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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure
Infection, Genetics and Evolution, Volume 19, Year 2013
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Description
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. © 2013 Elsevier B.V.
Authors & Co-Authors
Sangeda, Raphael Zozimus
Belgium, Leuven
Ku Leuven
Theys, Kristof
Belgium, Leuven
Ku Leuven
Beheydt, Gertjan
Belgium, Leuven
Ku Leuven
Rhee, Soo-yoon
Belgium, Leuven
Ku Leuven
United States, Palo Alto
Stanford University
Deforche, Koen
Belgium, Rotselaar
Mybiodata
Vercauteren, Jurgen
Belgium, Leuven
Ku Leuven
Libin, Pieter J.K.
Belgium, Leuven
Ku Leuven
Belgium, Rotselaar
Mybiodata
Imbrechts, Stijn
Belgium, Leuven
Ku Leuven
Grossman, Zehava
Israel, Tel Hashomer Tel Aviv
Chaim Sheba Medical Center Israel
Camacho, Ricardo Jorge
Portugal, Lisbon
Centro Hospitalar de Lisboa Central
Portugal, Lisbon
Universidade Nova de Lisboa
van Laethem, Kristel V.
Belgium, Leuven
Ku Leuven
Zazzi, Maurizio
Italy, Siena
Università Degli Studi Di Siena
Sönnerborg, Anders B.
Sweden, Stockholm
Karolinska Institutet
Incardona, Francesca
Italy, Rome
Euresist Network
De Luca, Andrea
Italy, Siena
Azienda Ospedaliera Universitaria Senese
Torti, Carlo
Italy, Brescia
Università Degli Studi Di Brescia
Ruíz, Lídia
Unknown Affiliation
Van De Vijver, D. A.M.C.
Netherlands, Rotterdam
Erasmus Universiteit Rotterdam
Shafer, Robert William
United States, Palo Alto
Stanford University
Bruzzone, Bianca Marisa
Italy, Genoa
Irccs San Martino Polyclinic Hospital
van Wijngaerden, Eric
Belgium, Leuven
Ku Leuven
Vandamme, Anne Mieke
Belgium, Leuven
Ku Leuven
Portugal, Lisbon
Universidade Nova de Lisboa
Statistics
Citations: 4
Authors: 22
Affiliations: 14
Identifiers
Doi:
10.1016/j.meegid.2013.03.014
ISSN:
15677257
Research Areas
Cancer
Genetics And Genomics
Infectious Diseases
Study Design
Cross Sectional Study
Cohort Study