Pathway-specific differences between tumor cell lines and normal and tumor tissue cells

  • Adam Ertel1,

    Affiliated with

    • Arun Verghese1,

      Affiliated with

      • Stephen W Byers2,

        Affiliated with

        • Michael Ochs3 and

          Affiliated with

          • Aydin Tozeren1Email author

            Affiliated with

            Molecular Cancer20065:55

            DOI: 10.1186/1476-4598-5-55

            Received: 23 January 2006

            Accepted: 02 November 2006

            Published: 02 November 2006

            Abstract

            Background

            Cell lines are used in experimental investigation of cancer but their capacity to represent tumor cells has yet to be quantified. The aim of the study was to identify significant alterations in pathway usage in cell lines in comparison with normal and tumor tissue.

            Methods

            This study utilized a pathway-specific enrichment analysis of publicly accessible microarray data and quantified the gene expression differences between cell lines, tumor, and normal tissue cells for six different tissue types. KEGG pathways that are significantly different between cell lines and tumors, cell lines and normal tissues and tumor and normal tissue were identified through enrichment tests on gene lists obtained using Significance Analysis of Microarrays (SAM).

            Results

            Cellular pathways that were significantly upregulated in cell lines compared to tumor cells and normal cells of the same tissue type included ATP synthesis, cell communication, cell cycle, oxidative phosphorylation, purine, pyrimidine and pyruvate metabolism, and proteasome. Results on metabolic pathways suggested an increase in the velocity nucleotide metabolism and RNA production. Pathways that were downregulated in cell lines compared to tumor and normal tissue included cell communication, cell adhesion molecules (CAMs), and ECM-receptor interaction. Only a fraction of the significantly altered genes in tumor-to-normal comparison had similar expressions in cancer cell lines and tumor cells. These genes were tissue-specific and were distributed sparsely among multiple pathways.

            Conclusion

            Significantly altered genes in tumors compared to normal tissue were largely tissue specific. Among these genes downregulation was a major trend. In contrast, cell lines contained large sets of significantly upregulated genes that were common to multiple tissue types. Pathway upregulation in cell lines was most pronounced over metabolic pathways including cell nucleotide metabolism and oxidative phosphorylation. Signaling pathways involved in adhesion and communication of cultured cancer cells were downregulated. The three way pathways comparison presented in this study brings light into the differences in the use of cellular pathways by tumor cells and cancer cell lines.

            Background

            Cell lines derived from tumors and tissues comprise the most frequently used living systems in research on cell biology. Limitations on the abundance of tissue samples necessitate the use of animal models and cell lines in the studies of tumor-related phenomena. Cancer cell lines have been extensively used in screening studies involving drug sensitivity and effectiveness of anti cancer drugs [1]. Other studies using cultured cells aimed at the determination of the phenotypic properties of cancer cells such as proliferation rates, migration capacity and ability to induce angiogenesis [2]. In other studies, human cultured cells were used to create tumors in the mice models [3].

            Whether measurements on cell lines provide information about the metastatic behavior of cancer cells in vivo is currently under investigation. Unsupervised classification of gene expression profiles of cancer tissue and cancer cell lines result in separate clustering of cancer cell lines from tissue cells for both solid tumors and blood cancers [4]. Sets of genes responsible for differences between solid tumors and cell lines in their response to anti cancer drugs have been identified in the Serial Analysis of Gene Expression (SAGE) Database [5]. Most optimal cell lines to represent given tumor tissue types were determined with the use of a quantitative tissue similarity index [6]. Results were striking: only 34 of the 60 cell lines used in the analysis were most similar to the tumor types from which they were derived. The study provided valuable information about selection of most appropriate cell lines in pharmaceutical screening programs and other cancer research. In a more recent work Sandberg et al. [7] identified those gene function groups for which cell lines differed most significantly from tumors based on meta-analysis using Gene Ontology (GO). Genes involved in cell-cycle progression, protein processing and protein turnover as well as genes involved in metabolic pathways were found to be upregulated (an increase in expression reflected by mRNA transcript levels) in cell lines, whereas genes for cell adhesion molecules and membrane signaling proteins in cell lines were downregulated (a decrease in expression reflected by mRNA transcript levels) in comparison with tumors [7]. To build on this approach, functional enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [8, 9] can be used to illustrate causal relationships between genes (gene products). While GO is organized into hierarchical annotations in the context of normal cellular function, the KEGG database organizes the genes (gene products) into pathway reaction maps and functional complexes, including some disease-specific pathways.

            The present study focuses on pathway specific differences in gene expression patterns between cancer cell lines and tumors as well as cancer cell lines and normal tissue and tumors and normal tissue. Extension of microarray data analysis to three-way comparison allows for the identification of gene expression patterns unique to cell lines. Such patterns might have arisen due to factors related to the cell culture environment. We used publicly accessible microarray data available for normal and cancer tissues and associated NCI60 cell lines in a pathway-specific quantitative analysis of gene expression profiles. A dominant theme that emerged from our analysis was that pathway-specific gene expression differences between cancer cell lines and cancer tissue were similar both in magnitude and direction to corresponding differences between cell lines and normal tissue cells. Cell cycle associated differences between normal and tumor tissue were amplified in cell lines. Results on metabolic pathways suggested an increase in the velocity of RNA and DNA production and increased flow of metabolites in the oxidative phosphorylation pathway. On the other hand, a small fraction of significantly altered genes in tumor-to-normal comparison had similar expressions in cancer cell lines and tumor cells. These genes were tissue-specific and were positioned sparsely along multiple pathways.

