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Gastric cancers of Western European and African patients show different patterns of genomic instability

  • Tineke E Buffart1,
  • Melanie Louw2,
  • Nicole CT van Grieken1,
  • Marianne Tijssen1,
  • Beatriz Carvalho1,
  • Bauke Ylstra1,
  • Heike Grabsch3,
  • Chris JJ Mulder4,
  • Cornelis JH van de Velde5,
  • Schalk W van der Merwe6 and
  • Gerrit A Meijer1Email author
BMC Medical Genomics20114:7

DOI: 10.1186/1755-8794-4-7

Received: 7 October 2010

Accepted: 13 January 2011

Published: 13 January 2011

Abstract

Background

Infection with H. pylori is important in the etiology of gastric cancer. Gastric cancer is infrequent in Africa, despite high frequencies of H. pylori infection, referred to as the African enigma. Variation in environmental and host factors influencing gastric cancer risk between different populations have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We aim to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK) and South Africa (SA), in an attempt to support the African enigma hypothesis at the biological level.

Methods

DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis.

Results

Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p < 0.05). For the microsatellite stable tumors, geographical origin of the patients correlated with cluster membership, derived from unsupervised hierarchical cluster analysis (p = 0.001). Several chromosomal alterations showed significantly different frequencies in tumors from UK patients and native SA patients, but not between UK and Caucasian SA patients and between native and Caucasian SA patients.

Conclusions

Gastric cancers from SA and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms in patients from different geographical origin. This is of future clinical relevance for stratification of gastric cancer therapy.

Background

Gastric cancer is the second most common cause of cancer death worldwide, but incidence and mortality rates show large variations across different countries. Japan and China show the highest incidence rates of gastric cancer of 80-115 cancers/100,000 population and 32-59 cancers/100,000 population respectively, while in other Asian counties, such as India, Bangladesh, and Thailand, the incidence rates are much lower (10.6, 1.3, and 7.1 per 100,000 populations, respectively). Also within Europe, incidence and mortality rates differ between countries. Portugal has the highest incidence rates (33.2/100,000) whereas other countries in Western Europe show incidence rates of 19.4 per 100,000 populations. In the Netherlands it ranks fifth as a cause of cancer death with incidence rates of 14.6/100,000. In Africa, gastric cancer is infrequent, with incidence rates varying between 6.9/100,000 in Northern Africa, 12.9/100,000 in Eastern Africa, 11.9/100,000 in Southern Africa and 7.0/100,000 in Western Africa (Table 1) [13].
Table 1

Incidence rates of gastric cancers per 100,000 populations.

 

Incidence rates

 

Incidence rates

Japan

80-115

Netherlands

14.6

China

32-59

Western Europe

19.4

India

10.6

Northern Africa

6.9

Bangladesh

1.3

Eastern Africa

12.9

Thailand

7.1

Southern Africa

11.9

Portugal

33.2

Western Africa

7.0

According to the Correa model, intestinal type gastric cancers arise through a sequence of events, starting with chronic active gastritis due to infection with Helicobacter pylori (H. pylori). This chronic inflammatory process may lead to atrophy, intestinal metaplasia followed by dysplasia and eventually may lead to invasive adenocarcinoma [4].

The mechanism by which H. pylori contributes to gastric carcinogenesis is still largely unknown. However, we do know that gastric cancer is the result of accumulation of (epi)genetic changes. In gastric cancer, at least two types of genetic instability play a role. Microsatellite instability (MSI) occurs in cancers associated with Lynch syndrome or hereditary non-polyposis colorectal cancer (HNPCC), and in 10-15% of sporadic gastric cancers due to hMLH1 promoter hypermethylation [5, 6]. However, the majority of gastric cancers show chromosomal instability, resulting in DNA copy number aberrations that can be analyzed in detail by high resolution array comparative genomic hybridization (array CGH). In a previous study using chromosome based comparative genomic hybridization (CGH), we were unable to demonstrate that there are specific chromosomal alterations which are associated with H. pylori infection [7].

Infection with H. pylori is important in the etiology of gastric cancer, consequently high incidences of gastric cancer are observed in areas with high prevalence of H. pylori infection, like Asia. However, despite high frequencies of H. pylori infection in Africa, gastric cancer is infrequent in Africa, a phenomenon often referred to as the 'African enigma' [8, 9]. We hypothesize that geographical differences in environmental factors, including infection with H. pylori, and host factors are reflected by different biological characteristics of the tumors from those areas. Therefore, we compared MSI status and DNA copy number profiles in gastric cancer patients from United Kingdom (UK) and South Africa (SA).

Methods

Material

A total of 67 gastric adenocarcinomas were included in this study. Of these, 33 gastric adenocarcinomas were obtained from Leeds (Leeds, General Infirmary, UK) and 34 gastric adenocarcinomas were obtained from Pretoria (Prinshof Campus, Pretoria, South Africa), of which 25 were obtained from native South African patients (native SA) and 9 from Caucasian South African patients (Caucasian SA), respectively. All tumors were randomly selected after testing for proper DNA quality as previously described [10]. All gastric adenocarcinomas were staged according to the TNM classification (5th edition) for the grading and to the Laurén's classification for morphology [11]. The study was approved by the Institutional Review Board and was in accordance with local medical ethical regulations.

DNA isolation procedure

DNA was isolated from formalin-fixed and paraffin embedded gastric cancer material as described previously,[12, 13] using the QIAamp microkit (Qiagen, Hilden, Germany). DNA concentrations were measured using a Nanodrop ND-1000 spectrophotometer (Isogen, IJsselstein, The Netherlands) and DNA quality was assessed by isothermal amplification [10]. Genomic DNA isolated from peripheral blood obtained from eighteen healthy females or males was pooled to use as normal reference.

Microsatellite instability (MSI) analysis

MSI analysis was performed using the MSI Analysis System (MSI Multiplex System Version 1.1, Promega) consisting of five nearly monomorphic mononucleotide markers (BAT-25, BAT-26, NR-21, NR-24, MONO-27) according to the manufacturer's instructions. PCR products were separated by capillary electrophoresis using an ABI 3130 DNA sequencer (Applied Biosystems, Foster City, CA, USA), and analyzed using GeneScan 3100 (Applied Biosystems, Foster City, CA, USA). An internal lane size standard was added to the PCR samples for accurate sizing of alleles and to adjust for run-to run variations. When all markers were stable, the tumor was interpreted as microsatellite stable (MSS). The tumor was interpreted as MSI-low (MSI-L) if one marker was instable and MSI-high (MSI-H) if two or more markers showed instability. MSI-L tumors were included in the MSS category in further analysis. Due to polymorphisms[14] in the South African population, native South African tumors were classified as MSI when three or more markers were instable.

