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Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape.

Theys K, Deforche K, Beheydt G, Moreau Y, van Laethem K, Lemey P, Camacho RJ, Rhee SY, Shafer RW, Van Wijngaerden E, Vandamme AM - BMC Bioinformatics (2010)

Bottom Line: Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment.By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).

View Article: PubMed Central - HTML - PubMed

Affiliation: Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be

ABSTRACT

Background: Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.

Results: We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).

Conclusions: Thus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.

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Related in: MedlinePlus

Genotypic correlates of genetic barrier. Impact of protease mutations and polymorphisms on the estimated genetic barrier to nelfinavir (NFV) resistance. For each mutation, the prevalence is indicated in the data set of protease inhibitor naive patients, which are all predicted as fully susceptible to NFV.
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Figure 1: Genotypic correlates of genetic barrier. Impact of protease mutations and polymorphisms on the estimated genetic barrier to nelfinavir (NFV) resistance. For each mutation, the prevalence is indicated in the data set of protease inhibitor naive patients, which are all predicted as fully susceptible to NFV.

Mentions: A step-wise model search was performed to identify a best linear model for log GR, which thus included the independent, multiplicative contributions of single mutations. In the final model, 22 mutations (10F/I/V, 12K, 13V, 20R/T, 33F, 35 D, 36I/V, 45R, 62V, 64M/V, 70R, 71T/V, 72V, 75I, 77I and 88D) independently decreased the genetic barrier (p < .05), while 7 mutations (12P, 17 D, 37A, 41K, 69Y and 89I/M) increased the genetic barrier (Figure 1). Although Figure 1 indicates contributions of pro-tease mutations to the genetic barrier with a fixed extent, these values resulted from averaging over the entire population (of 2764 sequences) and, since only independent and individual mutational contributions were considered, as well over mutations epistatically interacting with the respective mutation listed (see additional file 1 for the full model). As such, these findings do not contradict with the observation that the genetic context contributes to fitness in the landscape, and consequently to the genetic barrier to resistance. For example, mutation 71V was present in 85 isolates (3.1%), of which 45 (53%) selected 30N as first mutation and 9 (11%) 90 M (which are considered major resistance mutations by Rega). Baseline sequences lacking this mutation only selected in 487 (18%) and in 106 cases (4%) 30N and 90 M respectively. On the other hand, 9 isolates harboured mutation 17 D and 30N was only selected in 1 (11%) and 90 M (0%) zero cases, compared to 531 (19%) and 115 (4%) for isolates lacking 17 D.


Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape.

Theys K, Deforche K, Beheydt G, Moreau Y, van Laethem K, Lemey P, Camacho RJ, Rhee SY, Shafer RW, Van Wijngaerden E, Vandamme AM - BMC Bioinformatics (2010)

Genotypic correlates of genetic barrier. Impact of protease mutations and polymorphisms on the estimated genetic barrier to nelfinavir (NFV) resistance. For each mutation, the prevalence is indicated in the data set of protease inhibitor naive patients, which are all predicted as fully susceptible to NFV.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2921410&req=5

Figure 1: Genotypic correlates of genetic barrier. Impact of protease mutations and polymorphisms on the estimated genetic barrier to nelfinavir (NFV) resistance. For each mutation, the prevalence is indicated in the data set of protease inhibitor naive patients, which are all predicted as fully susceptible to NFV.
Mentions: A step-wise model search was performed to identify a best linear model for log GR, which thus included the independent, multiplicative contributions of single mutations. In the final model, 22 mutations (10F/I/V, 12K, 13V, 20R/T, 33F, 35 D, 36I/V, 45R, 62V, 64M/V, 70R, 71T/V, 72V, 75I, 77I and 88D) independently decreased the genetic barrier (p < .05), while 7 mutations (12P, 17 D, 37A, 41K, 69Y and 89I/M) increased the genetic barrier (Figure 1). Although Figure 1 indicates contributions of pro-tease mutations to the genetic barrier with a fixed extent, these values resulted from averaging over the entire population (of 2764 sequences) and, since only independent and individual mutational contributions were considered, as well over mutations epistatically interacting with the respective mutation listed (see additional file 1 for the full model). As such, these findings do not contradict with the observation that the genetic context contributes to fitness in the landscape, and consequently to the genetic barrier to resistance. For example, mutation 71V was present in 85 isolates (3.1%), of which 45 (53%) selected 30N as first mutation and 9 (11%) 90 M (which are considered major resistance mutations by Rega). Baseline sequences lacking this mutation only selected in 487 (18%) and in 106 cases (4%) 30N and 90 M respectively. On the other hand, 9 isolates harboured mutation 17 D and 30N was only selected in 1 (11%) and 90 M (0%) zero cases, compared to 531 (19%) and 115 (4%) for isolates lacking 17 D.

Bottom Line: Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment.By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).

View Article: PubMed Central - HTML - PubMed

Affiliation: Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be

ABSTRACT

Background: Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.

Results: We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).

Conclusions: Thus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.

Show MeSH
Related in: MedlinePlus