Optimization of cell lines as tumour models by integrating multi-omics data. (2nd October 2016)

• Record Type: Journal Article
• Authors: Zhao, Ning; Liu, Yongjing; Wei, Yunzhen; Yan, Zichuang; Zhang, Qiang; Wu, Cheng; Chang, Zhiqiang; Xu, Yan
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 3(2017:May)
• Page Start: 515
• Page End: 529
• Publication Date: 2016-10-02
• Subjects: cell line -- multi-omics -- cancer in vitro model -- optimization; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw082
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Cell lines are widely used as in vitro models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines. Then, the ideal cell lines to use for experiments for eight types of cancers are found by combining the results with Gene Ontology functional similarity. After verification, the optimal cell lines have the same genomic characteristics as their homologous tumour samples. The contaminated cell lines identified in previous research are also determined to be unsuitable in vitro cancer models here. Moreover, our study suggests that some of the commonly used cell lines are not suitable cancer models. In summary, we provide a reference for ideal cell lines to use in in vitro experiments and contribute to improving the accuracy of future cancer research. Furthermore, this research provides a foundation for identifying more effective treatment strategies.