Ethier Stephen P, Guest Stephen T, Garrett-Mayer Elizabeth, Armeson Kent, Wilson Robert C, Duchinski Kathryn, Couch Daniel, Gray Joe W, Kappler Christiana
Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC USA.
Present Address: Department of Biomedical Informatics, University of Michigan Medical School, Ann Arbor, MI USA.
NPJ Breast Cancer. 2020 Jul 21;6:30. doi: 10.1038/s41523-020-0173-z. eCollection 2020.
Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.
几年前,人类乳腺癌细胞系SUM小组得以组建,这些细胞系已被分发给全球数百个实验室。我们实验室和其他实验室已从这些细胞中开发出大量组学数据集。最近,我们对整个SUM细胞系小组以及MCF10A细胞、MCF - 7细胞和MCF - 7LTED细胞进行了全基因组规模的shRNA必需性筛选。这些基因必需性数据集使我们能够进行正交分析,从而将从传统基因组学平台获得的原本描述性的基因组数据功能化。为了让SUM细胞系的用户能够使用这些组学数据集,并允许用户挖掘这些数据集,我们开发了SUM乳腺癌细胞系知识库。该知识库提供了每个细胞系的来源信息,提供了细胞妥善保存的方案,并提供了一系列数据挖掘工具,可快速识别每个细胞系的癌基因特征、利用筛选命中和基因表达数据对KEGG通路进行富集、分析细胞系的蛋白质和磷酸化蛋白质表达,以及提供基因搜索工具和功能可药物化特征工具。最近,我们扩展了数据库,纳入了另外27种常用乳腺癌细胞系的基因组数据。因此,SLKBase为用户深入了解人类乳腺癌细胞系的生物学特性提供了帮助,这些特性可用于制定针对单个乳腺癌细胞系的逆向工程策略。