Abstract 2984: A comprehensive bioinformatic analysis of the representativity of 460 cancer cell lines as model systems for 88 different clinical cancer types
Authors: Ojala Kalle , Kilpinen Sami , Kallioniemi Olli 
Published: August 2012

Abstract
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. However, a systematic evaluation on the extent to which each cell line accurately represent the corresponding in vivo tumors has not been done across all types of tumors. The aim of this study was to comprehensively examine how well cancer cell lines resemble the uncultured primary tumors they supposedly are derived from and to identify genes and pathways which are particularly coherent or divergent. We examined 1268 Affymetrix gene expression profiles from 460 established cell lines and compared them against reference gene expression data from 13 961 in vivo cancer samples. These data originated from the In Silico Transcriptomics (IST TM) database (www.medisapiens.com), a more comprehensive version of the GeneSapiens database (Kilpinen et al. 2008), and were analyzed by the Alignment of Gene Expression Profiles (AGEP) method (Kilpinen et al. 2011). AGEP compares gene expression profiles from individual samples to groups of reference samples, and returns a gene-by-gene analysis of how much, as well as how uniquely, the sample resembles each reference group. We compared each of the 1268 cell line samples against 32 main cancer groups as well as 88 specific cancer reference groups. Thus, our comparison was unique in that it measured not only how well each of the cancer cell lines resembled the expected in vivo tumor tissue, but also how much they resembled all other tumor types. Our results showed that 38% of the cell lines had the highest similarity to the expected main cancer type. This percentage was lower when looking at the more specific groups (21%). Similar classification of individual clinical tumors to the average tumor classes with the AGEP method achieved 88% accuracy. A few cell line types had a high percentage of matches with the corresponding clinical tumors, with melanoma (95% match), T-ALL (74%) and colorectal cancer (79%) showing highest similarities. In contrast, pancreatic and ovarian tumor cell lines showed the lowest match percentages to the corresponding primary tumors (0%). We also studied the results on a gene-by-gene basis and identified those genes that were contributing to the match or discordant classification in each case. Interestingly, several genes were common to the majority of cell lines that clearly deviated from each of the primary tumor types (mostly cell cycle and cell adhesion genes), while other differences were more cancer-specific, e.g. MLANA and SILV genes, which were lost from some melanoma cell lines. These results will be useful for selecting optimal cancer model systems for each cancer type, and for each gene and pathway of interest.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2984. doi:1538-7445.AM2012-2984