Suppr超能文献

用于功能药物测试和治疗反应预测的肿瘤培养系统。

tumor culture systems for functional drug testing and therapy response prediction.

作者信息

Meijer Titia G, Naipal Kishan At, Jager Agnes, van Gent Dik C

机构信息

Department of Molecular Genetics, Erasmus Medical Center, Rotterdam, The Netherlands.

Department of Medical Oncology, Erasmus Medical Center, Rotterdam, The Netherlands.

出版信息

Future Sci OA. 2017 Mar 27;3(2):FSO190. doi: 10.4155/fsoa-2017-0003. eCollection 2017 Jun.

Abstract

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional assays requires model systems derived from viable tumor samples, which should closely resemble tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental 'cancer-on-chip' procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor-stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

摘要

在个性化医疗时代,优化患者分层至关重要。通过功能测定预测个体治疗反应需要源自活肿瘤样本的模型系统,该系统应与肿瘤特征和微环境极为相似。本文综述了广泛的模型系统,从经典的二维单层培养技术到更具实验性的“芯片上的癌症”程序。我们主要关注考虑到肿瘤异质性和肿瘤-基质相互作用的器官型肿瘤切片。这些三维模型系统可用于患者选择以及基础研究。为每项具体研究选择合适的模型系统至关重要,需要仔细权衡每种技术的利弊。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c22/5481868/fb79cda3eeb7/fsoa-03-190-g1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验