Mutation position is an important determinant for predicting cancer neoantigens
| Autor: | Cecile de la Cruz, Patrick J. Lupardus, Zhiyuan Fan, Lucia Taraborrelli, Richard Bourgon, Ira Mellman, Manmeet Singh, Samuel B. Pollock, Jennie R. Lill, Jim K. Wong, James Cevallos, Nicolas Lounsbury, Yajun Chestnut, Tamaki Nozawa, Lélia Delamarre, Benjamin Haley, Ajay Fernandez, Qui T. Phung, Lena Hänsch, Aude-Hélène Capietto, Suchit Jhunjhunwala |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Tumor Immunology medicine.medical_treatment Immunology Antibody Affinity Epitopes T-Lymphocyte Peptide binding Computational biology Biology CD8-Positive T-Lymphocytes medicine.disease_cause Epitope Article 03 medical and health sciences Interferon-gamma Mice 0302 clinical medicine Antigen Antigens Neoplasm Cell Line Tumor Neoplasms Exome Sequencing medicine Immunology and Allergy Cytotoxic T cell Animals RNA-Seq Amino Acids Exome sequencing Mutation Mice Inbred BALB C integumentary system Immunogenicity Histocompatibility Antigens Class I High-Throughput Nucleotide Sequencing Immunotherapy Mice Inbred C57BL Disease Models Animal 030104 developmental biology 030220 oncology & carcinogenesis Female Peptides |
| Zdroj: | The Journal of Experimental Medicine |
| ISSN: | 1540-9538 |
| Popis: | Cancer mutations create neoantigens that are of great interest for immunotherapy, but their accurate identification remains a challenge. Capietto et al. show that mutation position is an important determinant for immunogenicity, and incorporating this feature into prediction algorithms improves their identification. Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies. Graphical Abstract |
| Databáze: | OpenAIRE |
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