Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

The influence of tumor size and environment on gene expression in commonly used human tumor lines

  • Michael A Gieseg1Email author,
  • Michael Z Man1,
  • Nicholas A Gorski1,
  • Steven J Madore1,
  • Eric P Kaldjian1 and
  • Wilbur R Leopold1
BMC Cancer20044:35

https://doi.org/10.1186/1471-2407-4-35

Received: 05 March 2004

Accepted: 15 July 2004

Published: 15 July 2004

Abstract

Background

The expression profiles of solid tumor models in rodents have been only minimally studied despite their extensive use to develop anticancer agents. We have applied RNA expression profiling using Affymetrix U95A GeneChips to address fundamental biological questions about human tumor lines.

Methods

To determine whether gene expression changed significantly as a tumor increased in size, we analyzed samples from two human colon carcinoma lines (Colo205 and HCT-116) at three different sizes (200 mg, 500 mg and 1000 mg). To investigate whether gene expression was influenced by the strain of mouse, tumor samples isolated from C.B-17 SCID and Nu/Nu mice were also compared. Finally, the gene expression differences between tissue culture and in vivo samples were investigated by comparing profiles from lines grown in both environments.

Results

Multidimensional scaling and analysis of variance demonstrated that the tumor lines were dramatically different from each other and that gene expression remained constant as the tumors increased in size. Statistical analysis revealed that 63 genes were differentially expressed due to the strain of mouse the tumor was grown in but the function of the encoded proteins did not link to any distinct biological pathways. Hierarchical clustering of tissue culture and xenograft samples demonstrated that for each individual tumor line, the in vivo and in vitro profiles were more similar to each other than any other profile. We identified 36 genes with a pattern of high expression in xenograft samples that encoded proteins involved in extracellular matrix, cell surface receptors and transcription factors. An additional 17 genes were identified with a pattern of high expression in tissue culture samples and encoded proteins involved in cell division, cell cycle and RNA production.

Conclusions

The environment a tumor line is grown in can have a significant effect on gene expression but tumor size has little or no effect for subcutaneously grown solid tumors. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments. This could be used as a quality control tool for preclinical oncology studies.

Background

Preclinical animal models of human tumors represent a major tool for the selection and development of effective anticancer agents. There is a considerable number of well characterized human cancer cell lines, many of which can be grown as solid tumors (either subcutaneously or orthotopicaly) in immunodeficient mice. The ease of use and low cost of these models make them desirable for screening in vivo activity of anticancer compounds as compared to induced or transgenic rodent tumor models. Rodent models also allow pharmacodynamic and pharmacokinetic parameters to be directly measured and related to antitumor efficacy. Commonly used human cancer cell lines, such as the panel of 60 lines (NCI60) used by the Developmental Therapeutics Program at the National Cancer Institute, have been extensively studied at the molecular level in vitro and to a lesser extent in vivo. Recent advances in genomic technology allow molecular characterization of these models to an extent never before possible using RNA expression profiling, comparative genomic hybridization, proteomic and metabonomic profiling. Ross et al. [1] looked at the in vitro gene expression in the NCI60 panel. The gene expression pattern for many lines indicated a relationship with the tumor tissue of origin and also correlated with doubling time and drug metabolism. Virtanen et al. [2] have analyzed the expression patterns of 85 lung tumor samples from both clinical samples and established tumor lines. The fresh tumors clustered according to pathological subtype with many of the cell lines also clustering within the same groups. Pedersen et al. [3] performed a similar study with human small cell lung cancer cell lines and compared them to ressected tissue samples. Dan et al. [4] recently performed RNA expression profiling on 39 human cancer cell lines and related the gene expression patterns to chemosensitivity of 55 anticancer agents. Zembutsu et al. [5] performed a similar study but profiled 85 human cancer xenografts.

What was apparent in all these studies was that each tumor model was distinctly different and could be distinguished from related models using routine data analysis methods. Furthermore, it appears that it is possible to identify models that are no longer true to their origins. Ross et al. [1] identified MDA-MB-435 as a possible melanoma derived line, which is at odds with its supposed origins of a metastatic breast carcinoma [6]. However, such identification also raises the possibility that the sample that Ross et al. obtained was not the true "MDA-MB-435." Clearly there is the opportunity to use profiling technology as an advanced quality control method that could not only identify mislabeled tumor lines but possibly genetic drift.

While these reports have tremendous value they do not address some basic questions about human tumor models that could impact the design of in vivo drug development studies. For example, most, if not all, xenografts demonstrate Gompertzian growth kinetics with continuously increasing doubling times as they grow larger [7]. Despite this obvious change in biology, it is unknown whether xenografts alter their expression as they increase in size or whether expression differences exist for the same tumor line grown both as a solid tumor in a mouse or in tissue culture. To investigate these questions we grew human colon tumors (HCT-116 and Colo205) in Nu/Nu mice and harvested tumors at three sizes (200 mg, 500 mg, 1000 mg). Isolated RNA was analyzed on Affymetrix GeneChips. The effect of mouse strain on tumor gene expression was also investigated by comparing tumors at 500 mg grown in Nu/Nu and C.B-17 SCID mouse strains. Lastly we profiled additional tumor models to investigate the changes in gene expression that occur when a tumor line is grown in vivo or in vitro.

Methods

Xenograft and tissue culture methods

Cell lines were grown in DMEM/F12 supplemented with 10% fetal bovine serum (Invitrogen, Carlsbad, California). They were passaged in 75-cm2 tissue culture flasks in an atmosphere of 5% CO2 in air and were subcultured weekly, using 0.05% trypsin EDTA (Invitrogen, Carlsbad, California). RNA from tissue culture samples was harvested at mid-log phase. Human tumor xenografts were grown in either Nu/Nu mice or C.B-17 SCID female mice obtained from Charles River Laboratories (Wilmington, Massachusetts) between four and five weeks of age. Mice were housed five to a cage in animal rooms maintained at between 21–25°C with a 12 h alternating dark/light cycle. All animal studies were conducted under Veterinary Use Protocols approved by the Institutional Animal Care and Use Committee. Tumors were maintained by serial passage of 30 mg tumor fragments between animals, implanted subcutaneously into the right axillary region using a trocar needle aseptically. Tumors were passed when the primary had reached between 500 and 1000 mg and were never passed more than ten times. Tumor growth was followed by caliper measurements of perpendicular measures of the tumor. The weight in mg was estimated by the formula: tumor weight = a(b2)/2, where a and b are the tumor length and width respectively in mm. Tumor tissue was harvested immediately following animal sacrifice by excising the tumor and powdering it in a liquid nitrogen cooled crucible and pestle. Tumor powder was stored at -80°C until RNA isolation.

