Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nat Comput Sci. 2022 Dec;2(12):785-796. doi: 10.1038/s43588-022-00377-z. Epub 2022 Dec 19.
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
癌症免疫治疗建模方面正在取得令人鼓舞的进展，特别是在以下关键领域：基于个体患者参数制定个性化治疗策略、预测治疗结果以及优化免疫疗法与其他治疗方法联合使用时的协同作用。在这里，我们重点回顾了最近在癌症免疫治疗方面的数学建模工作，重点关注其临床转化。可以看出，该领域正从纯粹的基础科学向能够对患者生活产生重大影响的应用领域转变。我们讨论了研究人员如何整合实验和临床数据来充分为模型提供信息，以便能够将其应用于临床预测，并提出了如果要实现广泛的临床应用仍需克服的挑战。最后，我们讨论了最有前途的未来应用和预计将成为广泛未来建模研究重点的领域。
