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个性化化疗的黄金窗口:恰到好处的免疫反应。

The Goldilocks Window of Personalized Chemotherapy: Getting the Immune Response Just Right.

机构信息

Department of Zoology, University of Oxford, Oxford, United Kingdom.

Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.

出版信息

Cancer Res. 2019 Oct 15;79(20):5302-5315. doi: 10.1158/0008-5472.CAN-18-3712. Epub 2019 Aug 6.

DOI:10.1158/0008-5472.CAN-18-3712
PMID:31387920
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6801094/
Abstract

The immune system is a robust and often untapped accomplice of many standard cancer therapies. A majority of tumors exist in a state of immune tolerance where the patient's immune system has become insensitive to the cancer cells. Because of its lymphodepleting effects, chemotherapy has the potential to break this tolerance. To investigate this, we created a mathematical modeling framework of tumor-immune dynamics. Our results suggest that optimal chemotherapy scheduling must balance two opposing objectives: maximizing tumor reduction while preserving patient immune function. Successful treatment requires therapy to operate in a "Goldilocks Window" where patient immune health is not overly compromised. By keeping therapy "just right," we show that the synergistic effects of immune activation and chemotherapy can maximize tumor reduction and control. SIGNIFICANCE: To maximize the synergy between chemotherapy and antitumor immune response, lymphodepleting therapy must be balanced in a "Goldilocks Window" of optimal dosing. http://cancerres.aacrjournals.org/content/canres/79/20/5302/F1.large.jpg.

摘要

免疫系统是许多标准癌症疗法的强大且往往未被充分利用的伙伴。大多数肿瘤处于免疫耐受状态,患者的免疫系统对癌细胞变得不敏感。由于其淋巴耗竭作用,化疗有可能打破这种耐受。为了研究这一点,我们创建了一个肿瘤免疫动力学的数学建模框架。我们的研究结果表明,最佳化疗方案必须平衡两个相互矛盾的目标:最大限度地减少肿瘤,同时保持患者的免疫功能。成功的治疗需要在一个“金发姑娘区”中进行治疗,其中患者的免疫健康不会受到过度损害。通过保持治疗“恰到好处”,我们表明免疫激活和化疗的协同作用可以最大程度地减少肿瘤并控制肿瘤。意义:为了使化疗和抗肿瘤免疫反应之间的协同作用最大化,淋巴耗竭疗法必须在最佳剂量的“金发姑娘区”中进行平衡。http://cancerres.aacrjournals.org/content/canres/79/20/5302/F1.large.jpg。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/211830a5935e/nihms-1536901-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/e43c440b4b52/nihms-1536901-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/7dfc32ceb673/nihms-1536901-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/6b95e38369a4/nihms-1536901-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/29f848af7b09/nihms-1536901-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/59a3b96535e2/nihms-1536901-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/211830a5935e/nihms-1536901-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/e43c440b4b52/nihms-1536901-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/7dfc32ceb673/nihms-1536901-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/6b95e38369a4/nihms-1536901-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/29f848af7b09/nihms-1536901-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/59a3b96535e2/nihms-1536901-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a7/6801094/211830a5935e/nihms-1536901-f0006.jpg

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