Suppr超能文献

通过使用多维组学数据来推进 CAR T 细胞疗法。

Advancing CAR T cell therapy through the use of multidimensional omics data.

机构信息

Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA.

Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA.

出版信息

Nat Rev Clin Oncol. 2023 Apr;20(4):211-228. doi: 10.1038/s41571-023-00729-2. Epub 2023 Jan 31.

Abstract

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

摘要

尽管嵌合抗原受体 (CAR) T 细胞疗法在治疗某些血液恶性肿瘤方面取得了显著成功,但在优化 CAR 设计和细胞产品、提高反应率、延长缓解期的持久性、降低毒性以及将这种治疗模式扩展到其他癌症类型方面仍存在挑战。来自多维组学分析的数据,包括基因组学、表观基因组学、转录组学、T 细胞受体库分析、蛋白质组学、代谢组学和/或微生物组学,为剖析 CAR T 细胞的复杂和动态多因素表型、过程和反应提供了独特的机会,并发现新的肿瘤靶点和耐药途径。在这篇综述中,我们总结了用于推进我们对 CAR T 细胞疗法的机制理解的多维细胞和分子分析技术。此外,我们讨论了当前应用和潜在策略,利用多组学数据来识别最佳的靶抗原和其他分子特征,这些特征可以被利用来增强 CAR T 细胞治疗的抗肿瘤活性并最小化其毒性。实际上,充分利用多组学数据将为 CAR T 细胞治疗的生物学提供新的见解,进一步加速具有改善疗效和安全性特征的产品的开发,并使临床医生能够更好地预测和监测患者的反应。

相似文献

1
Advancing CAR T cell therapy through the use of multidimensional omics data.通过使用多维组学数据来推进 CAR T 细胞疗法。
Nat Rev Clin Oncol. 2023 Apr;20(4):211-228. doi: 10.1038/s41571-023-00729-2. Epub 2023 Jan 31.
6
CAR T-cell-associated neurotoxicity: A comprehensive review.嵌合抗原受体 T 细胞相关神经毒性:全面综述。
Rev Neurol (Paris). 2024 Nov;180(9):989-994. doi: 10.1016/j.neurol.2024.07.005. Epub 2024 Sep 16.

引用本文的文献

本文引用的文献

1
High-content CRISPR screening.高内涵CRISPR筛选
Nat Rev Methods Primers. 2022;2(1). doi: 10.1038/s43586-022-00098-7. Epub 2022 Feb 10.
7
Race is a key determinant of the human intratumor microbiome.种族是人类肿瘤内微生物群的关键决定因素。
Cancer Cell. 2022 Sep 12;40(9):901-902. doi: 10.1016/j.ccell.2022.08.007. Epub 2022 Aug 25.
10
Big data in basic and translational cancer research.基础和转化癌症研究中的大数据。
Nat Rev Cancer. 2022 Nov;22(11):625-639. doi: 10.1038/s41568-022-00502-0. Epub 2022 Sep 5.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验