Open Access

The chemiluminescence based Ziplex® automated workstation focus array reproduces ovarian cancer Affymetrix GeneChip® expression profiles

  • Michael CJ Quinn1,
  • Daniel J Wilson2,
  • Fiona Young2,
  • Adam A Dempsey2,
  • Suzanna L Arcand3,
  • Ashley H Birch1,
  • Paulina M Wojnarowicz1,
  • Diane Provencher4, 5, 6,
  • Anne-Marie Mes-Masson4, 6,
  • David Englert2 and
  • Patricia N Tonin1, 3, 7Email author
Journal of Translational Medicine20097:55

DOI: 10.1186/1479-5876-7-55

Received: 07 April 2009

Accepted: 06 July 2009

Published: 06 July 2009

Abstract

Background

As gene expression signatures may serve as biomarkers, there is a need to develop technologies based on mRNA expression patterns that are adaptable for translational research. Xceed Molecular has recently developed a Ziplex® technology, that can assay for gene expression of a discrete number of genes as a focused array. The present study has evaluated the reproducibility of the Ziplex system as applied to ovarian cancer research of genes shown to exhibit distinct expression profiles initially assessed by Affymetrix GeneChip® analyses.

Methods

The new chemiluminescence-based Ziplex® gene expression array technology was evaluated for the expression of 93 genes selected based on their Affymetrix GeneChip® profiles as applied to ovarian cancer research. Probe design was based on the Affymetrix target sequence that favors the 3' UTR of transcripts in order to maximize reproducibility across platforms. Gene expression analysis was performed using the Ziplex Automated Workstation. Statistical analyses were performed to evaluate reproducibility of both the magnitude of expression and differences between normal and tumor samples by correlation analyses, fold change differences and statistical significance testing.

Results

Expressions of 82 of 93 (88.2%) genes were highly correlated (p < 0.01) in a comparison of the two platforms. Overall, 75 of 93 (80.6%) genes exhibited consistent results in normal versus tumor tissue comparisons for both platforms (p < 0.001). The fold change differences were concordant for 87 of 93 (94%) genes, where there was agreement between the platforms regarding statistical significance for 71 (76%) of 87 genes. There was a strong agreement between the two platforms as shown by comparisons of log2 fold differences of gene expression between tumor versus normal samples (R = 0.93) and by Bland-Altman analysis, where greater than 90% of expression values fell within the 95% limits of agreement.

Conclusion

Overall concordance of gene expression patterns based on correlations, statistical significance between tumor and normal ovary data, and fold changes was consistent between the Ziplex and Affymetrix platforms. The reproducibility and ease-of-use of the technology suggests that the Ziplex array is a suitable platform for translational research.

Background

During the last decade, the advent of high-throughput techniques such as DNA microarrays, has allowed investigators to interrogate the expression level of thousands of genes concurrently. Due to the heterogeneous nature of many cancers in terms of both their genetic and molecular origins and their response to treatment, individualizing patient treatment based on the expression levels of signature genes may impact favorably on patient management [1, 2]. In ovarian cancer, discrete gene signatures have been determined from microarray analysis of ovarian cancer versus normal ovarian tissue [36], correlating gene expression profiles to survival or prognosis [7, 8], studies of chemotherapy resistance [9, 10], and functional studies such as chromosome transfer experiments [11, 12]. Recent studies have focused on a biomarker approach [13], with specific prognostic markers being discovered by relating gene expression profiles to clinical variables [1416]. In addition, there is a trend towards offering patient-tailored therapy, where expression profiles are related to key clinical features such as TP53 or HER2 status, surgical outcome and chemotherapy resistance [1, 17].

A major challenge in translating promising mRNA-based expression biomarkers has been the reproducibility of results when adapting gene expression assays to alternative platforms that are specifically developed for clinical laboratories. Xceed Molecular has recently developed a multiplex gene expression assay technology termed the Ziplex® Automated Workstation, designed to facilitate the expression analysis of a discrete number of genes (up to 120) specifically intended for clinical translational laboratories. The Ziplex array is essentially a three-dimensional array comprised of a microporous silicon matrix containing oligonucleotides probes mounted on a plastic tube. The probes are designed to overlap the target sequences of the probes used in large-scale gene expression array platforms from which the expression signature of interest was initially detected, such as the 3' UTR target sequences of the Affymetrix GeneChip®. However unlike most large-scale expression platforms, gene expression detection is by chemiluminescence. Recently, the Ziplex technology was compared to five other commercially available and well established gene expression profiling systems following the methods introduced by the MicroArray Quality Control (MAQC) consortium [1820] and reported in a white paper by Xceed Molecular [21]. The original MAQC study (MAQC Consortium, 2006) was undertaken because of concerns about the reproducibility and cross-platform concordance between gene expression profiling platforms, such as microarrays and alternative quantitative platforms. By assessing the expression levels of the MAQC panel of 53 genes on universal RNA samples, it was determined that the reproducibility, repeatability and sensitivity of the Ziplex system were at least equivalent to that of other MAQC platforms [21].