Affymetrix GeneChip

RNA was extracted using TRIZOL® reagent (Invitrogen, Carlsbad, California) according to the manufacturer's protocol. RNA integrity was monitored using denaturing agarose gel electrophoresis in 1X MOPS. Biotinylated target RNA was prepared from 15 μg of total RNA using the Affymetrix protocol. Briefly, double-stranded cDNA was prepared from the RNA template using a modified oligo-dT primer containing a 5' T7 RNA polymerase promoter sequence and the Superscript Choice System for cDNA Synthesis (Invitrogen, Carlsbad, California). Following phenol-chloroform extraction and ethanol precipitation, one-half of the cDNA reaction (0.5 – 1.0 μg) was used as the template in an in vitro transcription reaction containing T7 RNA polymerase, a mixture of unlabeled ATP, CTP, GTP, and UTP, and biotin-11-CTP and biotin-16-UTP (BioArray High Yield Kit, ENZO, Farmingdale, New York). The resulting biotinylated-cRNA "target" was purified on an affinity resin (RNeasy, Qiagen, Valencia, California) and quantified using the convention that 1 O.D. 260 nm corresponds to 40 μg/mL of RNA. Typical yields ranged from 50 – 100 μg with transcript sizes between 3.0 to 0.25 kilonucleotides as determined by denaturing gel electrophoresis. Fifteen micrograms of biotinylated cRNA was randomly fragmented to an average size of 50 nucleotides by incubating at 94°C for 35 minutes in 40 mM TRIS-acetate, pH 8.1, 100 mM potassium acetate, and 30 mM magnesium acetate. The fragmented cRNA was hybridized in a solution containing 100 mM MES, 1 M [Na+], 20 mM EDTA, 0.01% TWEEN 20, 50 pM of Control Oligonucleotide B2, 0.1 mg/mL of sonicated herring sperm DNA, and 0.5 mg/mL BSA for 16 hours at 45°C on either the Human U95A or the Mouse U74A Affymetrix GeneChips® (Affymetrix, Santa Clara, California). Each hybridization included a mixture of four bacterial biotinylated-RNA transcripts (BioB, BioC, BioD, and cre) spiked at 1.5, 5, 25, and 100 pM, respectively. The hybridization reactions were processed and scanned according to the standard Affymetrix protocols.

Data analysis

All arrays were global scaled to a target intensity value of 600 using the standard Affymetrix protocol. Calculation of the scaling factor, background, noise and percent present, was performed according to Affymetrix protocols using the Data Mining Tool (Affymetrix Santa Clara, California). All resulting data sets were filtered using the absolute call metric (present or absent) using Microsoft Access (Microsoft Corporation, Redmond, Washington). Genes selected had expression levels classified as present at least once in the samples selected for the particular analysis.

To determine the relationship between tumor samples harvested at different sizes the filtered RNA profiling data was analyzed with classic multidimensional scaling (MDS), implemented in R [8, 9]. MDS is an unsupervised learning technique that attempts to preserve the relationship between points from high dimensional space at lower dimensional spaces. The program R is a free integrated suite of software facilities for data manipulation, calculation and graphical display http://www.r-project.org.

Analysis of variance (ANOVA) was used to test the effects of tumor size and tumor line, using the following model: Expression of gene i ~ tumor.size + tumor.line + tumor.line*tumor.size. To test the effect due to mouse background, we used the following model: Expression of gene i ~ mouse background. RNA profiling data filtered on the absolute call metric was used for this analysis and ANOVA was implemented in R. ANOVA is a statistical linear modeling procedure that partitions the total variance into parts corresponding to various sources in the model [10, 11]. It has been used previously in microarray data analysis [1215].

To rigorously select genes with expression differences between samples, ANOVA p-values were adjusted using multiple comparison procedures. Multiple comparison procedures are tools to adjust p-values that might be inflated as a result of performing multiple hypothesis tests. The Benjamini and Hochberg procedure controls the false discovery rate, which is the expected fraction of false discoveries in all rejected hypothesis [16]. This procedure is less stringent than methods controlling the family wise error rate (e.g. the Bonferroni correction); hence it is more powerful.

Hierarchical clustering was performed in GeneSpring 5.0 (Silicon Genetics, Redwood City, California). The distinction calculation from Spotfire DecisionSite 6.2 (Spotfire Inc. Somerville, Massachusetts) was used to select genes differentially expressed in xenograft samples or tissue culture samples. All data from the tissue culture samples that had an in vivo pair (8 samples) were selected into one group and all data from the xenograft samples (8 samples) were selected into a second group. Genes were prefiltered using the absolute call metric by selecting genes that were present at least once in the selected samples. A distinction value score and p-value was calculated for each gene. The score (≥1) and p-value (≤0.001) was then used to select genes that were differentially expressed between xenograft samples and tissue culture samples. To functionally classify gene lists, web resources such as NCBI (National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov/) were searched and the data compiled. Further searching for gene associations in PubMed (NCBI) was also performed.

Results

Variation in tumor xenograft gene expression due to size

We focused on two human colon carcinoma xenografts (HCT-116 and Colo205) to investigate the effects of tumor size and mouse strain on gene expression. Samples were harvested in quadruplicate at three different tumor sizes (200 mg, 500 mg and 1000 mg) for both tumor models grown in Nu/Nu mice (except for the 500 mg sample of Colo205 where five samples were harvested). These sizes were selected as they represent the range at which sensitivity to anticancer agents are traditionally tested and because most models approximate log-linear growth at these sizes. RNA expression profiling data was obtained from Affymetrix U95A GeneChips containing approximately 12600 genes. Genes present (above background) once or more across all samples were selected for further analysis (approximately 7600 genes).

