Abstract 2887: A comprehensive multidimensional bioinformatic analysis of representativity of a thousand cancer cell lines as model systems for cancer.
Authors: Ojala Kalle A. , Kilpinen Sami K. 
Published: August 2014

Cell lines are routinely used as model systems in cancer research, drug discovery and pharmacogenomics. An alarming number of cell lines have been reported not to represent their original cancer type due to cross contamination or due to having evolved or dedifferentiated extensively, or for reasons currently unknown. However, a systematic evaluation on the extent to which each cell line accurately represents the corresponding in vivo tumors has not been done.

The aim of this study was to comprehensively examine how well cancer cell lines resemble the uncultured primary tumors they supposedly are derived from. We started by examining over two thousand Affymetrix gene expression profiles from a thousand established cell lines and comparing them against gene expression data from 14 000 in vivo cancer samples, from the In Silico Transcriptomics (IST TM) database (www.medisapiens.com). We extended the analysis by including other data types, such as copy number- and genomic variation data. We compared nearly a thousand cell lines to in vivo data from various large-scale cancer genome profiling efforts, such as TCGA.

With all data types we established an overall resemblance between each cell line and various cancer types. This was done over the genome as a whole. We also looked at the individual genes responsible for the (dis)similarity in each case, to determine exactly which processes, target genes, or other phenomena of interest differed between the in vivo tumors and cell lines.

From gene expression, we determined that solid tumor cell lines, on a genome wide level, do no resemble the cancers they are supposed to model. Among the cancer types, only melanoma and colorectal cancer were among those whose cell line samples featured &gt;50% match rate. That means that the majority of solid tumors are such that cell lines derived from them resemble most not the (supposed) cancer of origin, but rather some other cancer type. Also, when zooming into the individual gene level, drastic differences between cell lines could be seen in the conformity of expression of genes that had observed unique expression levels in the cancer type in question.

According to copy number- and genomic variation data, cell lines resemble in vivo cancers better than according to gene expression data. However, as in the case of expression data, large individual differences between cell lines from the same cancer type could be seen.

Overall, the results clearly indicate the need to carefully screen the cell line to be used when planning an experiment. When selecting a cell line, it is imperative that it models the studied in vivo cancer populations as well as possible. Most importantly, it must have the same behavior in the pathway or process that the experiment aims to perturb, else the experiment's results are severely compromised in terms of in vitro to in vivo translation efficiency and accuracy.

Citation Format: Kalle A. Ojala, Sami K. Kilpinen. A comprehensive multidimensional bioinformatic analysis of representativity of a thousand cancer cell lines as model systems for cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2887. doi:10.1158/1538-7445.AM2013-2887