There is a need to implement reliable gene expression technologies that are readily adaptable to clinical laboratories in order to screen individual or multiple gene expression profiles ("signature") identified by large-scale gene expression assays of cancer samples. Our ovarian cancer research group (as well as other independent groups) has identified specific gene expression profiles from mining Affymetrix GeneChip expression data illustrating the utility of this approach at identifying gene signature patterns associated with specific parameters of the disease [14, 22]. Ovarian cancer specimens are typically large and exhibit less tumor heterogeneity and thus may be amenable to gene expression profiling in a reproducible way. However, until recently the gene expression technologies available that could easily be adapted to a clinical setting have been limited primarily by the expertise required to operate them. The recently developed Ziplex Automated Workstation offers a opportunity to develop RNA expression-based biomarkers that could readily be adapted to clinical settings as the 'all-in-one' technology appears to be relatively easy to use. However, this system has not been applied to ovarian cancer disease nor has its use been reported in human systems. In the present study we have evaluated the reproducibility of the Ziplex system using 93 genes, selected based on their expression profile as initially assessed by Affymetrix GeneChip microarray analyses from a number of ovarian cancer research studies from our group [6, 14, 2226]. These include genes which are highly differentially expressed between ovarian tumor samples and normal ovary samples that were identified using both newer and older generation GeneChips [6, 22, 25, 26]. In addition, to address the question of sensitivity, genes known to have a wide range of expression values were tested some of which show comparable values of expression between representative normal and ovarian tumor tissue samples but represent a broad range of expression values [25, 26]. Other genes known to be relevant to ovarian cancer including tumor suppressor genes and oncogenes were included in the analysis. Selected highly differentially expressed genes from an independent microarray analysis of ovarian tumors compared to short term cultures of normal epithelial cells was also included [3]. In many cases, the level of gene expression identified by Affymetrix GeneChip analysis was independently validated by semi-quantitative RT-PCR, real-time RT-PCR, or Northern Blot analysis [6, 14, 22, 2426]. Expression assays were performed using RNA from serous ovarian tumors, short term cultures of normal ovarian surface epithelial cells, and four well characterized ovarian cancer cell lines which were selected based on their known expression profiles using Affymetrix microarray analyses. Comparisons were made between the Ziplex system and expression profiles generated using the U133A Affymetrix GeneChip platform. An important aspect of this study was that gene expression profiling of Ziplex system was performed in a blinded fashion where the sample content was not known to the immediate users. It is envisaged that both the nature of the candidates chosen and their range of gene expression will permit for a direct comment on the sensitivity, reproducibility and overall utility of the Ziplex array as a platform for gene expression array analysis for translational research.

Methods

Source of RNA

Total RNA was extracted with TRIzol reagent (Gibco/BRL, Life Technologies Inc., Grand Island, NY) from primary cultures of normal ovarian surface epithelial (NOSE) cells, frozen malignant serous ovarian tumor (TOV) samples and epithelial ovarian cancer (EOC) cell lines as described previously [27]. Additional File 1 provides a description of samples used in the expression analyses.

The NOSE and TOV samples were attained from the study participants at the Centre de recherche du Centre hospitalier de l'Université de Montréal – Hôpital Hotel-Dieu and Institut du cancer de Montréal with signed informed consent as part of the tissue and clinical banking activities of the Banque de tissus et de données of the Réseau de recherche sur le cancer of the Fonds de la Recherche en Santé du Québec (FRSQ). The study was granted ethical approval from the Research Ethics Boards of the participating research institutes.

Ziplex array and probe design

The 93 genes used for assessing the reproducibility of the Ziplex array are shown in Table 1. The criteria for gene selection were: genes exhibiting statistically significant differential expression between NOSE and TOV samples as assessed by Affymetrix U133A microarray analysis; genes exhibiting a range of expression values (nominally low, medium or high) based on Affymetrix U133A microarray analysis, in order to assess sensitivity; genes exhibiting differential expression profiles based on older generation Affymetrix GeneChips (Hs 6000 [6] and Hu 6800 [23]); and genes known or suspected to play a role in ovarian cancer (Table 1). Initial selection criteria for genes in their original study included individual two-way comparisons [25, 26], fold-differences [6, 23], and fold change analysis using SAM (Significance Analysis of Microarrays) [3] between TOV and NOSE groups. Some genes were selected based on their low, mid or high range of expression values that did not necessarily exhibit statistically significant differences between TOV and NOSE groups.
Table 1

Selection Criteria of Genes Assayed by Ziplex Technology

Selection Criteria Categories

Affymetrix U133A Probe Set

GeneID*

Gene Name

Reference

A: Differentially expressed genes based on Affymetrix U133A analysis

208782_at

11167

FSTL1

25

 

213069_at

57493

HEG1

25

 

218729_at

56925

LXN

25

 

202620_s_at

5352

PLOD2

25

 

217811_at

51714

SELT

25

 

213338_at

25907

TMEM158

25

 

203282_at

2632

GBE1

25

 

204846_at

1356

CP

25

 

221884_at

2122

EVI1

25

 

202310_s_at

1277

COL1A1

26

 

201508_at

3487

IGFBP4

26

 

200654_at

5034

P4HB

26

 

212372_at

4628

MYH10

26

 

216598_s_at

6347

CCL2

26

 

208626_s_at

10493

VAT1

26

 

41220_at

10801

SEPT9

26

 

208789_at

284119

PTRF

26

 

206295_at

3606

IL18

22

 

202859_x_at

3576

IL8

22

 

209969_s_at

6772

STAT1

22

 

209846_s_at

11118

BTN3A2

22

 

220327_at

389136

VGLL3

11

 

203180_at

220

ALDH1A3

26

 

204338_s_at

5999

RGS4

26

 

204879_at

10630

PDPN

26

 

207510_at

623

BDKRB1

26

 

208131_s_at

5740

PTGIS

26

 

211430_s_at

3500

IGHG1

26

 

216834_at

5996

RGS1

26

 

266_s_at

100133941

CD24

26

 

213994_s_at

10418

SPON1

26

 

221671_x_at

3514

IGKC

26

B: Genes exhibiting a range of expression values based on Affymetrix U133A analysis

218304_s_at

114885

OSBPL11

25

 

219295_s_at

26577

PCOLCE2

25

 

205329_s_at

8723

SNX4

25

 

219036_at

80321

CEP70

25

 

218926_at

55892

MYNN

25

 

208836_at

483

ATP1B3

25

 

204992_s_at

5217

PFN2

25

 

214143_x_at

6152

RPL24

25

 

208691_at

7037

TFRC

25

 

203002_at

51421

AMOTL2

25

 

221492_s_at

64422

ATG3

25

 

218286_s_at

9616

RNF7

25

 

212058_at

23350

SR140

25

 

201519_at

9868

TOMM70A

25

 

209933_s_at

11314

CD300A

26

 