            Materials and methods

            Microarray datasets

            Microarray datasets used in this study consisted of the publicly accessible gene expression profile dataset for NCI60 cell lines [10] and similar data for a panel of tumors and normal tissue samples [11]. This dataset contains measurements obtained using the Affymetrix Hu6800 arrays (Table 1). The tissue types considered in this study (breast, CNS, colon, prostate, ovary, and renal tissue) were restricted to those where the microarray results were available for normal and tumor tissue as well as corresponding cell lines. MDA-MB-435 and MDN cell line samples were excluded from these datasets because their tissue of origin, previously thought to be breast, is now suspect [6].
            Table 1

            Microarray data presented by Staunton et al. [10] and Ramaswamy et al. [11] used in the three way comparison of gene expression patterns in cell lines, tumors and normal tissue.

             

            Cell lines*

            Normal tissue**

            Tumor tissue**

            Array

            Breast

            6

            5

            10

            Affymetrix

            CNS

            6

            5

            20

            Affymetrix

            Colon

            7

            10

            9

            Affymetrix

            Ovary

            6

            4

            9

            Affymetrix

            Prostate

            2

            7

            7

            Affymetrix

            Renal

            8

            11

            8

            Affymetrix

            Sum

            35

            42

            63

             

            * Data obtained from Staunton et al [10]

            ** Data obtained from Ramaswamy et al [11]

            Quality of probe set annotations

            Quality of the Hu6800 GeneChip annotation was assessed because this platform is several versions away from current human microarrays. While the Hu6800 design is old and probe designs have since been greatly improved, the quality of probe annotation is maintained through regular updates by Affymetrix. The annotations used in this study are based on a July 12th 2006 update of Affymetrix annotations according to the March 2006 (NCBI Build 36.1) version of the human genome. A comparison was done between gene annotations for the Hu6800 GeneChip obtained from Webgestalt (web-based gene set analysis toolkit) [12] and from the Affymetrix website on August 7th, 2006. Out of the 7129 probesets on the chip, 6058 had the same annotations from both Webgestalt and Affymetrix. Of the remaining 1071 probesets, 692 were not annotated, 288 were annotated in the Affymetrix list but not in Webgestalt, 28 were annotated in Webgestalt but not Affymetrix, and 63 (~1%) probesets had conflicting annotations in Webgestalt and Affymetrix. Only 42 (~0.70% of all genes) genes belonging to any known KEGG pathway had discrepancies between Webgestalt and Affymetrix. While there were very few probes with discrepant annotations in any given pathway, this list of 42 probes was enriched for Antigen processing and presentation, Natural killer cell mediated cytotoxicity, Cell adhesion molecules (CAMs), Type I diabetes mellitus, and SNARE interactions in vesicular transport pathways. A review of this probe list revealed that discrepancies were merely due to updates and minor revisions to the official gene symbol that may reflect increased understanding of these genes functions. Genes associated with KEGG pathways represent a subset of well-studied and sequenced genes. Overall, the probe sets of genes belonging to KEGG pathways have well established and reliable annotations on the Hu6800 GeneChip. Annotations retrieved from Webgestalt were used for the remainder of the analysis.

            Normalization

            Gene expression data was normalized for each tissue type by computing the Robust Multichip Average (RMA) [13, 14] directly from the Affymetrix .CEL files for cell line, tumor, and normal samples. RMA consists of three steps: a background adjustment, quantile normalization and finally summarization. Quantile normalization method utilizes data from all arrays in an experiment in order to form the normalization relation [13, 14] RMA generated expression measure is on the log base 2 scale.

            Normalized data was generated using the Bioconductor (package for R) [15] implementation of RMA. R 2.3.1 [16] was first installed on an Intel Xeon machine running a Windows Professional Operating System. The Biobase 1.10.1 (dated 20 June 2006) package which contains the base functions for Bioconductor was installed by accessing the getBioC.R script directly from the Bioconductor website [17]. The "readaffy" command was used to load all .CEL files for a single tissue type. The RMA expression measures for each tissue type were computed using the "rma" function with default settings, including the Perfect Match Adjustment Method setting as Perfect Match Only so that expression signal calculation was based upon the perfect match values from each probe set as described in [13]. The RMA computed expression values were written out to a comma separated text file.

            The resulting expression values for each sample were checked against the average expression across cell line, tumor, and normal populations by calculating their correlation coefficients. Two anomalous samples (one normal tissue sample from colon and one tumor sample from prostate) were identified having correlations well outside the remaining population (R < 0.9) and removed; RMA for those tissues was recomputed excluding the suspect samples. The RMA generated gene expression data for the Affymetrix chips was clustered using a hierarchical clustering algorithm with Pearson correlation coefficient as the distance metric using average linkage using TIGR MeV Version 3.1. For each of the six tissues under consideration, the cell line samples clustered together in a single branch distinct from the branches containing tumor and normal tissue samples. This result confirmed that all the cell line samples have characteristics that are significantly different from the tumor tissue.

