A Statistical Framework for Modeling HLA-Dependent T Cell Response Data

The identification of T cell epitopes and their HLA (human leukocyte antigen) restrictions is important for applications such as the design of cellular vaccines for HIV. Traditional methods for such identification are costly and time-consuming. Recently, a more expeditious laboratory technique using...

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Main Authors:	Listgarten, Jennifer, Frahm, Nicole, Kadie, Carl, Brander, Christian, Heckerman, David
Format:	Online
Language:	English
Published:	Public Library of Science 2007
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014793/

id	pubmed-2014793
recordtype	oai_dc
spelling	pubmed-20147932007-10-25 A Statistical Framework for Modeling HLA-Dependent T Cell Response Data Listgarten, Jennifer Frahm, Nicole Kadie, Carl Brander, Christian Heckerman, David Research Article The identification of T cell epitopes and their HLA (human leukocyte antigen) restrictions is important for applications such as the design of cellular vaccines for HIV. Traditional methods for such identification are costly and time-consuming. Recently, a more expeditious laboratory technique using ELISpot assays has been developed that allows for rapid screening of specific responses. However, this assay does not directly provide information concerning the HLA restriction of a response, a critical piece of information for vaccine design. Thus, we introduce, apply, and validate a statistical model for identifying HLA-restricted epitopes from ELISpot data. By looking at patterns across a broad range of donors, in conjunction with our statistical model, we can determine (probabilistically) which of the HLA alleles are likely to be responsible for the observed reactivities. Additionally, we can provide a good estimate of the number of false positives generated by our analysis (i.e., the false discovery rate). This model allows us to learn about new HLA-restricted epitopes from ELISpot data in an efficient, cost-effective, and high-throughput manner. We applied our approach to data from donors infected with HIV and identified many potential new HLA restrictions. Among 134 such predictions, six were confirmed in the lab and the remainder could not be ruled as invalid. These results shed light on the extent of HLA class I promiscuity, which has significant implications for the understanding of HLA class I antigen presentation and vaccine development. Public Library of Science 2007-10 2007-10-12 /pmc/articles/PMC2014793/ /pubmed/17937494 http://dx.doi.org/10.1371/journal.pcbi.0030188 Text en © 2007 Listgarten et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Listgarten, Jennifer Frahm, Nicole Kadie, Carl Brander, Christian Heckerman, David
spellingShingle	Listgarten, Jennifer Frahm, Nicole Kadie, Carl Brander, Christian Heckerman, David A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
author_facet	Listgarten, Jennifer Frahm, Nicole Kadie, Carl Brander, Christian Heckerman, David
author_sort	Listgarten, Jennifer
title	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
title_short	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
title_full	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
title_fullStr	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
title_full_unstemmed	A Statistical Framework for Modeling HLA-Dependent T Cell Response Data
title_sort	statistical framework for modeling hla-dependent t cell response data
description	The identification of T cell epitopes and their HLA (human leukocyte antigen) restrictions is important for applications such as the design of cellular vaccines for HIV. Traditional methods for such identification are costly and time-consuming. Recently, a more expeditious laboratory technique using ELISpot assays has been developed that allows for rapid screening of specific responses. However, this assay does not directly provide information concerning the HLA restriction of a response, a critical piece of information for vaccine design. Thus, we introduce, apply, and validate a statistical model for identifying HLA-restricted epitopes from ELISpot data. By looking at patterns across a broad range of donors, in conjunction with our statistical model, we can determine (probabilistically) which of the HLA alleles are likely to be responsible for the observed reactivities. Additionally, we can provide a good estimate of the number of false positives generated by our analysis (i.e., the false discovery rate). This model allows us to learn about new HLA-restricted epitopes from ELISpot data in an efficient, cost-effective, and high-throughput manner. We applied our approach to data from donors infected with HIV and identified many potential new HLA restrictions. Among 134 such predictions, six were confirmed in the lab and the remainder could not be ruled as invalid. These results shed light on the extent of HLA class I promiscuity, which has significant implications for the understanding of HLA class I antigen presentation and vaccine development.
publisher	Public Library of Science
publishDate	2007
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2014793/
_version_	1611404213435236352

A Statistical Framework for Modeling HLA-Dependent T Cell Response Data

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