Array CGH

Array CGH was performed as described before [12, 15]. Briefly, 600 ng tumor and normal reference DNAs were labeled by random priming (Bioprime DNA Labeling System, Invitrogen, Breda, The Netherlands) and hybridized onto a BAC array containing approximately 6000 clones, consisting of the Sanger BAC clone set with an average resolution along the whole genome of 1.0 Mb, the OncoBac set, containing approximately 600 clones corresponding to 200 cancer-related genes, and selected clones of interest obtained from the Children's Hospital Oakland Research Institute (CHORI) to fill gaps larger than 1 Mb on chromosome 6 and to have full coverage contigs of regions on chromosome 8, 13 and 20. All clones were printed in triplicate on Nexterion slides (Schott Nexterion, Jena, Germany). Subsequent analysis was performed according to the clone position from the UCSC May 2004 freeze of the Human Genome Golden Path http://genome.ucsc.edu.

Image acquisition and data analysis

Images of the arrays were acquired by scanning (Agilent DNA Microarray scanner, Agilent Technologies, Palo Alto, USA) and Bluefuse software version 3.4 (BlueGnome, Cambridge, UK) was used for automatic feature extraction. Spots were excluded when the quality flag was below 1 or the confidence value was below 0.1. Log2 tumor to normal ratio was calculated for each clone and median block normalization was used to normalize the data. Quality of array CGH profiles was measured by calculating a median absolute deviation value of chromosome 2 (MAD2) [10]. Array CGH profiles with MAD2 values >0.18 were excluded from further analysis. For determining copy number gains and losses, the R package CGH call was used [16]. Output of the CGH call analysis was used for CGH region analysis to compress the data, using a threshold for average error rate of 0.001 [17]. Hierarchical cluster analysis was performed using the WECCA program, with the parameter total linkage [18].

Array data can be accessed using the Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo/, under accession number GSE22789.

Statistical analysis

Significance of differences for categorical variables between different categories was tested using a chi-square test. One-way ANOVA with Bonferroni correction was used to calculate significant differences for continuous variables between Caucasian SA, native SA, and UK patients (SPSS 12.0.1 for Windows, SPSS Inc, Chicago, IL, USA). P values less than 0.05 were considered to be significant.

Supervised analysis was performed using the non-parametric Mann-Whitney two-sample test (CGH test [19]). Alterations in patterns between different tumor groups were compared using a binomial differential proportion test. The test procedure included a permutation-based false discovery rate correction for multiple testing [20]. Two-sided p values less than 0.05 and false discovery rates below 0.15 were considered to be significant.

Results

Clinicopathological data

The mean age of the UK gastric cancer patients was 73.3 years (range 51-96), mean age of the Caucasian SA patients was 68.0 years (range 56-84) and the mean age of the native SA patients was 56.5 years (range 29-79). One-way ANOVA with Bonferroni correction yielded a significant difference between the mean age of the patients between native and Caucasian SA patients (p = 0.03) and between native SA and UK patients (p < 0.001), but not between Caucasian SA and UK patients (n.s).

There was no significant difference between patients of different geographical location and gender, tumor stage (T-category) and lymph node stage (N-category). UK gastric cancers showed significantly more diffuse type morphology compared to South African gastric cancers (p = 0.002). Overview of patient and tumor characteristics is given in Table 2.
Table 2

Tumor and patient characteristics of the 67 tumors used for MSI and array CGH analysis.