219184_x_at

29928

TIMM22

26

 

204683_at

3384

ICAM2

26

 

212529_at

124801

LSM12

26

 

211899_s_at

9618

TRAF4

26

 

218014_at

79902

NUP85

26

 

200816_s_at

5048

PAFAH1B1

26

 

202395_at

4905

NSF

26

 

201388_at

5709

PSMD3

26

 

220975_s_at

114897

C1QTNF1

26

 

210561_s_at

26118

WSB1

26

 

202856_s_at

9123

SLC16A3

26

 

212279_at

27346

TMEM97

26

 

37408_at

9902

MRC2

26

 

201140_s_at

5878

RAB5C

26

 

214218_s_at

7503

XIST

24

 

200600_at

4478

MSN

24

 

201136_at

5355

PLP2

24

C: Genes exhibiting differential expression profiles based on older generation Affymetrix GeneChips (Hs 6000 (6), Hu 6800 (22))

202431_s_at

4609

MYC

6

 

203752_s_at

3727

JUND

6

 

205009_at

7031

TFF1

6

 

205067_at

3553

IL1B

6

 

200807_s_at

3329

HSPD1

6

 

203139_at

1612

DAPK1

6

 

200886_s_at

5223

PGAM1

6

 

203083_at

7058

THBS2

6

 

202284_s_at

1026

CDKN1A

6

 

212667_at

6678

SPARC

6

 

202627_s_at

5054

SERPINE1

6

 

203382_s_at

348

APOE

6

 

211300_s_at

7157

TP53

6

 

200953_s_at

894

CCND2

6

 

201700_at

896

CCND3

6

 

205881_at

7625

ZNF74

23

 

207081_s_at

5297

PI4KA

23

 

205576_at

3053

SERPIND1

23

 

203412_at

8216

LZTR1

23

 

206184_at

1399

CRKL

23

D: Known oncogenes and tumour U133A analysis suppressor genes relevant to ovarian cancer biology

203132_at

5925

RB1

 
 

204531_s_at

672

BRCA1

 
 

214727_at

675

BRCA2

 
 

202520_s_at

4292

MLH1

 
 

216836_s_at

2064

ERBB2

 
 

204009_s_at

3845

KRAS

 
 

206044_s_at

673

BRAF

 
 

209421_at

4436

MSH2

 
 

211450_s_at

2956

MSH6

 

*GeneID (gene identification number) is based on the nomenclature used in the Entrez Gene database available through the National Center for Biotechnology Information (NCBI)

http://www.ncbi.nlm.nih.gov.

The Ziplex array or TipChip is a three-dimensional array comprised of a microporous silicon matrix containing oligonucleotide probes that is mounted on a plastic tube. Each probe was spotted in triplicate. In order to replicate gene expression assays derived from the Affymetrix GeneChip analysis, probe set design was based on the Affymetrix U133A probe set target sequences for the selected gene (refer to Table 1). Gene names were assigned using UniGene ID Build 215 (17 August 2008). To improve accuracy of probe design, and to account for variation of probe hybridization, up to three probes were designed for each gene. From this exercise, a single probe was chosen to provide the most reliable and consistent quantification of gene expression. Gene accession numbers corresponding to the Affymetrix probe set sequences for each gene were verified by BLAST alignment searches of the NCBI Transcript Reference Sequences (RefSeq) database http://www.ncbi.nlm.nih.gov/projects/RefSeq/. Array Designer (Premier Biosoft, Palo Alto, CA) was used to generate three probes from each verified RefSeq transcript that were between 35 to 50 bases in length (median 46 base pairs), exhibited a melting temperature of approximately 70°C, represent a maximum distance of 1,500 base pairs from the from 3' end of the transcript, and exhibited minimal homology to non-target RefSeq sequences. Using this approach it was possible to design three probes for 92 of the 93 selected genes: APOE was represented by only two probes. For the 93 genes analyzed, the median distance from the 3' end was 263 bases, whereas less than 12% of the probes were more than 600 bases from the 3' end. Ten probes were also designed for genes that were not expected to vary significantly between TOV and NOSE samples based on approximately equal expression in the two sample types and relatively low coefficients of variation (18 to 20%) as assessed by Affymetrix U133A microarray analysis of the samples; such probes were potential normalization controls. Based on standard quality control measures of the manufacturer, three probes representing ACTB, GAPDH, and UBC and a set of standard control probes, including a set of 5' end biased probes for RPL4, POLR2A, ACTB, GAPDH and ACADVL were printed on each array for data normalization and quality assessment. The probes were printed on two separate TipChip arrays.

Hybridization and raw data collection

Total RNA from NOSE and TOV samples and the four EOC cell lines were prepared as described above and provided to Xceed Molecular for hybridization and data collection in a blinded manner. RNA quality (RNA integrity number (RIN)) using the Agilent 2100 Bioanalyzer Nano, total RNA assay was assessed for each sample (Additional File 1). For each sample, approximately 500 ng of RNA was amplified and labeled with the Illumina® TotalPrep™ RNA Amplification Kit (Ambion, Applied Biosystems Canada, Streetsville, ON, CANADA). Although sample MG0026 (TOV-1150G) had a low RIN number, it was carried through the study. Sample MG0001 (TOV-21G) had no detectable RIN number and MG0013 (NOV-1181) failed to produce amplified RNA. Neither of these samples were carried through the study. Five μg of the resulting biotin-labeled amplified RNA was hybridized on each TipChip. The target molecules were biotin labeled, and an HRP-streptavidin complex was used for imaging of bound targets by chemiluminescence. Hybridization, washing, chemiluminescent imaging and data collection were automatically performed by the Ziplex Workstation (Xceed Molecular, Toronto, ON, Canada).

