transcriptase inhibitors (NRTIs) and boosted lopinavir or atazanavir is recommended. Materials and methods Plasma was obtained from subjects with first (n = 174) or second-line (n = 99) treatment failure, as defined by clinical or immunological criteria, as well as from a control group of ART naïve subjects (n = 17) in Dar es Salaam, Tanzania. Amplification of the pol region was performed locally and the amplified DNA fragment was sent to Sweden for sequencing (split genotyping procedure). The therapeutic options after failure were assessed by the genotypic sensitivity score and the EuResist predictive engine. Viral load was quantified in a subset of subjects with second-line failure (n = 52). Results The HIV-1 pol region was successfully amplified from 55/174 (32%) and 28/99 (28%) subjects with first- or second-line failure, respectively, and 14/17 (82%) ART-naïve individuals. HIV-1 pol sequence was obtained in 82 of these 97 cases (84.5%). Undetectable or very low (<2.6 log10 copies/10−3 L) viral load explained 19 out of 25 (76%) amplification failures in subjects at second-line ART failure. At first and second line failure, extensive accumulation of NRTI (88% and 73%, respectively) and NNRTI (93% and 73%, respectively) DRMs but a limited number of PI DRMs (11% at second line failure) was observed. First line failure subjects displayed a high degree of cross-resistance to second-generation NNRTIs etravirine (ETR; 51% intermediate and 9% resistant) and rilpivirine (RPV; 12% intermediate and 58% resistant), and to abacavir (ABC; 49% resistant) which is reserved for second line therapy in Tanzania. The predicted probability of success with the best salvage regimen at second-line failure decreased from 93.9% to 78.7% when restricting access to the NRTIs, NNRTIs and PIs currently available in Tanzania compared to when including all approved drugs. Discussion The split genotyping procedure is potential tool to analyse drug resistance in Tanzania but the sensitivity should be evaluated further. The lack of viral load monitoring likely results in a high false positive rate of treatment failures, unnecessary therapy switches and massive accumulation of NRTI and NNRTI mutations. The introduction of regular virological monitoring should be prioritized and integrated with drug resistance studies in resource limited settings.

Etravirine in Protease Inhibitor-Free Antiretroviral Combination Therapies

Etravirine (ETR) is a next generation non‐nucleoside reverse transcriptase inhibitor (NNRTI). The studies for ETR EMA approval were almost exclusively performed together with the protease inhibitor (PI) darunavir. However the fact that ETR can be active against NNRTI‐pretreated HIV variants and that it is well tolerated suggests its application in PI‐free antiretroviral combination therapies. Although approved only for PI‐containing therapies, a number of ETR treatments without PIs are performed currently. To evaluate the performance of ETR in PI‐free regimens, we analyzed the EURESIST database. We observed a total of 70 therapy switches to a PI‐free, ETR containing antiretroviral combination with detectable baseline viral load. 50/70 switches were in male patients and 20/70 in females. The median of previous treatments was 10. The following combinations were detected in the EURESIST database: ETR+MVC+RAL (20.0%); ETR+FTC+TDF (18.6%); 3TC+ETR+RAL (7.1%); 3TC+ABC+ETR (5.7%); other combinations (31.4%). A switch was defined as successful when either ≤50 copies/mL or a decline of the viral load of 2 log10, both at week 24 (range 18–30) were achieved. The overall success rate (SR) was 77% (54/70), and for the different combinations: ETR+MVC+RAL=78.6% (11/14); ETR+FTC+TDF=92.3% (12/13); 3TC+ETR+RAL =80.0% (4/5), 3TC+ABC+ETR=100% (SR 4/4); and for other combinations=67.6% (23/34). These SR values are comparable to those for other therapy combinations in such pretreated patients.