The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Paul T. Edlefsen - One of the best experts on this subject based on the ideXlab platform.
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Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial
PLoS computational biology, 2015Co-Authors: Paul T. Edlefsen, Morgane Rolland, Tomer Hertz, Andrew J. Gartland, Sodsai Tovanabutra, Allan C. Decamp, Craig A. Magaret, Hasan Ahmed, Raphael Gottardo, Michal JuraskaAbstract:The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through Sieve Analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 Analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome Analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to Analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.
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Leaky vaccines protect highly exposed recipients at a lower rate: implications for vaccine efficacy estimation and Sieve Analysis.
Computational and mathematical methods in medicine, 2014Co-Authors: Paul T. EdlefsenAbstract:“Leaky” vaccines are those for which vaccine-induced protection reduces infection rates on a per-exposure basis, as opposed to “all-or-none” vaccines, which reduce infection rates to zero for some fraction of subjects, independent of the number of exposures. Leaky vaccines therefore protect subjects with fewer exposures at a higher effective rate than subjects with more exposures. This simple observation has serious implications for Analysis methodologies that rely on the assumption that the vaccine effect is homogeneous across subjects. We argue and show through examples that this heterogeneous vaccine effect leads to a violation of the proportional hazards assumption, to incomparability of infected cases across treatment groups, and to nonindependence of the distributions of the competing failure processes in a competing risks setting. We discuss implications for vaccine efficacy estimation, correlates of protection Analysis, and mark-specific efficacy Analysis (also known as Sieve Analysis).
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Sieve Analysis in HIV-1 vaccine efficacy trials.
Current opinion in HIV and AIDS, 2013Co-Authors: Paul T. Edlefsen, Peter B Gilbert, Morgane RollandAbstract:Purpose of review The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence Analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine. Recent findings The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 (HIV Vaccine Trials Network-502) and RV144, led to numerous studies in the last 5 years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical Analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses. Summary Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons, whereas correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, Sieve analyses can shed light on potential mechanisms of vaccine protection.
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T-cell based Sieve Analysis ties HLA A*02 to vaccine efficacy and IgA-C1 immune correlate in RV144 Thai trial
Retrovirology, 2012Co-Authors: Tomer Hertz, Andrew J. Gartland, Holly Janes, Youyi Fong, Georgia D. Tomaras, Daryl E. Morris, Daniel E. Geraghty, Gustavo H. Kijak, Paul T. EdlefsenAbstract:T-cell based Sieve Analysis ties HLA A*02 to vaccine efficacy and IgA-C1 immune correlate in RV144 Thai trial T Hertz, A Gartland, H Janes, S Li, Y Fong, GD Tomaras, D Morris, D Geraghty, GH Kijak, PT Edlefsen, M Rolland, BB Larsen, S Tovanabutra, E Sanders-Buell, AC DeCamp, CA Magaret, H Ahmed, S Nariya, K Wong, H Zhao, W Deng, BS Maust, M Bose, S Howell, M Lazzaro, A Bates, E Lei, A Bradfield, G Ibitamuno, V Assawadarachai, RJ O’Connel, MS deSouza, S Nitayaphan, S Rerks-Ngarm, ML Robb, MJ McElrath, BF Haynes, NL Michael, PB Gilbert, JI Mullins, JH Kim
Morgane Rolland - One of the best experts on this subject based on the ideXlab platform.
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Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial
PLoS computational biology, 2015Co-Authors: Paul T. Edlefsen, Morgane Rolland, Tomer Hertz, Andrew J. Gartland, Sodsai Tovanabutra, Allan C. Decamp, Craig A. Magaret, Hasan Ahmed, Raphael Gottardo, Michal JuraskaAbstract:The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through Sieve Analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 Analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome Analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to Analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.
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Sieve Analysis in HIV-1 vaccine efficacy trials.
