Back

Viral antigen mismatch affects antiviral T-cell response and may impair immunotherapeutic efficacy against ATL

Sugata, K.; Takatori, M.; Reda, O.; Tan, B. J. Y.; Tokunaga, M.; Sato, T.; Ueda, M.; Yamano, Y.; Utsunomiya, A.; Satou, Y.

2024-01-29 immunology
10.1101/2024.01.25.576615 bioRxiv
Show abstract

Human T-cell leukemia virus type 1 (HTLV-1) has the potential to transform primary CD4+ T cells in vitro within a short time; however, the majority of infected individuals maintain an asymptomatic and disease-free condition, suggesting the existence of an equilibrium between the proliferation of infected cells and host immunity. The decline in anti-viral immunity contributes to the transformation of the infected cells, leading to the development of adult T-cell leukemia/lymphoma (ATL). This study identified a variation in a major viral antigen, HTLV-1 Tax, in human leukocyte antigen-A24 (HLA-A24) positive individuals. Two variants of Tax301-309 peptides, SFHNLHLLF (Tax301-309 A) and SFHSLHLLF (Tax301-309 B) were found to induce distinct T-cell immune responses in HLA-A24 positive individuals. There was a disparity between two Tax301-309 peptides in the detection of anti-Tax301-309 cytotoxic T-lymphocytes (CTLs) binding to A24/peptide multimers by flow cytometry analysis. More importantly, over half of the anti-Tax TCRs of anti-Tax CTLs from infected individuals did not recognize mismatched Tax301-309 peptides by Enzyme-Linked Immunospot (ELISpot) assay using Jurkat T cells expressing the anti-Tax301-309 specific TCR. These findings underscore the importance of matching the viral antigen epitope type in T-cell-based immunotherapy against ATL by using viral antigen Tax. Key pointsO_LIEpitope heterogeneity in the major viral antigen in HTLV-1 infection causes different T-cell responses in infected individuals. C_LIO_LIRecommended guideline; performing virus typing to obtain optimal efficacy in T-cell-mediated immunotherapy against the viral antigen Tax C_LI

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.