ID

gender

age

Tumor type

T

N

origin

MSI status

Cluster number

Cluster order

% events

% gains

% losses

1

F

62

intestinal

T2

N1

Cauc SA

MSS

5

34

3.2

3.2

0

2

M

73

intestinal

T2

N2

Cauc SA

MSS

5

28

16.9

13.4

3.4

3

F

59

intestinal

T2

N1

Cauc SA

MSS

5

26

26.5

15.1

11.4

4

F

74

intestinal

T2

N1

Cauc SA

MSS

5

27

15.7

9.3

6.5

5

M

56

intestinal

T3

N1

Cauc SA

MSI

-

-

15.6

10.3

5.3

6

M

57

intestinal

T2

N1

Cauc SA

MSS

6

48

3.4

2.4

1.0

7

F

84

intestinal

T1

N0

Cauc SA

MSS

6

43

10.9

7.9

3.1

8

F

79

intestinal

T3

N0

Cauc SA

MSI

-

-

17.6

16.8

0.8

9

M

68

intestinal

T3

N2

Cauc SA

MSS

5

25

32.1

24.3

7.7

10

M

65

intestinal

T2

N0

native SA

MSI

-

-

1.7

1.7

0

11

M

57

intestinal

T1

N0

native SA

MSI

-

-

0

0

0

12

F

29

intestinal

T4

N0

native SA

MSS

6

37

28.3

26.2

2.1

13

F

59

intestinal

T3

N1

native SA

MSS

6

42

10.6

5.3

5.3

14

M

66

intestinal

T2

N1

native SA

MSS

5

31

6.4

6.4

0

15

M

46

intestinal

T3

N2

native SA

MSS

5

35

3.2

3.2

0

16

F

-

diffuse

T4

N2

native SA

MSS

6

44

13.2

12.4

0.7

17

M

51

intestinal

T3

N1

native SA

MSS

5

32

6.1

6.1

0

18

F

49

intestinal

T3

N1

native SA

MSS

4

20

29.1

18.2

10.9

19

M

56

intestinal

T3

N1

native SA

MSS

4

19

26.0

10.3

15.7

20

M

48

intestinal

T3

N2

native SA

MSS

5

36

5.4

5.4

0

21

M

65

mixed

T3

N1

native SA

MSI

-

-

18.2

16.3

1.9

22

M

60

intestinal

T2

-

native SA

MSS

6

41

14.0

11.3

2.7

23

F

63

intestinal

-

-

native SA

MSS

6

38

17.1

10.7

6.4

24

F

54

papillary

T2

N0

native SA

MSS

3

14

44.8

20.6

24.2

25

M

67

intestinal

T3

N1

native SA

MSS

-

-

-

-

-

26

M

31

intestinal

T3

N1

native SA

MSS

5

24

41.1

28.4

12.7

27

M

43

intestinal

T3

N1

native SA

MSI

-

-

9.9

9.9

0

28

F

71

intestinal

T3

-

native SA

MSS

5

33

3.7

2.7

1.0

29

F

77

intestinal

T3

N1

native SA

MSS

5

30

12.7

7.1

5.5

30

M

57

intestinal

T2

N0

native SA

MSS

-

-

-

-

-

31

M

79

intestinal

T3

N2

native SA

MSI

-

-

2.5

2.5

0

32

M

57

intestinal

T4

N0

native SA

MSI

-

-

17.8

16.8

1.0

33

M

56

mixed

T3

N1

native SA

MSS

4

17

42.2

21.3

20.9

34

F

49

mixed

T3

N3

native SA

MSS

5

29

22.3

19.2

3.2

35

F

82

diffuse

T1

N0

UK

MSS

6

46

11.2

10.3

1.0

36

M

81

diffuse

T3

N2

UK

MSS

4

22

15.7

7.2

8.5

37

M

71

diffuse

T2

N1

UK

MSS

7

53

18.4

14.7

3.7

38

M

73

intestinal

T2

N0

UK

MSS

2

11

26.0

17.2

8.8

39

F

65

diffuse

T2

N3

UK

MSS

6

45

15.0

12.0

3.1

40

F

58

diffuse

T3

N3

UK

MSS

1

6

25.7

13.7

12.0

41

F

51

diffuse

T3

N3

UK

MSS

1

7

22.4

16.5

6.0

42

M

91

intestinal

T1

N0

UK

MSS

7

56

49.2

27.6

21.6

43

M

71

diffuse

T3

N2

UK

MSS

7

49

40.6

19.8

20.8

44

M

73

intestinal

T2

N1

UK

MSS

1

5

21.0

8.2

12.9

45

M

64

diffuse

T2

N0

UK

MSS

7

50

14.9

10.3

4.6

46

F

71

intestinal

T1

N0

UK

MSS

1

4

40.8

13.4

27.4

47

F

60

intestinal

T4

N1

UK

MSS

3

16

30.1

15.6

14.5

48

F

96

diffuse

T3

N0

UK

MSS

2

12

41.0

25.0

16.0

49

M

91

mixed

T3

N0

UK

MSS

6

40

16.0

11.4

4.6

50

M

81

diffuse

T2

N0

UK

MSS

2

10

37.7

17.2

20.4

51

F

83

intestinal

T2

N0

UK

MSS

7

51

14.5

10.5

4.0

52

F

82

intestinal

T3

N1

UK

MSS

6

47

2.2

2.2

0

53

F

77

intestinal

T3

N1

UK

MSS

6

39

19.5

11.1

8.5

54

M

74

mixed

T2

N1

UK

MSS

2

8

44.8

22.4

22.3

55

F

59

diffuse

T3

N1

UK

MSS

3

15

26.4

18.1

8.2

56

M

77

mixed

T3

N2

UK

MSS

1

3

24.1

13.7

10.3

57

M

75

intestinal

T2

N0

UK

MSS

2

9

36.9

18.2

18.7

58

M

64

diffuse

T3

N3

UK

MSS

2

13

35.3

20.2

15.1

59

M

71

intestinal

T3

N1

UK

MSS

1

2

31.0

11.7

19.3

60

F

74

diffuse

T3

N2

UK

MSS

4

23

23.2

14.7

8.4

61

M

81

intestinal

T2

N0

UK

MSI

-

-

10.7

8.7

2.0

62

F

74

mixed

T3

N2

UK

MSS

4

18

30.4

17.9

12.5

63

M

67

mixed

T3

N1

UK

MSS

7

52

12.3

6.4

5.9

64

M

73

intestinal

T3

N1

UK

MSS

4

21

15.2

7.3

7.9

65

F

66

mixed

T3

N2

UK

MSS

7

55

34.3

18.7

15.6

66

M

82

intestinal

T1

N1

UK

MSS

7

54

46.7

27.4

19.3

67

F

62

mixed

T3

N0

UK

MSS

1

1

44.0

20.7

23.3

Percentages of events, gains and losses are given for all tumors of sufficient array CGH quality. Cluster number and order are listed for the 56 cases included in the cluster analysis.

F: female, M: male, T: T-stage, N: N-stage MSS: microsatellite stable, MSI: microsatellite instable, Cauc SA: Caucasian South African patients, native SA: native South African patients, UK: patients from United Kingdom, -: unknown.

Microsatellite instability (MSI) analysis

Two out of nine (22%) Caucasian SA gastric cancers, six out of 25 (24%) native SA gastric cancers, and one out of 33 (3%) UK gastric cancers showed MSI. All other gastric cancers were MSS (Table 2). Pearson chi-square yielded a significant difference between the three different tumor groups and MSI status (p < 0.05).

Hierarchical cluster analysis

We analyzed DNA from all gastric cancers by genome-wide array CGH analysis to unravel DNA copy number changes in tumors from different geographical location. MSI positive gastric cancers and gastric cancers with array CGH profiles with a MAD2 value above 0.18 were excluded for cluster analysis leaving 56 tumors (from 32 UK, 17 native SA and 7 Caucasian SA patients) for further analysis.

Hierarchical cluster analysis yielded seven clusters which were significantly correlated with gastric cancers of different geographical origin (p < 0.001) (Figure 1). Clusters 1, 2 and 7 obtained only gastric cancers from UK patients. Clusters 3 and 4 comprised of gastric cancers from UK and native SA patients. Cluster 5 contained only gastric cancers from SA patients, and cluster 6 contained a mixture of tumors of all three groups (Table 2).
https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-7/MediaObjects/12920_2010_Article_204_Fig1_HTML.jpg
Figure 1

Cluster analysis of 56 gastric adenocarcinomas of which 32, 17 and 7 were obtained from UK, native SA and Caucasian SA patients, respectively. Hierarchical cluster analysis yielded 7 clusters significantly correlated with geographical origin of the tumors (p < 0.001). Columns represent the different tumors and rows represent the different chromosomal regions, with chromosome 1 at the bottom and chromosome 22 at the top of the heatmap. DNA copy number gains and losses are indicated in green and red, respectively. The yellow and blue bar next to the cluster represents the chromosome separation.