Data normalization

The mean ratio of the intensities of the replicate probes that were printed on both of the ovarian cancer arrays were used to scale the data between the two TipChip arrays hybridized with each sample. The mean scaling factor for the 27 samples was 1.03 with a maximum of 1.23. The coefficients of variation (CV) across 27 samples and the expression differences between NOSE and TOV samples was calculated from the raw data for each of the 10 genes included on the arrays as potential normalization genes (Additional File 2). The geometric means of the signals for probes for PARK7, PI4KB, TBCB, and UBC with small CVs (mean of 25%) and insignificant differences between NOSE and TOV (p > 0.48) were used to normalize the data (refer to Additional File 2 for all normalization gene results). The data were analyzed with and without normalization.

Selection of optimal probe design

The hybridization intensities of the replicate probes designed for each gene for the 27 samples were compared to choose a single probe per gene with optimal performance. This assessment was based on signal intensity (well above the noise level and within the dynamic range of the system), minimum distance from the 3' end of the target sequence and correlation between different probe designs. Minimum distance from the 3' end is a consideration since the RNA sample preparation process is somewhat biased to the 3' end of the transcripts. The signals for probes for the same target should vary proportionally between different samples if both probes bind to and only to the nominal target. Good correlation between different Ziplex probe designs for genes in the RefSeq database, as well as good correlation with the Affymetrix data and discrimination between sample types, infers that probes bind to the intended target sequences. Data from the chosen probe was used for all subsequent analysis. Correlations of signal intensities for pairs of probes for the same genes are presented in Additional File 3.

Comparative analysis of Ziplex and Affymetrix data

Correlations between Ziplex and Affymetrix array datasets were calculated. The Affymetrix U133A data was previously derived from RNA expression analysis of the NOSE and TOV samples and EOC cell lines. Hybridization and scanning was performed at the McGill University and Genome Quebec Innovation Centre http://www.genomequebecplatforms.com. MAS5.0 software (Affymetrix® Microarray Suite) was used to quantify gene expression levels. Data was normalized by multiplying the raw value for an individual probe set (n = 22,216) by 100 and dividing by the mean of the raw expression values for the given sample data set, as described previously [23, 28]. Affymetrix and Ziplex data were matched by gene, and correlations (p < 0.01, using values only of greater than 4) and a graphical representation was determined using Mathematica (Version 6.03) software (Wolfram Research, Inc., Champaign, IL, USA). Mean signal intensity values were log2 transformed and compared between NOSE and TOV data using a Welch Rank Sum Test, for both Affymetrix microarray and Ziplex array data. A p-value of less than 0.001 was used as the significance level.

Composition of mean-difference plots followed the method of Bland and Altman [29]. Briefly, the mean of the log2 fold change and the difference between the log2 fold change for the platforms under comparison were calculated and plotted. The 95% limits of agreement were calculated as follows: log2 fold change difference ± 1.96 × standard deviation of the log2 fold change difference.

Quality control of Ziplex array data

The percent CVs were greater for probes with signals below 30. The overall median of the median probe percent CV was 4.7%. The median of the median percent CVs was 4.4% for probes with median intensities greater than 30, and 8.0% for probes with median CVs less than or equal to 30. The signal to noise (SNR) values is the average of the ratios for the net signals of the replicate spots to the standard deviation of the pixel values used to evaluate background levels (an image noise estimate). Average SNR ranged from -0.3 to 32.8. The signal intensities and ratios of intensity signals derived from 3' and 5' probes are shown in Additional File 4. Sample MG0001, which included many high 3'/5' ratios, was not included for subsequent analysis. The 3'/5' signal intensity ratios correlated with the RIN numbers and 28 S/18 S ratios (Additional File 5), indicating that, as expected, amplified RNA fragment lengths vary according to the integrity of the total RNA sample.

Results

Correlation of Affymetrix U133A and Ziplex array expression profiles

Normalized Affymetrix U133A and Ziplex gene expression data were matched by gene. For each gene expression platform, values less than 4 were considered to contribute to censoring bias and were not included in the correlation analysis. Correlations (log10 transformed) for paired gene expression data ranged from 0.0277 to 0.998, with an average correlation of 0.811 between Affymetrix and Ziplex gene expression data (Additional File 6). For a detailed summary of the correlation analysis, see also Additional File 7. The expression profiles of 82 of the 93 (88.2%) genes were significantly positively correlated (p < 0.01) in a comparison of the two platforms. As shown with the selected examples, genes exhibiting under-expression, such as ALDH1A3 and CCL2, or over-expression, such as APOE and EVI1, in the TOV samples relative to the NOSE samples by Affymetrix U133A microarray analysis also exhibited similar patterns of expression by Ziplex array (Figure 1). In contrast, TRAF4 expression was not correlated between the platforms (R2 = 0.0003). However, both platforms yielded low expression values for this gene. Although gene expression at very low levels may be difficult to assay and can be affected by technical variability, a good correspondence between platforms can be achieved with specific probes, as shown in the comparison of the BRCA1 expression profiles (R2 = 0.870) (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1479-5876-7-55/MediaObjects/12967_2009_Article_366_Fig1_HTML.jpg
Figure 1

Correlation plots of selected genes underexpressed in TOV (A, B), over-expressed in TOV (C, D) and showing low expression (E, F) across samples. Xceed Ziplex (XZP) expression data is plotted on the x axis and Affymetrix (AFX) microarray data on the y axis. The EOC cell lines are indicated in green (n = 3), TOV samples in red (n = 12) and NOSE samples in blue (n = 11). Correlation coefficients are shown at the bottom right.

