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利用拉曼光谱研究放射治疗后体内肿瘤生物分子的变化

Investigating In Vivo Tumor Biomolecular Changes Following Radiation Therapy Using Raman Spectroscopy.

作者信息

Karunakaran Varsha, Dadgar Sina, Paidi Santosh K, Mordi April F, Lowe Whitney A, Mim Umme Marium, Ivers Jesse D, Rodriguez Troncoso Joel I, McPeake Jared A, Fernandes Alric, Tripathi Sanidhya D, Barman Ishan, Rajaram Narasimhan

机构信息

Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas 72701, United States.

Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.

出版信息

ACS Omega. 2024 Oct 9;9(42):43025-43033. doi: 10.1021/acsomega.4c06096. eCollection 2024 Oct 22.

DOI:10.1021/acsomega.4c06096
PMID:39464461
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11500151/
Abstract

Treatment resistance is a major bottleneck in the success of cancer therapy. Early identification of the treatment response or lack thereof in patients can enable an earlier switch to alternative treatment strategies that can enhance response rates. Here, Raman spectroscopy was applied to monitor early tumor biomolecular changes in sensitive (UM-SCC-22B) and resistant (UM-SCC-47) head and neck tumor xenografts for the first time in in vivo murine tumor models in response to radiation therapy. We used a validated multivariate curve resolution-alternating least-squares (MCR-ALS) model to resolve complex multicomponent Raman spectra into individual pure spectra and their respective contributions. We observed a significant radiation-induced increase in the contributions of lipid-like species ( = 0.0291) in the radiation-sensitive UM-SCC-22B tumors at 48 h following radiation compared to the nonradiated baseline (prior to commencing treatment). We also observed an increase in the contribution of collagen-like species in the radiation-resistant UM-SCC-47 tumors at 24 h following radiation compared to the nonradiated baseline ( = 0.0125). In addition to the in vivo analysis, we performed ex vivo confocal Raman microscopic imaging of frozen sections derived from the same tumors. A comparison of all control and treated tumors revealed similar trends in the contributions of lipid-like and collagen-like species in both in vivo and ex vivo measurements; however, when evaluated as a function of time, longitudinal trends in the scores of collagen-like and lipid-like components were not consistent between the two data sets, likely due to sample numbers and differences in sampling depth at which information is obtained. Nevertheless, this study demonstrates the potential of fiber-based Raman spectroscopy for identifying early tumor microenvironmental changes in response to clinical doses of radiation therapy.

摘要

治疗耐药性是癌症治疗成功的主要瓶颈。早期识别患者的治疗反应或缺乏治疗反应,能够使治疗更早地转向可提高反应率的替代治疗策略。在此,首次在体内小鼠肿瘤模型中应用拉曼光谱来监测敏感型(UM-SCC-22B)和耐药型(UM-SCC-47)头颈肿瘤异种移植对放射治疗的早期肿瘤生物分子变化。我们使用经过验证的多元曲线分辨交替最小二乘法(MCR-ALS)模型,将复杂的多组分拉曼光谱分解为各个纯光谱及其各自的贡献。我们观察到,与未接受辐射的基线(开始治疗前)相比,在辐射敏感的UM-SCC-22B肿瘤中,辐射后48小时脂类物质的贡献显著增加(P = 0.0291)。我们还观察到,与未接受辐射的基线相比,在辐射耐药的UM-SCC-47肿瘤中,辐射后24小时类胶原物质的贡献增加(P = 0.0125)。除了体内分析,我们还对来自相同肿瘤的冰冻切片进行了体外共聚焦拉曼显微镜成像。所有对照肿瘤和治疗后肿瘤的比较显示,脂类物质和类胶原物质在体内和体外测量中的贡献趋势相似;然而,当作为时间的函数进行评估时,两个数据集之间类胶原和脂类成分得分的纵向趋势不一致,这可能是由于样本数量以及获取信息的采样深度不同所致。尽管如此,本研究证明了基于光纤的拉曼光谱在识别临床剂量放射治疗引起的早期肿瘤微环境变化方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/e7b87b180800/ao4c06096_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/c3cf3ee63823/ao4c06096_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/05a300c6f134/ao4c06096_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/708d39817f5c/ao4c06096_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/424c38d1ce4a/ao4c06096_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/e7b87b180800/ao4c06096_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/c3cf3ee63823/ao4c06096_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/05a300c6f134/ao4c06096_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/708d39817f5c/ao4c06096_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/424c38d1ce4a/ao4c06096_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5080/11500151/e7b87b180800/ao4c06096_0004.jpg

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本文引用的文献

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Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.拉曼光谱和卷积神经网络监测乳腺癌异种移植的生化辐射反应。
Sci Rep. 2023 Jan 27;13(1):1530. doi: 10.1038/s41598-023-28479-2.
2
Profiling of Tumor Cell-Delivered Exosome by Surface Enhanced Raman Spectroscopy-Based Biosensor for Evaluation of Nasopharyngeal Cancer Radioresistance.基于表面增强拉曼光谱生物传感器的肿瘤细胞外泌体分析在评估鼻咽癌放射抵抗中的应用。
Adv Healthc Mater. 2023 Mar;12(8):e2202482. doi: 10.1002/adhm.202202482. Epub 2022 Dec 25.
3
Raman spectroscopy reveals phenotype switches in breast cancer metastasis.
拉曼光谱揭示乳腺癌转移中的表型转换。
Theranostics. 2022 Jul 11;12(12):5351-5363. doi: 10.7150/thno.74002. eCollection 2022.
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Adverse Radiation Therapy Effects in the Treatment of Head and Neck Tumors.头颈部肿瘤治疗中的放射治疗不良反应
Radiographics. 2022 May-Jun;42(3):806-821. doi: 10.1148/rg.210150. Epub 2022 Mar 18.
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Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.拉曼光谱和机器学习揭示免疫治疗诱导的早期肿瘤微环境变化。
Cancer Res. 2021 Nov 15;81(22):5745-5755. doi: 10.1158/0008-5472.CAN-21-1438. Epub 2021 Oct 13.
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Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential.粗拉曼和光学衍射层析成像实现了对不同转移潜能的同源乳腺癌细胞的无标记表型分析。
Biosens Bioelectron. 2021 Mar 1;175:112863. doi: 10.1016/j.bios.2020.112863. Epub 2020 Nov 27.
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Spectroscopic investigation of radiation-induced reoxygenation in radiation-resistant tumors.辐射抗性肿瘤中辐射诱导再氧合的光谱研究。
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Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares.利用拉曼光谱、非负矩阵分解和非负最小二乘法监测电离辐射诱导的细胞反应。
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Front Oncol. 2019 Nov 5;9:1152. doi: 10.3389/fonc.2019.01152. eCollection 2019.
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