超越MHC结合:免疫原性预测工具完善癌症患者新抗原选择

I. Carri, Erika Schwab, Enrique Podaza, Heli M. Garcia Alvarez, J. Mordoh, M. Nielsen, M. M. Barrio
{"title":"超越MHC结合:免疫原性预测工具完善癌症患者新抗原选择","authors":"I. Carri, Erika Schwab, Enrique Podaza, Heli M. Garcia Alvarez, J. Mordoh, M. Nielsen, M. M. Barrio","doi":"10.37349/ei.2023.00091","DOIUrl":null,"url":null,"abstract":"In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.","PeriodicalId":93552,"journal":{"name":"Exploration of immunology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients\",\"authors\":\"I. Carri, Erika Schwab, Enrique Podaza, Heli M. Garcia Alvarez, J. Mordoh, M. Nielsen, M. M. Barrio\",\"doi\":\"10.37349/ei.2023.00091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.\",\"PeriodicalId\":93552,\"journal\":{\"name\":\"Exploration of immunology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Exploration of immunology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37349/ei.2023.00091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Exploration of immunology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37349/ei.2023.00091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在过去的几年里,人们做出了多项努力来准确预测癌症患者体细胞突变产生的新抗原,或者开发个性化的治疗疫苗,或者研究癌症免疫疗法后的免疫反应。在这种情况下,肿瘤活检和匹配正常组织的配对全外显子组测序(WES)以及RNA测序(RNA-Seq)的可及性不断增加,为开发预测和优先考虑新抗原候选物的生物信息学工具提供了基础。大多数管道依赖于候选肽与患者主要组织相容性复合体(MHC)的结合预测,但这些方法返回大量假阳性,因为它们缺乏与影响T细胞对新抗原反应的其他特征相关的信息。这篇综述探索了现有的计算方法,这些方法结合了T细胞偏好的信息,以预测它们在遇到肽MHC复合物后的激活。具体而言,预测i)可能增加新肽暴露于免疫系统的可用性的生物学特征,ii)表示新抗原破坏免疫耐受的机会的自相似性指标,iii)病原体免疫原性和iv)肿瘤免疫原性的方法。此外,这篇综述描述了这些工具的特性,并在一个新的基准数据集的背景下讨论了它们的性能,该数据集是在一项II期临床研究中使用黑色素瘤疫苗(VACCIMEL)治疗的患者的实验验证的新抗原。评估的总体结果表明,目前的工具预测针对新抗原的细胞毒性反应激活的能力有限。基于这一结果,讨论了使这一问题成为免疫信息学中尚未解决的挑战的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.00
自引率
0.00%
发文量
0
期刊最新文献
Exploring the impact of immune response on tumor heterogeneity through mathematical modeling Recent advances in the study of the structure and function of the epididymis The immune response of nano carbon-based photic-driving vaccines to severe acute respiratory syndrome coronavirus 2 Targeted treatments for immune dysregulation in inborn errors of immunity Physical activity, immune system and hypertension: reflections and challenges for future pandemics based on learning from coronavirus disease 2019
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1