title: The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.
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.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.forename: Hesam
authors.initials: H
authors.lastname: Montazeri
authors.forename: Heike
authors.initials: H
authors.lastname: Schuhmacher
authors.forename: Patrick
authors.initials: P
authors.lastname: Knupfer
authors.forename: Viktor
authors.initials: V
authors.lastname: von Wyl
authors.forename: Hansjakob
authors.initials: H
authors.lastname: Furrer
authors.forename: Manuel
authors.initials: M
authors.lastname: Battegay
authors.forename: Bernard
authors.initials: B
authors.lastname: Hirschel
authors.forename: Matthias
authors.initials: M
authors.lastname: Cavassini
authors.forename: Pietro
authors.initials: P
authors.lastname: Vernazza
authors.forename: Enos
authors.initials: E
authors.lastname: Bernasconi
authors.forename: Sabine
authors.initials: S
authors.lastname: Yerly
authors.forename: Jürg
authors.initials: J
authors.lastname: Böni
authors.forename: Thomas
authors.initials: T
authors.lastname: Klimkait
authors.forename: Cristina
authors.initials: C
authors.lastname: Cellerai
authors.forename: Huldrych F
authors.initials: HF
authors.lastname: Günthard
mesh_terms: D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D015331:Cohort Studies; D024882:Drug Resistance, Viral; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics* / Q000821:virology; D015497:HIV-1; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D016017:Odds Ratio; D012372:ROC Curve; D016896:Treatment Outcome
chemical_list: D019380:Anti-HIV Agents