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小分子细胞毒性药物扩散屏障对乳腺癌免疫治疗疗效的影响。

Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer.

机构信息

Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, USA.

出版信息

PLoS One. 2013 Apr 19;8(4):e61398. doi: 10.1371/journal.pone.0061398. Print 2013.

DOI:10.1371/journal.pone.0061398
PMID:23620747
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3631240/
Abstract

Molecular-focused cancer therapies, e.g., molecularly targeted therapy and immunotherapy, so far demonstrate only limited efficacy in cancer patients. We hypothesize that underestimating the role of biophysical factors that impact the delivery of drugs or cytotoxic cells to the target sites (for associated preferential cytotoxicity or cell signaling modulation) may be responsible for the poor clinical outcome. Therefore, instead of focusing exclusively on the investigation of molecular mechanisms in cancer cells, convection-diffusion of cytotoxic molecules and migration of cancer-killing cells within tumor tissue should be taken into account to improve therapeutic effectiveness. To test this hypothesis, we have developed a mathematical model of the interstitial diffusion and uptake of small cytotoxic molecules secreted by T-cells, which is capable of predicting breast cancer growth inhibition as measured both in vitro and in vivo. Our analysis shows that diffusion barriers of cytotoxic molecules conspire with γδ T-cell scarcity in tissue to limit the inhibitory effects of γδ T-cells on cancer cells. This may increase the necessary ratios of γδ T-cells to cancer cells within tissue to unrealistic values for having an intended therapeutic effect, and decrease the effectiveness of the immunotherapeutic treatment.

摘要

分子靶向治疗和免疫治疗等以分子为靶点的癌症疗法,迄今为止在癌症患者中仅显示出有限的疗效。我们假设,低估影响药物或细胞毒性细胞递送至靶部位的生物物理因素的作用(与相关的优先细胞毒性或细胞信号转导调节有关)可能是临床疗效不佳的原因。因此,除了专门研究癌细胞中的分子机制外,还应考虑细胞毒性分子的对流-扩散和杀伤癌细胞在肿瘤组织内的迁移,以提高治疗效果。为了验证这一假设,我们开发了一个数学模型,用于研究 T 细胞分泌的小分子细胞毒性物质的间质扩散和摄取,该模型能够预测体外和体内测量的乳腺癌生长抑制作用。我们的分析表明,细胞毒性分子的扩散屏障与组织中 γδ T 细胞的缺乏共同限制了 γδ T 细胞对癌细胞的抑制作用。这可能会增加组织中 γδ T 细胞与癌细胞的必要比例,使其达到实现预期治疗效果的不切实际的数值,并降低免疫治疗的效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/ebe78abc123a/pone.0061398.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/60ac01c7c01f/pone.0061398.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/1b956c6fdea7/pone.0061398.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/e9d1cec96a17/pone.0061398.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/ebe78abc123a/pone.0061398.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/60ac01c7c01f/pone.0061398.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/1b956c6fdea7/pone.0061398.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/e9d1cec96a17/pone.0061398.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ff3/3631240/ebe78abc123a/pone.0061398.g004.jpg

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本文引用的文献

1
Breast tumor cell detection at single cell resolution using an electrochemical impedance technique.利用电化学阻抗技术实现单细胞分辨率的乳腺肿瘤细胞检测。
Lab Chip. 2012 Jul 7;12(13):2362-8. doi: 10.1039/c2lc21174b. Epub 2012 Apr 19.
2
Human ovarian tumor cells escape γδ T cell recognition partly by down regulating surface expression of MICA and limiting cell cycle related molecules.人卵巢肿瘤细胞通过下调表面 MICA 的表达和限制细胞周期相关分子来部分逃避 γδ T 细胞的识别。
PLoS One. 2011;6(9):e23348. doi: 10.1371/journal.pone.0023348. Epub 2011 Sep 14.
3
Evolution of tumor invasiveness: the adaptive tumor microenvironment landscape model.
一种用于估计肝转移结直肠癌患者肿瘤部位化疗浓度并预测治疗反应的数学模型。
Cancers (Basel). 2021 Jan 25;13(3):444. doi: 10.3390/cancers13030444.
4
A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden.用于量化患者对检查点抑制剂的敏感性和长期肿瘤负担的数学模型。
Nat Biomed Eng. 2021 Apr;5(4):297-308. doi: 10.1038/s41551-020-00662-0. Epub 2021 Jan 4.
5
Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy.数学预测接受检查点抑制剂免疫治疗的晚期癌症患者的临床结局。
Sci Adv. 2020 Apr 29;6(18):eaay6298. doi: 10.1126/sciadv.aay6298. eCollection 2020 May.
6
Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors.一种用于量化纳米颗粒在肿瘤中分布的基于生理学的数学模型的开发。
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2852-2855. doi: 10.1109/EMBC.2019.8856503.
7
Mathematical Modeling to Address Challenges in Pancreatic Cancer.数学建模应对胰腺癌挑战。
Curr Top Med Chem. 2020;20(5):367-376. doi: 10.2174/1568026620666200101095641.
8
Digital pathology and artificial intelligence.数字病理学与人工智能。
Lancet Oncol. 2019 May;20(5):e253-e261. doi: 10.1016/S1470-2045(19)30154-8.
9
Mathematical modeling in cancer nanomedicine: a review.癌症纳米医学中的数学建模:综述。
Biomed Microdevices. 2019 Apr 4;21(2):40. doi: 10.1007/s10544-019-0380-2.
10
Dynamic Targeting in Cancer Treatment.癌症治疗中的动态靶向
Front Physiol. 2019 Feb 14;10:96. doi: 10.3389/fphys.2019.00096. eCollection 2019.
肿瘤侵袭性的演变:适应肿瘤微环境景观模型。
Cancer Res. 2011 Oct 15;71(20):6327-37. doi: 10.1158/0008-5472.CAN-11-0304. Epub 2011 Aug 22.
4
Identification of Critical Molecular Components in a Multiscale Cancer Model Based on the Integration of Monte Carlo, Resampling, and ANOVA.基于蒙特卡罗、重采样和方差分析集成的多尺度癌症模型中关键分子成分的鉴定。
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5
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Nat Med. 2011 Mar;17(3):320-9. doi: 10.1038/nm.2328.
6
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CA Cancer J Clin. 2011 Mar-Apr;61(2):69-90. doi: 10.3322/caac.20107. Epub 2011 Feb 4.
7
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Math Med Biol. 2012 Mar;29(1):95-108. doi: 10.1093/imammb/dqq023. Epub 2010 Dec 8.
8
Histopathological image analysis: a review.组织病理学图像分析:综述。
IEEE Rev Biomed Eng. 2009;2:147-71. doi: 10.1109/RBME.2009.2034865. Epub 2009 Oct 30.
9
Effects of anti-VEGF treatment duration on tumor growth, tumor regrowth, and treatment efficacy.抗血管内皮生长因子治疗持续时间对肿瘤生长、肿瘤复发和治疗效果的影响。
Clin Cancer Res. 2010 Aug 1;16(15):3887-900. doi: 10.1158/1078-0432.CCR-09-3100. Epub 2010 Jun 16.
10
Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model.基于代理的非小细胞肺癌模型的跨尺度、跨途径评估。
Bioinformatics. 2009 Sep 15;25(18):2389-96. doi: 10.1093/bioinformatics/btp416. Epub 2009 Jul 4.