Assessment of cancer and virus antigens for cross-reactivity in human tissues.
Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens.



We developed a methodology for the calculation of quantitative CR for any defined peptide epitope. Using this approach, we performed assessment of 4 groups of 283 currently known human MHC-class-I epitopes including differentiation antigens, overexpressed proteins, cancer-testis 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 modelled by the method of Keşmir et al. which combines proteasomal cleavage, TAP affinity and MHC-binding predictions. The results of these analyses for many previously defined peptides are presented as CR 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.



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 CR to the self-antigens in 22 human tissues. The program is implemented as part of the iCrossR webserver, which is publicly available at http://webclu.bio.wzw.tum.de/icrossr/ CONTACT: d.frishman@wzw.tum.deSupplementary information: Supplementary data are available at Bioinformatics online.
Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising 85354, Germany.
Victor
V
Jaravine
Medigene Immunotherapies GmbH, Martinsried/Planegg 82152, Germany.
Silke
S
Raffegerst
Medigene Immunotherapies GmbH, Martinsried/Planegg 82152, Germany.
Dolores J
DJ
Schendel
Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising 85354, Germany.|Helmholtz Center Munich - German Research Center for Environmental Health (GmbH), Institute of Bioinformatics and Systems Biology, Neuherberg 85764, Germany.|St Petersburg State Polytechnic University, St Petersburg 195251, Russia.
Dmitrij
D
Frishman
D000951:Antigens, Neoplasm / Q000276:immunology*; D000956:Antigens, Viral / Q000276:immunology*; D003429:Cross Reactions; D000939:Epitopes / Q000276:immunology; D005260:Female; D015395:Histocompatibility Antigens Class I / Q000276:immunology; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000276:immunology; D009369:Neoplasms / Q000276:immunology* / Q000378:metabolism; D010455:Peptides / Q000276:immunology; D040901:Proteomics / Q000379:methods*; D012984:Software
D000951:Antigens, Neoplasm; D000956:Antigens, Viral; D000939:Epitopes; D015395:Histocompatibility Antigens Class I; D009363:Neoplasm Proteins; D010455:Peptides