Assessment of cancer and virus antigens for cross-reactivity in human tissues.
Bioinformatics 33, 104-111 (2017)
MOTIVATION: Cross-reactivity or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict cross-reactivity (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens. RESULTS: We developed a methodology for the calculation of quantitative cross-reactivity for any defined peptide epitope. Using this approach, we performed assessment of four groups of 283 currently known human MHC-class-I epitopes including differentiation antigens, overexpressed proteins, cancer-testis (CT) antigens, and mutations displayed by tumor cells. In addition, 89 epitopes originating from viral sources were investigated. The natural occurrence of these epitopes in human tissues was assessed based on proteomics abundance data, while the probability of their presentation by MHC-class-I molecules was modeled by the method of Kesmir et al. (2002), which combines proteasomal cleavage, TAP affinity and MHC-binding predictions. The results of these analyses for many previously defined peptides are presented as cross-reactivity indices and tissue profiles. The methodology thus allows for quantitative comparisons of epitopes, and is suggested to be suited for the assessment of epitopes of candidate antigens in an early stage of development of adoptive immunotherapy. AVAILABILITY: Our method is implemented as a Java program, with curated datasets stored in a MySQL database. It predicts all naturally possible self-antigens for a given sequence of a therapeutic antigen (or epitope), and after filtering for predicted immunogenicity outputs results as an index and profile of cross-reactivity to the self-antigens in 22 human tissues.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Tap Transport Efficiency; T-cell-receptor; Neural-networks; Tumor-antigens; Gene-therapy; Prediction; Immunotherapy; Affinities; Regression; Peptides
ISSN (print) / ISBN 1367-4803
Quellenangaben Band: 33, Heft: 1, Seiten: 104-111
Verlag Oxford University Press