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Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction

Author

Listed:
  • Hendrik Weisser
  • André Altmann
  • Saleta Sierra
  • Francesca Incardona
  • Daniel Struck
  • Anders Sönnerborg
  • Rolf Kaiser
  • Maurizio Zazzi
  • Monika Tschochner
  • Hauke Walter
  • Thomas Lengauer

Abstract

Background: Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood. Materials and Methods: We developed a method for predicting RC from genotype using support vector machines (SVMs) trained on about 300 genotype-RC pairs. Next, we studied the impact of predicted viral RC (pRC) on the change of viral load (VL) and CD4+ T-cell count (CD4) during the course of therapy on about 3,000 treatment change episodes (TCEs) extracted from the EuResist integrated database. Specifically, linear regression models using either treatment activity scores (TAS), the drug combination, or pRC or any combination of these covariates were trained to predict change in VL and CD4, respectively. Results: The SVM models achieved a Spearman correlation (ρ) of 0.54 between measured RC and pRC. The prediction of change in VL (CD4) was best at 180 (360) days, reaching a correlation of ρ = 0.45 (ρ = 0.27). In general, pRC was inversely correlated to drug resistance at treatment start (on average ρ = −0.38). Inclusion of pRC in the linear regression models significantly improved prediction of virological response to treatment based either on the drug combination or on the TAS (t-test; p-values range from 0.0247 to 4 10−6) but not for the model using both TAS and drug combination. For predicting the change in CD4 the improvement derived from inclusion of pRC was not significant. Conclusion: Viral RC could be predicted from genotype with moderate accuracy and could slightly improve prediction of virological treatment response. However, the observed improvement could simply be a consequence of the significant correlation between pRC and drug resistance.

Suggested Citation

  • Hendrik Weisser & André Altmann & Saleta Sierra & Francesca Incardona & Daniel Struck & Anders Sönnerborg & Rolf Kaiser & Maurizio Zazzi & Monika Tschochner & Hauke Walter & Thomas Lengauer, 2010. "Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-10, February.
  • Handle: RePEc:plo:pone00:0009044
    DOI: 10.1371/journal.pone.0009044
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    References listed on IDEAS

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    1. André Altmann & Michal Rosen-Zvi & Mattia Prosperi & Ehud Aharoni & Hani Neuvirth & Eugen Schülter & Joachim Büch & Daniel Struck & Yardena Peres & Francesca Incardona & Anders Sönnerborg & Rolf Kaise, 2008. "Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-9, October.
    2. Birkner Merrill D. & Sinisi Sandra E. & van der Laan Mark J., 2005. "Multiple Testing and Data Adaptive Regression: An Application to HIV-1 Sequence Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-30, April.
    3. Segal Mark R & Barbour Jason D & Grant Robert M, 2004. "Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-20, February.
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