1
|
Su X, Cho Y, Ni L, Liu L, Dusseldorp E. Refined moderation analysis with categorical outcomes in precision medicine. Stat Med 2023; 42:470-486. [PMID: 36513372 DOI: 10.1002/sim.9627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 11/09/2022] [Accepted: 11/30/2022] [Indexed: 12/15/2022]
Abstract
Moderation analysis is an integral part of precision medicine research. Concerning moderation analysis with categorical outcomes, we start with an interesting observation, which shows that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and the treatment variable in logistic regression models. Hence two estimators of moderating effects can be obtained. We then established the joint asymptotic normality for the two estimators, on which basis refined inference can be made for moderation analysis. The improved precision is helpful in addressing the lack-of-power problem that is common in search of moderators. The above-mentioned results hold for both experimental and observational data. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.
Collapse
Affiliation(s)
- Xiaogang Su
- Department of Mathematical Sciences, University of Texas at El Paso, El Paso, Texas, USA
| | - Youngjoo Cho
- Department of Applied Statistics, Konkuk University, Gwangjin-gu, Seoul, Republic of Korea
| | - Liqiang Ni
- Department of Statistics and Data Science, University of Central Florida, Orlando, Florida, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | | |
Collapse
|
2
|
Yang G, Balzer LB, Benkeser D. Causal inference methods for vaccine sieve analysis with effect modification. Stat Med 2022; 41:1513-1524. [PMID: 35044691 PMCID: PMC10517342 DOI: 10.1002/sim.9302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/08/2021] [Accepted: 12/08/2021] [Indexed: 11/06/2022]
Abstract
The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.
Collapse
Affiliation(s)
- Guandong Yang
- Department of Biostatistics and Epidemiology, University of Massachusetts, Massachusetts, USA
| | - Laura B. Balzer
- Department of Biostatistics and Epidemiology, University of Massachusetts, Massachusetts, USA
| | - David Benkeser
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Georgia, USA
| |
Collapse
|
3
|
Thomas R, Chansinghakul D, Limkittikul K, Gilbert PB, Hattasingh W, Moodie Z, Shangguan S, Frago C, Dulyachai W, Li SS, Jarman RG, Geretz A, Bouckenooghe A, Sabchareon A, Juraska M, Ehrenberg P, Michael NL, Bailleux F, Bryant C, Gurunathan S. Associations of human leukocyte antigen with neutralizing antibody titers in a tetravalent dengue vaccine phase 2 efficacy trial in Thailand. Hum Immunol 2022; 83:53-60. [PMID: 34635391 PMCID: PMC10536818 DOI: 10.1016/j.humimm.2021.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/12/2021] [Accepted: 09/20/2021] [Indexed: 11/04/2022]
Abstract
The recombinant, live, attenuated, tetravalent dengue vaccine CYD-TDV has shown efficacy against all four dengue serotypes. In this exploratory study (CYD59, NCT02827162), we evaluated potential associations of host human leukocyte antigen (HLA) alleles with dengue antibody responses, CYD-TDV vaccine efficacy, and virologically-confirmed dengue (VCD) cases. Children 4-11 years old, who previously completed a phase 2b efficacy study of CYD-TDV in a single center in Thailand, were included in the study. Genotyping of HLA class I and II loci was performed by next-generation sequencing from DNA obtained from 335 saliva samples. Dengue neutralizing antibody titers (NAb) were assessed as a correlate of risk and protection. Regression analyses were used to assess associations between HLA alleles and NAb responses, vaccine efficacy, and dengue outcomes. Month 13 NAb log geometric mean titers (GMTs) were associated with decreased risk of VCD. In the vaccine group, HLA-DRB1*11 was significantly associated with higher NAb log GMT levels (beta: 0.76; p = 0.002, q = 0.13). Additionally, in the absence of vaccination, HLA associations were observed between the presence of DPB1*03:01 and increased NAb log GMT levels (beta: 1.24; p = 0.005, q = 0.17), and between DPB1*05:01 and reduced NAb log GMT levels (beta: -1.1; p = 0.001, q = 0.07). This study suggests associations of HLA alleles with NAb titers in the context of dengue outcomes. This study was registered with clinicaltrials.gov: NCT02827162.
