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

Endogenous or Exogenous Spreading of {HIV-1} in Nordrhein-Westfalen, Germany, Investigated by Phylodynamic Analysis of the {RESINA} Study Cohort

HIV's genetic instability means that sequence similarity can illuminate the underlying transmission network. Previous application of such methods to samples from the United Kingdom has suggested that as many as 86% of UK infections arose outside of the country, a conclusion contrary to usual patterns of disease spread. We investigated transmission networks in the Resina cohort, a 2,747 member sample from Nordrhein-Westfalen, Germany, sequenced at therapy start. Transmission networks were determined by thresholding the pairwise genetic distance in the pol gene at 96.8% identity. At first blush the results concurred with the UK studies. Closer examination revealed four large and growing transmission networks that encompassed all major transmission groups. One of these formed a supercluster containing 71% of the sex with men (MSM) subjects when the network was thresholded at levels roughly equivalent to those used in the UK studies, though methodological differences suggest that this threshold may be too generous in the current data. Examination of the endo- versus exogenesis hypothesis by testing whether infections that were exogenous to Cologne or to Dusseldorf were endogenous to the greater region supported endogenous spread in MSM subjects and exogenous spread in the endemic transmission group. In intravenous drug using group subjects, it depended on viral strain, with subtype B sequences appearing to have origin exogenous to the Resina data, while non-B sequences (primarily subtype A) were almost completely endogenous to their local community. These results suggest that, at least in Germany, the question of endogenous versus exogenous linkages depends on subject group

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

Proviral {DNA} as a Target for {HIV}-1 Resistance Analysis

Background: Resistance analysis from viral RNA is restricted to detectable viral load. Therefore, analysis from proviral DNA could help in cases with low-level or suppressed viremia. Methods: Viral plasma RNA and the corresponding cellular proviral DNA of 78 EDTA samples from 48 therapy-naive (TN) and 30 therapy-experienced (TE) HIV-1-infected patients were isolated and analyzed for their resistance profiles in the protease and reverse transcriptase genes. Results: Overall, 175 drug-resistance mutations (DRMs) were detected in 25/30 TE (83.3%) and 5/48 TN (10.4%) samples. The TE patients displayed a mean number of 6.68 DRMs in RNA and 5.20 in DNA. In the TN patients, a mean of 0.8 DRMs was found in RNA and 1.0 in DNA; 75% of the DRMs were detected in RNA and DNA simultaneously. In the TE samples, 76% of the DRMs were detected simultaneously in RNA and DNA, 23% exclusively in RNA and 1% in DNA only. The TN samples revealed a significantly higher frequency of DRMs in DNA than in RNA. Conclusions: Proviral DNA resistance testing provides additional resistance information for TN patients. It is also a reliable alternative for TE patients with unsuccessful RNA testing and can provide valuable information when no records are available. (C) 2015 S. Karger AG, Base

Arevir: A Secure Platform for Designing Personalized Antiretroviral Therapies Against HIV

Despite the availability of antiretroviral combination therapies, success in drug treatment of HIV-infected patients is limited. One reason for therapy failure is the development of drug-resistant genetic variants. In principle, the viral genomic sequence provides resistance information and could thus guide the selection of an optimal drug combination. In practice however, the benefit of this procedure is impaired by (1) the difficulty in inferring the clinically relevant information from the genotype of the virus and (2) the restricted availability of this information. We have developed a secure platform for collaborative research aimed at optimizing anti-HIV therapies, called Arevir. A relational database schema was designed and implemented together with a web-based user interface. Our system provides a basis for monitoring patients, decision-support, and computational analyses. Thus, it merges clinical, diagnostic and bioinformatics efforts to exploit genomic and patient therapy data in clinical practice

Predicting response to combination antiretroviral therapy: retrospective validation of geno2pheno-THEO on a large clinical database

BACKGROUND: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure. METHODS: We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega. RESULTS: The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed. CONCLUSION: Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org