The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.
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 mutati...
Main Authors: | , , , , , , , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2013-01-01
|
Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC3757085?pdf=render |
id |
doaj-63320910d54b4d1d833ba0d08ccd6111 |
---|---|
record_format |
Article |
spelling |
doaj-63320910d54b4d1d833ba0d08ccd61112020-11-25T02:31:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0198e100320310.1371/journal.pcbi.1003203The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.Niko BeerenwinkelHesam MontazeriHeike SchuhmacherPatrick KnupferViktor von WylHansjakob FurrerManuel BattegayBernard HirschelMatthias CavassiniPietro VernazzaEnos BernasconiSabine YerlyJürg BöniThomas KlimkaitCristina CelleraiHuldrych F GünthardSwiss HIV Cohort StudyThe 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.http://europepmc.org/articles/PMC3757085?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Niko Beerenwinkel Hesam Montazeri Heike Schuhmacher Patrick Knupfer Viktor von Wyl Hansjakob Furrer Manuel Battegay Bernard Hirschel Matthias Cavassini Pietro Vernazza Enos Bernasconi Sabine Yerly Jürg Böni Thomas Klimkait Cristina Cellerai Huldrych F Günthard Swiss HIV Cohort Study |
spellingShingle |
Niko Beerenwinkel Hesam Montazeri Heike Schuhmacher Patrick Knupfer Viktor von Wyl Hansjakob Furrer Manuel Battegay Bernard Hirschel Matthias Cavassini Pietro Vernazza Enos Bernasconi Sabine Yerly Jürg Böni Thomas Klimkait Cristina Cellerai Huldrych F Günthard Swiss HIV Cohort Study The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. PLoS Computational Biology |
author_facet |
Niko Beerenwinkel Hesam Montazeri Heike Schuhmacher Patrick Knupfer Viktor von Wyl Hansjakob Furrer Manuel Battegay Bernard Hirschel Matthias Cavassini Pietro Vernazza Enos Bernasconi Sabine Yerly Jürg Böni Thomas Klimkait Cristina Cellerai Huldrych F Günthard Swiss HIV Cohort Study |
author_sort |
Niko Beerenwinkel |
title |
The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. |
title_short |
The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. |
title_full |
The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. |
title_fullStr |
The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. |
title_full_unstemmed |
The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients. |
title_sort |
individualized genetic barrier predicts treatment response in a large cohort of hiv-1 infected patients. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3757085?pdf=render |
work_keys_str_mv |
AT nikobeerenwinkel theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT hesammontazeri theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT heikeschuhmacher theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT patrickknupfer theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT viktorvonwyl theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT hansjakobfurrer theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT manuelbattegay theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT bernardhirschel theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT matthiascavassini theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT pietrovernazza theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT enosbernasconi theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT sabineyerly theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT jurgboni theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT thomasklimkait theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT cristinacellerai theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT huldrychfgunthard theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT swisshivcohortstudy theindividualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT nikobeerenwinkel individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT hesammontazeri individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT heikeschuhmacher individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT patrickknupfer individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT viktorvonwyl individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT hansjakobfurrer individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT manuelbattegay individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT bernardhirschel individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT matthiascavassini individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT pietrovernazza individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT enosbernasconi individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT sabineyerly individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT jurgboni individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT thomasklimkait individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT cristinacellerai individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT huldrychfgunthard individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients AT swisshivcohortstudy individualizedgeneticbarrierpredictstreatmentresponseinalargecohortofhiv1infectedpatients |
_version_ |
1724822101751234560 |