A modified gene trap approach for improved high-throughput cancer drug discovery

一种改进的基因陷阱方法,用于提高高通量癌症药物的发现

阅读:9
作者:Shelli M Morris, Andrew J Mhyre, Savanna S Carmack, Carrie H Myers, Connor Burns, Wenjuan Ye, Marc Ferrer, James M Olson, Richard A Klinghoffer

Abstract

While advances in laboratory automation has dramatically increased throughout of compound screening efforts, development of robust cell-based assays in relevant disease models remain resource-intensive and time-consuming, presenting a bottleneck to drug discovery campaigns. To address this issue, we present a modified gene trap approach to efficiently generate pathway-specific reporters that result in a robust "on" signal when the pathway of interest is inhibited. In this proof-of-concept study, we used vemurafenib and trametinib to identify traps that specifically detect inhibition of the mitogen-activated protein kinase (MAPK) pathway in a model of BRAFV600E driven human malignant melanoma. We demonstrate that insertion of our trap into particular loci results in remarkably specific detection of MAPK pathway inhibitors over compounds targeting any other pathway or cellular function. The accuracy of our approach was highlighted in a pilot screen of ~6000 compounds where 40 actives were detected, including 18 MEK, 10 RAF, and 3 ERK inhibitors along with a few compounds representing previously under-characterized inhibitors of the MAPK pathway. One such compound, bafetinib, a second generation BCR/ABL inhibitor, reduced phosphorylation of ERK and when combined with trametinib, both in vitro and in vivo, reduced growth of vemurafenib resistant melanoma cells. While piloted in a model of BRAF-driven melanoma, our results set the stage for using this approach to rapidly generate reporters against any transcriptionally active pathway across a wide variety of disease-relevant cell-based models to expedite drug discovery efforts.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。