title: Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.
abstract: OBJECTIVE
  Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost.
  
  
  METHODS
  Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement.
  
  
  METHODS
  Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count.
  
  
  RESULTS
  Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05).
  
  
  CONCLUSIONS
  Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.
authors.affiliation: B.C. Centre for Excellence in HIV/AIDS St. Paul's Hospital, Vancouver, British Columbia, Canada.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Low
authors.forename: Winnie
authors.initials: W
authors.lastname: Dong
authors.forename: Dennison
authors.initials: D
authors.lastname: Chan
authors.forename: Tobias
authors.initials: T
authors.lastname: Sing
authors.forename: Ronald
authors.initials: R
authors.lastname: Swanstrom
authors.forename: Mark
authors.initials: M
authors.lastname: Jensen
authors.forename: Satish
authors.initials: S
authors.lastname: Pillai
authors.forename: Benjamin
authors.initials: B
authors.lastname: Good
authors.forename: P Richard
authors.initials: PR
authors.lastname: Harrigan
mesh_terms: D000465:Algorithms; D018791:CD4 Lymphocyte Count; D003001:Cloning, Molecular / Q000379:methods; D003430:Cross-Sectional Studies; D005822:Genetic Vectors / Q000235:genetics; D005838:Genotype; D006678:HIV / Q000235:genetics*; D015658:HIV Infections / Q000235:genetics / Q000821:virology; D006801:Humans; D010641:Phenotype; D019713:Receptors, CCR5 / Q000235:genetics; D019718:Receptors, CXCR4 / Q000235:genetics; D019707:Receptors, Chemokine / Q000235:genetics*; D014759:Viral Envelope Proteins / Q000235:genetics; D019562:Viral Load
chemical_list: D019713:Receptors, CCR5; D019718:Receptors, CXCR4; D019707:Receptors, Chemokine; D014759:Viral Envelope Proteins