title: Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations.
abstract: BACKGROUND
  Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.
  
  
  RESULTS
  Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.
  
  
  CONCLUSIONS
  We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.
authors.affiliation: Tibotec-Virco, Beerse, Belgium. kvdborgh@its.jnj.com
authors.forename: Koen
authors.initials: K
authors.lastname: Van der Borght
authors.forename: Elke
authors.initials: E
authors.lastname: Van Craenenbroeck
authors.forename: Pierre
authors.initials: P
authors.lastname: Lecocq
authors.forename: Margriet
authors.initials: M
authors.lastname: Van Houtte
authors.forename: Barbara
authors.initials: B
authors.lastname: Van Kerckhove
authors.forename: Lee
authors.initials: L
authors.lastname: Bacheler
authors.forename: Geert
authors.initials: G
authors.lastname: Verbeke
authors.forename: Herman
authors.initials: H
authors.lastname: van Vlijmen
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology / Q000627:therapeutic use*; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D016014:Linear Models; D009154:Mutation; D009570:Nitriles; D011724:Pyridazines / Q000494:pharmacology / Q000627:therapeutic use*; D011743:Pyrimidines; D018894:Reverse Transcriptase Inhibitors / Q000494:pharmacology / Q000627:therapeutic use*
chemical_list: D019380:Anti-HIV Agents; D009570:Nitriles; D011724:Pyridazines; D011743:Pyrimidines; D018894:Reverse Transcriptase Inhibitors; C451734:etravirine