            Significance analysis for gene expression

            The Significance Analysis of Microarray Data (SAM) implementation [18] in the TIGR MeV Version 3.1 software [19] was used to identify those genes that had statistically significant differences in expression between tumor samples, cell lines, and normal tissue. SAM analysis was performed using all default parameters and adjusting the delta-value to obtain a maximum number of genes while maintaining a conservative false discovery rate of zero. A list of significant genes was identified for cell line-tumor cell line-normal and normal-tumor combinations for each of the six tissue types. When the set of significant genes was deleted from the microarray data, clustering analysis based on the remaining genes interspersed microarray datasets for cell lines with corresponding datasets for tissue.

            Identification of significantly altered pathways

            Two different methods were used for identifying significantly altered pathways. First, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [8, 9] were identified as significantly altered by performing a functional enrichment analysis on genes identified as significant by SAM analysis. The analysis was carried out using the Webgestalt system [12], comparing significant genes obtained by SAM against all genes in the Affymetrix HU6800 array, for each comparison under study. A p-value for pathway enrichment was obtained using the hypergeometiric test documented in [12]. Four different p-value cutoffs (0.001, 0.01, 0.05 and 0.1) were used in order to assess the dependence of the significant pathway identification on p value. This process was also applied to subsets of significant genes, for example, the intersection of significant genes from (CL - N) and (T - N).

            A second method was applied to KEGG pathway genes in order to detect changes that were not apparent on a single-gene basis. For this method, KEGG pathways were deemed significantly altered if at least 80% of the genes for that pathway contained on the HU6800 array were shifted in the same direction for a given comparison. For each of the six tissues, three-way comparisons were performed between averaged cell line, tumor, and normal samples. Similar examples of how significant changes in functional pathways are revealed by a population of related genes that are not evident from observations of a single gene are found in [20, 21].

            Results

            Significant genes

            This article presents a pathway-specific analysis of gene expression profile differences between cancer cell lines and normal and tumor tissue. The microarray data used in the three-way comparison of gene expression profiles covered breast, CNS, colon, ovary, prostate, and renal tissue (Table 1). Gene expression profiles of cancer cell lines derived from this data clustered together in a branch exclusive of tumor and normal tissue (3) within each tissue type and for all tissue types combined. Lists of significant genes (SAM genes) were determined using SAM analysis from the microarray data pairs of cell lines and tumors (CL - T), cell lines and normal tissue (CL - N) and tumor and normal tissue (T - N) for each of the six tissue types under consideration. Table 2 provides a summary of the numbers of significant genes for the three-way comparison. The table shows that the significant genes for (CL - T) and (CL - N) pairs ranged in numbers from low hundreds to thousands, depending on the tissue type. Significant genes for (T - N) pairs were lower in number than those for (CL - T) and (CL - N) pairs in all six tissues under consideration. Downregulation of significant genes was a trend in (T - N) comparisons while a majority of SAM genes were upregulated in cell lines compared to tumor and normal (CL - T; CL - N). Moreover, an overwhelming majority of the SAM genes in (T - N) comparison were not found as significantly altered in (CL - T) comparisons. The gene set (T - N) - (T - N ∩ CL - T) listed in Table 2 shows a vast majority of SAM genes in (T - N) comparison are not significantly altered in expression in (CL - T) comparison, suggesting that cancer cell lines may be good representation models for tumor cells in gene expression profile studies. On the other hand, the set (CL - T) contains many more genes than the (T - N) comparison, revealing that cancer cell lines have a large number of genes that are significantly altered in expression compared to tumor cells. The same trend holds true when cell lines are compared with normal tissue cells. These results indicate that global gene expression profiles of cultured cancer cell lines contain significantly different gene expression patterns compared to the corresponding profiles for normal and tumor tissue.
            Table 2

            Number of significant genes identified by SAM in comparisons of cell line-to-tumor (CL - T), cell line-to-normal (CL - N), and tumor-to-normal (T - N) comparisons.

            Comparison

            Breast

            CNS

            Colon

            Ovary

            Prostate

            Renal

            Common Genes

            CL-T (upregulated %)

            572 (66%)

            576 (86%)

            503 (62%)

            603 (41%)

            190 (94%)

            1637 (44%)

            51

            CL-N (upregulated %)

            269 (61%)

            560 (72%)

            983 (63%)

            225 (62%)

            469 (72%)

            2047 (45%)

            29

            T-N (upregulated %)

            243 (10%)

            153 (61%)

            166 (45%)

            94 (14%)

            30 (0%)

            65 (0%)

            0

            CL-T ∩ CL-N

            132

            328

            431

            145

            164

            1481

            16

            (T-N) - (T-N ∩ CL-T)

            236

            138

            143

            83

            26

            43

            0

            (T-N ∩ CL-N) - (T-N ∩ CL-N ∩ CL-T)

