Skip to content

Advertisement

  • Research
  • Open Access

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

  • 1,
  • 1,
  • 2,
  • 3 and
  • 1Email author
Molecular Cancer20065:55

https://doi.org/10.1186/1476-4598-5-55

©  Ertel et al; licensee BioMed Central Ltd. 2006

  • Received: 23 January 2006
  • Accepted: 02 November 2006
  • Published:

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.

Keywords

  • Functional Enrichment Analysis
  • Gene Ontology Category
  • Pyrimidine Metabolism
  • Robust Multichip Average
  • NCI60 Cell Line

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.
Figure 1
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).
Figure 2
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].
Figure 3
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.
Figure 4
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 [26–29]. 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, 3143 Chestnut Street, Philadelphia, PA 19104, USA
(2)
Lombardi Comprehensive Cancer Center at Georgetown University, 3800 Reservoir Road, Washington, DC 20057, USA
(3)
Division of Bioinformatics, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111-2497, USA

References

  1. Yamori T: Panel of human cancer cell lines provides valuable database for drug discovery and bioinformatics. Cancer Chemother Pharmacol. 2003, 52 (Suppl 1): S74-9. 10.1007/s00280-003-0649-1View ArticlePubMedGoogle Scholar
  2. Kim JB, Stein R, O'Hare MJ: Three-dimensional in vitro tissue culture models of breast cancer – a review. Breast Cancer Res Treat. 2004, 85 (3): 281-91. 10.1023/B:BREA.0000025418.88785.2bView ArticlePubMedGoogle Scholar
  3. Price JE, Zhang RD: Studies of human breast cancer metastasis using nude mice. Cancer Metastasis Rev. 1990, 8: 285-297. 10.1007/BF00052605View ArticlePubMedGoogle Scholar
  4. Ross DT, Perou CM: A comparison of gene expression signatures from breast tumors and breast tissue derived cell lines. Dis Markers. 2001, 17 (2): 99-109.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Stein WD, Litman T, Fojo T, Bates SE: A Serial Analysis of Gene Expression (SAGE) database analysis of chemosensitivity: comparing solid tumors with cell lines and comparing solid tumors from different tissue origins. Cancer Res. 2004, 64 (8): 2805-16. 10.1158/0008-5472.CAN-03-3383View ArticlePubMedGoogle Scholar
  6. Sandberg R, Ernberg I: Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI). Proc Natl Acad Sci USA. 2005, 102 (6): 2052-2057. 10.1073/pnas.0408105102PubMed CentralView ArticlePubMedGoogle Scholar
  7. Sandberg R, Ernberg I: The molecular portrait of in vitro growth by meta-analysis of gene-expression profiles. Genome Biol. 2005, 6 (8): R65- 10.1186/gb-2005-6-8-r65PubMed CentralView ArticlePubMedGoogle Scholar
  8. Kanehisa M: A database for post-genome analysis. Trends Genet. 1997, 13: 375-376. 10.1016/S0168-9525(97)01223-7View ArticlePubMedGoogle Scholar
  9. Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28: 27-30. 10.1093/nar/28.1.27PubMed CentralView ArticlePubMedGoogle Scholar
  10. Staunton JE, Slonim DK, Coller HA, Tamayo P, Angelo MJ, Park J, Scherf U, Lee JK, Reinhold WO, Weinstein JN, Mesirov JP, Lander ES, Golub TR: Chemosensitivity prediction by transcriptional profiling. Proc Natl Acad Sci USA. 2001, 98 (19): 10787-92. 10.1073/pnas.191368598PubMed CentralView ArticlePubMedGoogle Scholar
  11. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang CH, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov JP, Poggio T, Gerald W, Loda M, Lander ES, Golub TR: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA. 2001, 98 (26): 15149-54. 10.1073/pnas.211566398PubMed CentralView ArticlePubMedGoogle Scholar
  12. Zhang B, Kirov S, Snoddy J: WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005, 33: W741-8. 10.1093/nar/gki475PubMed CentralView ArticlePubMedGoogle Scholar