UK patients showed significantly more gastric adenocarcinomas of the diffuse type according to the Laurén classification[11] compared to SA patients (p = 0.002). We therefore repeated the cluster analysis including only intestinal type gastric carcinomas. Cluster membership of the remaining 12 tumors from UK patients and 12 and 7 tumors from native SA and Caucasian SA patients, respectively, was again significantly correlated to geographical origin of the patient (p < 0.001). Moreover, when analyzing only UK gastric cancers, hierarchical cluster analysis did not separate intestinal and diffuse type gastric cancers, nor were any significant differences observed between these two morphological tumor types with supervised analysis using CGH test.

Cluster membership was independent of gender, tumor stage, lymph node stage and of age of the patients (categorized as < 50 years of age versus ≥ 50 years of age).

DNA copy number changes

We first compared the number of events, which was defined as percentage of clones showing a gain or loss. Gastric cancers from UK patients showed a higher number of events (27% (range 2-49%)) compared to cancers from Caucasian SA (16% (range 3-32%)) and native SA patients (16% (range 0-45%)) (p = 0.005). Cancers from UK, Causasian SA and native SA patients showed 15% (range 2-28%), 11% (range 2-24%) and 11% (range 0-28%) of gained clones respectively, and 12% (range 0-27%), 4% (range 0-11%) and 5% (0-24%) clones showing a loss, respectively. A significant difference in the percentage of clones showing a loss was observed between UK patients and Caucasian SA patients (p = 0.002) and between UK patients and native SA patients (p = 0.02).

Also, when looking only at microsatellite stable gastric cancers UK patients showed a higher number of events (27% (range 2-49%)) compared to microsatellite stable cancers from Caucasian SA and native SA patients (16% (range 3-32%) and 19% (range 3-45%), respectively; p = 0.04). Microsatellite stable cancers from UK, Caucasian SA and native SA patients showed 15% (range 2-28%), 12% (range 2-24%) and 13% (range 3-28%) of gained clones, respectively. There was again a significant difference in percentages of clones showing a loss between cancers from UK and Caucasian SA patients (12% (range 0-27%) and 4% (range 0-11%), respectively; p = 0.04) and between cancers from UK and native SA patients (12% (range 0-27%) and 7% (range 0-24%) respectively; p = 0.04).

An overview of frequently altered (>30%) chromosomal regions with gains and losses per tumor group is given in Tables 3, 4 and 5. Most frequently altered (>30%) chromosomal regions observed in the UK tumors were gains on chromosomes 1p, 1q, 5p, 6p, 7p, 7q, 8q, 9q, 10p, 10q, 11p, 11q, 13q, 14q, 16p, 16q, 17p, 17q, 19p, 19q, 20p, 20q, 21q and 22q and losses on chromosomes 1p, 1q, 3p, 4p, 4q, 5q, 9p, 12q, 13q, 14q, 15q, 17q, 18q and 21q (Table 3). Most frequent DNA copy number aberrations in the native SA patients were gains on the chromosomal regions 7p, 7q, 8q, 9q, 17q, 19p, 19q, 20p and 20q, and losses on 3p and 4q (Table 4). Most frequently altered chromosomal regions in Caucasian SA patients were gains on 3q, 5p, 7p, 7q, 8p, 8q, 9q, 11q, 16p, 17q, 19p, 19q, 20p and 20q and losses on 3p, 4q and 9p (Table 5). A summary of frequencies of gains and losses of all gastric cancers per tumor group is presented in Figures 2 (UK), 3 (native SA) and 4 (Caucasian SA).
Table 3

Detailed overview of frequent DNA copy number aberrations (>30%) of tumors from UK patients.

chromosomal aberrations

flanking clones

position (bp)

 

segment size

gains

losses

start

end

start

end

(Mb)

1p36.33-p36.21

 

RP11-206L10

RP4-636F13

672780

12417597

11.74

 

1p31.1

RP5-944F13

RP11-246O4

69815162

83112098

13.30

 

1p21.3-p13.3

RP11-146P11

RP5-1077K16

95695632

107389118

11.69

1q21.2-q23.1

 

RP4-790G17

RP11-214H6

146971278

153444622

6.47

 

1q31.1-q31.3

RP11-134C1

RP11-75C23

184717073

194242465

9.53

1q32.1-q32.2

 

RP11-150l7

RP11-564A8

197877387

203602276

5.72

 

3p26.3

RP11-385A18

RP11-129K1

46140

2377366

2.33

 

3p25.1-p24.1

RP11-255O19

RP11-99M10

15780361

30799547

15.02

 

3p14.2

RP11-170K19

RP11-114P15

59701329

62639806

2.94

 

3p12.3-p11.2

RP11-103P13

RP11-91M15

75146113

96627928

21.48

 

4p16.1-q35.2

RP11-61G19

CTC-963K6

10275012

191158370

180.88

5p15.33

 

RP11-811I15

CTD-2265D9

70262

2671745

2.60

 

5q11.1-q23.3

RP11-269M20

RP11-114H7

49913067

130460728

80.55

6p21.32-p21.1

 

RP11-79I1

RP11-121G20

33123932

44385866

11.26

7p22.3-p22.1

 

RP11-713A20

RP11-161C7

106471

6396697

6.29

7p11.2

 

RP11-449G3

RP11-34J24

54413814

55403627

0.99

7q22.1

 

RP11-10D8

RP11-163M5

98067793

101528379

3.46

7q36.1

 

RP11-89P11

RP11-43l19

147485335

151131938

3.65

7q36.3

 

RP11-58F7

RP11-120H14

157072238

158524109

1.45

8q24.12-q24.3

 

RP11-22A24

RP5-1109M23

120711365

146238749

25.53

 

9p24.3-p21.1

RP11-48M17

RP11-141J7

2136329

32469400

30.33

9q33.3-q34.3

 

RP11-205K6

RP11-424E7

126296075

138363252

12.07

10p15.3

 

RP11-631M21

RP11-74N14

50000

1789100

1.74

10p15.2

 

RP11-195B3

 

3293007

3338470

0.05

10q22.1

 