Comparative analysis of fold changes of Affymetrix U133A and Ziplex array expression profiles

The fold change differences in gene expression were compared between the two platforms. There was a strong correspondence of gene expression patterns across the platforms when compared for each gene (Table 2). In terms of overall concordance of statistical significance between NOSE and TOV samples, there were consistent results for 75 of 93 genes by Affymetrix and Ziplex analysis (p < 0.001) by Welch rank sum test, in each platform. The fold change differences were concordant for 87 of 93 (94%) genes where there was agreement between the platforms regarding statistical significance for 71 (76%) of the 87 genes. The fold change differences were discordant for 6 genes, but the differences were statistically insignificant on both platforms for four of these genes. For example for the gene SERPIND1, there is no concordance in terms of fold change between the two platforms, but these fold change differences are not significant for either platform (p > 0.001). These results exemplifies that caution should be used when relying on fold change results alone. Notably, for two of the discordantly expressed genes (MSH6 and TFF1), the fold change differences were statistically significant (p < 0.001) only on the Ziplex platform but not for the Affymetrix platform.
Table 2

Comparison of mean signal intensity (SI) values for the 93 gene probes between NOSE and TOV samples

  

Affymetrix U133A Array

Ziplex Automated Workstation

Platform Comparison

Selection Criteria1

Gene Probe

NOSE mean SI (n = 11)

TOV mean SI (n = 12)

ratio (N/T)2

ratio (T/N)2

p-value3

NOSE mean SI (n = 11)

TOV mean SI (n = 12)