Initial analysis of the expression data with multi-dimensional scaling (MDS) showed that samples from the same tumor line clustered together and that there was clear separation between samples from HCT-116 and Colo205 (Figure 1). Compared to the profound effect due to tumor line, there was no clear separation among samples of different sizes in the MDS plot, suggesting that there was little alteration in gene expression due to differences in tumor size (tumor size effect).
Figure 1

Multidimensional scaling plot of Colo205 and HCT-116 samples. Multidimensional scaling plot showing the relatedness of individual samples from Colo205 and HCT-116 to each other in 2D space. The color indicates the size of the tumor sample when harvested: red, 200 mg; blue, 500 mg; black, 1000 mg. The size of text indicates the mouse strain the tumor was grown in: small, Nu/Nu; large, C.B-17 SCID. Samples that are more related to each other are closer together. For Colo205 five samples were analyzed.

The result from MDS was further confirmed by analysis of variance (ANOVA). Using ANOVA we modeled the effects of tumor line and tumor size on gene expression. Since in the ANOVA we conducted approximately 7000 statistical tests (on the selected genes), with a p-value cutoff of 0.01, we would expect approximately 70 genes (1% of 7000) scored as significantly changed due to chance alone. Indeed, the observed number of significantly changed genes (p ≤ 0.01) due to tumor size effect was 154, approximately twice what was predicted by chance alone (Figure 2). In contrast, the number of significantly changed genes due to tumor line effect (4731 genes p-value < 0.01) was far greater than chance alone, indicating the two tumor lines were extremely different as suggested by MDS. The distribution of p-values confirmed the profound effect of tumor line on gene expression with genes affected at all expression levels (Figure 3).
Figure 2

Expected verses observed number of significantly changed genes. The graph shows the number of genes (y-axis) that fall into specific categories (x-axis) based on the ANOVA calculated p-values. The categories are as follows: Predicted, number of genes expected by chance alone; Tumor line effect, number of genes that fall within the specified ranges due to differences in the tumor line; Tumor size effect, number of genes that fall within the specified ranges due to changes in the tumor size; Mouse strain effect, number of genes that fall within the specified range due to changes in the mouse strain the tumors were grown in.

Figure 3

P-value distribution/volcano plot of line and size effect. Scatter plot with mean expression level (log 10) on the x-axis and – p-value (log 10) on y-axis. Each gene is plotted twice, once for the p-value resulting from the tumor-line effect ANOVA (black) and once for the p-value resulting from the tumor-size effect ANOVA (red). The blue line represents a p-value of 0.01. Genes with a lower p-value (more significant) have a higher – p-value log10. There are far more genes with significant p-values due to differences in the tumor lines than due to changes in tumor size.

To rigorously identify genes that may suggest functionally significant changes as a tumor increases in size from 200 mg to 1000 mg the ANOVA p-values were adjusted using multiple comparison procedures. Following this analysis none of the previously identified 154 genes had p-values < 0.05. This indicated that there was no change in gene expression as a human tumor xenograft increased in size from 200 mg to 1000 mg. This was true for both the HCT-116 and the Colo205 tumor lines.

Variation in tumor xenograft gene expression due to mouse strain

Additional RNA samples were prepared from 500 mg HCT-116 tumors grown in C.B-17 SCID mice and hybridized to Affymetrix U95A GeneChips. The expression data were compared to data from the same line grown in Nu/Nu mice harvested at 500 mg. The MDS analysis showed there was no distinct separation or grouping for samples from the two different mouse strains (Figure 1). This suggests that mouse strain played only a small role in altering the expression profiles of HCT-116 tumor samples. However, ANOVA did reveal that considerably more genes were altered in their expression due to the mouse background (493 genes) compared to chance alone (p < 0.01). To identify genes that may suggest biologically significant differences due to the mouse background effect, the ANOVA p-values were adjusted using multiple comparison procedures. Using the Benjamini and Hockberg procedure [16] 63 genes were found to have a p-value < 0.05 with 32 genes increased in the C.B17-SCID strain and 31 increased in the Nu/Nu strain (Table 1 and 2). Functional classification of these genes using a gene ontology approach did not identify functions that could be linked to known biological pathways. Therefore a biological understanding of the changes in gene expression due to mouse strain remains elusive.
Table 1

Genes with high expression in HCT-116 tumors grown in C.B-17 SCID mice

Function

Affymetrix Probe Set ID

ANOVA p-value

BH p-value1

Log2 FC2

Gene Symbol

LocusLink ID

Sequence Probe Derived From

Gene Title

Translation/RNA binding/RNA splicing

 

34733_at

0.00029

0.046

0.5

SF3A1

10291

X85237

splicing factor 3a, subunit 1, 120 kDa

 

34829_at

0.00020

0.046

0.4

DKC1

1736

U59151

dyskeratosis congenita 1, dyskerin

 

35174_i_at

0.00020

0.046

0.4

EEF1A2

1917

X70940

eukaryotic translation elongation factor 1 alpha 2

 

37462_i_at

0.00013

0.042

0.5

SF3A2

8175

L21990

splicing factor 3a, subunit 2, 66 kDa

 

39047_at

0.00007

0.030

0.5

SART3

9733

AB020880

squamous cell carcinoma antigen recognised by T cells 3

 

40490_at

0.00041

0.049

0.7

DDX21

9188

U41387

DEAD/H box polypeptide 21

Signal transduction

 

33887_at

0.00029

0.046

0.5

HGS

9146

D84064

hepatocyte growth factor-regulated tyrosine kinase substrate

 

38019_at

0.00002

0.028

1.0

CSNK1E

1454

L37043

casein kinase 1, epsilon

 

38779_r_at

0.00039

0.048

0.8

HDGF

3068

D16431

hepatoma-derived growth factor

 

40864_at

0.00029

0.046

0.4

RAC1

5879

D25274

ras-related C3 botulinum toxin substrate

Small Molecule transport

 

32186_at

0.00023

0.046

0.7

SLC7A5

8140

M80244

solute carrier family 7, member 5

 

36557_at

0.00003

0.028

0.9

CACNB1

782

M92303

calcium channel, voltage-dependent, beta 1 subunit

 

38029_at

0.00031

0.046

0.4

SLC3A2

6520

J02939

solute carrier family, member 2

Protein Complex Assembly

 