            31

            43

            64

            26

            9

            28

            0

            The percentage of upregulated genes is shown in parentheses for cell line-to-tumor (CL - T), cell line-to-normal (CL - N), and tumor-to-normal (T - N) comparisons. The intersection (CL - T ∩ CL - N) contains genes that were altered in cell lines compared to both normal and tumor tissue, representing expression profiles that are specific to cell lines. The set (T - N) - (T - N ∩ CL - T) contains SAM genes in the (T - N) comparisons that are not significantly altered in (CL - T) comparisons. The set (T - N ∩ CL - N) - (T - N ∩ CL - N ∩ CL - T) contains genes significantly altered in both cell lines and tumors relative to normal tissue (T - N; CL - N) but with no significant difference between cell lines and tumor tissue (CL - T); tumor-specific expression profiles that may be adequately modeled by cell lines.

            SAM genes common in (CL - T) comparisons for all six tissues were all upregulated. Table 3 shows the list of 51 significant genes in (CL - T) comparisons that are common to the six tissue types under consideration. In this list of 51 genes, the overrepresented KEGG pathways with a p-value cutoff of 0.01 are cell cycle, oxidative phosphorylation, proteasome, pyrimidine metabolism, and ubiquitin mediated proteolysis. The 18 genes shown in italics also appeared among 29 significant genes that were common to all (CL - N) comparisons. The 18 genes common to both lists again showed overrepresentation of cell cycle and ubiquitin mediated proteolysis pathways under a p-value cutoff of 0.01. Moreover these eighteen genes showed the same trend of upregulation in cell line-to-tumor (CL - T) and cell line-to-normal (CL - N) comparisons. No significant genes identified in the (T - N) comparisons were common to all six tissues.
            Table 3

            SAM genes that were upregulated in cell lines compared to tumors in all the 6 tissues considered in the study (CL - T).

            Gene Symbol

            Gene Name

            Kegg Pathway(s)

            ATP5B

            ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide

            Oxidative phosphorylation, ATP synthesis

            ATP5G3

            ATP synthase, H+ transporting, mitochondrial F0 complex, subunit C3 (subunit 9)

            ATP synthesis, Oxidative phosphorylation

            C1QBP

            complement component 1, q subcomponent binding protein

            (Immune Response)

            CBX3

            chromobox homolog 3 (HP1 gamma homolog, Drosophila)

            N/A

            CCNB1

            cyclin B1

            Cell cycle

            CCT5

            chaperonin containing TCP1, subunit 5 (epsilon)

            N/A

            CDC20

            CDC20 cell division cycle 20 homolog (S. cerevisiae)

            Ubiquitin mediated proteolysis, Cell cycle

            CDKN3

            cyclin-dependent kinase inhibitor 3 (CDK2-associated dual specificity phosphatase)

            N/A

            CHAF1A

            chromatin assembly factor 1, subunit A (p150)

            N/A

            CKAP1

            cytoskeleton associated protein 1

            N/A

            CKS1B

            CDC28 protein kinase regulatory subunit 1B

            N/A

            CKS2

            CDC28 protein kinase regulatory subunit 2

            N/A

            COX8A

            cytochrome c oxidase subunit 8A (ubiquitous)

            Oxidative phosphorylation

            CYC1

            cytochrome c-1

            Oxidative phosphorylation

            DNMT1

            DNA (cytosine-5-)-methyltransferase 1

            Methionine metabolism

            DYNLL1

            dynein, light chain, LC8-type 1

            N/A

            EBNA1BP2

            EBNA1 binding protein 2

            N/A

            HMGB2

            high-mobility group box 2

            N/A

            KIAA0101

            KIAA0101

            N/A

            KIF2C

            kinesin family member 2C

            N/A

            LMNB2

            lamin B2

            Cell communication

            MCM3

            MCM3 minichromosome maintenance deficient 3 (S. cerevisiae)

            Cell cycle

            MCM4

            MCM4 minichromosome maintenance deficient 4 (S. cerevisiae)

            Cell cycle

            MCM7

            MCM7 minichromosome maintenance deficient 7 (S. cerevisiae)

            Cell cycle

            MRPL12

            mitochondrial ribosomal protein L12

            N/A

            NDUFS8

            NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa (NADH-coenzyme Q reductase)

            Oxidative phosphorylation

            PAICS

            phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole succinocarboxamide synthetase

            Purine metabolism

            PCNA

            proliferating cell nuclear antigen

            Cell cycle

            POLR2G

            polymerase (RNA) II (DNA directed) polypeptide G

            Purine metabolism, RNA polymerase, Pyrimidine metabolism

            PRMT1

            protein arginine methyltransferase 1

            Selenoamino acid metabolism, Nitrobenzene degradation, Aminophosphonate metabolism, Tryptophan metabolism, Histidine metabolism, Androgen and estrogen metabolism, Tyrosine metabolism