Collapse
Affiliation(s)
- Rasmi Thomas
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | | | - Kriengsak Limkittikul
- Department of Tropical Pediatrics, Faculty of Tropical Medicine, Mahidol University, Bangkok 73170, Thailand
| | - Peter B Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA
| | - Weerawan Hattasingh
- Department of Tropical Pediatrics, Faculty of Tropical Medicine, Mahidol University, Bangkok 73170, Thailand
| | - Zoe Moodie
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA
| | - Shida Shangguan
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA
| | - Carina Frago
- Global Clinical Sciences, Sanofi Pasteur, 048580, Singapore
| | - Wut Dulyachai
- Ratchaburi Hospital, Amphoe Muang Ratchaburi, 70000, Thailand
| | - Shuying Sue Li
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA
| | - Richard G Jarman
- Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Aviva Geretz
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA
| | | | - Arunee Sabchareon
- Department of Tropical Pediatrics, Faculty of Tropical Medicine, Mahidol University, Bangkok 73170, Thailand
| | - Michal Juraska
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA
| | - Philip Ehrenberg
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Nelson L Michael
- US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | | | | | | |
Collapse
|
4
|
Dai JY, LeBlanc M. Case-only trees and random forests for exploring genotype-specific treatment effects in randomized clinical trials with dichotomous endpoints. J R Stat Soc Ser C Appl Stat 2019; 68:1371-1391. [PMID: 32489221 PMCID: PMC7266264 DOI: 10.1111/rssc.12366] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Discovering gene-treatment interactions in clinical trials is of rising interest in the era of precision medicine. Nonparametric statistical learning methods such as trees and random forests are useful tools for building prediction rules. In this article, we introduce trees and random forests to the recently proposed case-only approach for discovering gene-treatment interactions and estimating marker-specific treatment effects for a dichotomous trial endpoints. The motivational example is a case-control genetic association study in the Prostate Cancer Prevention Trial (PCPT), which tested the hypothesis whether finasteride can prevent prostate cancer. We compare this novel approach to the interaction tree method previously proposed. Because of the modeling simplicity - directly targeting at interaction - and the statistical efficiency of the case-only approach, case-only trees and random forests yield more accurate prediction of heterogeneous treatment effects and better measure of variable importance, relative to the interaction tree method which uses data from both cases and controls. Application of the proposed case-only trees and random forests to the PCPT study yielded a discovery of genotypes that may influence the prevention effect of finasteride.
Collapse
Affiliation(s)
- James Y Dai
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Michael LeBlanc
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| |
Collapse
|
5
|
Li SS, Gilbert PB, Carpp LN, Pyo CW, Janes H, Fong Y, Shen X, Neidich SD, Goodman D, deCamp A, Cohen KW, Ferrari G, Hammer SM, Sobieszczyk ME, Mulligan MJ, Buchbinder SP, Keefer MC, DeJesus E, Novak RM, Frank I, McElrath MJ, Tomaras GD, Geraghty DE, Peng X. Fc Gamma Receptor Polymorphisms Modulated the Vaccine Effect on HIV-1 Risk in the HVTN 505 HIV Vaccine Trial. J Virol 2019; 93:e02041-18. [PMID: 31434737 PMCID: PMC6803257 DOI: 10.1128/jvi.02041-18] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 08/14/2019] [Indexed: 12/19/2022] Open
Abstract
HIV Vaccine Trials Network (HVTN) 505 was a phase 2b efficacy trial of a DNA/recombinant adenovirus 5 (rAd5) HIV vaccine regimen. Although the trial was stopped early for lack of overall efficacy, later correlates of risk and sieve analyses generated the hypothesis that the DNA/rAd5 vaccine regimen protected some vaccinees from HIV infection yet enhanced HIV infection risk for others. Here, we assessed whether and how host Fc gamma receptor (FcγR) genetic variations influenced the DNA/rAd5 vaccine regimen's effect on HIV infection risk. We found that vaccine receipt significantly increased HIV acquisition compared with placebo receipt among participants carrying the FCGR2C-TATA haplotype (comprising minor alleles of four FCGR2C single-nucleotide polymorphism [SNP] sites) (hazard ratio [HR] = 9.79, P = 0.035) but not among participants without the haplotype (HR = 0.86, P = 0.67); the interaction of vaccine and haplotype effect was significant (P = 0.034). Similarly, vaccine receipt increased HIV acquisition compared with placebo receipt among participants carrying the FCGR3B-AGA haplotype (comprising minor alleles of the 3 FCGR3B SNPs) (HR = 