Despite the availability of an effective vaccine, hepatitis B virus (HBV) constitutes a significant disease burden in East Asia. While some infected individuals functionally clear the virus, many develop a chronic hepatitis B (CHB) infection which can ultimately cause liver cirrhosis and hepatocellular carcinoma. Attempts to understand the immune response to HBV have revealed that HBV-specific CD8+ T cells are indispensable in viral control, prompting interest in understanding this T cell subset. However virus-specific T cells are rare and difficult to identify and isolate for study. To date, some HBV-specific CD8+ T cell epitopes have been identified. However, this research has focused on epitopes recognized by major histocompatibility complex (MHC) allele HLA-A*02:01, but the predominant MHC allele (HLA- A*11:01) differs in individuals from East Asia. Because virus-specific T cells have implications for immunotherapy, the paucity of data regarding immune control of CHB in East Asian patients decreases potential therapeutic interventions for a large group of people. Yang Cheng (Singapore Immunology Network) of the Newell lab (Vaccine and Infectious Disease Division; formerly of the Singapore Immunology Network) performed a large screen to identify and characterize virus-specific HLA-A*11:01-restricted T cells and published their findings in Science Immunology.
In order to study HBV-specific T cells, Cheng and colleagues first needed the ability to find these cells, which are of extremely low frequency among an individual’s total T cell repertoire. To begin, the authors deep-sequenced HBV DNA from the serum of 15 CHB patients, yielding 484 new HBV epitopes that could potentially bind a T cell receptor (TCR). Using a highly multiplexed peptide-MHC tetramer strategy previously developed by Newell, the authors screened patient T cells for specificity to the nearly epitopes they had discovered. Monomers of HLA-A*11:01 were combined into tetramers and loaded with the nearly 500 putative antigens, which were then each tagged with a unique combination of metals, pooled, and allowed to bind with T cells from patients at different stages of CHB infection. Additionally, T cells were stained with antibodies against markers pertaining to T cell activation, differentiation, and trafficking. The tagged peptide-MHC:T cell complexes and T cells were then detectable by mass cytometry, a technique similar to flow cytometry that uses heavy metal ions instead of fluorochromes that tagged to antibodies that label the MHC. This combinatorial strategy facilitated simultaneous probing of many epitopes at once, as the limited number of T cells does not allow for smaller, consecutive screens and identified previously known and unknown epitopes bound by CD8+ T cells. The authors chose to focus on T cells specific for epitope HBVcore169 as it was found across many patients at all stages of CHB. Interestingly, HBVcore169 had previously been described in HLA-A*02:01-restricted T cells, implying its relevance to the larger population.
Using t-distributed stochastic neighbor embedding (tSNE), a dimensionality reduction tool for high-dimensional datasets, the T cells of interest were clustered based on their expression of phenotypic markers. The authors then applied a computational technique known as trajectory inference method to determine the relationship of the T cell phenotypes across stages of CHB. The authors found that different combinations of memory and inhibitory markers delineated stages of CHB infection and that these phenotypes correlated with severity of viral disease. For example, activated T cells were abundant in patients with high viral load, while memory T cells predominated in those with suppressed viremia. These results imply that T cell phenotype may serve as an indicator of viral antigen, which can be used as a proxy for measuring patients’ disease status and predicting their prognosis.
Lastly, to understand which TCRs are selected of the course of CHB, the authors sequenced the TCR repertoires of HBV-specific T cells from people at various stages of infection. They found that TCR usage differed by epitope and clinical stage, and that certain TCR sequences were only found in the T cells of individuals with low viral load. To further investigate these dynamics, the authors screened for T cells that bound HBV epitopes in longitudinal samples from CHB patients receiving antiviral treatment and then phenotyped these T cells using the the memory and inhibitory markers used earlier. They found that patients who went on to better control virus shared T cell phenotypes at parallel stages of infection. Together, these results suggest that certain TCR clones are essential for viral control, and that like T cell phenotype, TCR could be used to predict the disease stage and prognosis of individual patients based on their initial HBV-specific T cell phenotypes.
Newell, who recently moved his lab to Fred Hutch, plans to continue to investigate antigen-specific cells and their potentials as biomarkers. However, this work is not limited to viral infection, as T cells also respond to tumor antigens. He explained that in the case of a tumor, a population of antigen-specific T cells sometimes suddenly switch from effector to memory phenotypes, suggesting that the T cells can no longer “see” their antigen and that the tumor cells may be evading detection by thwarting presentation of the tumor antigen. Therefore, as in HBV, T cells might serve as the most accurate indicator of the presence or absence of antigen, which could predict patient prognoses and inform treatment decisions. Likewise, Newell is also interested in HIV-specific T cells and their roles as biomarkers, and the lab plans to continue to use their high-throughput screens to find and characterize antigen-specific T cells in the context of many different diseases.
Cheng Y, Zhu YO, Becht E, Aw P, Chen J, Poidinger M, Flórez de Sessions P, Hibberd ML, Bertoletti A, Lim SG, Newell EW. 2019. Multifactorial heterogeneity of virus-specific T cells and association with the progression of human chronic hepatitis B infection. Science Immunology. 2019 Feb 8;4(32). pii: eaau6905. doi: 10.1126/sciimmunol.aau6905
This study was supported by the Translational & Clinical Research Flagship program from the National Medical Research Council of Singapore; Singapore Immunology Network core funding of Agency for Science, Technology and Research (A*STAR), Singapore; and the A*STAR Singapore International Graduate Award (SINGA). The flow cytometry and CyTOF platforms are part of the SIgN Immunomonitoring platform, supported by BMRC.
Fred Hutch/UW Cancer Consortium member Evan Newell contributed to this work.