癌症免疫研究技术进展：从免疫基因组学到单细胞分析和人工智能

　　2021年8月20日，英国《自然》旗下《信号转导与靶向治疗》（影响因子：18.187）在线发表复旦大学附属肿瘤医院徐颖、苏冠华、马丁、肖毅、邵志敏、江一舟等学者的长篇综述，全面总结了免疫基因组学、单细胞分析和人工智能领域的常规广泛使用和目前先进技术，并分析展望提出了未来研究的发展方向。全文共计23页，参考文献多达295篇。

　　近年来，随着肿瘤免疫研究的蓬勃发展，越来越多的基础研究及临床试验证明免疫治疗对于癌症治疗具有关键作用。不过，由于免疫检查点阻断剂以及其他免疫治疗策略仅对少数患者有效，故需更多新技术破译肿瘤细胞与肿瘤免疫微环境组成部分之间复杂的相互作用。

　　肿瘤免疫组学借助快速发展的新一代测序技术，利用免疫基因组学、免疫蛋白质组学、免疫生物信息学等多组学数据进行全面研究，可综合全面剖析反映肿瘤免疫状态。

　　高通量基因组和转录组数据可用于计算免疫细胞丰度和预测肿瘤抗原，为免疫基因组学提供参考。可是，由于混池测序只能代表异质细胞群平均特征，无法区分不同细胞亚型。利用单细胞技术可以实现精准的免疫细胞分群单细胞层面研究，以及肿瘤免疫微环境空间结构的研究，更好地剖析肿瘤免疫微环境。此外，影像组学和数字化病理学深度学习模型也在很大程度上有助于癌症免疫研究。这些人工智能技术对预测免疫治疗效果表现良好，对癌症治疗具有深远意义。

　　因此，肿瘤免疫组学相关研究技术的飞速发展，在很大程度上能够推动肿瘤免疫研究、肿瘤免疫治疗的进步。

Signal Transduct Target Ther. 2021 Aug 20;6(1):312.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence.

Ying Xu, Guan-Hua Su, Ding Ma, Yi Xiao, Zhi-Ming Shao, Yi-Zhou Jiang.

Fudan University Shanghai Cancer Center, Shanghai, China; Shanghai Medical College, Fudan University, Shanghai, China; Institutes of Biomedical Sciences, Fudan University, Shanghai, China.

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

DOI: 10.1038/s41392-021-00729-7