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

Fiche du document

Date

2013

Type de document
Périmètre
Langue
Identifiants
Relations

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1003203

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/24009493

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1553-7358

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_CCDA351C11275

Licences

info:eu-repo/semantics/openAccess , Copying allowed only for non-profit organizations , https://serval.unil.ch/disclaimer


Sujets proches En

Therapy

Citer ce document

N. Beerenwinkel et al., « The Individualized Genetic Barrier Predicts Treatment Response in a Large Cohort of HIV-1 Infected Patients. », Serveur académique Lausannois, ID : 10.1371/journal.pcbi.1003203


Métriques


Partage / Export

Résumé 0

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.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Exporter en