PMID- 37197645 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20240402 IS - 2730-5848 (Electronic) IS - 2730-583X (Print) IS - 2730-583X (Linking) VI - 3 IP - 2 DP - 2023 Apr TI - In Silico Pipeline to Identify Tumor-Specific Antigens for Cancer Immunotherapy Using Exome Sequencing Data. PG - 130-137 LID - 10.1007/s43657-022-00084-9 [doi] AB - Tumor-specific antigens or neoantigens are peptides that are expressed only in cancer cells and not in healthy cells. Some of these molecules can induce an immune response, and therefore, their use in immunotherapeutic strategies based on cancer vaccines has been extensively explored. Studies based on these approaches have been triggered by the current high-throughput DNA sequencing technologies. However, there is no universal nor straightforward bioinformatic protocol to discover neoantigens using DNA sequencing data. Thus, we propose a bioinformatic protocol to detect tumor-specific antigens associated with single nucleotide variants (SNVs) or "mutations" in tumoral tissues. For this purpose, we used publicly available data to build our model, including exome sequencing data from colorectal cancer and healthy cells obtained from a single case, as well as frequent human leukocyte antigen (HLA) class I alleles in a specific population. HLA data from Costa Rican Central Valley population was selected as an example. The strategy included three main steps: (1) pre-processing of sequencing data; (2) variant calling analysis to detect tumor-specific SNVs in comparison with healthy tissue; and (3) prediction and characterization of peptides (protein fragments, the tumor-specific antigens) derived from the variants, in the context of their affinity with frequent alleles of the selected population. In our model data, we found 28 non-silent SNVs, present in 17 genes in chromosome one. The protocol yielded 23 strong binders peptides derived from the SNVs for frequent HLA class I alleles for the Costa Rican population. Although the analyses were performed as an example to implement the pipeline, to our knowledge, this is the first study of an in silico cancer vaccine using DNA sequencing data in the context of the HLA alleles. It is concluded that the standardized protocol was not only able to identify neoantigens in a specific but also provides a complete pipeline for the eventual design of cancer vaccines using the best bioinformatic practices. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-022-00084-9. CI - (c) International Human Phenome Institutes (Shanghai) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. FAU - Morazan-Fernandez, Diego AU - Morazan-Fernandez D AD - Caja Costarricense de Seguro Social, San Jose, 10104 Costa Rica. GRID: grid.466544.1. ISNI: 0000 0001 2112 4705 FAU - Mora, Javier AU - Mora J AUID- ORCID: 0000-0002-9288-1858 AD - Centro de Investigacion de Enfermedades Tropicales, Centro de Investigacion en Cirugia y Cancer, and Facultad de Microbiologia, Universidad de Costa Rica, San Jose, 2060 Costa Rica. GRID: grid.412889.e. ISNI: 0000 0004 1937 0706 FAU - Molina-Mora, Jose Arturo AU - Molina-Mora JA AUID- ORCID: 0000-0001-9764-4192 AD - Centro de Investigacion de Enfermedades Tropicales, Centro de Investigacion en Cirugia y Cancer, and Facultad de Microbiologia, Universidad de Costa Rica, San Jose, 2060 Costa Rica. GRID: grid.412889.e. ISNI: 0000 0004 1937 0706 LA - eng PT - Journal Article DEP - 20221208 PL - Switzerland TA - Phenomics JT - Phenomics (Cham, Switzerland) JID - 9918351171706676 PMC - PMC10110822 OTO - NOTNLM OT - Cancer vaccine OT - Colorectal cancer OT - Costa Rica OT - Human leukocyte antigen OT - Neoantigens OT - Neopeptide COIS- Conflict of InterestThe authors declare that there is no conflict of interest. EDAT- 2023/05/18 01:07 MHDA- 2023/05/18 01:08 PMCR- 2024/04/01 CRDT- 2023/05/17 20:05 PHST- 2022/04/26 00:00 [received] PHST- 2022/11/09 00:00 [revised] PHST- 2022/11/15 00:00 [accepted] PHST- 2023/05/18 01:07 [pubmed] PHST- 2023/05/18 01:08 [medline] PHST- 2023/05/17 20:05 [entrez] PHST- 2024/04/01 00:00 [pmc-release] AID - 84 [pii] AID - 10.1007/s43657-022-00084-9 [doi] PST - epublish SO - Phenomics. 2022 Dec 8;3(2):130-137. doi: 10.1007/s43657-022-00084-9. eCollection 2023 Apr.