Current opinion in HIV and AIDS, 2013Co-Authors: Paul T. Edlefsen, Peter B Gilbert, Morgane RollandAbstract:Purpose of review The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence Analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine. Recent findings The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 (HIV Vaccine Trials Network-502) and RV144, led to numerous studies in the last 5 years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical Analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses. Summary Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons, whereas correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, Sieve analyses can shed light on potential mechanisms of vaccine protection.
Peter B Gilbert - One of the best experts on this subject based on the ideXlab platform.
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Assessing trends in vaccine efficacy by pathogen genetic distance.
Journal de la Societe francaise de statistique (2009), 2020Co-Authors: David Benkeser, Michal Juraska, Peter B GilbertAbstract:Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine Sieve Analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to Sieve Analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.
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Sieve Analysis in HIV-1 vaccine efficacy trials.
Current opinion in HIV and AIDS, 2013Co-Authors: Paul T. Edlefsen, Peter B Gilbert, Morgane RollandAbstract:Purpose of review The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence Analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine. Recent findings The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 (HIV Vaccine Trials Network-502) and RV144, led to numerous studies in the last 5 years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical Analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses. Summary Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons, whereas correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, Sieve analyses can shed light on potential mechanisms of vaccine protection.
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Interpretability and robustness of Sieve Analysis models for assessing HIV strain variations in vaccine efficacy.
Statistics in medicine, 2001Co-Authors: Peter B GilbertAbstract:From data on HIV-1 characteristics measured on viruses isolated from vaccinated and unvaccinated persons infected while enrolled in preventive HIV-1 vaccine trials, interpretable inferences into strain variations of vaccine efficacy can be made with recently developed Sieve Analysis models. Four assumptions are needed for the parameters in these models to have meaningful interpretations in terms of vaccine-induced reductions in strain-specific per-contact transmission probabilities: (A1) vaccination impacts each strain-specific transmission probability homogeneously in vaccinated persons (leaky vaccine effect); (A2) for each strain biological susceptibility to infection given exposure is homogeneous among vaccinated trial participants and among unvaccinated trial participants; (A3) the distribution of exposure is equal in vaccinated and unvaccinated trial participants; (A4) the relative prevalence of circulating HIV-1 strains during the trial follow-up period is constant. Through theoretical considerations and simulations of an ongoing phase III HIV-1 vaccine efficacy trial in Bangkok, we evaluate the importance and necessity of these assumptions. We show that the models still provide estimates of biologically interpretable parameters when A1 is violated, but with bias the extent to which vaccine protection is heterogeneous. We also show that the models are highly robust to departures from A4, with implication that the time-independent models are adequate for applications. In addition, we suggest extensions of the Sieve Analysis models which incorporate random effects that account for unmeasured heterogeneity in infection risk. With these mixed models, usefully interpretable strain-specific vaccine efficacy parameters can be estimated without requiring A2. The conclusion is that A3, which is justified by randomization and blinding, is the essential assumption for the Sieve models to provide reliable interpretable inferences into strain variations in vaccine efficacy.
Michal Juraska - One of the best experts on this subject based on the ideXlab platform.
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Assessing trends in vaccine efficacy by pathogen genetic distance.
Journal de la Societe francaise de statistique (2009), 2020Co-Authors: David Benkeser, Michal Juraska, Peter B GilbertAbstract:Preventive vaccines are an effective public health intervention for reducing the burden of infectious diseases, but have yet to be developed for several major infectious diseases. Vaccine Sieve Analysis studies whether and how the efficacy of a vaccine varies with the genetics of the infectious pathogen, which may help guide future vaccine development and deployment. A standard statistical approach to Sieve Analysis compares the effect of the vaccine to prevent infection and disease caused by pathogen types defined dichotomously as genetically near or far from a reference pathogen strain inside the vaccine construct. For example, near may be defined by amino acid identity at all amino acid positions considered in a multiple alignment and far defined by at least one amino acid difference. An alternative approach is to study the efficacy of the vaccine as a function of genetic distance from a pathogen to a reference vaccine strain where the distance cumulates over the set of amino acid positions. We propose a nonparametric method for estimating and testing the trend in the effect of a vaccine across genetic distance. We illustrate the operating characteristics of the estimator via simulation and apply the method to a recent preventive malaria vaccine efficacy trial.
