Current methods of epitope identification for cancer vaccine design. Issue 51 (16th December 2015)

• Record Type: Journal Article
• Authors: Cherryholmes, Gregory A.; Stanton, Sasha E.; Disis, Mary L.
• Is Part Of: Vaccine. Volume 33:Issue 51(2015)
• Page Start: 7408
• Page End: 7414
• Publication Date: 2015-12-16
• Subjects: Vaccine -- Epitope detection -- Sequence-based prediction -- Structure-based prediction -- Self-protein -- Peptide -- In silico prediction; MHC major histocompatibility complex -- pMHC peptide–MHC complex -- CTA cancer testis antigen -- QM quantitative matrix -- DS-QM docking simulation-quantitative matrix -- ANN artificial neural network -- SVM support vector machine -- HMM hidden markov model -- ACS ant colony strategies -- IEDB internet epitope database -- SDR specific determining residue -- QSAR quantitative structure activity relationship -- HPLC high performance liquid chromatography; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2015.06.116
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: The importance of the immune system in tumor development and progression has been emerging in many cancers. Previous cancer vaccines have not shown long-term clinical benefit possibly because were not designed to avoid eliciting regulatory T-cell responses that inhibit the anti-tumor immune response. This review will examine different methods of identifying epitopes derived from tumor associated antigens suitable for immunization and the steps used to design and validate peptide epitopes to improve efficacy of anti-tumor peptide-based vaccines. Focusing on in silico prediction algorithms, we survey the advantages and disadvantages of current cancer vaccine prediction tools.