edoc

The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients

Beerenwinkel, N. and Montazeri, H. and Schuhmacher, H. and Knupfer, P. and von Wyl, V. and Furrer, H. and Battegay, M. and Hirschel, B. and Cavassini, M. and Vernazza, P. and Bernasconi, E. and Yerly, S. and Boni, J. and Klimkait, T. and Cellerai, C. and Gunthard, H. F. and Swiss HIV Cohort Study, . (2013) The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients. PLoS Computational Biology, Vol. 9, H. 8 , e1003203.

Full text not available from this repository.

Official URL: http://edoc.unibas.ch/dok/A6338385

Downloads: Statistics Overview

Abstract

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.
Faculties and Departments:03 Faculty of Medicine > Departement Biomedizin > Department of Biomedicine, University Hospital Basel > Infection Biology (Khanna)
03 Faculty of Medicine > Departement Biomedizin > Division of Medical Microbiology > Molecular Virology (Klimkait)
UniBasel Contributors:Klimkait, Thomas and Battegay, Manuel E.
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Library of Science
ISSN:1553-734X
e-ISSN:1553-7358
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:12 Oct 2017 10:13
Deposited On:10 Apr 2015 09:13

Repository Staff Only: item control page