A statistical framework for modeling HLA-dependent T cell response data

Anti-hepatitis C virus T-cell immunity in the context of multiple exposures to the virus

Characterisation of Hepatitis C virus (HCV)-specific CD8⁺ T-cell responses in the context of multiple HCV exposures is critical to identify broadly protective immune responses necessary for an effective HCV vaccine against the different HCV genotypes. However, host and viral genetic diversity complicates vaccine development. To compensate for the observed variation in circulating autologous viruses and host molecules that restrict antigen presentation (human leucocyte antigens; HLA), this study used a reverse genomics approach that identified sites of viral adaptation to HLA-restricted T-cell immune pressure to predict genotype-specific HCV CD8⁺ T-cell targets. Peptides representing these putative HCV CD8⁺ T-cell targets, and their adapted form, were used in individualised IFN-γ ELISpot assays to screen for HCV-specific T-cell responses in 133 HCV-seropositive subjects with high-risk of multiple HCV exposures. The data obtained from this study i) confirmed that genetic studies of viral evolution is an effective approach to detect novel in vivo HCV T-cell targets, ii) showed that HCV-specific T-cell epitopes can be recognised in their adapted form and would not have been detected using wild-type peptides and iii) showed that HCV-specific T-cell (but not antibody) responses against alternate genotypes in chronic HCV-infected subjects are readily found, implying clearance of previous alternate genotype infection. In summary, HCV adaptation to HLA Class I-restricted T-cell responses plays a central role in anti-HCV immunity and multiple HCV genotype exposure is highly prevalent in at-risk exposure populations, which are important considerations for future vaccine design.

A population response analysis approach to assign class II HLA-epitope restrictions

Identification of the specific HLA locus and allele presenting an epitope for recognition by specific TCRs (HLA restriction) is necessary to fully characterize the immune response to Ags. Experimental determination of HLA restriction is complex and technically challenging. As an alternative, the restricting HLA locus and allele can be inferred by genetic association, using response data in an HLA-typed population. However, simple odds ratio (OR) calculations can be problematic when dealing with large numbers of subjects and Ags, and because the same epitope can be presented by multiple alleles (epitope promiscuity). In this study, we develop a tool, denominated Restrictor Analysis Tool for Epitopes, to extract inferred restriction from HLA class II-typed epitope responses. This automated method infers HLA class II restriction from large datasets of T cell responses in HLA class II-typed subjects by calculating ORs and relative frequencies from simple data tables. The program is validated by: 1) analyzing data of previously determined HLA restrictions; 2) experimentally determining in selected individuals new HLA restrictions using HLA-transfected cell lines; and 3) predicting HLA restriction of particular peptides and showing that corresponding HLA class II tetramers efficiently bind to epitope-specific T cells. We further design a specific iterative algorithm to account for promiscuous recognition by calculation of OR values for combinations of different HLA molecules while incorporating predicted HLA binding affinity. The Restrictor Analysis Tool for Epitopes program streamlines the prediction of HLA class II restriction across multiple T cell epitopes and HLA types.

Statistical resolution of ambiguous HLA typing data

High-resolution HLA typing plays a central role in many areas of immunology, such as in identifying immunogenetic risk factors for disease, in studying how the genomes of pathogens evolve in response to immune selection pressures, and also in vaccine design, where identification of HLA-restricted epitopes may be used to guide the selection of vaccine immunogens. Perhaps one of the most immediate applications is in direct medical decisions concerning the matching of stem cell transplant donors to unrelated recipients. However, high-resolution HLA typing is frequently unavailable due to its high cost or the inability to re-type historical data. In this paper, we introduce and evaluate a method for statistical, in silico refinement of ambiguous and/or low-resolution HLA data. Our method, which requires an independent, high-resolution training data set drawn from the same population as the data to be refined, uses linkage disequilibrium in HLA haplotypes as well as four-digit allele frequency data to probabilistically refine HLA typings. Central to our approach is the use of haplotype inference. We introduce new methodology to this area, improving upon the Expectation-Maximization (EM)-based approaches currently used within the HLA community. Our improvements are achieved by using a parsimonious parameterization for haplotype distributions and by smoothing the maximum likelihood (ML) solution. These improvements make it possible to scale the refinement to a larger number of alleles and loci in a more computationally efficient and stable manner. We also show how to augment our method in order to incorporate ethnicity information (as HLA allele distributions vary widely according to race/ethnicity as well as geographic area), and demonstrate the potential utility of this experimentally. A tool based on our approach is freely available for research purposes at http://microsoft.com/science.

Extensive HLA class I allele promiscuity among viral CTL epitopes

Promiscuous binding of T helper epitopes to MHC class II molecules has been well established, but few examples of promiscuous class I-restricted epitopes exist. To address the extent of promiscuity of HLA class I peptides, responses to 242 well-defined viral epitopes were tested in 100 subjects regardless of the individuals' HLA type. Surprisingly, half of all detected responses were seen in the absence of the originally reported restricting HLA class I allele, and only 3% of epitopes were recognized exclusively in the presence of their original allele. Functional assays confirmed the frequent recognition of HLA class I-restricted T cell epitopes on several alternative alleles across HLA class I supertypes and encoded on different class I loci. These data have significant implications for the understanding of MHC class I-restricted antigen presentation and vaccine development.