RP11-91A1

RP11-28E3

72033907

73573433

1.54

11p15.5-p15.4

 

CTC-908H22

RP11-304P12

178227

3140168

2.96

11q12.2-q13.5

 

RP11-286N22

RP11-30J7

60851860

76232373

15.38

 

12q21.2-q22

RP1-97G4

RP11-2K12

76228586

91346299

15.12

13q11-q12.11

 

RP11-94A1

RP11-61K9

18360157

19386914

1.03

 

13q21.2-q21.33

RP11-310K10

RP11-451E2A

60721181

71574154

10.85

13q32.3

 

RP11-19J14

RP11-113F15

97851594

99328275

1.48

13q33.3-q34

 

RP11-61I17

RP11-569D9

108847725

114103243

5.26

 

14q12

RP11-330O19

RP11-109D12

25538832

26345513

0.81

 

14q21.1-q21.3

RP11-88D14

RP11-94K16

36949212

48298851

11.35

 

14q31.1-q31.3

RP11-46l17

RP11-88N20

78630860

86772926

8.14

14q32.31-q32.33

 

RP11-367F11

RP11-815P21

101467534

105159201

3.69

 

15q14

RP11-294M6

RP11-79A5

33841939

35953471

2.11

16p13.3

 

CTD-2148K8

RP11-89M4

87754

4697230

4.61

16p13.2-p13.11

 

RP11-475D10

RP11-489O1

8598165

15572359

6.97

16p12.1-p11.2

 

RP11-142A12

RP11-18H23

26595069

31443695

4.85

16q21-q22.1

 

RP11-52B24

RP11-394B2

63708677

69365102

5.66

16q23.3-q24.3

 

RP11-483P21

RP11-566K11

82361609

88613383

6.25

17p13.3-p13.1

 

RP11-411G7

RP11-89A15

427024

8365794

7.94

17p11.2

 

RP11-524F11

RP1-162E17

17343389

19251691

1.91

17q11.2-q21.31

 

RP11-138P22

RP11-374N3

23133763

41096064

17.96

17q21.32-q21.33

 

RP11-234J24

RP11-506D12

42655422

46333070

3.68

 

17q22-q23.2

RP11-143M4

RP11-139B3

47607556

51363278

3.76

17q24.3-q25.3

 

RP11-65C22

RP11-258N23

68165339

78308832

10.14

 

18q11.2-q23

RP11-5G23

RP11-396D4

21431314

71337306

49.91

19p13.3-q13.43

 

RP11-110A24

GS1-1129C9

134914

63771717

63.64

20p13-q13.33

 

RP11-530N10

CTB-81F12

9943

62393015

62.38

 

21q11.2-q22.11

RP11-72P4

RP11-41N19

13857799

30673984

16.82

22q11.1-q11.21

 

RP11-81H21

RP11-586I18

14754982

18976359

4.22

22q12.3-q13.33

 

RP11-90I17

CTB-99K24

35686144

49397088

13.71

Table 4

Detailed overview of frequent DNA copy number aberrations (>30%) of tumors from native SA patients.

chromosomal aberrations

flanking clones

position (bp)

 

segment size

gains

losses

start

end

start

end

(Mb)

 

3p14.2

RP11-734E15

RP11-137N22

59105371

61252524

2.15

 

4q35.2

RP11-354H17

CTC-963K6

190095484

191158370

1.06

7p22.3-p11.2

 

RP11-713A20

RP11-80l24

106471

55784518

55.68

7q22.1

 

RP11-10D8

RP11-163M5

98067793

101528379

3.46

8q24.3

 

RP11-472K18

RP5-1109M23

144481535

146238749

1.76

9q33.3-q34.3

 

RP11-91G7

RP11-424E7

124316484

138363252

14.05

17q12-q21.31

 

RP11-893G17

RP11-392O1

31506328

39091575

7.59

17q21.32-q21.33

 

RP1-62O9

RP11-506D12

44647598

46333070

1.69

17q23.2-q25.3

 

RP11-579A4

RP11-258N23

54149948

78451750

24.30

19p13.3

 

RP11-110A24

CTC-1482H14

134914

5154803

5.02

19p13.2-p13.11

 

RP11-197O4

RP11-88I12

10248852

19023254

8.77

19q12-q13.34

 

CTC-1459F4

GS1-1129C9

32889410

63771717

30.88

20p13-q13.33

 

RP11-530N10

CTB-81F12

9943

62393015

62.38

Table 5

Detailed overview of frequent DNA copy number aberrations (>30%) of tumors from Caucasian SA patients.

chromosomal aberrations

flanking clones

position (bp)

 

segment size

gains

losses

start

end

start

end

(Mb)

 

3p14.2

RP11-48E21

RP11-641C17

60380670

60705094

0.32

3q26.2-q26.31

 

RP11-669J9

RP11-44A1

172392313

173855790

1.46

 

4q32.1-q35.2

RP11-192D11

CTC-963K6

159886665

191158370

31.27

5p13.1-p12

 

RP11-17J3

RP11-55O15

40113135

44396362

4.28

7p22.3-p21.3

 

RP11-713A20

RP11-505D17

106471

7932634

7.83

7q22.1

 

RP4-550A13

RP11-333G13

98512376

101153193

2.64

8p23.1

 

RP11-241P12

RP11-589N15

9788949

11803111

2.01

8q22.1-q22.3

 

RP11-664H21

RP11-132E3

98618965

105402542

6.78

8q24.21

 

RP11-28I2

RP11-1142f3

127563658

129620230

2.06

 

9p24.1-p23

RP11-165O14

RP11-91E3

5873408

9689968

3.82

9q33.3-q34.3

 

RP11-62A6

RP11-424E7

124479347

138363252

13.88

11q13.3-q13.5

 

RP11-554A11

RP11-98G24

68509550

77008323

8.50

16p11.2

 

RP11-110P16

RP11-388M20

28675396

31163676

2.49

17q12-q21.1

 

RP5-986F12

RP11-94L15

33099924

35227135

2.13

17q25.1-q25.3

 

RP11-41E12

RP11-258N23

68729134

78451750

9.72

19p13.3-p13.11

 

RP11-110A24

RP11-88I12

134914

19023254

18.89

19q13.11-q13.43

 

CTC-1325L16

GS1-1129C9

37623641

63771717

26.15

20p13-q13.33

 