31842_at

0.00044

0.050

0.5

BCS1L

617

AF038195

BCS1-like (yeast)

 

32575_at

0.00019

0.046

0.4

NAP1L4

4676

U77456

nucleosome assembly protein 1-like 4

Protein transport and folding

 

36945_at

0.00036

0.047

0.5

C12orf8

10961

X94910

chromosome 12 open reading frame 8

 

40756_at

0.00017

0.046

0.4

NPM3

10360

AF081280

nucleophosmin/nucleoplasmin, 3

Unknown or other functions

 

31670_s_at

0.00023

0.046

0.3

SRP72

6731

U81554

signal recognition particle 72 kDa

 

34279_at

0.00005

0.030

2.0

FLJ20719

55672

AL050141

hypothetical protein FLJ20719

 

36192_at

0.00003

0.028

0.7

KIAA0193

9805

D83777

KIAA0193 gene product

 

36209_at

0.00003

0.028

0.8

BRD2

6046

S78771

bromodomain containing 2

 

36210_g_at

0.00035

0.047

1.0

BRD2

6046

S78771

bromodomain containing 2

 

36668_at

0.00035

0.047

0.4

DIA1

1727

M28713

diaphorase (NADH)

 

37907_at

0.00006

0.030

0.4

F8A

8263

M34677

coagulation factor VIII-associated

 

38885_at

0.00000

0.007

1.0

DNA2L

1763

D42046

DNA2 DNA replication helicase 2-like

 

39436_at

0.00010

0.039

1.5

BNIP3L

665

AF079221

BCL2/adenovirus E1B 19 kDa interacting protein 3-like

 

39758_f_at

0.00030

0.046

0.4

LAMP1

3916

J04182

lysosomal-associated membrane protein 1

 

39832_at

0.00007

0.030

0.5

ARS2

51593

AL096723

arsenate resistance protein ARS2

 

40589_at

0.00041

0.049

0.9

SNTB2

6645

U40572

syntrophin, beta 2

 

40604_at

0.00012

0.040

1.1

DYRK2

8445

Y13493

dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2

 

41062_at

0.00028

0.046

0.7

NSPC1

84759

AA037278

likely ortholog of mouse nervous system polycomb 1

 

982_at

0.00011

0.039

0.4

MCM5

4174

X74795

MCM5 minichromosome maintenance deficient 5, cell division cycle 46

1p-value resulting from Benjamini and Hochberg procedure 2 log2 fold change calculated by dividing expression level from C.B-17 SCID sample by expression level of Nu/Nu sample

Table 2

Genes with high expression in HCT-116 tumors grown in Nu/Nu mice

Function

Affymetrix Probe Set ID

ANOVA p-value

BH p-value1

Log2 FC2

Gene Symbol

LocusLink ID

Sequence Probe Derived From

Gene Title

Translation/RNA binding/RNA splicing

 

32276_at

0.00036

0.047

-0.5

RPL32

6161

X03342

ribosomal protein L32

 

32432_f_at

0.00032

0.046

-0.6

RPL15

6138

L25899

ribosomal protein L15

 

33614_at

0.00028

0.046

-0.5

RPL18A

6142

X80822

ribosomal protein L18a

 

33660_at

0.00039

0.048

-0.5

RPL5

6125

U14966

ribosomal protein L5

 

34317_g_at

0.00029

0.046

-0.5

RPS15A

6210

W52024

ribosomal protein S15a

 

34345_at

0.00043

0.049

-0.1

C20orf14

24148

AF026031

chromosome 20 open reading frame 14

 

398_at

0.00029

0.046

-0.6

DDX18

8886

X98743

DEAD/H box polypeptide 18 (Myc-regulated)

 

40887_g_at

0.00017

0.046

-0.5

EEF1A1

1915

L41498

eukaryotic translation elongation factor 1 alpha 1

Signal Transduction

 

35772_at

0.00029

0.046

-0.9

ARHGEF12

23365

AB002380

Rho guanine nucleotide exchange factor (GEF) 12

 

36091_at

0.00038

0.048

-0.6

SCAP2

8935

AF051323

src family associated phosphoprotein 2

 

40784_at

0.00003

0.028

-0.7

PPP2R5C

5527

Z69030

protein phosphatase 2, regulatory subunit B (B56), gamma isoform

 

769_s_at

0.00032

0.046

-0.3

ANXA2

302

D00017

annexin A2

 

777_at

0.00010

0.039

-0.6

GDI2

2665

D13988

GDP dissociation inhibitor 2

Unknown or Other Functions

 

1160_at

0.00024

0.046

-0.3

CYC1

1537

J04444

cytochrome c-1

 

1226_at

0.00010

0.039

-0.5

ADAM17

6868

U69611

a disintegrin and metalloproteinase domain 17

 

31531_g_at

0.00007

0.030

-0.5

ACACB

32

U89344

acetyl-Coenzyme A carboxylase beta

 

31684_at

0.00004

0.030

-0.8

ANXA2P1

303

M62896

annexin A2 pseudogene 1

 

32518_at

0.00014

0.042

-0.6

ZNF259

8882

AF019767

zinc finger protein 259

 

33363_at

0.00041

0.049

-0.1

JTV1

7965

W25934

JTV1 gene

 

33389_at

0.00017

0.046

-1.0

CYP51

1595

U23942

cytochrome P450, 51

 

33558_at

0.00012

0.039

-0.5

TBX5

6910

Y09445

T-box 5

 

33820_g_at

0.00000

0.007

-0.7

HSPA8

3312

X13794

heat shock 70 kDa protein 8

 

33937_at

0.00021

0.046

-0.6

  

AJ011981

Homo sapiens mRNA sequence, IMAGE clone 417820

 

34808_at

0.00022

0.046

-0.4

  

AB023216

KIAA0999 protein

 

37218_at

0.00029

0.046

-0.4

BTG3

10950

D64110

BTG family, member 3

 

38553_r_at

0.00025

0.046

-1.3

BAP29

55973

AI984786

B-cell receptor-associated protein BAP29

 

38589_i_at

0.00042

0.049

-0.6

PTMA

5757

M14630

prothymosin, alpha (gene sequence 28)

 

39167_r_at

0.00045

0.050

-1.2

SERPINH2

872

D83174

serine (or cysteine) proteinase inhibitor, clade H, member 2

 

