tetano
Editor, Senior Moderator
Eur J Immunol. 2013 Oct 17. doi: 10.1002/eji.201343657. [Epub ahead of print]
Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response.
Tan Y, Tamayo P, Nakaya H, Pulendran B, Mesirov JP, Haining WN.
Source
Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
Abstract
Vaccines are very effective at preventing infectious disease but not all recipients mount a protective immune response to vaccination. Recently, gene expression profiles of peripheral blood mononuclear cell samples in vaccinated individuals have been used to predict the development of protective immunity. However, the magnitude of change in gene expression that separates vaccine responders and non-responders is likely to be small and distributed across networks of genes, making the selection of predictive and biologically relevant genes difficult. Here we apply a new approach to predicting vaccine response based on coordinate upregulation of sets of biologically informative genes in post-vaccination gene expression profiles. We found that enrichment of gene sets related to proliferation and immunoglobulin genes accurately segregated high responders to influenza vaccination from low responders and achieved a prediction accuracy of 88% in an independent clinical trial. Many of the genes in these gene sets would not have been identified using conventional, single-gene level approaches because of their subtle upregulation in vaccine responders. Our results demonstrate that gene set enrichment method can capture subtle transcriptional changes and may be a generally useful approach for developing and interpreting predictive models of the human immune response. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
KEYWORDS:
B-Cell proliferation, gene expression, immune response, systems biology, vaccine efficacy
PMID:
24136404
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24136404
Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response.
Tan Y, Tamayo P, Nakaya H, Pulendran B, Mesirov JP, Haining WN.
Source
Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA; Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
Abstract
Vaccines are very effective at preventing infectious disease but not all recipients mount a protective immune response to vaccination. Recently, gene expression profiles of peripheral blood mononuclear cell samples in vaccinated individuals have been used to predict the development of protective immunity. However, the magnitude of change in gene expression that separates vaccine responders and non-responders is likely to be small and distributed across networks of genes, making the selection of predictive and biologically relevant genes difficult. Here we apply a new approach to predicting vaccine response based on coordinate upregulation of sets of biologically informative genes in post-vaccination gene expression profiles. We found that enrichment of gene sets related to proliferation and immunoglobulin genes accurately segregated high responders to influenza vaccination from low responders and achieved a prediction accuracy of 88% in an independent clinical trial. Many of the genes in these gene sets would not have been identified using conventional, single-gene level approaches because of their subtle upregulation in vaccine responders. Our results demonstrate that gene set enrichment method can capture subtle transcriptional changes and may be a generally useful approach for developing and interpreting predictive models of the human immune response. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
KEYWORDS:
B-Cell proliferation, gene expression, immune response, systems biology, vaccine efficacy
PMID:
24136404
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24136404