S2: How to choose the right cancer cell line for your research

How to choose the right cancer cell line for your research: a transcriptomes-guided approach

How to choose the right cancer cell line for your research, OpenTox USA 2018
PRESENTING AUTHOR: 

Xiaohui Fan

INSTITUTION / COMPANY : 

College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China

POSITION: 

Professor

AUTHOR(S): 

Xin Shao, Xuechun Chen, Jie Liao, Ni Ai, Yi Wang and Xiaohui Fan

ABSTRACT CONTENT / DETAILS: 

Cancer cell lines are widely used model systems in a variety of important research, from carcinogenesis elucidation to drug evaluation, due to obvious advantage on availability and expense. However, increasing evidence have indicated phenotypic responses vary between tissue samples and cancer cell lines, which are related to intrinsic heterogeneity, e.g. mutations, copy number variations, gene expressions, of tumors. Therefore, two linked questions rise, if a cancer can be studied by one cancer cell line and how to find this proper one. The accumulating data on transcriptional profiles of tumor samples are now available for systemic analysis, as well as for cancer cell lines through several well-known projects. Here a comprehensive comparison of tumors samples with cancer cell lines on genomic characteristics are carried out. First, high concordance on genomic signatures of cancer cell lines was observed among the NCI60 Project, the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project. We observed a few cancer cell lines transcriptionally correlated with the corresponding tissue samples across 22 types, however this does not hold for a large part of them. Thus, a computational method is proposed to prioritize cell lines that resemble the corresponding cancer type based on gene expression pattern. Validation experiments on predicted top cell line with the closest resemblance to a certain tumor group were conducted using FDA-approved drugs or phase III or IV drugs on breast, prostate and thyroid carcinoma. Additionally, a web tool is under development to allow the on-the-fly evaluation, which offers good insight for drug screening or mechanism research. Our study bridges the gap between tumors and cell lines and presents a helpful guide of selecting the most suitable cell lines model for cancer studies.