  13. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4 (2): 249-64. 10.1093/biostatistics/4.2.249View ArticlePubMedGoogle Scholar
  14. Bolstad BM, Irizarry RA, Astrand M, Speed TP: A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics. 2003, 19 (2): 185-193. Supplemental information, 10.1093/bioinformatics/19.2.185View ArticlePubMedGoogle Scholar
  15. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80- 10.1186/gb-2004-5-10-r80PubMed CentralView ArticlePubMedGoogle Scholar
  16. Ihaka R, Gentleman R: R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics. 1996, 3 (5): 299-314. 10.2307/1390807. 10.2307/1390807Google Scholar
  17. Bioconductor for R Installation Script. [http://www.bioconductor.org/getBioC.R]
  18. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98: 5116-5121. 10.1073/pnas.091062498PubMed CentralView ArticlePubMedGoogle Scholar
  19. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34: 374-378.PubMedGoogle Scholar
  20. Segal E, Friedman N, Koller D, Regev A: A module map showing conditional activity of expression modules in cancer. Nat Genet. 2004, 36 (10): 1090-8.View ArticlePubMedGoogle Scholar
  21. Segal E, Friedman N, Kaminski N, Regev A, Koller D: From signatures to models: understanding cancer using microarrays. Nat Genet. 2005, S38-45. 37 Suppl.Google Scholar
  22. Chevrollier A, Loiseau D, Gautier F, Malthiery Y, Stepien G: ANT2 expression under hypoxic conditions produces opposite cell-cycle behavior in 143B and HepG2 cancer cells. Mol Carcinog. 2005, 42 (1): 1-8. 10.1002/mc.20059View ArticlePubMedGoogle Scholar
  23. A predicted increase in the velocity of RNA and DNA base production in cell lines is in line with trend of increasing rates of cell division observed in cell cultures.Google Scholar
  24. Kufe Donald W, Pollock Raphael E, Weichselbaum Ralph R, Bast Robert C, Gansler Ted S, Holland James F, Frei Emil : Pyrimidine and Purine Antimetabolites. Cancer Medicine Inc. Edited by: Decker BC. 2003, 50-6.Google Scholar
  25. Vogel TW, Zhuang Z, Li J, Okamoto H, Furuta M, Lee YS, Zeng W, Oldfield EH, Vortmeyer AO, Weil RJ: Proteins and protein pattern differences between glioma cell lines and glioblastoma multiforme. Clin Cancer Res. 2005, 11 (10): 3624-32. 10.1158/1078-0432.CCR-04-2115View ArticlePubMedGoogle Scholar
  26. Dipasquale B, Colombatti M, Tridente G: Morphological heterogeneity and phenotype modifications during long term in vitro cultures of six new glioblastoma cell lines. Tumori. 1990, 76 (2): 172-8.PubMedGoogle Scholar
  27. Chen MH, Yang WK, Whang-Peng J, Lee LS, Huang TS: Differential inducibilities of GFAP expression, cytostasis and apoptosis in primary cultures of human astrocytic tumours. Apoptosis. 1998, 3: 171-82. 10.1023/A:1009698822305View ArticlePubMedGoogle Scholar
  28. Pandita A, Aldape KD, Zadeh G, Guha A, James CD: Contrasting in vivo and in vitro fates of glioblastoma cell subpopulations with amplified EGFR. Genes Chromosomes Cancer. 2004, 39: 29-33. 10.1002/gcc.10300View ArticlePubMedGoogle Scholar
  29. Pieper RO: Defined human cellular systems in the study of glioma development. Front Biosci. 2003, 8: s19-27.View ArticlePubMedGoogle Scholar
  30. Faure Vigny H, Heddi A, Giraud S, Chautard D, Stepien G: Expression of oxidative phosphorylation genes in renal tumors and tumoral cell lines. Mol Carcinog. 1996, 16 (3): 165-72. 10.1002/(SICI)1098-2744(199607)16:3<165::AID-MC7>3.0.CO;2-GView ArticlePubMedGoogle Scholar
  31. Franks SE, Kuesel AC, Lutz NW, Hull WE: 31P MRS of human tumor cells: effects of culture media and conditions on phospholipid metabolite concentrations. Anticancer Res. 1996, 16 (3B): 1365-74.PubMedGoogle Scholar

Copyright

© 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.

Advertisement

Molecular Cancer

ISSN: 1476-4598