Szeto Gregory L, Finley Stacey D
Department of Chemical, Biochemical, and Environmental Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA; Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA.
Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, DRB 140, Los Angeles, CA 90089, USA; Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, HED 216, Los Angeles, CA 90089, USA; Department of Biological Sciences, University of Southern California, 3616 Trousdale Parkway, AHF 107, Los Angeles, CA 90089, USA.
Trends Cancer. 2019 Jul;5(7):400-410. doi: 10.1016/j.trecan.2019.05.010.
Cancer immunotherapy aims to arm patients with cancer-fighting immunity. Many new cancer-specific immunotherapeutic drugs have gained approval in the past several years, demonstrating immunotherapy's efficacy and promise as an anticancer modality. Despite these successes, several outstanding questions remain for cancer immunotherapy, including how to make immunotherapy more efficacious in a broader range of cancer types and patients, and how to predict which patients will respond or not respond to therapy. We present a case for integrative systems approaches that will answer these questions. This involves applying mechanistic and statistical modeling, establishing consistent and widely adopted experimental tools to generate systems-level data, and creating sustained mechanisms of support. If implemented, these approaches will lead to major advances in cancer treatment.
癌症免疫疗法旨在使患者具备抗癌免疫力。在过去几年中，许多新型癌症特异性免疫治疗药物已获批准，这证明了免疫疗法作为一种抗癌方式的有效性和前景。尽管取得了这些成功，但癌症免疫疗法仍存在几个突出问题，包括如何使免疫疗法在更广泛的癌症类型和患者中更有效，以及如何预测哪些患者会对治疗有反应或无反应。我们提出了一种综合系统方法来回答这些问题。这包括应用机制和统计建模，建立一致且广泛采用的实验工具以生成系统层面的数据，以及创建持续的支持机制。如果得以实施，这些方法将引领癌症治疗取得重大进展。