            PSMA1

            proteasome (prosome, macropain) subunit, alpha type, 1

            Proteasome

            PSMB2

            proteasome (prosome, macropain) subunit, beta type, 2

            Proteasome

            PSMB5

            proteasome (prosome, macropain) subunit, beta type, 5

            Proteasome

            PSMB6

            proteasome (prosome, macropain) subunit, beta type, 6

            Proteasome

            PSMD14

            proteasome (prosome, macropain) 26S subunit, non-ATPase, 14

            Proteasome

            RANBP1

            RAN binding protein 1

            N/A

            SFRS9

            splicing factor, arginine/serine-rich 9

            N/A

            SNRPA

            small nuclear ribonucleoprotein polypeptide A

            N/A

            SNRPB

            small nuclear ribonucleoprotein polypeptides B and B1

            N/A

            SNRPC

            small nuclear ribonucleoprotein polypeptide C

            N/A

            SNRPD2

            small nuclear ribonucleoprotein D2 polypeptide 16.5kDa

            N/A

            SNRPD3

            small nuclear ribonucleoprotein D3 polypeptide 18kDa

            N/A

            SNRPE

            small nuclear ribonucleoprotein polypeptide E

            N/A

            SNRPF

            small nuclear ribonucleoprotein polypeptide F

            N/A

            SNRPG

            small nuclear ribonucleoprotein polypeptide G

            N/A

            TCEB1

            transcription elongation factor B (SIII), polypeptide 1 (15kDa, elongin C)

            Ubiquitin mediated proteolysis

            TUBG1

            tubulin, gamma 1

            N/A

            TXNRD1

            thioredoxin reductase 1

            Pyrimidine metabolism

            TYMS

            thymidylate synthetase

            Pyrimidine metabolism, One carbon pool by folate

            UBE2C

            ubiquitin-conjugating enzyme E2C

            Ubiquitin mediated proteolysis

            UBE2S

            ubiquitin-conjugating enzyme E2S

            N/A

            SAM genes shown in italic also belonged to cell lines and normal tissue comparison. There were no downregulated genes common to all tissue types in cell line-tumor (CL - T) comparisons.

            Significant pathways

            KEGG pathways whose gene expression profiles differed significantly in (CL - T), (CL - N), and (T - N) pair comparisons were identified using a hypergeometric test as described in the Methods section. Figure 1 shows the most frequently observed KEGG pathways with altered gene expression profiles for (CL - T), (CL - N) and (T - N) pairs for breast, CNS, colon, ovary, prostate, and renal tissue. Cell cycle and a number of metabolic and transcription-related pathways emerged as significantly altered in almost all (CL - T) and (CL - N) comparison pairs. Cellular pathways that were significantly altered in cell lines compared to tumor cells and normal cells of the same tissue type in at least two tissue types included cell cycle, oxidative phosphorylation, purine and pyrimidine metabolism, proteasome, ribosome, and RNA polymerase. The most striking difference between cell lines and tumor tissue in Figure 1 is in the oxidative phosphorylation pathway. Oxidative phoshorylation is the final stage of cellular metabolism following glycolysis and the citric acid cycles. The loss of cancer cell dependence on oxidative metabolism may be an important factor in the development of tumors [22]. ECM-receptor interaction, which is thought to affect cell migration, appeared with more subtle differences between all comparisons (CL - T), (CL - N), and (T - N). This may reflect more tissue-specific composition of the migration machinery utilized in tumor cell invasion.
            http://static-content.springer.com/image/art%3A10.1186%2F1476-4598-5-55/MediaObjects/12943_2006_Article_187_Fig1_HTML.jpg
            Figure 1

            KEGG pathways identified to be significantly altered in cell lines and tumors (CL - T), cell lines and normal tissue (CL - N), and tumor and normal tissue (T - N) comparisons. The term frequency shown in the figure is defined as the ratio of tissue types for which a pathway identified as significantly altered to the total number of tissue types (6). KEGG pathways were identified as significantly altered by using a hypergeometric test with a p-value cutoff. The minimum number of SAM genes in each significantly altered pathway has been set to two. The error bars indicate the standard deviation of frequency for different p- value cutoffs (p = 0.001, 0.01, 0.05 and 0.1).

            Next we used pathway-specific analysis to identify up- and downregulation patterns in three-way comparisons. Figure 2 provides module maps showing the direction of regulation of KEGG pathways that were identified to be significantly different in at least 2 tissue types in (CL - T) comparisons. The pathways presented in Figure 2a were deemed significantly altered if the average gene expression between two conditions was altered in the same direction for at least 80% of the genes in the pathway. This criterion captured seven of the significant pathways from Figure 1 along with 23 additional pathways. The Figure 2a indicates a high degree of correlation in the direction of Aminoacyl-tRNA synthetases, Monoterpenoid biosynthesis, Proteosome, and RNA polymerase pathway shifts in cell line – tumor and cell line – normal comparisons. Many more pathways appear to be significantly altered in the module map if the criterion for percentage of genes altered in the same direction is reduced from 80% to 70% (Figure 2b). These two module maps illustrate how extensive the pathway alterations are in cell lines compared to tumor and normal tissue (CL - T; CL - N).
            http://static-content.springer.com/image/art%3A10.1186%2F1476-4598-5-55/MediaObjects/12943_2006_Article_187_Fig2_HTML.jpg
            Figure 2

            A module map showing the direction of regulation of cellular pathways that were identified as significantly altered in cell lines compared to tumor tissue (CL - T) in at least 2 of the 6 tissues considered in this study. In (a), a pathway is deemed significantly altered if at least 80% of the genes in the pathway are shifted in a common direction. In (b), a pathway is deemed significantly altered if at least 70% of the genes in the pathway are shifted in a common direction. The color red indicates an upregulated pathway, the color green indicates a downregulated pathway, and the color black indicates that the pathway was not significant in that comparison.