418_at

0.00004

0.030

-0.5

MKI67

4288

X65550

antigen identified by monoclonal antibody Ki-67

 

893_at

0.00003

0.028

-0.4

E2-EPF

27338

M91670

ubiquitin carrier protein

 

932_i_at

0.00031

0.046

-0.8

ZNF91

7644

L11672

zinc finger protein 91

1p-value resulting from Benjamini and Hochberg procedure 2 log2 fold change calculated by dividing expression level from C.B-17 SCID sample by expression level of Nu/Nu sample

Comparison of lines grown in both tissue culture and solid tumor xenografts

To explore the relatedness of different tumor lines to each other and to investigate the gene expression differences between growth in tissue culture and growth as a subcutaneous solid tumor, RNA samples were prepared from the 13 human tumor lines grown in tissue culture to mid-log phase (Table 3). An additional eight RNA samples were obtained of the same lines grown as xenografts in Nu/Nu mice. The gene expression profiling data resulting from hybridizing the 21 samples to Affymetrix U95A GeneChips was filtered for genes classified as present at least once across all samples.
Table 3

Human tumor lines used in this study

Tumor

Tissue of Origin

Xenograft sample

Tissue culture sample

BT-474

Breast

No

Yes

MCF-7

Breast

No

Yes

MDA-MB-231

Breast

Yes

Yes

MDA-MB-435

Breast

Yes

Yes

MDA-MB-453

Breast

No

Yes

MDA-MB-468

Breast

Yes

Yes

SKBr-3

Breast

No

Yes

ZR-75-1

Breast

Yes

Yes

Colo205

Colon

Yes

Yes

HCT-116

Colon

Yes

Yes

H125

Lung

Yes

Yes

H2009

Lung

No

Yes

A431

Squamous cell

Yes

Yes

Hierarchical clustering analysis using Spearman rank for samples and Pearson correlation for genes was used to determine the relationship between 13 different human tumor lines (Figure 4). The clustering analysis revealed that for each individual tumor line the xenograft and tissue culture profiles were more similar to each other than any other profile (with one exception). That is, tumor lines clustered together based on their genotypes rather than their growth conditions, suggesting "nature" (genotype) was more influential than "nurture" (growth conditions). This result was confirmed with MDS and principle component analysis (data not shown). The one exception from this pattern was the xenograft sample of ZR-75-1, which did not cluster on the same node as the ZR-75-1 sample grown in tissue culture. However, subsequently we have found that our xenograft ZR-75-1 line has aberrant biological characteristics that are inconsistent with the known phenotypes of this line.
Figure 4

Hierarchical clustering analysis of 13 tumor lines grown in different environments. Hierarchical clustering analysis showing the structure within the data of the 13 tissue culture samples (suffix – TC) and 8 xenograft samples (suffix – X). Samples are displayed vertically, genes are displayed horizontally. A dendrogram of relatedness of the samples is at the top in green. For any two samples, the vertical distance from the sample roots to the first node joining them is a measure of their similarity; the shorter the distance the more similar. The color in each cell of the table represents the median adjusted expression value of each gene. The color scale used to represent the expression ratios is shown on the right, with yellow indicating increased expression relative to the median and blue decreased.

Although each tissue culture-xenograft sample did cluster together there were many gene changes between the paired samples (Table 4). When fold change values were calculated for each tissue culture xenograft pair and the number of genes with ≥2 fold-change tabulated there was a median of 425.5 genes increased in xenografts and 387 decreased. Again the ZR-75-1 sample was aberrant with many more gene changes than the other lines.
Table 4

Number of genes with fold change values greater than or equal to 2 with tissue culture sample as the baseline

Tumor line

Number of genes with ≥2 fold increase

Number of genes with ≥2 fold decrease

Total with ≥2 fold change

A431

542

650

1192

Colo205

447

328

775

H125

335

304

639

HCT-116

351

393

744

MDA-MB-231

489

381

870

MDA-MB-435

404

519

923

MDA-MB-468

381

204

585

ZR-75-1

889

1218

2107

Median

425.5

387

822.5

Another striking feature revealed by clustering analysis was that none of the tumor lines were particularly similar to each other. The tumor lines did not necessarily cluster based upon the tissue of origin from which each line was thought to be derived. However, a group of breast carcinoma lines that did cluster together (BT474, ZR-75-1, MDA-MB-453, MCF-7, SKBr-3 and MDA-MB-468), although MDA-MB-435 and MDA-MB-231 did not cluster with this group. The two lung carcinoma lines (H2009 and H125) did cluster closely, but MDA-MB-231 and HCT-116 (colon carcinoma) also clustered with this group. MDA-MB-435 and Colo205 clustered independently from all groups.

Selection and functional assessment of differential expressed genes in xenograft or tissue culture samples

Since genes that are co-expressed in a particular environment may provide information about the adaptive changes to the environment, we identified genes that showed a pattern of high expression in xenograft samples or tissue culture samples using Spotfire's DecisionSite calculation. This analysis identified a small number of genes that matched either pattern. There were 36 genes with increased expression in the xenograft samples relative to the tissue cultures samples and 17 genes that showed high expression in the tissue culture samples relative to the xenograft samples (Figure 5). Functional classification of these genes showed that they separated into distinct functional groups (see Table 5 and 6). Many of the genes consistently expressed in tissue culture samples encoded proteins involved in cell division, cell cycle, transcription and translation. In contrast, genes expressed in xenograft samples encoded proteins involved in extracellular matrix, cell adhesion, cell surface receptors transcription and translation.
Figure 5

Heat-map plot of differential expressed genes in xenograft or tissue culture samples. Heat-map plot showing the genes selected as differentially expressed between the tissue culture and xenograft samples. The samples are displayed vertically, genes are displayed horizontally. Yellow indicates high expression while blue indicates low expression, relative to the median expression for each gene across all samples

Table 5

Genes with high expression in xenograft samples

Function

Affymetrix Probe Set ID

Gene Symbol

LocusLink ID

Sequence Probe Set Derived From

Gene Title

Extracellular Matrix/Cell Adhesion

 

31574_i_at

  

M14087

Beta-galactoside-binding lectin, mRNA sequence

 

31810_g_at

CNTN1

1272

Z21488

contactin 1

 

