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

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 [3‐6], 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 [14‐16]. 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 [18‐20] 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, 22‐26]. 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, 24‐26]. 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.

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.

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.