4.6 Article

Development of a High-Throughput Three-Dimensional Invasion Assay for Anti-Cancer Drug Discovery

期刊

PLOS ONE
卷 8, 期 12, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0082811

关键词

-

资金

  1. Baldwin Breast Cancer Foundation
  2. National Cancer Institute [1R01CA166936]

向作者/读者索取更多资源

The lack of three-dimensional (3-D) high-throughput (HT) screening assays designed to identify anti-cancer invasion drugs is a major hurdle in reducing cancer-related mortality, with the key challenge being assay standardization. Presented is the development of a novel 3-D invasion assay with HT potential that involves surrounding cell-collagen spheres within collagen to create a 3-D environment through which cells can invade. Standardization was achieved by designing a tooled 96-well plate to create a precisely designated location for the cell-collagen spheres and by using dialdehyde dextran to inhibit collagen contraction, maintaining uniform size and shape. This permits automated readout for determination of the effect of inhibitory compounds on cancer cell invasion. Sensitivity was demonstrated by the ability to distinguish varying levels of invasiveness of cancer cell lines, and robustness was determined by calculating the Z-factor. A Z-factor of 0.65 was obtained by comparing the effects of DMSO and anti-beta 1-integrin antibody, an inhibitory reagent, on the invasion of Du145 cancer cells, suggesting this novel assay is suitable for large scale drug discovery. As proof of principle, the NCI Diversity Compound Library was screened against human invasive cancer cells. Nine compounds exhibiting high potency and low toxicity were identified, including DX-52-1, a compound previously reported to inhibit cell migration, a critical determinant of cancer invasion. The results indicate that this innovative HT platform is a simple, precise, and easy to replicate 3-D invasion assay for anti-cancer drug discovery.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据