32305_at

COL1A2

1278

J03464

collagen, type I, alpha 2

 

32701_at

ARVCF

421

U51269

armadillo repeat gene deletes in velocardiofacial syndrome

 

36298_at

PRPH

5630

L14565

peripherin

 

38181_at

MMP11

4320

X57766

matrix metalloproteinase 11 (stromelysin 3)

 

38570_at

HLA-DOB

3112

X03066

major histocompatibility complex, class II, DO beta

 

38614_s_at

OGT

8473

U77413

O-linked N-acetylglucosamine (GlcNAc) transferase

Cell surface receptor

 

33950_g_at

CRHR2

1395

AF011406

corticotropin releasing hormone receptor 2

 

35485_at

GRM4

2914

X80818

glutamate receptor, metabotropic 4

 

35503_at

HTR1B

3351

M81590

5-hydroxytryptamine (serotonin) receptor 1B

 

36277_at

CD3E

916

M23323

CD3E antigen, epsilon polypeptide (TiT3 complex)

 

41190_at

TNFRSF25

8718

U83598

tumor necrosis factor receptor superfamily, member 25

Tanscription/transcription factor

 

34786_at

JMJD1

55818

AB018285

zinc finger protein

 

36944_f_at

PLAGL1

5325

U72621

pleiomorphic adenoma gene-like 1

 

37491_at

TAF1

6872

D90359

TAF1 RNA polymerase II, TATA box binding protein-associated factor

 

38216_at

TRIP8

9323

L40411

thyroid hormone receptor interactor 8

Intracellular transport

 

32228_at

AP2A2

161

AB020706

adaptor-related protein complex 2, alpha 2 subunit

 

33779_at

VAMP1

6843

AF060538

vesicle-associated membrane protein 1 (synaptobrevin 1)

Translation/RNA binding/RNA splicing

 

1556_at

RBM5

10181

U23946

RNA binding motif protein 5

 

31896_at

NAG

51594

AL050281

neuroblastoma-amplified protein

Unknown or other functions

 

1155_at

  

J03069

 
 

1529_at

13CDNA73

10129

U50534

hypothetical protein CG003

 

31525_s_at

  

J00153

 
 

31652_at

KIAA1000

22989

AB023217

KIAA1000 protein

 

32355_at

DKFZP564D166

26115

AL050270

putative ankyrin-repeat containing protein

 

36811_at

  

U24389

 
 

37979_at

YAP1

10413

X80507

Yes-associated protein 1, 65 kDa

 

39499_s_at

PARD3

56288

W25794

par-3 partitioning defective 3 homolog

 

40928_at

WSB1

26118

W26496

SOCS box-containing WD protein SWiP-1

 

41113_at

KIAA0557

26048

AI871396

KIAA0557 protein

 

41328_s_at

EML2

24139

AL096717

echinoderm microtubule associated protein like 2

 

40856_at

SERPINF1

5176

U29953

serine (or cysteine) proteinase inhibitor, clade F, member 1

 

32833_at

CLK1

1195

M59287

CDC-like kinase 1

 

39451_i_at

IDS

3423

AF050145

iduronate 2-sulfatase (Hunter syndrome)

 

36386_at

PDK1

5163

L42450

pyruvate dehydrogenase kinase, isoenzyme 1

Table 6

Genes with high expression in tissue culture samples

Function

Affymetrix ProbeSet ID

Gene Symbol

LocusLink ID

Sequence ProbeSet Derived From

Gene Title

Cell division

 

34851_at

STK6

6790

AF011468

serine/threonine kinase 6

 

33346_r_at

TUBG1

7283

M61764

tubulin, gamma 1

 

35995_at

ZWINT

11130

AF067656

ZW10 interactor

 

37171_at

KNSL5

9493

X67155

kinesin-like 5 (mitotic kinesin-like protein 1)

Cell cycle

 

34736_at

CCNB1

891

M25753

cyclin B1

 

37458_at

CDC45L

8318

AJ223728

CDC45 cell division cycle 45-like (S. cerevisiae)

 

38804_at

CSE1L

1434

AF053641

CSE1 chromosome segregation 1-like (yeast)

Translation/RNA binding/RNA splicing

 

35323_at

EIF3S9

8662

U78525

eukaryotic translation initiation factor 3, subunit 9 eta, 116 kDa

 

38679_g_at

SNRPE

6635

AA733050

small nuclear ribonucleoprotein polypeptide E

 

41460_at

RBM14

10432

AF080561

RNA binding motif protein 14

Transcription/transcription factor

 

39664_at

CKN1

1161

U28413

Cockayne syndrome 1 (classical)

 

41332_at

POLR2E

5434

D38251

polymerase (RNA) II (DNA directed) polypeptide E, 25 kDa

Unknown or other functions

 

35715_at

DKFZP564M082

25906

AL080071

DKFZP564M082 protein

 

40758_at

ICT1

3396

X81788

immature colon carcinoma transcript 1

 

1749_at

GCDH

2639

AD000092

glutaryl-Coenzyme A dehydrogenase

 

40134_at

ATP5J2

9551

AF047436

ATP synthase, subunit f, isoform 2

 

41400_at

TK1

7083

K02581

thymidine kinase 1, soluble

Discussion

The first part of our study was designed to address whether gene expression patterns in xenografts varied with tumor size. No significant changes (p-value < 0.05) were found in any genes after statistical analysis of the RNA profiling data. This was a somewhat surprising result as there is evidence to suggest that tumors show size-dependent biological variation in both a clinical setting and as implanted solid tumors in rodents. It is generally assumed clinically that the larger the tumor mass the lower the likelihood of curing the patient, regardless of the treatment [17, 18]. Pathological and genetic heterogeneity increase with increasing tumor cell number and contribute to the emergence of clinical drug resistance [19]. As tumors grow larger their vascular surface decreases, intercapillary distance increases, interstial pressure increases and necrotic foci develop [20]. Tumor doubling times and cell loss also increase with increasing tumor size [17].

Implanted animal tumor models most likely represent clinical end-stage disease and may not necessarily recapitulate clinical behavior seen during tumor development in humans. There are few reports on the molecular and biochemical changes that occur as implanted solid tumors increase in size. Massaad et al. [21] found that some drug metabolizing enzyme systems, including glutathione-S-transferases (GST) did change both activity and expression with increasing tumor size in the mouse colon adenocarcinoma Co38.