ratio (N/T)2

ratio (T/N)2

p-value3

significance based on p-value3

concordance based on ratio fold-change direction

A

RGS4

291

2

181.2

0.01

<0.0001

863

41

21.1

0.05

<0.0001

agree

concordance

C

SERPINE1

1912

12

162.4

0.01

<0.0001

1426

17

82.2

0.01

<0.0001

agree

concordance

A

PDPN

57

2

23.9

0.04

0.0008

100

35

2.9

0.35

0.0023

disagree

concordance

A

ALDH1A3

661

29

22.6

0.04

0.0020

1887

76

24.8

0.04

0.0051

agree

concordance

A

IL8

1353

69

19.7

0.05

0.0151

4465

231

19.3

0.05

0.0015

agree

concordance

A

PTGIS

1470

80

18.4

0.05

<0.0001

3474

184

18.9

0.05

<0.0001

agree

concordance

A

HEG1

923

66

14.1

0.07

<0.0001

3184

252

12.6

0.08

<0.0001

agree

concordance

A

TMEM158

461

33

13.9

0.07

<0.0001

869

46

18.8

0.05

<0.0001

agree

concordance

C

CDKN1A

598

53

11.4

0.09

<0.0001

385

63

6.1

0.16

<0.0001

agree

concordance

A

CCL2

570

54

10.6

0.09

0.0010

1923

207

9.3

0.11

0.0001

agree

concordance

A

LXN

731

73

10.1

0.10

<0.0001

926

124

7.5

0.13

0.0002

agree

concordance

C

SPARC

1037

108

9.6

0.10

<0.0001

2841

341

8.3

0.12

<0.0001

agree

concordance

C

IL1B

666

70

9.6

0.10

0.0247

1559

46

34.0

0.03

0.0035

agree

concordance

A

BDKRB1

152

18

8.7

0.11

0.0004

464

22

21.0

0.05

<0.0001

agree

concordance

B

SLC16A3

425

63

6.8

0.15

<0.0001

197

37

5.3

0.19

<0.0001

agree

concordance

A

FSTL1

1837

277

6.6

0.15

<0.0001

5293

732

7.2

0.14

<0.0001

agree

concordance

C

THBS2

846

135

6.3

0.16

<0.0001

668

105

6.4

0.16

0.0009

agree

concordance

A

IGFBP4

1484

238

6.2

0.16

<0.0001

692

122

5.7

0.18

0.0001

agree

concordance

A

PTRF

976

168

5.8

0.17

<0.0001

217

77

2.8

0.35

<0.0001

agree

concordance

A

GBE1

775

136

5.7

0.18

<0.0001

988

173

5.7

0.17

<0.0001

agree

concordance

A

PLOD2

654

123

5.3

0.19

<0.0001

926

132

7.0

0.14

<0.0001

agree

concordance

A

VAT1

874

175

5.0

0.20

<0.0001

255

78

3.3

0.31

<0.0001

agree

concordance

A

COL1A1

2940

614

4.8

0.21

0.0001

1502

289

5.2

0.19

0.0003

agree

concordance

C

CCND2

324

70

4.7

0.21

0.0127

481

117

4.1

0.24

0.0337

agree

concordance

A

SELT

558

148

3.8

0.27

0.0010

166

137

1.2

0.8

>0.05

disagree

concordance

B

C1QTNF1

169

48

3.6

0.28

<0.0001

30

3

11.7

0.09

<0.0001

agree

concordance

A

VGLL3

35

10

3.5

0.29

<0.0001

75

12

6.1

0.16

0.0015

disagree

concordance

C

PGAM1

1482

473

3.1

0.32

<0.0001

1603

504

3.2

0.31

<0.0001

agree

concordance

C

TP53

55

18

3.0

0.33

0.0178

197

226

0.9

1.1

>0.05

agree

discordance

B

MSN

746

250

3.0

0.33

<0.0001

818

354

2.3

0.43

<0.0001

agree

concordance

B

PSMD3

196

66

3.0

0.34

<0.0001

735

384

1.9

0.5

<0.0001

agree

concordance

B

WSB1

300

103

2.9

0.34

0.0003

313

155

2.0

0.50

0.0006

agree

concordance

B

MRC2

313

109

2.9

0.35

<0.0001

528

138

3.8

0.26

<0.0001

agree

concordance

A

MYH10

1113

420

2.6

0.38

0.0006

1096

464

2.4

0.42

0.0106

disagree

concordance

B

NSF

180

72

2.5

0.40

<0.0001

304

170

1.8

0.6

0.0023

disagree

concordance

A

P4HB

2276

917

2.5

0.40

<0.0001

4567

1553

2.9

0.34

<0.0001

agree

concordance

C

SERPIND1

7

3

2.2

0.45

>0.05

79

117

0.7

1.5

0.0363

agree

discordance

B

RAB5C

309

142

2.2

0.46

0.0106

132

61

2.2

0.46

<0.0001

disagree

concordance

B

PFN2

800

392

2.0

0.49

<0.0001

699

444

1.6

0.6

0.0005

agree

concordance

B

TRAF4

47

23

2.0

0.50

0.0363

30

27

1.1

0.9

>0.05

agree

concordance

B

LSM12

59

31

1.9

0.5

0.0023

53

36

1.5

0.7

0.0106

agree

concordance

B

PLP2

294

157

1.9

0.5

0.0051

270

190

1.4

0.7

0.0151

agree

concordance

B

PAFAH1B1

181

98

1.9

0.5

0.0006

556

387

1.4

0.7

0.0089

disagree

concordance

B

TIMM22

42

23

1.8

0.5

0.0392

126

82

1.5

0.6

0.0001

disagree

concordance

B

AMOTL2

308

173

1.8

0.6

0.0015

776

484

1.6

0.6

0.0113

agree

concordance

B

ATP1B3

668

386

1.7

0.6

<0.0001

832

449

1.9

0.5

0.0015

disagree

concordance

C

DAPK1

181

117

1.5

0.6

>0.05

186

146

1.3

0.8

>0.05

agree

concordance

B

TFRC

894

606

1.5

0.7

0.0089

386

216

1.8

0.6

0.0062

agree

concordance

B

ATG3

200

139

1.4

0.7

0.0106

342

319

1.1

0.9

>0.05

agree

concordance

B

RNF7

177

125

1.4

0.7

0.0178

54

63

0.9

1.2

>0.05

agree

concordance

A

IL18

21

16

1.4

0.7

0.0148

125

104

1.2

0.8

0.0210

agree

concordance

C

CRKL

38

28

1.4

0.7

>0.05

18

23

0.8

1.3

>0.05

agree

concordance

B

XIST

103

76

1.4

0.7

>0.05

256

378

0.7

1.5

>0.05

agree

discordance

C

PI4KA

59

44

1.4

0.7

0.0127

110

113

1.0

1.0

>0.05

agree

concordance

D

MSH6

62

47

1.3

0.8

>0.05

227

519

0.4

2.3

0.0010

disagree

discordance

C

LZTR1

82

69

1.2

0.8

>0.05

81

74

1.1

0.9

>0.05

agree

concordance

D

MLH1

171

150

1.1

0.9

>0.05

143

150

1.0

1.0

>0.05

agree

concordance

C

MYC

151

142

1.1

0.9

>0.05

119

212

0.6

1.8

>0.05

agree

discordance

B

PCOLCE2

22

21

1.0

1.0

>0.05

39

39

1.0

1.0

>0.05

agree

concordance

C

CCND3

136

139

1.0

1.0

>0.05

101

134

0.7

1.3

0.0127

agree

concordance

D

KRAS

157

162

1.0

1.0

>0.05

150

200

0.8

1.3

>0.05

agree

concordance

A

SEPT9

880

918

1.0

1.0

>0.05

543

394

1.4

0.7

>0.05

agree

concordance

D

RB1

67

73

0.9

1.1

>0.05

166

225

0.7

1.4

>0.05

agree

concordance

D

BRCA2

10

12

0.8

1.2

>0.05

15

23

0.6

1.6

0.0210

agree

concordance

B

SNX4

43

52

0.8

1.2

>0.05

199

339

0.6

1.7

0.0042

agree

concordance

A

BTN3A2

40

48

0.8

1.2

>0.05

89

173

0.5

1.9

0.0005

disagree

concordance

C

TFF1

12

16

0.7

1.4

>0.05

226

61

3.7

0.3

<0.0001

disagree

discordance

B

NUP85

71

101

0.7

1.4

>0.05

85

134

0.6

1.6

0.0028

agree

concordance

C

JUND

759

1181

0.6

1.6

>0.05

1725

2479

0.7

1.4

>0.05

agree

concordance

B

OSBPL11

46

74

0.6

1.6

0.0151

56

148

0.4

2.6

<0.0001

disagree

concordance

D

BRCA1

15

24

0.6

1.6

>0.05

27

40

0.7

1.5

>0.05

agree

concordance

B

SR140

144

243

0.6

1.7

0.0089

13

64

0.2

5.0

<0.0001

disagree

concordance

D

BRAF

27

46

0.6

1.7

0.0089

22

47

0.5

2.1

<0.0001

disagree

concordance

C

ZNF74

12

21

0.6

1.8

0.0042

16

44

0.4

2.8

0.0002

disagree

concordance

B

TOMM70A

212

383

0.6

1.8

0.0004

115

306

0.4

2.7

<0.0001

agree

concordance

B

RPL24

1895

3503

0.5

1.8

0.0002

1834

4179

0.4

2.3

0.0003

agree

concordance

C

HSPD1

899

1682

0.5

1.9

0.0002

461

1189

0.4

2.6

0.0004

agree

concordance

D

MSH2

27

53

0.5

2.0

0.0023

112

495

0.2

4.4

<0.0001

disagree

concordance

B

MYNN

27

55

0.5

2.1

0.0001

16

40

0.4

2.5

0.0005

agree

concordance

D

ERBB2

99

230

0.4

2.3

0.0003

50

142

0.4

2.8

0.0002

agree

concordance

B

ICAM2

14

34

0.4

2.5

0.0011

13

25

0.5

1.9

0.0089

agree

concordance

B

CEP70

