Abstract 243: Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond

L. Piccotti, L. Mirandola, M. Chiriva-Internati
{"title":"Abstract 243: Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond","authors":"L. Piccotti, L. Mirandola, M. Chiriva-Internati","doi":"10.1158/1538-7445.AM2021-243","DOIUrl":null,"url":null,"abstract":"Adoptive cell therapy has been proven a powerful approach for the cure of cancer and other diseases. In particular, the selection of appropriate immunogenic targets has been key to positive outcomes in clinical settings. The availability of RNA-Seq analysis, the accessibility to large data repositories such as TCGA and GTEx, and the creation of new bioinformatic tools have accelerated the process of neoantigen discovery. However, most of the current algorithms are encumbered by the intrinsic complexity of predicting antigen immunogenicity. Diamond™ is a novel artificial intelligence and cognitive machine and deep learning platform to predict peptide processing, HLA binding, and T cell activation. To validate the predictive value of DIAMOND algorithms, the meta-analyses of expression data of cancer-testis antigen New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1) and predictions for the immunogenic peptides were compared to experimental data in the literature. In agreement with published clinical observations, DIAMOND metanalysis showed NY-ESO-1 genic overexpression in skin cutaneous melanoma, lung adenocarcinoma, and sarcoma. Moreover, DIAMOND predicted an MHC binding affinity of 0.289 with Supertype A2 for a new NY-ESO-1 peptide, which has been successfully targeted in clinical trials for patients with HLA-A*02:01, as well as it mirrored published data in its prediction of peptide affinity binding in NY-ESO-1–specific MHC II–restricted T cell receptors. Taken together these data support DIAMOND as a reliable platform for the discovery of new immunogenic targets for cancer therapy. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 243.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of bioinformatics and systems biology : Open access","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1158/1538-7445.AM2021-243","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Adoptive cell therapy has been proven a powerful approach for the cure of cancer and other diseases. In particular, the selection of appropriate immunogenic targets has been key to positive outcomes in clinical settings. The availability of RNA-Seq analysis, the accessibility to large data repositories such as TCGA and GTEx, and the creation of new bioinformatic tools have accelerated the process of neoantigen discovery. However, most of the current algorithms are encumbered by the intrinsic complexity of predicting antigen immunogenicity. Diamond™ is a novel artificial intelligence and cognitive machine and deep learning platform to predict peptide processing, HLA binding, and T cell activation. To validate the predictive value of DIAMOND algorithms, the meta-analyses of expression data of cancer-testis antigen New York Esophageal Squamous Cell Carcinoma 1 (NY-ESO-1) and predictions for the immunogenic peptides were compared to experimental data in the literature. In agreement with published clinical observations, DIAMOND metanalysis showed NY-ESO-1 genic overexpression in skin cutaneous melanoma, lung adenocarcinoma, and sarcoma. Moreover, DIAMOND predicted an MHC binding affinity of 0.289 with Supertype A2 for a new NY-ESO-1 peptide, which has been successfully targeted in clinical trials for patients with HLA-A*02:01, as well as it mirrored published data in its prediction of peptide affinity binding in NY-ESO-1–specific MHC II–restricted T cell receptors. Taken together these data support DIAMOND as a reliable platform for the discovery of new immunogenic targets for cancer therapy. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of novel epitopes of NY-ESO-1 for solid malignancies by Kiromic proprietary search engine Diamond [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 243.
243:通过Kiromic专有搜索引擎Diamond鉴定NY-ESO-1实体恶性肿瘤的新表位
过继细胞疗法已被证明是治疗癌症和其他疾病的有力方法。特别是,选择适当的免疫原性靶点是临床环境中取得积极结果的关键。RNA-Seq分析的可用性、TCGA和GTEx等大型数据库的可访问性以及新的生物信息学工具的创建加速了新抗原发现的过程。然而,目前大多数算法都受到预测抗原免疫原性的固有复杂性的阻碍。Diamond™是一种新型的人工智能、认知机器和深度学习平台,用于预测肽加工、HLA结合和T细胞活化。为了验证DIAMOND算法的预测价值,我们将癌症-睾丸抗原纽约食管鳞状细胞癌1 (NY-ESO-1)的表达数据和免疫原性肽的预测数据与文献中的实验数据进行了比较。与已发表的临床观察一致,DIAMOND meta分析显示NY-ESO-1基因在皮肤黑色素瘤、肺腺癌和肉瘤中过表达。此外,DIAMOND预测了一种新的NY-ESO-1肽与Supertype A2的MHC结合亲和力为0.289,该肽已在HLA-A*02:01患者的临床试验中成功靶向,并且在预测NY-ESO-1特异性MHC ii限制性T细胞受体的肽亲和力结合方面反映了已发表的数据。综上所述,这些数据支持DIAMOND作为发现新的癌症治疗免疫原性靶点的可靠平台。引文格式:Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati。通过Kiromic专有搜索引擎Diamond鉴定NY-ESO-1实体恶性肿瘤的新表位[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志,2021;81(13 -增刊):摘要nr 243。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信