乳腺癌居民t细胞受体CDR3结构域和癌症抗原ARMC3的化学互补性与更高水平的生存率和颗粒酶表达相关

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231177269
Nagehan Pakasticali, Andrea Chobrutskiy, Dhruv N Patel, Monica Hsiang, Saif Zaman, Konrad J Cios, George Blanck, Boris I Chobrutskiy
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引用次数: 1

摘要

目前,癌症免疫治疗最紧迫的目标之一是确定可操作的抗原。方法:本研究基于以下考虑和途径来鉴定潜在的乳腺癌抗原:(1)适应性免疫受体、互补决定区-3 (CDR3)在抗原结合中的重要作用,以及癌睾丸抗原(cta)的存在;(ii)化学吸引力;(iii)告知(i)和(ii)项目整合与患者预后和肿瘤基因表达数据的相关性。结果:基于cta与肿瘤驻留t细胞受体(TCR) CDR3s的化学互补性,我们评估了cta与生存的关系。此外,我们已经建立了基因表达与高TCR CDR3-CTA化学互补性的相关性,用于颗粒酶B和其他免疫生物标志物。结论:总体而言,对于几个独立的TCR CDR3乳腺癌数据集,CTA ARMC3作为一种完全新颖的候选抗原脱颖而出,该抗原基于多种算法,方法高度一致。使用最近构建的Adaptive Match网络工具促进了这一结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression.

Introduction: One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens.

Methods: This study relies on the following considerations and approaches to identify potential breast cancer antigens: (i) the significant role of the adaptive immune receptor, complementarity determining region-3 (CDR3) in antigen binding, and the existence cancer testis antigens (CTAs); (ii) chemical attractiveness; and (iii) informing the relevance of the integration of items (i) and (ii) with patient outcome and tumor gene expression data.

Results: We have assessed CTAs for associations with survival, based on their chemical complementarity with tumor resident T-cell receptor (TCR), CDR3s. Also, we have established gene expression correlations with the high TCR CDR3-CTA chemical complementarities, for Granzyme B, and other immune biomarkers.

Conclusions: Overall, for several independent TCR CDR3 breast cancer datasets, the CTA, ARMC3, stood out as a completely novel, candidate antigen based on multiple algorithms with highly consistent approaches. This conclusion was facilitated by use of the recently constructed Adaptive Match web tool.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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