Geno2pheno and the Arevir database: tools for determination of clinically relevant cut-offs and prediction of viral load change from HIV-1 genotype.

Poster number: 23

Martin Däumer (1), Niko Beerenwinkel (2), Saleta Sierra (1), Joachim Buech (2), Tobias Sing (2), Daniel Hoffmann (3), Joachim Selbig (4), Thomas Lengauer (2), Mark Oette (5), Gerd Fätkenheuer (6), Jürgen K. Rockstroh (7), Hauke Walter (8), Herbert J. Pfister (1), and Rolf Kaiser (1)

  1. Institute of Virology, University of Cologne
  2. Max Planck Institute for Informatics, Saarbrücken
  3. Center of Advanced European Studies and Research (caesar), Bonn
  4. Max Planck Institute of Molecular Plant Physiology, Golm
  5. Internal Medicine I, University of Düsseldorf
  6. Internal Medicine I, University of Cologne
  7. Internal Medicine I, University of Bonn
  8. NRZ Retroviren, Erlangen, all Germany

Introduction: There is wide agreement that genotypic and phenotypic definitions of HIV drug resistance should rely on correlation with virologic and clinical outcome. Establishing these "clinical cut-offs" has proven to be a significant challenge because (i) monotherapy is obsolete, (ii) treatment response to a drug combination is usually attributed to more than one drug, and (iii) other factors apart from drug resistance may confound the analysis (e.g. drug adherence).

Methods: For determination of clinically relevant cut-offs we combined the quantitative phenotype prediction tool geno2pheno (Nucleic Acids Res. 31:3850, 2003; with the multi-center Arevir database. Geno2pheno is an HIV drug resistance interpretation system based on machine learning techniques. The Arevir database which contains clinical and virological data was screened for Lopinavir (LPV) add-on- and "quasi-monotherapies" (therapies where LPV was the only drug estimated to be active) to deduce virologic response to LPV. Resistance factors for LPV were predicted from HIV-genotypes before therapy switch and correlated with the observed viral load (VL) change four to six weeks post initiation of the LPV containing therapy.

Results and discussion: The maximum VL reduction after therapy switch was observed for genotypes with LPV-specific resistance factors (RFs) < 10 fold (lower cut-off). Patients with no VL change harboured viruses with genotypes of predicted RFs >30 fold (upper cut-off). Besides determination of clinically relevant cut-offs, this system enables prediction of VL change from HIV-genotype, which is most helpful in an intermediate level of resistance, where interpretation is most challenging, particularly for patients with limited therapy options.