RP11-48M7

CTB-81F12

3728265

62393015

58.66

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-7/MediaObjects/12920_2010_Article_204_Fig2_HTML.jpg
Figure 2

Frequencies of gains and losses throughout the genome in all gastric adenocarcinomas from UK patients. Clones are sorted by position per chromosome (1-22). Vertical lines indicate transition between chromosomes; dashed vertical lines indicate centromere position.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-7/MediaObjects/12920_2010_Article_204_Fig3_HTML.jpg
Figure 3

Frequencies of gains and losses throughout the genome in all gastric adenocarcinomas from native SA patients. Clones are sorted by position per chromosome (1-22). Vertical lines indicate transition between chromosomes; dashed vertical lines indicate centromere position.

https://static-content.springer.com/image/art%3A10.1186%2F1755-8794-4-7/MediaObjects/12920_2010_Article_204_Fig4_HTML.jpg
Figure 4

Frequencies of gains and losses throughout the genome in all gastric adenocarcinomas from Caucasian SA patients. Clones are sorted by position per chromosome (1-22). Vertical lines indicate transition between chromosomes; dashed vertical lines indicate centromere position.

Supervised analysis

To identify biological differences between gastric cancers from different geographical origin, native SA tumors were compared with UK tumors using CGH test. Only MSS tumors were included in the supervised analysis. In total, 133 regions, located on different chromosomes, were significantly different (p < 0.05 and fdr≤0.15) between these two patient groups. An overview of the significant chromosomal regions, including the fdr rates, is given in Table 6. No significant differences were found between gastric cancers from UK and Caucasian SA patients or between gastric cancers from native and Caucasian SA patients.
Table 6

Detailed overview of the supervised analysis using CGH test.

cytoband

region start (bp)

region end (bp)

p-value

fdr

cytoband

region start (bp)

region end (bp)

