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.

免疫疗法在癌症治疗中发挥着关键作用。然而，鉴于只有少数患者对免疫检查点阻断和其他免疫治疗策略有反应，因此需要更多新技术来破译肿瘤细胞与肿瘤免疫微环境（TIME）成分之间复杂的相互作用。肿瘤免疫组学是指利用免疫基因组学、免疫蛋白质组学、免疫生物信息学等反映肿瘤免疫状态的多组学数据对TIME进行综合研究，依赖于二代测序技术的快速发展。高通量基因组和转录组数据可用于计算免疫细胞的丰度并预测肿瘤抗原，参考免疫基因组学。然而，由于批量测序代表了异质细胞群的平均特征，因此无法区分不同的细胞亚型。基于单细胞的技术可以通过精确的免疫细胞亚群和空间结构研究更好地剖析 TIME。此外，基于放射组学和数字病理学的深度学习模型在很大程度上有助于癌症免疫的研究。这些人工智能技术在预测免疫治疗反应方面表现良好，对癌症治疗具有深远意义。在这篇综述中，我们简要总结了免疫基因组学、单细胞和人工智能领域的常规和最新技术，并对未来的研究提出了展望。