Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Nat Immunol. 2024 Dec;25(12):2186-2199. doi: 10.1038/s41590-024-02015-4. Epub 2024 Nov 25.
Immune checkpoint therapy has revolutionized cancer treatment, leading to dramatic clinical outcomes for a subset of patients. However, many patients do not experience durable responses following immune checkpoint therapy owing to multiple resistance mechanisms, highlighting the need for effective combination strategies that target these resistance pathways and improve clinical responses. The development of combination strategies based on an understanding of the complex biology that regulates human antitumor immune responses has been a major challenge. In this Review, we describe the current landscape of combination therapies. We also discuss how the development of effective combination strategies will require the integration of small, tissue-rich clinical trials, to determine how therapy-driven perturbation of the human immune system affects downstream biological responses and eventual clinical outcomes, reverse translation of clinical observations to immunocompetent preclinical models, to interrogate specific biological pathways and their impact on antitumor immune responses, and novel computational methods and machine learning, to integrate multiple datasets across clinical and preclinical studies for the identification of the most relevant pathways that need to be targeted for successful combination strategies.
免疫检查点疗法彻底改变了癌症治疗，为一部分患者带来了显著的临床效果。然而，由于存在多种耐药机制，许多患者在接受免疫检查点治疗后无法获得持久的缓解，这凸显了需要有效的联合策略来靶向这些耐药途径并改善临床反应。基于对调控人类抗肿瘤免疫反应的复杂生物学的理解，开发联合策略一直是一个主要挑战。在这篇综述中，我们描述了联合治疗的现状。我们还讨论了如何开发有效的联合策略将需要整合小型、富含组织的临床试验，以确定治疗驱动的人类免疫系统的扰动如何影响下游的生物学反应和最终的临床结果，将临床观察反向转化为免疫活性的临床前模型，以研究特定的生物学途径及其对抗肿瘤免疫反应的影响，以及新的计算方法和机器学习，以整合临床和临床前研究中的多个数据集，确定需要针对成功的联合策略进行靶向的最相关途径。
