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一种利用3D细胞培养鉴定耐药相关基因的功能强大的表型筛选方法。

A Functionally Robust Phenotypic Screen that Identifies Drug Resistance-associated Genes Using 3D Cell Culture.

作者信息

Lee Sun-Young, Bissell Mina J

机构信息

Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

出版信息

Bio Protoc. 2018 Nov 20;8(22). doi: 10.21769/BioProtoc.3083.

Abstract

Drug resistance is a major obstacle in cancer treatment: A case in point is the failure of cancer patients to respond to tyrosine kinase inhibitors (TKI) of EGFR, a receptor that is highly expressed in many cancers. Identification of the targets and delineation of mechanisms of drug resistance remain major challenges. Traditional pharmacological assays of drug resistance measure the response of tumor cells using cell proliferation or cell death as readouts. These assays performed using traditional plastic tissue culture plates (2D) do not translate to realities. Here, we describe a genetic screen based on phenotypic changes that can be completed over a period of 1-1½ months using functional endpoints in physiologically relevant 3D culture models. This phenotype-based assay could lead to the discovery of previously unknown therapeutic targets and could explain the source of the resistance and relapse. As a proof of principle, we performed our 3D culture assay with a small cDNA library in that yielded five unknown intermediates in EGFR and PI3K signaling pathways. Here, we describe the screening method and the characterization of one of the five molecules, but this approach could be easily expanded for a high-throughput screening to identify or evaluate many more unknown intermediates in oncogenic signaling pathways.

摘要

耐药性是癌症治疗中的一个主要障碍

一个典型的例子是癌症患者对表皮生长因子受体(EGFR)的酪氨酸激酶抑制剂(TKI)没有反应,而EGFR是一种在许多癌症中高度表达的受体。确定耐药靶点和阐明耐药机制仍然是重大挑战。传统的耐药性药理学检测使用细胞增殖或细胞死亡作为读数来测量肿瘤细胞的反应。在传统塑料组织培养板(二维)上进行的这些检测无法转化为实际情况。在此,我们描述了一种基于表型变化的基因筛选方法,该方法可以在1至1个半月的时间内,利用生理相关三维培养模型中的功能终点来完成。这种基于表型的检测方法可能会发现以前未知的治疗靶点,并可以解释耐药性和复发的根源。作为原理验证,我们用一个小的cDNA文库进行了三维培养检测,结果在EGFR和PI3K信号通路中产生了五个未知的中间产物。在此,我们描述了筛选方法以及五个分子之一的特性,但这种方法可以很容易地扩展为高通量筛选,以识别或评估致癌信号通路中更多的未知中间产物。

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The tumor microenvironment is a dominant force in multidrug resistance.肿瘤微环境是多药耐药的主要驱动力。
Drug Resist Updat. 2012 Feb-Apr;15(1-2):39-49. doi: 10.1016/j.drup.2012.01.006. Epub 2012 Feb 13.

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