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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.
Design: 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.
(C) 2007 Lippincott Williams & Wilkins, Inc.