            The pathway-specific results on cell line-tumor microarray data comparisons presented in this study are in agreement with the results recently published by Sandberg et al. [7] on the gene expressions patterns associated with gene ontology categories in cell lines and tumors. These authors have used the same microarray databases used in our study and reached highly similar conclusions on the directions of difference between cell lines and tumors along equivalent pathways and gene ontology categories. Table 4 provides a comparison of the KEGG pathways (from Figure 1) against the most related gene ontology categories from Sandberg et al. [7]. KEGG pathways for complement and coagulation cascade and phenylalanine metabolism passed the significance criteria based on the (T - N) comparison in our study but we could not located the corresponding GO categories in the Sandberg et al. study on cell lines vs. tumor tissue.
            Table 4

            Comparison of results obtained from this study with those based on Gene Ontology Processes by Sandberg et al. [7]

            KEGG Pathway

            Related GO category

            Direction of regulation in cell lines with respect to tumors

              

            This study

            Gene Ontology Study [7]

            ATP synthesis

            ATP synthesis coupled proton transport (GO:0015986)

            Cell cycle

            Cell cycle (GO:0007049)

            One carbon pool by folate

            Nucleotide biosynthesis (GO:0009165)

            Oxidative phosphorylation

            Oxidative phosphorylation (GO:0006119)

            Proteasome

            Ubiquitin-dependent protein catabolism (GO:0006511); Modification-dependent protein catabolism (GO:0019941)

            Purine metabolism

            Purine nucleotide metabolism (GO:0006163)

            Pyrimidine metabolism

            Nucleobase, nucleoside, nucleotide and nucleic acid metabolism (GO:0006139)

            Ribosome

            Protein biosynthesis (GO:0006412)

            RNA polymerase

            Nucleobase, nucleoside, nucleotide and nucleic acid metabolism (GO:0006139)

            Cell adhesion molecules (CAMs)

            Cell adhesion (GO:0007155)

            Cell communication

            Cell adhesion (GO:0007155)

            Complement and coagulation cascade

            Complement activation (GO:0006956)

            N/A

            ECM-receptor interaction

            Cell adhesion (GO:0007155)

            Focal Adhesion

            Cell adhesion (GO:0007155)

            Phenylalanine metabolism

            Phenol metabolism (GO:0018958)

            N/A

            The symbol [↑] indicates upregulation in cell lines with respect to tumors and [↓] indicates downregulation in cell lines with respect to tumors (CL - T).

            Gene expression changes in metabolic pathways

            Metabolic pathways such as oxidative phosphorylation, pyrimidine and purine metabolism account for some of the most significant alterations among the three-way comparisons. The alterations in the oxidative phosphorylation pathway were discussed briefly in the previous section. Purine and pyrimidine metabolic pathways synthesize the nucleotides that make RNA and DNA. All of the nitrogens in the purine and pyrimidine bases (as well as some of the carbons) are derived from amino acids glutamine, aspartic acid, and glycine, whereas the ribose and deoxyribose sugars are derived from glucose. Figure 3 shows the KEGG diagram of pyrimidine metabolism with the expression values (averaged over six tissues) overlaid for (CL - T) (3a), (CL - N) (3b), and (T - N) (3c) comparisons. This KEGG pathway is altered with upregulated expression for a majority of genes in cell lines and tumors when compared to normal tissue. The increased levels of pyrimidine metabolism gene expression are most pronounced in cell lines (Fig 3a). A predicted increase in the velocity of RNA and DNA base production in cell lines is consistent with trends of increasing rates of cell division observed in cell cultures [23]. The observation that nucleotide metabolism accelerates in cancer has been discussed in the literature. Development of pyrimidine and purine analogs as potential antineoplastic agents evolved from an early presumption that cancer is a disease of uncontrolled growth and nucleic acids are involved in growth control [24].
            http://static-content.springer.com/image/art%3A10.1186%2F1476-4598-5-55/MediaObjects/12943_2006_Article_187_Fig3_HTML.jpg
            Figure 3

            KEGG pyrimidine metabolism diagram. Gene expression shifts are projected from comparisons of cell line-to-tumor (CL - T), cell line-to-normal (CL - N), and tumor-to-normal (T - N) comparisons averaged over all six tissues. The color red indicates upregulated genes, green indicates downregulated genes and grey indicates the genes that are not on the microarray. Uncolored genes are not in the organism-specific pathway for Homo sapiens. A gene is identified as upregulated (downregulated) if its gene expression value averaged over 6 tissue types were greater (or lesser) in cell lines compared to tumor or normal tissue. Colored genes with white lettering were also identified with SAM in at least two tissues.