p-value

fdr

1p36.33

672780

1359795

0.04

0.15

11p14.2-p14.1

27033269

27371257

0.05

0.15

1p36.32-p36.31

3386389

6294064

0.03

0.12

11q13.3

68509550

69323966

0.04

0.14

1p36.21-p36.13

12798944

15683816

0.03

0.12

11q13.3-q13.4

69314721

70472869

0.04

0.14

1p31.2

67178936

69303906

0.04

0.14

11q22.1-q22.2

98930611

101405228

0.03

0.12

1p31.1

69815162

76679895

0.01

0.06

11q22.2-q22.3

102010610

102424014

0.03

0.12

1p31.1

77428804

77820126

<0.01

0.04

12q21.2

76570565

78724263

0.04

0.14

1p21.2-p21.1

101684496

104502748

0.03

0.12

13q21.31-q21.33

61335626

69275204

0.04

0.14

1q31.1-q31.2

184717073

188976520

0.01

0.09

14q21.1

39694531

42171623

0.02

0.10

1q31.2-q31.3

189822405

193082884

0.02

0.10

14q21.2

42965408

44043547

0.01

0.09

1q31.3

193336091

194242465

0.01

0.08

14q21.2-q21.3

45258184

48298851

0.03

0.12

1q31.3

195068870

195629725

0.03

0.12

15q14

33841939

35953471

0.04

0.14

3p26.3

46140

2377366

<0.01

0.03

15q22.2

57373165

61214280

0.02

0.09

3p24.3

17181327

18148477

<0.01

0.03

15q23

65816865

68768615

0.02

0.09

3p24.3

19033520

21742232

<0.01

0.03

15q23-q24.2

69188639

73645336

0.05

0.15

3p24.3-p24.1

22747912

27531283

0.02

0.10

15q24.2-q25.2

74334873

81024206

0.03

0.12

3p21.31

46545403

47371983

0.05

0.15

16p13.2-p13.12

8777494

12522798

0.05

0.15

3p21.31

47384745

50114898

0.04

0.14

16p11.2

28675396

31163676

<0.01

0.04

3p21.31-p21.2

50533656

51418837

0.01

0.09

16q24.1

82993991

83622386

0.05

0.15

3p21.31-p21.2

51390596

52007218

0.02

0.09

16q24.1

84123856

84922042

0.02

0.09

3p21.1

52658450

53621497

0.01

0.09

16q24.2-q24.3

86986672

87452904

0.03

0.12

3p12.3

76450939

79097142

0.02

0.09

16q24.3

87848795

88465228

0.02

0.09

3p12.3-p12.2

79197544

82066233

<0.01

0.04

16q24.3

88398231

88613383

0.01

0.07

3p12.2-p12.1

82657794

84801874

<0.01

0.03

17p13.3

427024

1071560

<0.01

0.03

3p12.1-p11.2

85234579

87730259

<0.01

0.03

17p13.3-p13.2

2026966

4169913

0.04

0.14

3p11.1

88915283

89771786

<0.01

0.04

17p13.2

4810523

5166678

0.01

0.09

3q11.2

95569618

96250439

0.01

0.05

17p13.1

6780962

7477414

<0.01

0.03

3q25.1-q25.33

151508469

161536133

0.03

0.12

17p13.1

7436435

7682367

<0.01

0.03

3q25.33-q26.1

162044657

164516640

0.03

0.12

17p11.2

17343389

19251691

0.04

0.14

3q26.1

165289729

167697600

0.05

0.15

17q11.2

23133763

28423820

0.02

0.09

3q26.31-q26.32

174848631

180449332

0.05

0.15

17q11.2-q12

28664889

29147086

0.02

0.09

4p15.32-p14

17799729

36260048

0.02

0.11

17q25.1

69709369

71238958

0.01

0.09

4p14

36998587

37497326

0.01

0.09

17q25.1-q25.3

71406319

77588058

0.03

0.12

4p14

37851044

38103870

0.02

0.11

18q11.2

21431314

21774952

0.01

0.05

4p14-p12

38087293

46781512

0.01

0.09

18q11.2

22104619

22931012

<0.01

0.04

4q12

58332155

59148507

0.04

0.14

18q12.1

23970882

24526449

0.02

0.10

4q12-q13.1

59679465

62653308

0.02

0.10

18q12.1

25034674

26878315

0.01

0.08

4q13.3

74322497

76429795

0.03

0.12

18q12.1

27447009

28415095

0.01

0.09

4q13.3-q21.21

76495364

79353372

0.02

0.09

18q12.1

29409483

30219417

0.01

0.09

4q21.21

79222718

80683287

0.03

0.12

18q12.1-q12.2

30773824

31588529

0.01

0.06

4q33-q34.3

172094999

178721484

0.02

0.10

18q22.1

60340433

61310295

0.03

0.12

4q34.3

178969859

179599683

0.01

0.09

18q22.1

61623805

62645202

0.03

0.12

4q34.3

180819690

183096645

0.02

0.10

18q22.1-q22.3

63092764

68041625

0.03

0.12

4q35.1

184503994

186178296

0.03

0.12

19p13.3

134914

913289

0.01

0.08

5q11.2

50971745

52055659

0.04

0.14

19p13.3

902641

5009969

<0.01

0.04

5q11.2

52909242

56437163

0.03

0.12

19p13.3

5663923

6519297

<0.01

0.01

5q11.2-q12.1

56921490

58947885

0.02

0.09

19p13.3-p13.2

6523443

9826740

<0.01

<0.01

5q14.3

82802677

86118543

0.05

0.15

19p13.2-p13.12

10248852

15116365

<0.01

0.03

5q23.2

122463056

123527915

0.05

0.15

19p13.12-p13.11

15415833

17777501

<0.01

0.01

7p22.1-p21.3

6983150

7932634

<0.01

0.02

19p13.11

18202507

19023254

<0.01

0.03

7p21.3-p21.2

9256088

14085902

<0.01

<0.01

19p12

19877150

21504328

0.01

0.05

7p21.2-p21.1

14342152

19957111

<0.01

0.01

19p12

22133662

22949959

0.01

0.05

7q22.1

98651132

100929260

<0.01

0.03

19q12

33315121

33507712

0.02

0.10

8q24.21

130562467

131126185

0.05

0.15

19q12

34159370

34664148

0.02

0.11

8q24.22

131641447

133561490

0.01

0.09

19q12

34960906

35766560

0.02

0.11

8q24.22

134537164

136154996

0.02

0.11

19q12-q13.11

36958851

37518517

0.05

0.15

8q24.22-q24.23

136472354

138543270

0.04

0.14

19q13.12-q13.13

40883472

43004503

0.03

0.13

8q24.3

141395868

142790557

0.01

0.05

19q13.2-q13.32

46498596

50725496

0.03

0.12

8q24.3

144790054

145357620

0.01

0.09

19q13.32-q13.33

52665374

54718281

0.03

0.12

8q24.3

145585590

145953950

0.02

0.11

19q13.33-q13.43

55461670

63771717

<0.01

0.01

8q24.3

145893230

146238749

0.04

0.14

21q11.2-q21.1

13857799

18774434

<0.01

0.03

9p24.1-p23

8398601

9689968

0.04

0.14

21q21.1

20982315

21411332

<0.01

0.03

9p23

9684353

10554235

0.04

0.14

21q21.1-q21.2

22151920

22943367

<0.01

0.03

10q22.1

70824040

71674097

0.03

0.12

21q21.2

23491399

25568510

<0.01

0.01

10q22.1

72033907

73573433

0.02

0.09

21q21.3

26174732

26923374

0.02

0.09

10q26.3

135110821

135301208

0.03

0.12

21q21.3-q22.11

28596969

30855176

0.03

0.12

11p15.5

178227

626401

0.01

0.05

22q13.33

48473404

49397088

0.04

0.15

11p15.5

1299306

1785278

<0.01

0.03

     

In total, 133 regions were significantly different (p < 0.05 and fdr≤0.15) between UK and native SA tumors. The chromosomal regions, including the start and end positions, and the fdr rates are listed.

Discussion

One of the main risk factors contributing to gastric cancer is infection with H. pylori, which causes a chronic active gastritis [4, 21]. In South Africa, gastric cancer is infrequent, while the prevalence of H. pylori infection is very high. Although differences in genotypes of H. pylori exist in different geographic areas, this African enigma can not only be explained by differences in virulent strains of H. pylori [2224]. High prevalence of vacA s1b strain is observed in South Africa as well as in Brazil and Portugal, countries with high incidences of gastric cancer,[2527] and frequencies of CagA antibodies were similar between patients with gastric neoplasia compared to healthy controls [28]. The prevalence of the different virulent strains in the present study is unknown. Since H. pylori is thought to play a major role in the initiation phase of gastric cancer development and most often already disappeared at time of gastric cancer diagnosis, it is impossible to accurately retrieve this information.

Besides the virulence of the infecting H. pylori strain, other factors influence gastric cancer risk, including environmental factors such as diet and socioeconomic status, and host factors, such as polymorphisms, which are involved in the inflammatory response to the infection [29, 30]. Knowing that the prevalence of H. pylori infection and incidences of gastric cancer are different in South Africa and Western Europe, we aimed to study if this would reflect in different patterns of gastric carcinogenesis.

The concept of the African enigma has been challenged since it has been suggested that the enigma could be explained due to lack of infrastructure and access to hospitals and care in African countries resulting in incomplete reporting of gastric cancer. However the incidence of gastric cancer would have been so dramatically underestimated that it has been stated that under-reporting by itself could not explain the lower frequencies of gastric cancer in African countries [31]. Also, when using the proportional frequency of gastric cancer compared to other cancer types in Africa, gastric cancer incidence remains very low [8]. Another criticism on the African enigma has been the high prevalence of HIV infection. A relatively large part of the African population would die of HIV before the age in which gastric cancer becomes more frequent. However, the low gastric cancer incidence in Africa was described before the HIV epidemic.

South African patients showed significantly more microsatellite instable gastric cancers compared to Western European patients. Also at the level of chromosomal instability clear differences were found, reflected by a significant correlation between cluster membership and geographical tumor origin, i.e. UK, native SA and Caucasian SA. Microsatellite instable gastric cancers are described to have fewer chromosomal aberrations compared to microsatellite stable gastric cancers [32, 33]. To rule out that tumors from South African patients cluster together by hierarchical cluster analysis due to the fact that these tumors show higher frequencies of microsatellite instability, only microsatellite stable gastric cancers were included in the hierarchical cluster analysis.

