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单细胞 RNA 测序推动下一代 AML 治疗策略:挑战与展望

英文原题:Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.

查看英文原题

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.

PubMed 2025/01/30(内容时间) Mol Med Q1 · IF 8.3(JCR 2025)

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中文摘要

急性髓系白血病(AML)由髓系母细胞的成熟和分化异常,以及转录/表观遗传改变引起,这些均导致骨髓中恶性血细胞过度增殖。由于获得新的体细胞改变导致的肿瘤异质性,使得对当前治疗产生高耐药率或降低造血干细胞移植(HSCT)的疗效,从而增加复发和死亡风险。单细胞RNA测序(scRNA-seq)将能够通过分析同一疾病不同方面的患者,进行风险分层,并在诊断时或治疗后识别新的潜在治疗靶点,从而对AML进行分类并指导治疗方法。ScRNA-seq能够识别静止的干细胞样细胞,以及导致治疗耐药和治疗后复发的白血病干细胞。该方法还引入了增强HSCT过程疗效的因素和机制。AML转录谱的生成数据甚至可能促进癌症疫苗和CAR-T 细胞疗法的开发,同时节省宝贵时间并减轻化疗和HSCT在体内的危险副作用。

然而,scRNA-seq应用面临诸多挑战,如高维分析的大量数据、技术噪声、批次效应以及寻找微小生物学模式,这些可通过结合人工智能模型加以改进。

展开英文摘要原文

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment.

ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo.

However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

论文信息

作者
Khosroabadi Z、Azaryar S、Dianat-Moghadam H、Amoozgar Z、Sharifi M
第一作者单位
Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.Iran
通讯作者单位
Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran. mo_sharifi@med.mui.ac.ir.Iran
文献类型
综述
期刊
Molecular medicine (Cambridge, Mass.)2025 Jan 30
原文标识
PubMed 39885388 · DOI 10.1186/s10020-025-01085-w