2.78, P = 0.058) but not among participants without the haplotype (HR = 0.73, P = 0.44); again, the interaction of vaccine and haplotype was significant (P = 0.047). The FCGR3B-AGA haplotype also influenced whether a combined Env-specific CD8+ T-cell polyfunctionality score and IgG response correlated significantly with HIV risk; an FCGR2A SNP and two FCGR2B SNPs influenced whether anti-gp140 antibody-dependent cellular phagocytosis correlated significantly with HIV risk. These results provide further evidence that Fc gamma receptor genetic variations may modulate HIV vaccine effects and immune function after HIV vaccination.IMPORTANCE By analyzing data from the HVTN 505 efficacy trial of a DNA/recombinant adenovirus 5 (rAd5) vaccine regimen, we found that host genetics, specifically Fc gamma receptor genetic variations, influenced whether receiving the DNA/rAd5 regimen was beneficial, neutral, or detrimental to an individual with respect to HIV-1 acquisition risk. Moreover, Fc gamma receptor genetic variations influenced immune responses to the DNA/rAd5 vaccine regimen. Thus, Fc gamma receptor genetic variations should be considered in the analysis of future HIV vaccine trials and the development of HIV vaccines.
Collapse
Affiliation(s)
- Shuying S Li
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Peter B Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Lindsay N Carpp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Chul-Woo Pyo
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Holly Janes
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Youyi Fong
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Xiaoying Shen
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
| | - Scott D Neidich
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
| | - Derrick Goodman
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
| | - Allan deCamp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Kristen W Cohen
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Guido Ferrari
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
- Department of Surgery, Duke University, Durham, North Carolina, USA
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, USA
| | - Scott M Hammer
- Division of Infectious Diseases, Columbia University College of Physicians and Surgeons, New York, New York, USA
| | - Magdalena E Sobieszczyk
- Division of Infectious Diseases, Columbia University College of Physicians and Surgeons, New York, New York, USA
| | - Mark J Mulligan
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Susan P Buchbinder
- Department of Medicine, University of California, San Francisco, California, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Michael C Keefer
- Division of Infectious Diseases, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
| | | | | | - Ian Frank
- Division of Infectious Diseases, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - M Juliana McElrath
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Georgia D Tomaras
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
- Department of Surgery, Duke University, Durham, North Carolina, USA
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, USA
- Department of Immunology, Duke University, Durham, North Carolina, USA
| | - Daniel E Geraghty
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Xinxia Peng
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, North Carolina, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| |
Collapse
|
6
|
Dai JY, Liang J, LeBlanc M, Prentice RL, Janes H. Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics 2018; 74:753-763. [PMID: 28960244 PMCID: PMC5874156 DOI: 10.1111/biom.12789] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 06/01/2017] [Accepted: 08/01/2017] [Indexed: 11/29/2022]
Abstract
Retrospectively measuring markers on stored baseline samples from participants in a randomized controlled trial (RCT) may provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene-environment interactions in the odds ratio scale, the case-only method has recently been advocated for assessing gene-treatment interactions on rare disease endpoints in randomized clinical trials. In this article, the case-only approach is shown to provide a consistent and efficient estimator of marker by treatment interactions and marker-specific treatment effects on the relative risk scale. The prohibitive rare-disease assumption is no longer needed, broadening the utility of the case-only approach. The case-only method is resource-efficient as markers only need to be measured in cases only. It eliminates the need to model the marker's main effect, and can be used with any parametric or nonparametric learning method. The utility of this approach is illustrated by an application to genetic data in the Women's Health Initiative (WHI) hormone therapy trial.