Preclinical tumor models show variability in response to treatment with a given anti-tumor agent despite being derived from a single tumor and being implanted into inbred mice [22]. This has been attributed to heterogeneity of the host or the tumor cell populations [23, 24] along with tumor-to-tumor variations in perfusion and drug distribution [25]. We found there was no significant difference in gene expression between tumors of either the same size or different size as assessed in four replicate animals. If differences in tumor population, host metabolism or perfusion exist between animals implanted with the same tumors, our data suggests they are not detectable using gene expression analysis of the whole tumor.

Our results suggest that once tumor xenografts have grown to 200 mg, physiological influences on tumor transcription (such as the interaction with host stroma, nutrient supply and oxygenation) have reached steady state and do not change appreciably as the tumor grows to 1000 mg. It is also conceivable that tumors smaller and larger than the sizes investigated in this study may show gene expression changes. When a tumor is first implanted the hypoxic environment is likely to drive the expression of pro-angiogenic cytokines that will recruit new vessels to the tumor. As a tumor becomes very large (>2 g) angiogenic factors may again become highly expressed due to the inability for the existing vasculature to adequately support the large tumor mass. We have not excluded the possibility that there may be changes in protein activities or levels, or that other tumor lines may show changes as the size increases. But since the phenomenon was independently observed in two tumor lines it suggests it may be common to many other lines. The results also imply that tumors harvested within this size range without drug treatment should be very comparable. It remains possible that selective isolation and analysis of tumor regions or subpopulations by microdissection may identify expression differences.

By comparing the same tumor line grown in different mouse strains we identified 63 genes with a significant change in gene expression with biological functions ranging from cellular signaling, RNA processing and translation. Although different genes were differentially expressed in different mouse strains, particular gene functions did not associate with one strain. Therefore, the biological significance of these changes remains unclear. It should be noted that although the changes were statistically significant, in general the magnitude of the change was small. Only 12% of the significant genes displayed a change greater or equal to 2-fold. This raises the question of whether statistical significance equates to biological significance and how relevant thresholds can be determined for analyzing expression profiling data.

In the second part of the study we compared the gene expression patterns of tumor lines grown as xenografts with the same lines grown in tissue culture. We found that each xenograft – tissue culture pair was more similar to one another than any other line. This has been previously observed with RNA expression profiling of small cell lung cancer lines grown as both xenografts and tissue culture [3]. In our data, the one exception to this clustering pattern, ZR-75-1, exhibited aberrant growth characteristics; it had a rapid doubling time (2–3 days) and was not estrogen dependent as has been reported [26, 27].

There was a gene expression pattern consistent with growth either in a xenograft or tissue culture environment, but remarkably it consisted of only a small number of genes. We identified 36 genes with high relative expression in xenograft samples and 17 genes with high relative expression in tissue culture samples. Their biological functions were consistent with the differences between a tissue culture sample and a xenograft sample. Genes expressed at increased levels in tissue culture samples were primarily involved in cell division, cell cycle, transcription and translation and were consistent with a greater percentage of cycling cells in the tissue culture samples, which we have previously observed with flow cytometry (data not shown). Many of the genes expressed in xenograft samples encoded for proteins involved in extracellular matrix, cell adhesion, cell surface receptors transcription and translation and suggest increased interactions with an in vivo stromal environment, including the development of a 3-dimensional matrix.

There are several reports in the literature of tumor line expression profiling studies demonstrating that lines derived from a common tissue of origin group together in most cases [1, 4, 5]. We observed that most of the breast tumor lines clustered together but the other lines generally did not show clustering patterns based upon tissue of origin. However, our study evaluated fewer samples and consisted predominantly of breast cancer tumor lines with only two representatives of non-small cell lung cancer and colon cancer.

Since each tumor line was substantially different to every other line tesed it suggests that it would be possible to identify a group of genes that could be used to distinguish individual lines. Also, it has been our experience that it is possible to identify aberrant samples resulting from labeling error by expression profiling that were missed by other methods. What is less clear is whether genetic drift causes changes in RNA expression that can be identified using RNA profiling technology. Recently a report has been published showing that MCF-7 sublines were substantially different to each other both at the genetic and RNA expression levels [28] suggesting that variation within individual lines can be identified. Given this, it seems reasonable that RNA expression profiling could be used as a comprehensive methodology for identifying aberrant or incorrectly labeled samples. This would provide an additional quality control tool to standardize tumor models and the in vivo testing of therapeutic agents.

Conclusions

Our data suggest that the environment a tumor line is grown in can have a significant effect on gene expression even though tumor size has little or no effect for a subcutaneously grown solid tumor. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments such as tissue culture or in vivo. Routine RNA expression profiling of a selected set of genes could be used as a quality control tool for preclinical oncology studies and would allow the standardization of tumor models used worldwide.

Declarations

Acknowledgements

We kindly acknowledge Dr Sarah Fowler for assistance with proofing this manuscript and Dr William Elliott for helpful discussion about the growth characteristics of solid tumor xenografts.

