Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka–Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

现代癌症免疫疗法已经彻底改变了肿瘤学，并具有从根本上改变癌症治疗方法的潜力。然而，仍有许多问题有待回答，以更好地了解免疫治疗反应并进一步改善未来癌症患者的益处。计算模型是很有前途的工具，可以通过提供新的线索和假设来加速免疫治疗研究，这些线索和假设可以在未来的试验中进行测试，基于先前的模拟以及经验原理。在这篇专题综述中，我们简要总结了癌症免疫治疗的历史，包括传统癌症免疫治疗的计算模型，并全面回顾了现代癌症免疫治疗的计算模型，例如免疫检查点抑制剂（作为单一疗法和联合治疗），共刺激激动抗体、双特异性抗体和嵌合抗原受体 T 细胞。建模方法分为以下类别之一：数据驱动的自上而下与机械自下而上、简单与详细、连续与离散以及混合。总结了几种常见的建模方法，例如药代动力学/药效学模型、Lotka-Volterra 模型、进化博弈论模型、定量系统药理学模型、时空模型、基于代理的模型和基于逻辑的模型。对每种建模方法的优缺点进行了批判性讨论，特别是关注成功转化为免疫肿瘤学研究和常规临床实践的潜力。特别注意每个模型的校准和验证，这是任何成功模式的必要先决条件，同时也是主要障碍之一。最后，我们为该领域的未来发展提供指导和建议。