Collapse
Affiliation(s)
- James Y. Dai
- Public Health Sciences Division and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Jason Liang
- Public Health Sciences Division and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Michael LeBlanc
- Public Health Sciences Division and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Ross L. Prentice
- Public Health Sciences Division and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| | - Holly Janes
- Public Health Sciences Division and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A
| |
Collapse
|
7
|
Methodical Considerations. HUMAN VACCINES 2017. [DOI: 10.1016/b978-0-12-802302-0.00006-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
8
|
Wang X, Dai JY. TwoPhaseInd: an R package for estimating gene-treatment interactions and discovering predictive markers in randomized clinical trials. Bioinformatics 2016; 32:3348-3350. [PMID: 27378290 DOI: 10.1093/bioinformatics/btw391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 06/16/2016] [Indexed: 11/13/2022] Open
Abstract
In randomized clinical trials, identifying baseline genetic or genomic markers for predicting subgroup treatment effects is of rising interest. Outcome-dependent sampling is often employed for measuring markers. The R package TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup treatment effects and gene-treatment interactions, exploiting the gene-treatment independence dictated by randomization, including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model. For rare failure events subject to censoring, we have proposed efficient augmented case-only designs, a variation of the case-cohort design, to estimate genetic associations and subgroup treatment effects in a Cox regression model. The R package is computationally scalable to genome-wide studies, as illustrated by an example from Women's Health Initiative. AVAILABILITY AND IMPLEMENTATION The R package TwoPhaseInd is available from http://cran.r-project.org/web/packages CONTACT: jdai@fredhutch.org.
Collapse
Affiliation(s)
- Xiaoyu Wang
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - James Y Dai
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA Department of Biostatistics, University of Washington, Seattle, WA, USA
| |
Collapse
|
9
|
Dai JY, Zhang XC, Wang CY, Kooperberg C. Augmented case-only designs for randomized clinical trials with failure time endpoints. Biometrics 2015; 72:30-8. [PMID: 26347982 DOI: 10.1111/biom.12392] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2014] [Revised: 07/01/2015] [Accepted: 07/01/2015] [Indexed: 02/05/2023]
Abstract
Under suitable assumptions and by exploiting the independence between inherited genetic susceptibility and treatment assignment, the case-only design yields efficient estimates for subgroup treatment effects and gene-treatment interaction in a Cox model. However it cannot provide estimates of the genetic main effect and baseline hazards, that are necessary to compute the absolute disease risk. For two-arm, placebo-controlled trials with rare failure time endpoints, we consider augmenting the case-only design with random samples of controls from both arms, as in the classical case-cohort sampling scheme, or with a random sample of controls from the active treatment arm only. The latter design is motivated by vaccine trials for cost-effective use of resources and specimens so that host genetics and vaccine-induced immune responses can be studied simultaneously in a bigger set of participants. We show that these designs can identify all parameters in a Cox model and that the efficient case-only estimator can be incorporated in a two-step plug-in procedure. Results in simulations and a data example suggest that incorporating case-only estimators in the classical case-cohort design improves the precision of all estimated parameters; sampling controls only in the active treatment arm attains a similar level of efficiency.