23

59

0.4

2.6

<0.0001

56

182

0.3

3.3

<0.0001

agree

concordance

B

TMEM97

70

195

0.4

2.8

0.0015

51

140

0.4

2.8

0.0004

disagree

concordance

B

CD300A

11

36

0.3

3.3

<0.0001

4

36

0.1

9.2

0.0006

agree

concordance

A

STAT1

30

109

0.3

3.6

0.0127

48

110

0.4

2.3

0.0210

agree

concordance

A

EVI1

11

197

0.06

17.5

<0.0001

36

636

0.06

17.5

<0.0001

agree

concordance

C

APOE

7

126

0.06

17.9

<0.0001

39

326

0.12

8.4

<0.0001

agree

concordance

A

CP

7

295

0.02

43.5

<0.0001

33

972

0.03

29.3

<0.0001

agree

concordance

A

RGS1

2

112

0.02

47.0

<0.0001

3

169

0.02

56.5

<0.0001

agree

concordance

A

SPON1

5

271

0.02

57.8

<0.0001

6

257

0.02

44.9

<0.0001

agree

concordance

A

CD24

6

481

0.01

77.2

<0.0001

63

3697

0.02

58.5

<0.0001

agree

concordance

A

IGKC

7

991

0.01

151.6

<0.0001

27

873

0.03

32.6

0.0008

agree

concordance

A

IGHG1

3

1262

0.003

374.3

<0.0001

19

203

0.10

10.5

<0.0001

agree

concordance

1See Table 1 for description of categories of selection criteria. 2Fold change >2 or <0.5 (bold) between NOSE (N) and TOV (T) gene expression comparison. 3Welch Rank Sum Test p<0.001 (italics) difference between NOSE (N) and TOV (T).

As shown in Figure 2A, there was a strong agreement between the two platforms as shown by comparisons of log2 fold differences of gene expression between TOV versus NOSE samples (R = 0.93) and by Bland-Altman analysis (Figure 2B), where the majority of probes exhibited expression profiles in comparative analyses that fell within the 95% limits of agreement. Both statistical methods of comparative analysis of log2 fold differences show minimal variance as the mean increases regardless of the direction of expression difference evaluated: genes selected based on over- or under-expression in TOV samples relative to NOSE samples. Although there were examples of expression differences which fell outside the 95% limits of agreement as observed in the Bland-Altman analysis such as for RGSF4, PDPN, IGKC, IGHG1, C1QTNF1, TFF1 and IL1B (Figure 2B), both the directionality and magnitude of TOV versus NOSE expression patterns were generally consistent (Figure 2A and Table 2).
https://static-content.springer.com/image/art%3A10.1186%2F1479-5876-7-55/MediaObjects/12967_2009_Article_366_Fig2_HTML.jpg
Figure 2

Comparison of the fold change difference in expression between NOSE and TOV samples for the Ziplex and Affymetrix platforms. A: The log2 fold change between the NOSE and TOV samples (mean NOSE signal intensity/mean TOV signal intensity) was calculated for the expression values of all 93 probes and plotted. Linear regression was performed resulting in the following model: log2 Affymetrix NOSE/TOV = 0.180098 + 1.0251794 log2 Ziplex NOSE/TOV with a Pearson's correlation coefficient (R) of 0.93. Probes that were not significant (p > 0.001 based on a Welch Rank Sum test) on either platform are indicated in grey, probes significant (p < 0.001 based on a Welch Rank Sum test) on both platforms are indicated in black, on only the Ziplex platform are indicated in blue and on only the Affymetrix platform in green. B: Bland-Altman plots for expression values of all probes. Values determined to be outliers are indicated in the mean-difference (of the log2 fold change values) plot. A difference in log2 fold change of 0 is indicated by a solid black line. The upper and lower 95% limits of agreement for the difference in log2 fold change are indicated by red dashed lines, and arrows on the right hand side. Expression values that fall outside of these lines are considered outliers and are identified by their gene name.

Discussion

The Ziplex array technology as applied to ovarian cancer research was capable of reproducing expression profiles of genes selected based on their Affymetrix GeneChip patterns. A high concordance of gene expression patterns was evident based on overall correlations, significance testing and fold-change comparisons derived from both platforms. The Ziplex array technology was validated by testing the expression of genes exhibiting not only significant differences in expression between normal tissues (NOSE) and ovarian cancer (TOV) samples but also the vast range in expression values exhibited by these samples using the Affymetrix microarray technology. Notable also is that comparisons were made between Affymetrix GeneChip data that was derived using MAS5 software rather than RMA analysis. We have routinely used MAS5 derived data in order to avoid potential skewing of low and high expression values which could occur with RMA treated data sets as this is more amenable to data sets of limited sample size [6, 23, 25, 26, 30]. MAS5 derived data also allows for exclusion of data that may represent ambiguous expression values as reflected in a reliability score based on comparison of hybridization to sets of probes representing matched and mismatch sequences complementary to the intended target RNA sequence. A recent study has re-evaluated the merits of using MAS5 data with detection call algorithms demonstrating its overall utility [31]. Our results are consistent with a previous study which had tested the analytical sensitivity, repeatability and differential expression of the Ziplex technology within a MAQC study framework [21]. As with all gene expression platforms, reproducibility is more variable within very low range of gene expression. Gene expression values in the low range across comparable groups would unlikely be developed as RNA expression biomarkers at the present time regardless of platform used. The MAQC study included a comparison of Xceed Molecular platform performance with at least three major gene expression platforms in current use in the research community, such as Affymetrix GeneChips, Agilent cDNA arrays, and real-time RT-PCR. The implementation of some of these various technology platforms in a clinical setting may require significant infrastructure which may be awkward to implement due to the level of expertise involved. In some cases, costs may also be prohibitive but this should diminish over time with increase in usage in clinical settings. It is also not clear that expression biomarkers are readily adaptable to all cancer types as this requires sufficient clinical specimens to extract amounts of good quality RNA for RNA biomarker screening to succeed. Tumor heterogeneity is also an issue. The large size and largely tumor cell composition of ovarian cancer specimens may render this disease more readily amenable to the development and implementation of RNA biomarker screening strategies in order to improve health care of ovarian cancer patients. The ease with which to use the Ziplex Automated Workstation focus array and the fact that it appears to perform overall as well as highly sensitive gene expression technologies including real-time RT-PCR, suggests that this new platform might be amenable to translational research of gene expression-based biomarkers for ovarian cancer initially identified from established large-scale gene expression platforms.