            Gene expression pattern changes in cell cycle

            In contrast to the pyrimidine metabolism pathway discussed above, the gene expression alterations along the cell cycle pathway appear to be more complex and tissue-specific. Figure 4a shows the KEGG diagram of cell division cycle with genes specific to Homo sapiens shaded light green. Figure 4b shows the extent of alteration of these genes in the three-way comparisons for each tissue type with a graded color map representing maximum upregulation in red and maximum downregulation in green.
            http://static-content.springer.com/image/art%3A10.1186%2F1476-4598-5-55/MediaObjects/12943_2006_Article_187_Fig4_HTML.jpg
            Figure 4

            KEGG cell cycle diagram. Genes are shown (a) in a pathway map with genes specific to homo-sapiens shaded light green and (b) tabulated with a color map showing average gene expression shifts for samples within the six tissues. Red indicates a positive change and green indicates a negative change in average RMA value for the respective cell line-tumor (CL - T), cell line-normal (CL - N), and tumor-normal (T - N) comparisons, with color scale limits set to -2 and +2.

            Perhaps the most obvious feature of this color map is how subtle the changes in (T - N) comparisons are relative to (CL - T) and (CL - N) comparisons in all six tissues under consideration. Genes such as CCNA2, CCNB1, CDC20, CDK4, and MDM2 through MDM7 are consistently upregulated in cell lines compared to tumors and normal tissue. On the other hand, genes such as CCND1, CCND3, CDC16, and CDK2 do not exhibit quickly a recognizable pattern. A multitude of gene expression profiles in cell cycle may point towards the same disease process.

            SAM genes common to cancer cell lines and tumor cells

            It is of interest to cell biologists to identify similarities between cancer cell lines and tumors. Towards that goal, one can determine the list of SAM genes belonging to both (T - N) and (CL - N) comparisons but do not appear to be significant in (CL - T) comparison. This list is shown in Table 5 for all six tissues under consideration. Table 5 gives an indication of the size of the SAM gene subsets that are preserved and commonly regulated in cell lines and tumors but not in normal tissues. The list of genes in Table 5 comprises mostly downregulated genes for breast, colon, ovary, prostate, and renal tissue, with CNS as the only exception. When these lists were projected onto KEGG pathways, the probability of enrichment score could not be used as an indication that the pathways are similar because KEGG pathways that include genes from these lists also included SAM genes from (CL - T) comparisons. In conclusion, it was not possible to assert pathway similarity with statistical confidence using this analysis.
            Table 5

            Genes that were identified by SAM in both (T - N) and (CL - N) comparisons but not in (CL - T) comparisons; (T - N ∩ CL - N) – (T - N ∩ CL - N ∩ CL - T).

            Breast

            CNS

            Colon

            Ovary

            Prostate

            Renal

            UP

            DOWN

            UP

            DOWN

            UP

            DOWN

            UP

            DOWN

            UP

            DOWN

            UP

            DOWN

            GALNS

            APP

            ACTB

            ATP5O

            ARD1A

            ADH1B

            MCM2

            ACTG2

             

            APOD

             

            ADH1B

            GP9

            AQP1

            CPSF1

            COX7A1

            ARPC1B

            BRD2

             

            AEBP1

             

            CCND2

             

            ALDH4A1

            LCAT

            ARHGEF6

            DDX11

            CTNNB1

            BCAT1

            C7

             

            C7

             

            CXCL12

             

            ANPEP

            RND2

            ATP6V1B2

            ECE1

            GYPE

            CCND1

            CA2

             

            CEBPD

             

            KCNMB1

             

            ASS

             

            BRD2

            EEF1A1

            ITGB7

            CPNE1

            CALCOCO2

             

            CNN1

             

            MATN2

             

            ATP6V1B1

             

            CTNNB1

            EEF1G

            KIAA0513

            CUL7

            CASC3

             

            DPYSL2

             

            PTGDS

             

            C7

             

            CXCL12

            FRAP1

            MEF2C

            ERCC1

            CES2

             

            DUSP1

             

            PTN

             

            CLCNKB

             

            DUSP1

            GNAI2

            MRPS21

            GPS1

            CHGA

             

            EGR1

             

            SERPING1

             

            ENG

             

            EGR1

            GNB1

            MYOM2

            MDK

            CLEC3B

             

            FOS

             

            SPARCL1

             

            EPHX2

             

            EGR3

            GNB2

            PCP4

            PDXK

            CNN1

             

            GYPC

               

            FABP1

             

            IGFBP4

            GPIAP1

            PVALB

            PEX6

            CRYAB

             

            IGFBP5

               

            GATA3

             

            JUND

            GPS1

            S100A1

            PHLDA2

            CTNNB1

             

            JUNB

               

            GATM

             

            KHSRP

            H3F3B

            SEPP1

            S100A11

            CUGBP2

             

            LMOD1

               

            GPX3

             

            KIT

            HNRPF

            SERPINI1

            TEAD4

            DMD

             

            LUM

               

            GSTA2

             

            KRT15

            KHDRBS1

              

            DPYSL2

             

            MYH11

               

            HMGCS2

             

            KRT5

            MAZ

              

            FABP4

             

            MYLK

               

            HPD

             

            MXI1

            NONO

              