Authors’ Affiliations

(1)
Pfizer Global Research and Development

References

  1. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de RM, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Shalon D, Myers TG, Weinstein JN, Botstein D, Brown PO: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000, 24: 227-235. 10.1038/73432.View ArticlePubMedGoogle Scholar
  2. Virtanen C, Ishikawa Y, Honjoh D, Kimura M, Shimane M, Miyoshi T, Nomura H, Jones MH: Integrated classification of lung tumors and cell lines by expression profiling. Proc Natl Acad Sci USA. 2002, 99: 12357-12362. 10.1073/pnas.192240599.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Pedersen N, Mortensen S, Sorensen SB, Pedersen MW, Rieneck K, Bovin LF, Poulsen HS: Transcriptional gene expression profiling of small cell lung cancer cells. Cancer Res. 2003, 63: 1943-1953.PubMedGoogle Scholar
  4. Dan S, Tsunoda T, Kitahara O, Yanagawa R, Zembutsu H, Katagiri T, Yamazaki K, Nakamura Y, Yamori T: An integrated database of chemosensitivity to 55 anticancer drugs and gene expression profiles of 39 human cancer cell lines. Cancer Res. 2002, 62: 1139-1147.PubMedGoogle Scholar
  5. Zembutsu H, Ohnishi Y, Tsunoda T, Furukawa Y, Katagiri T, Ueyama Y, Tamaoki N, Nomura T, Kitahara O, Yanagawa R, Hirata K, Nakamura Y: Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Res. 2002, 62: 518-527.PubMedGoogle Scholar
  6. Cailleau R, Olive M, Cruciger QV: Long-term human breast carcinoma cell lines of metastatic origin: preliminary characterization. In Vitro. 1978, 14: 911-915.View ArticlePubMedGoogle Scholar
  7. Jackson RC: From The Growth Kinetics of Large Tumors. In The Theoretical Foundations of Cancer Chemotherapy Introduced by Computer Models. Edited by: Jackson RC. 1992, San Diego, California: Academic Press Inc, 259-298.Google Scholar
  8. Ihaka R, Gentleman RR: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics. 1996, 5: 299-314.Google Scholar
  9. Venables WN, Ripley BD: Modern Applied Statistics with S-PLUS. 1997, Springer, SecondView ArticleGoogle Scholar
  10. Fisher RD: Statistical methods for research workers. 1950, London: Oliver and BoydGoogle Scholar
  11. Fisher LD, van Belle G: Biostatistics: a methodology for the health sciences. 1993, New York: John Wiley & Sons IncGoogle Scholar
  12. Kerr MK, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. J Comput Biol. 2000, 7: 819-837. 10.1089/10665270050514954.View ArticlePubMedGoogle Scholar
  13. Coombes KR, Highsmith WE, Krogmann TA, Baggerly KA, Stivers DN, Abruzzo LV: Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays. J Comput Biol. 2002, 9: 655-669. 10.1089/106652702760277372.View ArticlePubMedGoogle Scholar
  14. Pritchard CC, Hsu L, Delrow J, Nelson PS: Project normal: defining normal variance in mouse gene expression. Proc Natl Acad Sci USA. 2001, 98: 13266-13271. 10.1073/pnas.221465998.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Pavlidis P, Noble WS: Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol. 2001, 2: 1-15. 10.1186/gb-2001-2-10-research0042.View ArticleGoogle Scholar
  16. Benjamini Y, Hochberg Y: Controlling the false discovery rate; a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B. 1995, 57: 289-300.Google Scholar
  17. DeVita VT: The James Ewing lecture. The relationship between tumor mass and resistance to chemotherapy. Implications for surgical adjuvant treatment of cancer. Cancer. 1983, 51: 1209-1220.View ArticlePubMedGoogle Scholar
  18. Michaelson JS, Silverstein M, Wyatt J, Weber G, Moore R, Halpern E, Kopans DB, Hughes K: Predicting the survival of patients with breast carcinoma using tumor size. Cancer. 2002, 95: 713-723. 10.1002/cncr.10742.View ArticlePubMedGoogle Scholar
  19. Baylin SB: From Clonal selection and heterogeneity of human solid neoplasms. In Design of Models for Testing Cancer Therapeutic Agents. Edited by: Fidler IJ, White RJ. 1981, New York: Van Nostrand Reinhold, 50-63.Google Scholar
  20. Jain RK: Delivery of novel therapeutic agents in tumors: physiological barriers and strategies. J Natl Cancer Inst. 1989, 81: 570-576.View ArticlePubMedGoogle Scholar
  21. Massaad L, Chabot GG, Toussaint C, Koscielny S, Morizet J, Bissery MC, Gouyette A: Influence of tumor size on the main drug-metabolizing enzyme systems in mouse colon adenocarcinoma Co38. Cancer Chemother Pharmacol. 1994, 34: 497-502. 10.1007/s002800050179.View ArticlePubMedGoogle Scholar
  22. Schabel FM, Griswold DP, Corbett TH, Laster WR, Lloyd HH, Rose WC: From Variable response of advanced solid tumors of mice to treatment with anticancer drugs. In Design of Models for Testing Cancer Therapeutic Agents. Edited by: Fidler IJ, White RJ. 1981, New York: Van Nostrand Reinhold, 95-113.Google Scholar
  23. Nelson JA, Hokanson JA, Jenkins VK: Role of the host in the variable chemotherapeutic response of advanced Ridgway osteogenic sarcoma. Cancer Chemother Pharmacol. 1982, 9: 148-155.View ArticlePubMedGoogle Scholar
  24. Trope C: From Different susceptibilities of tumor cell subpopulations to cytotoxic agents. In Design of Models for Testing Cancer Therapeutic Agents. Edited by: Fidler IJ, White RJ. 1981, New York: Van Nostrand Reinhold, 64-79.Google Scholar
  25. Simpson-Herren L, Noker PE, Wagoner SD: Variability of tumor response to chemotherapy. II. Contribution of tumor heterogeneity. Cancer Chemother Pharmacol. 1988, 22: 131-136.View ArticlePubMedGoogle Scholar
  26. Gutman M, Couillard S, Roy J, Labrie F, Candas B, Labrie C: Comparison of the effects of EM-652 (SCH57068), tamoxifen, toremifene, droloxifene, idoxifene, GW-5638 and raloxifene on the growth of human ZR-75-1 breast tumors in nude mice. Int J Cancer. 2002, 99: 273-278. 10.1002/ijc.10302.View ArticlePubMedGoogle Scholar
  27. Cameron DA, Ritchie AA, Langdon S, Anderson TJ, Miller WR: Tamoxifen induced apoptosis in ZR-75-1 breast cancer xenografts antedates tumor regression. Breast Cancer Res Treat. 1997, 45: 99-107. 10.1023/A:1005850827825.View ArticlePubMedGoogle Scholar
  28. Nugoli M, Chuchana P, Vendrell J, Orsetti B, Ursule L, Nguyen C, Birnbaum D, Douzery EJ, Cohen P, Theillet C: Genetic variability in MCF-7 sublines: evidence of rapid genomic and RNA expression profile modifications. BMC Cancer. 2003, 3: 13-10.1186/1471-2407-3-13.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/4/35/prepub

Copyright

© Gieseg et al; licensee BioMed Central Ltd. 2004

This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

Advertisement