Collapse
Affiliation(s)
- James Y Dai
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle, Washington
| | - Xinyi Cindy Zhang
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle, Washington
| | - Ching-Yun Wang
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle, Washington
| | - Charles Kooperberg
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle, Washington
| |
Collapse
|
10
|
Li SS, Gilbert PB, Tomaras GD, Kijak G, Ferrari G, Thomas R, Pyo CW, Zolla-Pazner S, Montefiori D, Liao HX, Nabel G, Pinter A, Evans DT, Gottardo R, Dai JY, Janes H, Morris D, Fong Y, Edlefsen PT, Li F, Frahm N, Alpert MD, Prentice H, Rerks-Ngarm S, Pitisuttithum P, Kaewkungwal J, Nitayaphan S, Robb ML, O'Connell RJ, Haynes BF, Michael NL, Kim JH, McElrath MJ, Geraghty DE. FCGR2C polymorphisms associate with HIV-1 vaccine protection in RV144 trial. J Clin Invest 2014; 124:3879-90. [PMID: 25105367 DOI: 10.1172/jci75539] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Accepted: 06/19/2014] [Indexed: 02/02/2023] Open
Abstract
The phase III RV144 HIV-1 vaccine trial estimated vaccine efficacy (VE) to be 31.2%. This trial demonstrated that the presence of HIV-1-specific IgG-binding Abs to envelope (Env) V1V2 inversely correlated with infection risk, while the presence of Env-specific plasma IgA Abs directly correlated with risk of HIV-1 infection. Moreover, Ab-dependent cellular cytotoxicity responses inversely correlated with risk of infection in vaccine recipients with low IgA; therefore, we hypothesized that vaccine-induced Fc receptor-mediated (FcR-mediated) Ab function is indicative of vaccine protection. We sequenced exons and surrounding areas of FcR-encoding genes and found one FCGR2C tag SNP (rs114945036) that associated with VE against HIV-1 subtype CRF01_AE, with lysine at position 169 (169K) in the V2 loop (CRF01_AE 169K). Individuals carrying CC in this SNP had an estimated VE of 15%, while individuals carrying CT or TT exhibited a VE of 91%. Furthermore, the rs114945036 SNP was highly associated with 3 other FCGR2C SNPs (rs138747765, rs78603008, and rs373013207). Env-specific IgG and IgG3 Abs, IgG avidity, and neutralizing Abs inversely correlated with CRF01_AE 169K HIV-1 infection risk in the CT- or TT-carrying vaccine recipients only. These data suggest a potent role of Fc-γ receptors and Fc-mediated Ab function in conferring protection from transmission risk in the RV144 VE trial.
Collapse
|
11
|
Gartland AJ, Li S, McNevin J, Tomaras GD, Gottardo R, Janes H, Fong Y, Morris D, Geraghty DE, Kijak GH, Edlefsen PT, Frahm N, Larsen BB, Tovanabutra S, Sanders-Buell E, deCamp AC, Magaret CA, Ahmed H, Goodridge JP, Chen L, Konopa P, Nariya S, Stoddard JN, Wong K, Zhao H, Deng W, Maust BS, Bose M, Howell S, Bates A, Lazzaro M, O'Sullivan A, Lei E, Bradfield A, Ibitamuno G, Assawadarachai V, O'Connell RJ, deSouza MS, Nitayaphan S, Rerks-Ngarm S, Robb ML, Sidney J, Sette A, Zolla-Pazner S, Montefiori D, McElrath MJ, Mullins JI, Kim JH, Gilbert PB, Hertz T. Analysis of HLA A*02 association with vaccine efficacy in the RV144 HIV-1 vaccine trial. J Virol 2014; 88:8242-55. [PMID: 24829343 PMCID: PMC4135964 DOI: 10.1128/jvi.01164-14] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 05/07/2014] [Indexed: 11/20/2022] Open
Abstract
UNLABELLED The RV144 HIV-1 vaccine trial demonstrated partial efficacy of 31% against HIV-1 infection. Studies into possible correlates of protection found that antibodies specific to the V1 and V2 (V1/V2) region of envelope correlated inversely with infection risk and that viruses isolated from trial participants contained genetic signatures of vaccine-induced pressure in the V1/V2 region. We explored the hypothesis that the genetic signatures in V1 and V2 could be partly attributed to selection by vaccine-primed T cells. We performed a T-cell-based sieve analysis of breakthrough viruses in the RV144 trial and found evidence of predicted HLA binding escape that was greater in vaccine versus placebo recipients. The predicted escape depended on class I HLA A*02- and A*11-restricted epitopes in the MN strain rgp120 vaccine immunogen. Though we hypothesized that this was indicative of postacquisition selection pressure, we also found that vaccine efficacy (VE) was greater in A*02-positive (A*02(+)) participants than in A*02(-) participants (VE = 54% versus 3%, P = 0.05). Vaccine efficacy against viruses with a lysine residue at site 169, important to antibody binding and implicated in vaccine-induced immune pressure, was also greater in A*02(+) participants (VE = 74% versus 15%, P = 0.02). Additionally, a reanalysis of vaccine-induced immune responses that focused on those that were shown to correlate with infection risk suggested that the humoral responses may