            FCGBP

             

            NDN

               

            KCNJ1

             

            MYH11

            ODC1

              

            FGFR2

             

            NR4A1

               

            MT1G

             

            NFIB

            PCBP2

              

            FHL1

             

            PPAP2B

               

            MT1X

             

            NSMAF

            RAB7

              

            GDI1

             

            SEPP1

               

            PAH

             

            PCBP1

            RBM10

              

            GPD1L

             

            SERPINF1

               

            PALM

             

            SERPINA3

            RBM5

              

            HMGCS2

             

            SPARCL1

               

            PCK2

             

            SNTB2

            RHOB

              

            HSD11B2

             

            TNXB

               

            PRODH2

             

            SOX9

            SMARCA4

              

            HSPA1A

             

            ZBTB16

               

            PTHR1

             

            SPARCL1

            SRM

              

            IL11RA

             

            ZFP36

               

            SERPINA5

             

            VWF

            TRIM28

              

            IL6R

                 

            TACSTD1

             

            ZFP36

            TUBB

              

            ITGA7

                 

            UGT2B7

              

            UFM1

              

            ITPKB

                 

            UMOD

              

            YBX1

              

            LMOD1

                  
                 

            LPL

                  
                 

            MAOA

                  
                 

            NFIB

                  
                 

            NR3C2

                  
                 

            PCK1

                  
                 

            PLN

                  
                 

            PPAP2B

                  
                 

            PPP1R1A

                  
                 

            PRKCB1

                  
                 

            SEPP1

                  
                 

            SLC26A3

                  
                 

            SMTN

                  
                 

            SPIB

                  
                 

            SRPX

                  
                 

            TACR2

                  
                 

            TGFBR3

                  
                 

            TPM1

                  
                 

            TPM2

                  
                 

            TSPAN7

                  
                 

            TUBA3

                  
                 

            ZBTB16

                  

            Conclusion

            Our study shows that a large portion of genes implicated in the emergence and progression of cancer have similar gene expression values in tumors and cancer cell lines indicating the value of cultured cell lines in cancer research. However, the pair-wise comparisons of gene expression profiles of CL, T, and N across all tissues illustrate that there are pronounced changes in gene expression specific to cell lines (CL - T; CL - N) that may not represent a disease process. This study also identified the signaling and metabolic pathways in cell lines that have distinctly different gene expression patterns than those associated with normal and tumor tissue. Pathway-specific gene expression changes in (CL - T) and (CL - N) comparisons were more consistent than (T - N) comparisons in the set of six tissues under consideration. Just as the gene expression changes in tumor – normal tissue comparison were largely tissue-specific, the significantly altered pathways among tumor – normal comparisons were limited to a small number of tissues. Functional enrichment analysis allows us to explore significant changes in pathways despite having heterogeneous changes in gene expression across different tissues. Cellular pathways that were significantly upregulated in cell lines compared to tumor cells and normal cells of the same tissue type included ATP synthesis, cell cycle, oxidative phosphorylation, purine, pyrimidine and pyruvate metabolism, and proteasome. Results on metabolic pathways suggested an increase in the velocity nucleotide metabolism and RNA production.

            The dominant trend in the gene expression profiles along significantly altered pathways in cell lines appeared to be upregulation of genes when compared either to tumor or normal tissue. Exceptions included genes in the cell adhesion molecules, cell communication, and ECM-receptor interaction, focal adhesion, and complement/coagulation cascade pathways. The apparent downregulation of the complement/coagulation cascade in cell lines may be due to the heterogeneous mixture of cells in tumor samples including immune cells as well as tissue-specific cells.

            The composition of the cell culture medium may be the reason why gene expression patterns that differentiate cancer cell lines from tumor tissue are similar to those patterns that differentiate between cell lines and normal tissue. Typical cell culture medium is replete with metabolites, growth factors, and cytokines, among others, for which cells normally must compete in vivo [24]. Multicellular interfaces with which tumor cells interact in vivo are not replicated for cells grown in cell culture plates [2629]. The differences in environmental selection pressures may help explain the differential gene expression patterns between the tumor tissue and the cell lines. Our finding about the upregulation of oxidative phosphorylation in cell lines is supported by previous metabolic studies [30, 31]. The documentation of gene expression differences along signaling and metabolic pathways is important in compound screening during the drug discovery process. Compounds may affect significantly altered pathways between cell lines and tumor tissue differently. Recent studies are taking advantage of the technological advances in microfluidics and tissue engineering to develop three-dimensional cell culture systems that aim simulating in vivo culture conditions. Whether cell lines can be made to mimic tumor cell gene expression patterns by altering the culture medium conditions is a question yet to be fully explored.

            Declarations

            Acknowledgements

            This study was supported by the National institute of Health (NIH) grant #232240 and by the National science Foundation (NSF) grant # 235327.

            Authors’ Affiliations

            (1)
            Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health Systems, Bossone 714, Drexel University
            (2)
            Lombardi Comprehensive Cancer Center at Georgetown University
            (3)
            Division of Bioinformatics, Fox Chase Cancer Center

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            © Ertel et al; licensee BioMed Central Ltd. 2006

            This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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