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MALDI Mass Spectrometry Imaging for Evaluation of Therapeutics in Colorectal Tumor Organoids
Journal of the American Society for Mass Spectrometry ( IF 3.1 ) Pub Date : 2017-12-05 , DOI: 10.1007/s13361-017-1851-4
Xin Liu 1 , Colin Flinders 2 , Shannon M Mumenthaler 2 , Amanda B Hummon 1
Affiliation  

Patient-derived colorectal tumor organoids (CTOs) closely recapitulate the complex morphological, phenotypic, and genetic features observed in in vivo tumors. Therefore, evaluation of drug distribution and metabolism in this model system can provide valuable information to predict the clinical outcome of a therapeutic response in individual patients. In this report, we applied matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to examine the spatial distribution of the drug irinotecan and its metabolites in CTOs from two patients. Irinotecan is a prodrug and is often prescribed as part of therapeutic regimes for patients with advanced colorectal cancer. Irinotecan shows a time-dependent and concentration-dependent permeability and metabolism in the CTOs. More interestingly, the active metabolite SN-38 does not co-localize well with the parent drug irinotecan and the inactive metabolite SN-38G. The phenotypic effect of irinotecan metabolism was also confirmed by a viability study showing significantly reduced proliferation in the drug treated CTOs. MALDI-MSI can be used to investigate various pharmaceutical compounds in CTOs derived from different patients. By analyzing multiple CTOs from a patient, this method could be used to predict patient-specific drug responses and help to improve personalized dosing regimens.

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中文翻译:


MALDI 质谱成像用于评估结直肠肿瘤类器官的治疗



患者来源的结直肠肿瘤类器官(CTO)密切再现了体内肿瘤中观察到的复杂形态、表型和遗传特征。因此,对该模型系统中药物分布和代谢的评估可以提供有价值的信息来预测个体患者治疗反应的临床结果。在本报告中,我们应用基质辅助激光解吸/电离质谱成像 (MALDI-MSI) 来检查两名患者 CTO 中药物伊立替康及其代谢物的空间分布。伊立替康是一种前药,通常作为晚期结直肠癌患者治疗方案的一部分。伊立替康在 CTO 中表现出时间依赖性和浓度依赖性的渗透性和代谢。更有趣的是,活性代谢物 SN-38 与母体药物伊立替康和无活性代谢物 SN-38G 不能很好地共定位。一项生存力研究也证实了伊立替康代谢的表型效应,显示药物处理的 CTO 的增殖显着减少。 MALDI-MSI 可用于研究来自不同患者的 CTO 中的各种药物化合物。通过分析患者的多个 CTO,该方法可用于预测患者特定的药物反应并帮助改进个性化给药方案。


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更新日期:2017-12-05
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