Research article

Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy

Linda Wittkop^1,2 , Daniel Commenges^1,2 , Isabelle Pellegrin³ , Dominique Breilh⁴ , Didier Neau⁵ , Denis Lacoste⁵ , Jean-Luc Pellegrin⁵ , Geneviève Chêne^1,2 , François Dabis^1,2 and Rodolphe Thiébaut^1,2

¹INSERM, Unit 897, Bordeaux, France

²University Victor Segalen, Bordeaux school of public health (ISPED), Bordeaux, France

³Bordeaux University Hospital, Department of Virology and EA 2968, University Victor Segalen, Bordeaux, France

⁴Bordeaux University Hospital, Department of Clinical Pharmacokinetics and Pharmacy and EA2968, University Victor Segalen, France

⁵Bordeaux University Hospital, Department of Internal Medicine and Infectious Diseases, Bordeaux, France

author email corresponding author email

BMC Medical Research Methodology 2008, 8:68doi:10.1186/1471-2288-8-68

Published:	22 October 2008

Abstract

Background

Principal component analysis (PCA) and partial least square (PLS) regression may be useful to summarize the HIV genotypic information. Without pre-selection each mutation presented in at least one patient is considered with a different weight. We compared these two strategies with the construction of a usual genotypic score.

Methods

We used data from the ANRS-CO3 Aquitaine Cohort Zephir sub-study. We used a subset of 87 patients with a complete baseline genotype and plasma HIV-1 RNA available at baseline and at week 12. PCA and PLS components were determined with all mutations that had prevalences >0. For the genotypic score, mutations were selected in two steps: 1) p-value < 0.01 in univariable analysis and prevalences between 10% and 90% and 2) backwards selection procedure based on the Cochran-Armitage Test. The predictive performances were compared by means of the cross-validated area under the receiver operating curve (AUC).

Results

Virological failure was observed in 46 (53%) patients at week 12. Principal components and PLS components showed a good performance for the prediction of virological response in HIV infected patients. The cross-validated AUCs for the PCA, PLS and genotypic score were 0.880, 0.868 and 0.863, respectively. The strength of the effect of each mutation could be considered through PCA and PLS components. In contrast, each selected mutation contributes with the same weight for the calculation of the genotypic score. Furthermore, PCA and PLS regression helped to describe mutation clusters (e.g. 10, 46, 90).

Conclusion

In this dataset, PCA and PLS showed a good performance but their predictive ability was not clinically superior to that of the genotypic score.