Εξειδίκευση τύπου :	Κεφάλαιο βιβλίου
Τίτλος:	Computational Methods for Cancer Neoantigen Prediction
Δημιουργός/Συγγραφέας:	Moreno-Manuel, Andrea Ouzounis, Sotiris Eidsaa, Marius Fornelino-González, Roberto Ballesteros-Cuartero, Pilar Gómez-Garrido, Daniel Veiga-Chacón, Esteban [EL] Κατσίλα, Θεοδώρα[EN] Katsila, Theodora Callari, Maurizio Muñoz-Barrutia, Arrate Sanz-Pamplona, Rebeca
Επιμελητής έκδοσης:	Rani, Sweta Skalniak, Lukasz
Ημερομηνία:	2026
Γλώσσα:	Αγγλικά
ISBN:	978-1-0716-4733-2 978-1-0716-4734-9
ISSN:	1064-3745 1940-6029
DOI:	10.1007/978-1-0716-4734-9_17
Άλλο:	41028271
Περίληψη:	Neoantigens are mutated peptides arising from tumor genomic alterations, which can be recognized and attacked by the immune system, leading to antitumor immune responses. In the last decades, many immunotherapeutic strategies have been developed, which has increased the interest in neoantigens. This led to the development of computational tools that facilitate neoantigen identification and prioritization, prior to their validation using experimental approaches. This chapter aims at explaining the key steps that need to be conducted to identify potential neoantigens in silico, including an example of the most frequently used tools. This is followed by a description and comparison of the cutting-edge tools and pipelines for neoantigen prediction both for human and mouse. The last aim of this chapter is to depict the technical challenges that limit neoantigen prediction using bioinformatics, as well as the expected improvements, given the current revolution of artificial intelligence, which is implemented in most of the neoantigen-related tools. As exposed in this book chapter, we believe that advances in immunomics and computational biology will be key to implement personalized cancer immunotherapy in the clinical practice, to improve outcomes of cancer patients.
Τίτλος πηγής δημοσίευσης:	MMUNO-model in Cancer. Methods in Molecular Biology
Τόμος/Κεφάλαιο:	2959
Σελίδες:	253-289
Σειρά δημοσίευσης:	Methods in Molecular Biology
Θεματική Κατηγορία:	[EL] Νεοπλάσματα. Όγκοι. Ογκολογία (περ. Καρκίνος, κακινογόνες ουσίες)[EN] Neoplasms. Tumors. Oncology (Incl.cancer, carcinogens) [EL] Βιοπληροφορική[EN] Bioinformatics [EL] Ανοσολογία[EN] Immunology [EL] Μοριακή Βιολογία[EN] Molecular Biology
Λέξεις-Κλειδιά:	Bioinformatics Cancer HLA-binding affinity Neoantigen prediction MHC Mice Immunomics Immune microenvironment
Χρηματοδότης:	ASPANOA Foundation Instituto de Salud Carlos III European Cooperation in Science and Technology
Αναγνωριστικό χρηματοδοτικού προγράμματος:	MCIN/AEI/10.13039/501100011033 CNS2022-136176 PI22/01938 CA21135
Κάτοχος πνευματικών δικαιωμάτων:	© 2026 The Author(s)
Όροι και προϋποθέσεις δικαιωμάτων:	Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Ηλεκτρονική διεύθυνση στον εκδότη (link):	https://doi.org/10.1007/978-1-0716-4734-9_17
Εμφανίζεται στις συλλογές:	Ινστιτούτο Χημικής Βιολογίας - Επιστημονικό έργο