HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

• Published in: Infection, genetics and evolution Vol. 19; pp. 349 - 360
• Main Authors: Sangeda, Raphael Z., Theys, Kristof, Beheydt, Gertjan, Rhee, Soo-Yon, Deforche, Koen, Vercauteren, Jurgen, Libin, Pieter, Imbrechts, Stijn, Grossman, Zehava, Camacho, Ricardo J., Van Laethem, Kristel, Pironti, Alejandro, Zazzi, Maurizio, Sönnerborg, Anders, Incardona, Francesca, De Luca, Andrea, Torti, Carlo, Ruiz, Lidia, Van de Vijver, David A.M.C., Shafer, Robert W., Bruzzone, Bianca, Van Wijngaerden, Eric, Vandamme, Anne-Mieke
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.10.2013
• Subjects: algorithms; Antiretrovirals; Bayes Theorem; Bioinformatics; Computational Biology; Drug Resistance, Viral - drug effects; Drug Resistance, Viral - genetics; drugs; epistasis; Evolution; Evolution, Molecular; Genetic Fitness; HIV Infections - drug therapy; HIV Infections - virology; HIV Protease Inhibitors - pharmacology; HIV Protease Inhibitors - therapeutic use; HIV-1 - drug effects; HIV-1 - genetics; HIV-1 drug resistance; Human immunodeficiency virus 1; Humans; Immunologi inom det medicinska området; Indinavir - pharmacology; Indinavir - therapeutic use; Infektionsmedicin; Kaplan-Meier Estimate; Klinisk medicin; landscapes; Medicin och hälsovetenskap; Medicinska och farmaceutiska grundvetenskaper; Models, Statistical; mutation; patients; prediction; pressure treatment; proteinase inhibitors; therapeutics; Treatment Failure; Treatment response; Viral Load; Antiretrovirals; Evolution; Treatment response; HIV-1 drug resistance; Bioinformatics
• ISSN: 1567-1348; 1567-7257; 1567-7257
• DOI: 10.1016/j.meegid.2013.03.014

•Epistatic fitness interactions can be learned from other drugs that target the same protein.•Longitudinal fitness landscape can predict the failing genotype.•Fitness and genetic barrier to resistance are predictive for treatment outcome. 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 48weeks, 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.