title: HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure.
abstract: We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.
authors.affiliation: Rega Institute for Medical Research, Department of Microbiology and Immunology, KU Leuven, 3000 Leuven, Belgium.
authors.forename: Raphael Z
authors.initials: RZ
authors.lastname: Sangeda
authors.forename: Kristof
authors.initials: K
authors.lastname: Theys
authors.forename: Gertjan
authors.initials: G
authors.lastname: Beheydt
authors.forename: Soo-Yon
authors.initials: SY
authors.lastname: Rhee
authors.forename: Koen
authors.initials: K
authors.lastname: Deforche
authors.forename: Jurgen
authors.initials: J
authors.lastname: Vercauteren
authors.forename: Pieter
authors.initials: P
authors.lastname: Libin
authors.forename: Stijn
authors.initials: S
authors.lastname: Imbrechts
authors.forename: Zehava
authors.initials: Z
authors.lastname: Grossman
authors.forename: Ricardo J
authors.initials: RJ
authors.lastname: Camacho
authors.forename: Kristel
authors.initials: K
authors.lastname: Van Laethem
authors.forename: Alejandro
authors.initials: A
authors.lastname: Pironti
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Anders
authors.initials: A
authors.lastname: Sönnerborg
authors.forename: Francesca
authors.initials: F
authors.lastname: Incardona
authors.forename: Andrea
authors.initials: A
authors.lastname: De Luca
authors.forename: Carlo
authors.initials: C
authors.lastname: Torti
authors.forename: Lidia
authors.initials: L
authors.lastname: Ruiz
authors.forename: David A M C
authors.initials: DA
authors.lastname: Van de Vijver
authors.forename: Robert W
authors.initials: RW
authors.lastname: Shafer
authors.forename: Bianca
authors.initials: B
authors.lastname: Bruzzone
authors.forename: Eric
authors.initials: E
authors.lastname: Van Wijngaerden
authors.forename: Anne-Mieke
authors.initials: AM
authors.lastname: Vandamme
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology; D024882:Drug Resistance, Viral / Q000187:drug effects / Q000235:genetics; D019143:Evolution, Molecular; D056084:Genetic Fitness; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology*; D017320:HIV Protease Inhibitors / Q000494:pharmacology* / Q000627:therapeutic use; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics*; D006801:Humans; D019469:Indinavir / Q000494:pharmacology* / Q000627:therapeutic use; D053208:Kaplan-Meier Estimate; D015233:Models, Statistical; D017211:Treatment Failure; D019562:Viral Load
chemical_list: D017320:HIV Protease Inhibitors; D019469:Indinavir
keywords: Antiretrovirals; Bioinformatics; Evolution; HIV-1 drug resistance; Treatment response