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

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_CCDA351C1127
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients.
Périodique
PLOS Computational Biology
Auteur(s)
Beerenwinkel N., Montazeri H., Schuhmacher H., Knupfer P., von Wyl V., Furrer H., Battegay M., Hirschel B., Cavassini M., Vernazza P., Bernasconi E., Yerly S., Böni J. , Klimkait T., Cellerai C., Günthard H.F. 
Collaborateur(s)
Swiss HIV Cohort Study
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Statut éditorial
Publié
Date de publication
2013
Volume
9
Numéro
8
Pages
e1003203
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
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.
Pubmed
Web of science
Open Access
Oui
Création de la notice
27/09/2013 19:45
Dernière modification de la notice
20/08/2019 16:47
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