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靶向微转移灶:异质性放射性核素分布对肿瘤控制概率的影响

Targeting Micrometastases: The Effect of Heterogeneous Radionuclide Distribution on Tumor Control Probability.

作者信息

Falzone Nadia, Lee Boon Quan, Able Sarah, Malcolm Javian, Terry Samantha, Alayed Yasir, Vallis Katherine Anne

机构信息

Univesrity of Oxford, United Kingdom.

University of Oxford, United Kingdom.

出版信息

J Nucl Med. 2018 Jun 29;60(2):250-8. doi: 10.2967/jnumed.117.207308.

DOI:10.2967/jnumed.117.207308
PMID:29959216
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6330061/
Abstract

The spatial distribution of radiopharmaceuticals that emit short-range high linear-energy-transfer electrons greatly affects the absorbed dose and their biological effectiveness. The purpose of this study was to investigate the effect of heterogeneous radionuclide distribution on tumor control probability (TCP) in a micrometastases model. Cancer cell lines; MDA-MB-468, SQ20B and 231-H2N were grown as spheroids to represent micrometastases. The intracellular distribution of a representative radiopeptide (In-labelled epidermal growth factor, EGF) and radioimmunotherapeutic (In-labelled Trastuzumab) was determined in cell internalization experiments. The intratumoral distribution was evaluated by microautoradiography of spheroids. γH2AX staining was performed on spheroid sections to correlate DNA damage with radionuclide distribution. Experimental surviving fractions ( ) were obtained using clonogenic assays. A random closed-packed algorithm, which models the random packing behavior of cells and reflects variation in the radii of cells and nuclei, was used to simulate 3-D spheroids. Calculated survival fractions ( ) were generated using an iterative modelling method based on Monte Carlo determined absorbed dose with the PENELOPE code and were compared to ( ). Radiobiological parameters deduced from experimental results and MC simulations were used to predict the TCP for a 3-D spheroid model. Calculated SFs were in good agreement with experimental data, particularly when an increased value for relative biological effectiveness (RBE) was applied to self-dose deposited by sources located in the nucleus and when radiobiological parameters were adjusted to account for dose protraction. Only in MDA-MB-468 spheroids treated with In-EGF was a TCP>0.5 achieved, indicating that for this cell type the radiopeptide would be curative when targeting micrometastases. This is attributed to the relative radiosensitivity of MDA-MB-468 cells, high nuclear uptake of the radiopeptide and uniform distribution of radioactivity throughout the spheroid. It is imperative to include biological endpoints when evaluating the distribution of radionuclides in models emulating micrometastatic disease. The spatial distribution of radioactivity is a clear determinant of biological effect and TCP as demonstrated in this study.

摘要

发射短程高传能线密度电子的放射性药物的空间分布极大地影响吸收剂量及其生物学效应。本研究的目的是在微转移模型中研究异质放射性核素分布对肿瘤控制概率(TCP)的影响。癌细胞系MDA-MB-468、SQ20B和231-H2N培养成球体以代表微转移灶。在细胞内化实验中测定了一种代表性放射性肽(铟标记的表皮生长因子,EGF)和放射免疫治疗药物(铟标记的曲妥珠单抗)的细胞内分布。通过球体的微放射自显影评估肿瘤内分布。对球体切片进行γH2AX染色,以将DNA损伤与放射性核素分布相关联。使用克隆形成试验获得实验存活分数( )。一种随机密堆积算法用于模拟三维球体,该算法模拟细胞的随机堆积行为并反映细胞和细胞核半径的变化。使用基于蒙特卡罗方法确定的吸收剂量并结合PENELOPE代码的迭代建模方法生成计算存活分数( ),并与( )进行比较。从实验结果和蒙特卡罗模拟推导的放射生物学参数用于预测三维球体模型的TCP。计算得到的存活分数与实验数据吻合良好,特别是当对位于细胞核内的源自身沉积剂量应用增加的相对生物学效应(RBE)值,以及调整放射生物学参数以考虑剂量延长时。仅在用铟-EGF处理的MDA-MB-468球体中实现了TCP>0.5,这表明对于这种细胞类型,放射性肽在靶向微转移灶时具有治愈性。这归因于MDA-MB-468细胞的相对放射敏感性、放射性肽的高核摄取以及整个球体中放射性的均匀分布。在模拟微转移疾病的模型中评估放射性核素分布时,纳入生物学终点至关重要。如本研究所示,放射性的空间分布是生物学效应和TCP的明确决定因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/f1136c3947bf/250fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/cf3a467aa471/250fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/7f42cf06c53f/250fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/a499234f5c8f/250fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/adcaf8bafaed/250fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/cab82d08b126/250fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/82abc8d513fc/250fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/f1136c3947bf/250fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/cf3a467aa471/250fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/7f42cf06c53f/250fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/a499234f5c8f/250fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/adcaf8bafaed/250fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/cab82d08b126/250fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/82abc8d513fc/250fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f6/8833858/f1136c3947bf/250fig7.jpg

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