have differed in A*02(+) participants. These exploratory and hypothesis-generating analyses indicate there may be an association between a class I HLA allele and vaccine efficacy, highlighting the importance of considering HLA alleles and host immune genetics in HIV vaccine trials. IMPORTANCE The RV144 trial was the first to show efficacy against HIV-1 infection. Subsequently, much effort has been directed toward understanding the mechanisms of protection. Here, we conducted a T-cell-based sieve analysis, which compared the genetic sequences of viruses isolated from infected vaccine and placebo recipients. Though we hypothesized that the observed sieve effect indicated postacquisition T-cell selection, we also found that vaccine efficacy was greater for participants who expressed HLA A*02, an allele implicated in the sieve analysis. Though HLA alleles have been associated with disease progression and viral load in HIV-1 infection, these data are the first to suggest the association of a class I HLA allele and vaccine efficacy. While these statistical analyses do not provide mechanistic evidence of protection in RV144, they generate testable hypotheses for the HIV vaccine community and they highlight the importance of assessing the impact of host immune genetics in vaccine-induced immunity and protection. (This study has been registered at ClinicalTrials.gov under registration no. NCT00223080.).
Collapse
Affiliation(s)
- Andrew J Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Sue Li
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - John McNevin
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Georgia D Tomaras
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Raphael Gottardo
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Holly Janes
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Youyi Fong
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Daryl Morris
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Daniel E Geraghty
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Gustavo H Kijak
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Paul T Edlefsen
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Nicole Frahm
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Brendan B Larsen
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | | | | | - Allan C deCamp
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Craig A Magaret
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Hasan Ahmed
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | | | - Lennie Chen
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Philip Konopa
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Snehal Nariya
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Julia N Stoddard
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Kim Wong
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Hong Zhao
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Wenjie Deng
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Brandon S Maust
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Meera Bose
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Shana Howell
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Adam Bates
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Michelle Lazzaro
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | | | - Esther Lei
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Andrea Bradfield
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Grace Ibitamuno
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | | | | | | | | | | | - Merlin L Robb
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - John Sidney
- La Jolla Institute for Allergy and Immunology, La Jolla, California, USA
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, California, USA
| | | | - David Montefiori
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - M Juliana McElrath
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - James I Mullins
- Department of Microbiology, University of Washington, Seattle, Washington, USA
| | - Jerome H Kim
- U.S. Military HIV Research Program, Silver Spring, Maryland, USA
| | - Peter B Gilbert
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Tomer Hertz
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA Shraga Segal Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| |
Collapse
|
12
|
Leaky vaccines protect highly exposed recipients at a lower rate: implications for vaccine efficacy estimation and sieve analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:813789. [PMID: 24895500 PMCID: PMC4033482 DOI: 10.1155/2014/813789] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 03/14/2014] [Indexed: 11/17/2022]
Abstract
“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).
Collapse
|