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

RZ Sangeda, K Theys, G Beheydt, SY Rhee, K Deforche, J Vercauteren, P Libin, S Imbrechts, Z Grossman, RJ Camacho, K Van Laethem, A Pironti, M Zazzi, A Sonnerborg, F Incardona, A Luca, C Torti, L Ruiz, David van de Vijver, RW ShaferB Bruzzone, E van Wijngaerden, AM Vandamme

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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 naive 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 naive and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir 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 respon
Original languageUndefined/Unknown
Pages (from-to)349-360
Number of pages12
JournalInfection, Genetics and Evolution
Publication statusPublished - 2013

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