癌症免疫基因组学是连接基因组学和免疫学的新兴领域。大规模基因组合作努力的建立以及新的单细胞转录组学技术和多组学方法的发展使得许多癌症类型的突变和转录谱的表征成为可能，并有助于识别临床上可行的改变以及预测和预测。预后生物标志物。研究人员开发了计算方法和机器学习算法，以便从大量组织或单细胞的基因组和转录组测序数据中准确获取临床有用的信息，并探索肿瘤及其微环境。测序和计算方法的快速增长导致人们无法满足了解其真正潜力和局限性的需求，以改善正在接受免疫治疗的癌症患者的管理。在这篇综述中，我们描述了目前可用于分析来自癌症、基质细胞和免疫细胞的大量组织和单细胞测序数据的计算方法，以及如何最好地选择最合适的工具来解决各种临床问题，并最终改善患者结果。© 2023。Springer Nature Limited。

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.© 2023. Springer Nature Limited.