Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

测序技术和临床实验的进步彻底改变了实体瘤和血液恶性肿瘤的免疫治疗。然而，免疫疗法的好处仅限于特定的患者亚群，这给更广泛的应用带来了挑战。为了提高其有效性，识别可以预测患者反应的生物标志物至关重要。机器学习 (ML) 在利用多组学癌症数据集和解锁免疫治疗新见解方面发挥着关键作用。本综述概述了应用于免疫治疗分析组学数据的前沿机器学习模型，包括免疫治疗反应预测和免疫治疗相关的肿瘤微环境识别。我们阐明机器学习如何利用不同的数据类型来识别重要的生物标志物，增强我们对免疫治疗机制的理解并优化决策过程。此外，我们还讨论了机器学习在这个快速发展的领域中当前的局限性和挑战。最后，我们概述了旨在克服这些障碍并提高免疫治疗研究中机器学习效率的未来方向。