Gui Yu, He Xiujing, Yu Jing, Jing Jing
Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu 610041, China.
School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
J Clin Med. 2023 Feb 6;12(4):1279. doi: 10.3390/jcm12041279.
The emergence of immunotherapy has dramatically changed the cancer treatment paradigm and generated tremendous promise in precision medicine. However, cancer immunotherapy is greatly limited by its low response rates and immune-related adverse events. Transcriptomics technology is a promising tool for deciphering the molecular underpinnings of immunotherapy response and therapeutic toxicity. In particular, applying single-cell RNA-seq (scRNA-seq) has deepened our understanding of tumor heterogeneity and the microenvironment, providing powerful help for developing new immunotherapy strategies. Artificial intelligence (AI) technology in transcriptome analysis meets the need for efficient handling and robust results. Specifically, it further extends the application scope of transcriptomic technologies in cancer research. AI-assisted transcriptomic analysis has performed well in exploring the underlying mechanisms of drug resistance and immunotherapy toxicity and predicting therapeutic response, with profound significance in cancer treatment. In this review, we summarized emerging AI-assisted transcriptomic technologies. We then highlighted new insights into cancer immunotherapy based on AI-assisted transcriptomic analysis, focusing on tumor heterogeneity, the tumor microenvironment, immune-related adverse event pathogenesis, drug resistance, and new target discovery. This review summarizes solid evidence for immunotherapy research, which might help the cancer research community overcome the challenges faced by immunotherapy.
免疫疗法的出现极大地改变了癌症治疗模式，并在精准医学领域展现出巨大前景。然而，癌症免疫疗法受到其低响应率和免疫相关不良事件的极大限制。转录组学技术是一种很有前景的工具，可用于解读免疫疗法反应和治疗毒性的分子基础。特别是，应用单细胞RNA测序（scRNA-seq）加深了我们对肿瘤异质性和微环境的理解，为开发新的免疫疗法策略提供了有力帮助。转录组分析中的人工智能（AI）技术满足了高效处理和可靠结果的需求。具体而言，它进一步扩展了转录组学技术在癌症研究中的应用范围。人工智能辅助转录组分析在探索耐药性和免疫疗法毒性的潜在机制以及预测治疗反应方面表现出色，对癌症治疗具有深远意义。在本综述中，我们总结了新兴的人工智能辅助转录组学技术。然后，我们重点介绍了基于人工智能辅助转录组分析对癌症免疫疗法的新见解，重点关注肿瘤异质性、肿瘤微环境、免疫相关不良事件发病机制、耐药性和新靶点发现。本综述总结了免疫疗法研究的可靠证据，这可能有助于癌症研究界克服免疫疗法面临的挑战。