Not much has been reported about microsatellite status in gastric cancers from African patients. One study reported infrequent microsatellite instable gastric cancers in South African patients[34] which is in contrast with our findings which show a higher frequency of microsatellite instability in gastric cancers from SA patients compared to UK patients. Based on the present data, MSI does play an important role in gastric carcinogenesis in South Africa.

Several chromosomal aberrations are common in the three different tumor groups analyzed, including gains of chromosomes 7, 8q, 9q, 17q, 19 and 20 and losses of 3p and 4q, while other chromosomal changes are specific for each tumor group. In addition, gastric cancers from UK patients showed a significantly higher number of clones showing a loss compared to gastric cancers form South African patients. These results indicate different patterns of chromosomal instabilities in gastric cancers correlating to geographical origin of the patient.

The chromosomal aberrations of the UK tumors are comparable to other array CGH studies analyzing Western European tumors [12, 3537]. To the best of our knowledge, this is the first array CGH study on gastric cancers from South African patients. Since several chromosomal regions are significantly different between gastric cancers from different geographical origin, and each region comprises multiple genes, further studies are needed to pinpoint candidate genes contributing to the differences in genomic profiles.

The higher frequency of diffuse gastric cancers from UK patients compared to the SA patients in the present study could be considered as a confounding factor. Contradicting results have been published either describing different or similar patterns of DNA copy number aberrations between intestinal and diffuse type gastric cancers [7, 33, 3739]. In the context of the present study we believe that the differences in DNA copy number aberrations between UK and SA gastric cancers are independent of the histological tumor type. When repeating the cluster analysis with intestinal type carcinomas only, cluster membership again was significantly correlated with geographical origin of the tumors. Also supervised data analysis, i.e. testing copy number status of all genomic loci, did not reveal any significant differences in DNA copy number changes between intestinal and diffuse gastric cancers from UK patients. Furthermore, hierarchical cluster analysis including UK gastric cancers only did not separate intestinal and diffuse type gastric cancers. We therefore do not believe our findings to be influenced by distribution of histological types in this series. Question remains why diffuse type gastric cancers were more frequently observed in gastric cancers from UK patients compared to SA patients. Besides being a confounding factor, we can hypothesize that mutation of E-cadherin (CDH1), or other mechanisms disrupting the CDH1 gene function such as epigenetic mechanisms or miRNAs, playing an important role in diffuse type gastric cancer, might play a minor role in SA gastric cancer patients due to different pathways of carcinogenesis, as shown in the present study by differences in patterns of DNA copy number aberrations. Also, the prevalence of H. pylori infection is very high in South Africa, and H. pylori infection mainly plays a role in intestinal type gastric cancers. This could also explain the higher number of intestinal type gastric cancers in SA patients.

Further, with respect to copy number changes in relation to histological types, chromosomal gains of 8q and 17q and losses of 3p have been described to be associated with intestinal type gastric cancers [33]. On the other hand, gains of 8q and 17q have been reported to be altered predominantly in diffuse type gastric cancers [38]. In the present study gains on chromosomes 8q and 17q and losses on 3p were common to both intestinal and diffuse type gastric cancers. In addition, these aberrations also were common in tumors from both UK and SA patients. Gains on chromosomes 13q and 19q have been found more frequently in diffuse type gastric cancers [33, 36, 38]. Again, in the present study, gains of these chromosomes were observed equally in intestinal and diffuse type gastric cancers. Gain of 19q was frequently observed in tumors from both geographical origins. Although gain of 13q was observed less frequently in tumors from SA patients compared to tumors from UK patients, still around 20-25% of the tumors of native SA patients show a gain of chromosome 13q, making it unlikely that tumor type has influenced cluster membership.

A limitation of the present study is the fact that native SA gastric cancer patients were significantly younger compared to Caucasian SA and UK gastric cancer patients. We previously showed that gastric cancers of young and elderly patients have different patterns of chromosomal aberrations [12]. We cannot rule out that also in these series, age might contribute to differences in DNA copy number profiles, however cluster analysis showed that gastric cancers from native SA patients were more similar to cancers from Caucasian SA patients, who have similar age as UK patients, indicating that cluster membership is independent of age in this respect. Overall, most differences were observed between UK and native SA tumors.

We realize that the present study is based on a heterogeneous group of gastric cancer patients, with different genetic background and different environmental factors, including H. pylori, diet and socioeconomic status, influencing gastric cancer risk. Statements on genotype influencing gastric cancer are very difficult to make since the degree of heterogeneity within each different patient group, i.e. UK, native SA and Caucasian SA, is unknown.

The patterns of genomic alterations in gastric cancers from UK and SA patients could gain clinical relevance in the future. In addition to surgery, gastric cancer treatment increasingly includes (neo)adjuvant chemotherapy and/or radiotherapy, however still without patients being stratified based on biological characteristics of their tumors. Clinical trials are underway in which also the value of genetic markers for predicting response to therapy are studied. In the end, stratification for therapy may include genomic alterations observed in tumors of patients from different geographical origin.

Conclusions

We showed that gastric cancers of UK and SA patients are different in their patterns of genomic instability. Gastric cancers from SA patients show higher frequencies of microsatellite instability and different patterns of chromosomal aberrations compared to gastric cancers from UK patients. These results may suggest different molecular pathways of gastric carcinogenesis, consistent with the African enigma hypothesis. Further studies are needed to explore the link between H. pylori and other environmental factors, as well as host factors, such as polymorphisms influencing gastric cancer susceptibility, in relation to the patterns of genomic instability in gastric cancers from these different geographic areas.

Declarations

Acknowledgements

We thank the Mapping Core and Map Finishing groups of the Wellcome Trust Sanger Institute for initial clone supply and verification. We thank Peter van der Vlies and Klaas Kok for providing the BAC arrays. This work was financially supported by the Dutch Cancer Society grant-KWF 2004-3051 and the Pathological Society Pilot Study Grant - August 2007.

Authors’ Affiliations

(1)
Dept. of Pathology, VU University Medical Center
(2)
Dept. of Anatomical Pathology, University of Pretoria
(3)
Pathology and Tumour Biology, Leeds Institute of Molecular Medicine, University of Leeds
(4)
Dept. of Gastroenterology, VU University Medical Center Amsterdam
(5)
Dept. of Surgery, Leiden University Medical Center
(6)
Dept of Internal Medicine and Gastroenterology, University of Pretoria

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  40. Pre-publication history

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