Abstract
Knowing whether a protein can be processed and the resulting peptides presented by major histocompatibility complex (MHC) is highly important for immunotherapy design. MHC ligands can be predicted by in silico peptide-MHC class-I binding prediction algorithms. However, prediction performance differs considerably, depending on the selected algorithm, MHC class-I type, and peptide length. We evaluated the prediction performance of 13 algorithms based on binding affinity data of 8- to 11-mer peptides derived from the HPV16 E6 and E7 proteins to the most prevalent human leukocyte antigen (HLA) types. Peptides from high to low predicted binding likelihood were synthesized, and their HLA binding was experimentally verified by in vitro competitive binding assays. Based on the actual binding capacity of the peptides, the performance of prediction algorithms was analyzed by calculating receiver operating characteristics (ROC) and the area under the curve (AROC). No algorithm outperformed others, but different algorithms predicted best for particular HLA types and peptide lengths. The sensitivity, specificity, and accuracy of decision thresholds were calculated. Commonly used decision thresholds yielded only 40% sensitivity. To increase sensitivity, optimal thresholds were calculated, validated, and compared. In order to make maximal use of prediction algorithms available online, we developed MHCcombine, a web application that allows simultaneous querying and output combination of up to 13 prediction algorithms. Taken together, we provide here an evaluation of peptide-MHC class-I binding prediction tools and recommendations to increase prediction sensitivity to extend the number of potential epitopes applicable as targets for immunotherapy.
Highlights
Immunotherapy has emerged over the past decades to be a promising approach to personalize treatment of cancer patients
Thereby, we provide the means to increase the number of true human leukocyte antigen (HLA) ligands in the prediction output, and the number of potential T-cell epitopes considered as candidates for immunotherapy
To exploit the individual strengths of the various in silico methods, 13 prediction algorithms were used for HLA class-I binding predictions of peptides derived from the HPV16 proteins E6 and E7
Summary
Immunotherapy has emerged over the past decades to be a promising approach to personalize treatment of cancer patients. The key prerequisite of successful immunotherapy is a tumorspecific antigen that allows induction and focusing of an immune attack against tumor cells. Such tumor-specific antigens could be either viral proteins, in the case of virus-driven malignancies, or mutation-derived neoantigens. Not every possible antigenic peptide will be processed and presented on the cell surface by major histocompatibility complex (MHC) molecules and not all MHC-presented peptides are immu-. Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
