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High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery.
SLAS Discovery: Advancing the Science of Drug Discovery ( IF 3.1 ) Pub Date : 2020-05-22 , DOI: 10.1177/2472555220917115
Rebecca E Hughes 1 , Richard J R Elliott 1 , Alison F Munro 1 , Ashraff Makda 1 , J Robert O'Neill 2 , Ted Hupp 1 , Neil O Carragher 1
Affiliation  

Esophageal adenocarcinoma (EAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies, contributing to poor outcomes for patients. We describe the application of a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six EAC cell lines and two tissue-matched control lines. We built an automated high-content image analysis pipeline to identify compounds that selectively modified the phenotype of EAC cell lines. We further trained a machine-learning model to predict the mechanism of action of EAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes, including methotrexate and three other antimetabolites that are highly selective for EAC cell lines. We further identify a small number of hits from our diverse chemical library that show potent and selective activity for EAC cell lines and that do not cluster with the reference library of compounds, indicating they may be selectively targeting novel esophageal cancer biology. Overall, our results demonstrate that our EAC phenotypic screening platform can identify existing pharmacologic classes and novel compounds with selective activity for EAC cell phenotypes.

中文翻译:

食管腺癌的高内涵表型分析确定了选择性活性药物的药理学类别,用于重新利用和新药发现的化学起点。

食管腺癌 (EAC) 是一种高度异质性的疾病,以大规模基因组重排和拷贝数改变为主。这些特征阻碍了传统的靶向药物发现和个性化医疗策略,导致患者预后不佳。我们描述了应用高内涵细胞绘画分析来分析一组 6 个 EAC 细胞系和两个组织匹配的对照细胞系中 19,555 种化合物的表型反应。我们建立了一个自动化的高内涵图像分析管道来识别选择性修饰 EAC 细胞系表型的化合物。我们进一步训练了一个机器学习模型,以使用参考化合物库中的表型指纹来预测 EAC 选择性化合物的作用机制。我们确定了许多富含相似药理学类别的表型簇,包括甲氨蝶呤和其他三种对 EAC 细胞系具有高度选择性的抗代谢物。我们进一步从我们多样化的化学库中鉴定出少量的命中物,这些化合物对 EAC 细胞系显示出强效和选择性的活性,并且不与化合物参考库聚集在一起,表明它们可能选择性地靶向新型食管癌生物学。总体而言,我们的结果表明我们的 EAC 表型筛选平台可以识别现有的药理类别和对 EAC 细胞表型具有选择性活性的新型化合物。我们进一步从我们多样化的化学库中鉴定出少量的命中物,这些化合物对 EAC 细胞系显示出强效和选择性的活性,并且不与化合物参考库聚集在一起,表明它们可能选择性地靶向新型食管癌生物学。总体而言,我们的结果表明我们的 EAC 表型筛选平台可以识别现有的药理类别和对 EAC 细胞表型具有选择性活性的新型化合物。我们进一步从我们多样化的化学库中鉴定出少量的命中物,这些化合物对 EAC 细胞系显示出强效和选择性的活性,并且不与化合物参考库聚集在一起,表明它们可能选择性地靶向新型食管癌生物学。总体而言,我们的结果表明我们的 EAC 表型筛选平台可以识别现有的药理类别和对 EAC 细胞表型具有选择性活性的新型化合物。
更新日期:2020-05-22
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