BMC Genomics 2007, 8:117 http://www.biomedcentral.com/1471-2164/8/117 two dimensions into three dimensions. Organotypic cul- ture imitates the in vivo phenotypic structure of epithelial tissue and has been used in different types of research, including breast [6,7], skin [8,9], cervical [10], and head and neck [11] cancer, as well as to study epithelial differ- entiation [12] and individual gene expression [10,13]. Few attempts have been made to quantitatively assess how closely cell lines actually model tissue. Most results from analyses such as clustering of expression profiles of cell lines and tissues simply conclude that cell lines resem- ble their tissue of origin [14]. For example, Sandberg et al. used the NCI 60 microarray data to compare cell lines in culture to their respective cancer microarray data sets. They performed a singular value decomposition (SVD) cluster analysis and generated a quantitative value termed the tissue similarity index (TSI), which denotes how well various cell lines still represent their tissue of origin [15]. With this in mind, we wanted to quantitatively assess how well cervical cancer cell lines commonly used to study cer- vical cancer actually model the disease, focusing on late- stage invasive cervical cancer. Therefore, the gene expres- sion profiles of nine different cervical cell lines were cor- related separately to the expression profiles of nine cervical cancer and three normal cervical biopsies. In addi- tion to their American Type Culture Collection (ATCC) recommended culture media, HeLa and SiHa cell lines were additionally cultured in different media as well as in an organotypic environment to assess how their correla- tion to tissue changes in different culture environments. Conditions with higher correlations indicate better mod- els of cervical tissue. In order to move beyond a simple global comparison, the correlations of each condition were also compared to cervical tissue at individual path- way-specific levels. This information provides a more detailed view of our ability to model cancer using cell lines. Results and discussion Differential gene expression between cervical cancer and normal cervix Before comparing cervical cancer transcriptional profiles with cell line profiles, we first determined that the biop- sies used in the analysis accurately represented cervical tis- sue and invasive cervical cancer. We tested the differentially expressed genes between normal and cancer- ous biopsies by first examining transcriptional changes in light of presumed biological mechanisms of cancer biol- ogy; we then compared the differentially expressed genes with regard to previous cervical cancer research. We first used presumed biological mechanisms of cancer biology to assess our differentially expressed genes. First, groups of genes that represented common cancer path- ways were checked for correct expression changes [16,17]. A t-test using the Benjamini-Hochberg multiple hypothesis correction (p < 0.01) and a 2-fold minimum expression change was used to identify 140 genes that were differen- tially expressed between cervical cancer and normal cer- vix. The 140 genes [see Additional file 1] were involved in many expected pathways involved in tumorigenesis; the top 16 up- and down-regulated genes can be found in Table 1. Individual genes, as well as sets of pathways including cell proliferation, cell-cell adhesion, and cell differentiation, were identified as correctly changing expression according to the general biology of cancer. A second analysis provided further evidence that the expression profiles in these cervical cancer biopsies were consistent with previous observations in the literature and therefore suitable for further detailed analysis. Approxi- mately 650 differentially expressed genes identified in the literature, derived from small scale microarray studies [18- 22], differential RNA display [23-27], or single gene stud- ies [28,29], were compared to the 140 genes identified in our study (Figure 1). Nineteen genes were observed in both data sets (*) [see Additional file 1]; 30 genes in the same sequence family (homologs) were also identified (**) [see Additional file 1]. Despite the apparent small overlap in large scale datasets, the overlap is significant (p < 0.001, hypergeometric distribution) and indicates that our tissue biopsies are representative of the literature and can be used for further analysis. A transcriptional comparison of cervical tissue to cell lines was performed using hierarchical clustering (Figure 2A) and SVD (Figure 2B) [30]. With few exceptions, all repli- cates were seen to cluster together, indicating high data quality. The few exceptions were likely due to small changes in gene expression, for example the organotypic and organotypic control (without fibroblasts) samples only exhibit small numbers of gene changes that are over- shadowed when subjected to large scale clustering. Hierar- chical clustering separated cell lines from tissue but did not provide information on how well cell lines model cer- vical cancer. A more quantitative assessment was used next to determine which cell line and growth condition most resembled cervical cancer at a pathway level. We recognize that the particular cervical cancer biopsy samples that we used in this study do not represent the diversity of all possible cervical cancer samples. However, the comparisons described above provide strong evidence that the expression profiles collected accurately represent both cervical cancer and normal cervix. Once the reliabil- ity of the expression profiles was established, they could be used to compare the cell line expression profiles to identify which of the tested cell lines is a better model of cervical tissue. Page 2 of 13 (page number not for citation purposes)