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Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues.
Jaravine, Victor; Mösch, Anja; Raffegerst, Silke; Schendel, Dolores J; Frishman, Dmitrij.
Afiliación
  • Jaravine V; Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, 85354, Germany.
  • Mösch A; Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, 82152, Germany.
  • Raffegerst S; Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, 85354, Germany.
  • Schendel DJ; Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, 82152, Germany.
  • Frishman D; Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, 82152, Germany.
BMC Cancer ; 17(1): 892, 2017 12 28.
Article en En | MEDLINE | ID: mdl-29282079
BACKGROUND: Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression. The recent availability of proteome-wide human protein abundance information now facilitates a more direct approach for CR prediction. Here we present a new version 2.0 of Expitope, which computes all naturally possible epitopes of a peptide sequence and the corresponding CR indices using both protein and transcript abundance levels weighted by a proposed hierarchy of importance of various human tissues. RESULTS: We tested the tool in two case studies: The first study quantitatively assessed the potential CR of the epitopes used for cancer immunotherapy. The second study evaluated HLA-A*02:01-restricted epitopes obtained from the Immune Epitope Database for different disease groups and demonstrated for the first time that there is a high variation in the background CR depending on the disease state of the host: compared to a healthy individual the CR index is on average two-fold higher for the autoimmune state, and five-fold higher for the cancer state. CONCLUSIONS: The ability to predict potential side effects in normal tissues helps in the development and selection of safer antigens, enabling more successful immunotherapy of cancer and other diseases.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Linfocitos T / Proteínas / Enfermedad / Epítopos de Linfocito T / Bases de Datos de Proteínas / Inmunoterapia Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: BMC Cancer Asunto de la revista: NEOPLASIAS Año: 2017 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Programas Informáticos / Linfocitos T / Proteínas / Enfermedad / Epítopos de Linfocito T / Bases de Datos de Proteínas / Inmunoterapia Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: BMC Cancer Asunto de la revista: NEOPLASIAS Año: 2017 Tipo del documento: Article País de afiliación: Alemania