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Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial
PLoS computational biology, 2015Co-Authors: Paul T. Edlefsen, Morgane Rolland, Tomer Hertz, Andrew J. Gartland, Sodsai Tovanabutra, Allan C. Decamp, Craig A. Magaret, Hasan Ahmed, Raphael Gottardo, Michal JuraskaAbstract:The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through Sieve Analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 Analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome Analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to Analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.
Tomer Hertz - One of the best experts on this subject based on the ideXlab platform.
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Comprehensive Sieve Analysis of Breakthrough HIV-1 Sequences in the RV144 Vaccine Efficacy Trial
PLoS computational biology, 2015Co-Authors: Paul T. Edlefsen, Morgane Rolland, Tomer Hertz, Andrew J. Gartland, Sodsai Tovanabutra, Allan C. Decamp, Craig A. Magaret, Hasan Ahmed, Raphael Gottardo, Michal JuraskaAbstract:The RV144 clinical trial showed the partial efficacy of a vaccine regimen with an estimated vaccine efficacy (VE) of 31% for protecting low-risk Thai volunteers against acquisition of HIV-1. The impact of vaccine-induced immune responses can be investigated through Sieve Analysis of HIV-1 breakthrough infections (infected vaccine and placebo recipients). A V1/V2-targeted comparison of the genomes of HIV-1 breakthrough viruses identified two V2 amino acid sites that differed between the vaccine and placebo groups. Here we extended the V1/V2 Analysis to the entire HIV-1 genome using an array of methods based on individual sites, k-mers and genes/proteins. We identified 56 amino acid sites or "signatures" and 119 k-mers that differed between the vaccine and placebo groups. Of those, 19 sites and 38 k-mers were located in the regions comprising the RV144 vaccine (Env-gp120, Gag, and Pro). The nine signature sites in Env-gp120 were significantly enriched for known antibody-associated sites (p = 0.0021). In particular, site 317 in the third variable loop (V3) overlapped with a hotspot of antibody recognition, and sites 369 and 424 were linked to CD4 binding site neutralization. The identified signature sites significantly covaried with other sites across the genome (mean = 32.1) more than did non-signature sites (mean = 0.9) (p < 0.0001), suggesting functional and/or structural relevance of the signature sites. Since signature sites were not preferentially restricted to the vaccine immunogens and because most of the associations were insignificant following correction for multiple testing, we predict that few of the genetic differences are strongly linked to the RV144 vaccine-induced immune pressure. In addition to presenting results of the first complete-genome Analysis of the breakthrough infections in the RV144 trial, this work describes a set of statistical methods and tools applicable to Analysis of breakthrough infection genomes in general vaccine efficacy trials for diverse pathogens.
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T-cell based Sieve Analysis ties HLA A*02 to vaccine efficacy and IgA-C1 immune correlate in RV144 Thai trial
Retrovirology, 2012Co-Authors: Tomer Hertz, Andrew J. Gartland, Holly Janes, Youyi Fong, Georgia D. Tomaras, Daryl E. Morris, Daniel E. Geraghty, Gustavo H. Kijak, Paul T. EdlefsenAbstract:T-cell based Sieve Analysis ties HLA A*02 to vaccine efficacy and IgA-C1 immune correlate in RV144 Thai trial T Hertz, A Gartland, H Janes, S Li, Y Fong, GD Tomaras, D Morris, D Geraghty, GH Kijak, PT Edlefsen, M Rolland, BB Larsen, S Tovanabutra, E Sanders-Buell, AC DeCamp, CA Magaret, H Ahmed, S Nariya, K Wong, H Zhao, W Deng, BS Maust, M Bose, S Howell, M Lazzaro, A Bates, E Lei, A Bradfield, G Ibitamuno, V Assawadarachai, RJ O’Connel, MS deSouza, S Nitayaphan, S Rerks-Ngarm, ML Robb, MJ McElrath, BF Haynes, NL Michael, PB Gilbert, JI Mullins, JH Kim