Data normalization of gene expression values is a subject of intense study and is a major consideration when moving from one technology platform to another [4, 5]. In this study, data normalization of the Ziplex data was achieved by using the expression values derived from seven genes, each of which had low CV values across all samples tested. Since the input quantity of amplified RNA was equivalent for all Ziplex arrays, raw data could also have been used in our analysis. A statistical analysis based on correlations and fold-changes found negligible differences between raw and normalized data (not shown). Affymetrix GeneChip and Ziplex systems also differ in a number of technical ways that may affect the determination of gene expression. Affymetrix probe design is based on 11 oligonucleotide probes, 25 base pairs in size, within a target sequence of several hundred base pairs. The gene expression value is based on the median of the measured signal from the 11 probes. The probe design for the Ziplex system is based on oligonucleotide probes ranging from 35 to 50 bases. In this study three probes were designed and tested for each target gene and a single optimal probe was chosen. The visualization system for gene expression differs for both platforms where expression using the Ziplex array is measured by chemiluminescence, whereas fluorescence is used for the Affymetrix GeneChip. In spite of these differences, our findings along with an independent assessment of the Ziplex system [21] indicated a high degree of correspondence in expression profiles generated across both platforms. The overall findings are not surprising given that the probe design was intentionally targeted to similar 3'UTR sequences for the tested gene. Thus, the overall reproducibility of expression profiles along with the possibility of using raw data would be an attractive feature of applying the Ziplex system to validated biomarkers that were discovered using the Affymetrix platform.

The expression patterns of many of the tested genes were previously validated by an independent technique from our research group. RT-PCR analyses of ovarian cancer samples validated gene expression profiles of TMEM158, GBE1 and HEG1 from a chromosome 3 transcriptome analysis [25] and IGFBP4, PTRF and C1QTNF1 from a chromosome 17 transcriptome analysis [26]. The Ziplex platform also revealed over-expression of genes (ZNF74, PIK4CA, SERPIND1, LZTR1 and CRKL) associated with a chromosome 22q11 amplicon found in the OV90 EOC cell line and initially characterized by earlier generation Affymetrix expression microarrays and validated by RT-PCR and Northern blot analysis [23]. Differential expression of SPARC, a tumor suppressor gene implicated in ovarian cancer, has been shown to give consistent expression profiles in EOC cell lines and samples across a number of Affymetrix GeneChip® platforms and by RT-PCR from our group and others [6, 30, 32]. This indicates the utility of using older generation Affymetrix GeneChip data where good concordance can be observed with historical data and the accuracy of the earlier generation GeneChips has been evaluated by alternative techniques in the literature [6, 23]. This is an important consideration particularly given the large number of historical data sets that are available for further mining of potential gene expression biomarkers. Northern blot analysis has validated expression of MYC, HSPD1, TP53 and PGAM1 which were initially found to be differentially expressed in our EOC cell lines by the prototype Affymetrix GeneChip [6]. Concordance of gene expression was also evident from the 10 genes (see Table 1) selected based on an Affymetrix U133A microarray analysis of TOV samples and short term cultures of NOSE samples reported by an independent group [3]. BTF4 is a potential prognostic marker for ovarian cancer and was originally identified by Affymetrix microarray technology and then validated by real-time RT-PCR analysis [14]. Assaying the expression of BTF4 in clinical specimens is of particular interest because at the time of study there was no available antibody, illustrating the need for a reliable and accurate quantitative gene expression platform for RNA molecular markers.

Conclusion

It is becoming increasingly apparent that expression signatures involving multiple genes can be correlated with various clinical parameters of disease, and in turn that these signatures could be used as biomarkers [4, 5]. Although the expression signatures are gleaned from the statistical analyses of transcriptomes from genome-wide expression analyses, such as with use of Affymetrix GeneChip, the use of such arrays requires technical expertise and infrastructure that is not at the present time readily adaptable to clinical laboratories. In this study we have shown the concordance of the expression signatures derived from Affymetrix microarray analysis by the Ziplex array technology, suggesting that it is amenable for translational research of expression signature biomarkers for ovarian cancer.

List of abbreviations used

RNA: 

ribonucleic acid

mRNA: 

messenger ribonucleic acid

UTR: 

untranslated region

R: 

correlation coefficient

MAQC: 

MicroArray Quality Control

RT-PCR: 

reverse transcription polymerase chain reaction

NOSE cells: 

normal ovarian surface epithelial cells

TOV: 

ovarian tumor

EOC: 

epithelial ovarian cancer

BLAST: 

Basic Local Alignment Search Tool

NCBI: 

National Centre for Biotechnology Information

RIN: 

RNA integrity number

HRP: 

horseradish peroxidase

SNR: 

signal to noise ratio

SI: 

signal intensity.

Declarations

Acknowledgements

Manon Deladurantaye provided technical assistance with sample preparation. PT is an Associate Professor and Medical Scientist at The Research Institute of the McGill University Health Centre which receives support from the Fonds de la Recherche en Santé du Québec (FRSQ). AB is a recipient of a graduate scholarship from the Department of Medicine and the Research Institute of the McGill University Health Centre and PW is a recipient of a Canadian Institutes of Health Research doctoral research award. The ovarian tumor banking was supported by the Banque de tissus et de données of the Réseau de recherche sur le cancer of the FRSQ affiliated with the Canadian Tumour Respository Network (CRTNet). This work was supported by grants from the Genome Canada/Génome Québec, the Canadian Institutes of Health Research and joint funding from The Terry Fox Research Institute and Canadian Partnership Against Cancer Corporation (Project: 2008-03T) to PT, AMMM and DP.

Authors’ Affiliations

(1)
Department of Human Genetics, McGill University
(2)
Xceed Molecular
(3)
The Research Institute of the McGill University Health Centre
(4)
Centre de Recherche du Centre hospitalier de l'Université de Montréal/Institut du cancer de Montréal
(5)
Département de Médicine, Université de Montréal
(6)
Département de Obstétrique et Gynecologie, Division of Gynecologic Oncology, Université de Montréal
(7)
Department of Medicine, McGill University

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© Quinn et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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