Identification of EpCAM as a molecular target of prostate cancer stroma.

To delineate the molecular changes that occur in the tumor microenvironment, we previously performed global transcript analysis of human prostate cancer specimens using tissue microdissection and expression microarrays. Epithelial and stromal compartments were individually studied in both tumor and normal fields. Tumor-associated stroma showed a distinctly different expression pattern compared with normal stroma, having 44 differentially expressed transcripts, the majority of which were up-regulated. In the present study, one of the up-regulated transcripts, epithelial cell adhesion activating molecule, was further evaluated at the protein level in 20 prostate cancer cases using immunohistochemistry and a histomathematical analysis strategy. The epithelial cell adhesion activating molecule showed a 76-fold expression increase in the tumor-associated stroma, as compared with matched normal stroma. Moreover, Gleason 4 or 5 tumor stroma was increased 170-fold relative to matched normal stroma, whereas the Gleason 3 tumor area showed only a 36-fold increase, indicating a positive correlation with Gleason tumor grade. Since the stromal compartment may be particularly accessible to vascular-delivered agents, epithelial cell adhesion activating molecule could become a valuable molecular target for imaging or treatment of prostate cancer.

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies.

BACKGROUND: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. RESULTS: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations. CONCLUSION: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

Global expression analysis of prostate cancer-associated stroma and epithelia.

Characterization of gene expression profiles in tumor cells and the tumor microenvironment is an important step in understanding neoplastic progression. To date, there are limited data available on expression changes that occur in the tumor-associated stroma as either a cause or consequence of cancer. In the present study, we employed a 54,000 target oligonucleotide microarray to compare expression profiles in the 4 major components of the microenvironment: tumor epithelium, tumor-associated stroma, normal epithelium, and normal stroma. Cells from 5 human, whole-mount prostatectomy specimens were microdissected and the extracted and amplified mRNA was hybridized to an Affymetrix Human Genome U133 Plus 2.0 GeneChip. Using the intersection of 2 analysis methods, we identified sets of differentially expressed genes among the 4 components. Forty-four genes were found to be consistently differentially expressed in the tumor-associated stroma; 35 were found in the tumor epithelium. Interestingly, the tumor-associated stroma showed a predominant up-regulation of transcripts compared with normal stroma, in sharp contrast to the overall down-regulation seen in the tumor epithelium relative to normal epithelium. These data provide insight into the molecular changes occurring in tumor-associated stromal cells and suggest new potential targets for future diagnostic, imaging, or therapeutic intervention.

Nonclassic functions of human topoisomerase I: genome-wide and pharmacologic analyses.

The biological functions of nuclear topoisomerase I (Top1) have been difficult to study because knocking out TOP1 is lethal in metazoans. To reveal the functions of human Top1, we have generated stable Top1 small interfering RNA (siRNA) cell lines from colon and breast carcinomas (HCT116-siTop1 and MCF-7-siTop1, respectively). In those clones, Top1 is reduced approximately 5-fold and Top2alpha compensates for Top1 deficiency. A prominent feature of the siTop1 cells is genomic instability, with chromosomal aberrations and histone gamma-H2AX foci associated with replication defects. siTop1 cells also show rDNA and nucleolar alterations and increased nuclear volume. Genome-wide transcription profiling revealed 55 genes with consistent changes in siTop1 cells. Among them, asparagine synthetase (ASNS) expression was reduced in siTop1 cells and in cells with transient Top1 down-regulation. Conversely, Top1 complementation increased ASNS, indicating a causal link between Top1 and ASNS expression. Correspondingly, pharmacologic profiling showed L-asparaginase hypersensitivity in the siTop1 cells. Resistance to camptothecin, indenoisoquinoline, aphidicolin, hydroxyurea, and staurosporine and hypersensitivity to etoposide and actinomycin D show that Top1, in addition to being the target of camptothecins, also regulates DNA replication, rDNA stability, and apoptosis. Overall, our studies show the pleiotropic nature of human Top1 activities. In addition to its classic DNA nicking-closing functions, Top1 plays critical nonclassic roles in genomic stability, gene-specific transcription, and response to various anticancer agents. The reported cell lines and approaches described in this article provide new tools to perform detailed functional analyses related to Top1 function.

Characterization of mismatch and high-signal intensity probes associated with Affymetrix genechips.

MOTIVATION: For Affymetrix microarray platforms, gene expression is determined by computing the difference in signal intensities between perfect match (PM) and mismatch (MM) probesets. Although the use of PM is not controversial, MM probesets have been associated with variance and ultimately inaccurate gene expression calls. A principal focus of this study was to investigate the nature of the MM signal intensities and demonstrate its contribution to the experimental results. RESULTS: While most MM intensities were likely associated with random noise, a subset of approximately 20% (99,485) of the MM probes displayed relatively high signal intensities to the corresponding PM probes (MM > PM) in a non-random fashion; 13,440 of these probes demonstrated exceptionally high 'outlier' intensities. About 15,938 PM probes also demonstrated exceptionally high outlier intensities consistently across all hybridizations. About 92% of the MM > PM probes had either a dThymidine (dT) or a dCytidine (dC) at the 13th position of the probe sequence. MM and PM probes displaying extremely high outlier intensities contained high dC rich nucleotides, and low dA contents at other nucleotides positions along the 25mer probe sequence. Differentially expressed genes generated using Genechip Operating System (GCOS) or modified PM-only methods were also examined. Of those candidate genes identified in the PM-only method, 157 of them were designated by GCOS as absent across all datasets and many others contained probes with MM > PM signal intensities. Our data suggests that MM intensity from PM signal can be a major source of error analysis, leading to fewer potentially biologically important candidate genes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Manufacturing of microarrays.

DNA microarray technology has become a powerful tool in the arsenal of the molecular biologist. Capitalizing on high precision robotics and the wealth of DNA sequences annotated from the genomes of a large number of organisms, the manufacture of microarrays is now possible for the average academic laboratory with the funds and motivation. Microarray production requires attention to both biological and physical resources, including DNA libraries, robotics, and qualified personnel. While the fabrication of microarrays is a very labor-intensive process, production of quality microarrays individually tailored on a project-by-project basis will help researchers shed light on future scientific questions.

Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project.

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

The end of the microarray Tower of Babel: will universal standards lead the way?

Microarrays are the most common method of studying global gene expression, and may soon enter the realm of FDA-approved clinical/diagnostic testing of cancer and other diseases. However, the acceptance of array data has been made difficult by the proliferation of widely different array platforms with gene probes ranging in size from 25 bases (oligonucleotides) to several kilobases (complementary DNAs or cDNAs). The algorithms applied for image and data analysis are also as varied as the microarray platforms, perhaps more so. In addition, there is a total lack of universally accepted standards for use among the different platforms and even within the same array types. Due to this lack of coherency in array technologies, confusion in interpretation of data within and across platforms has often been the norm, and studies of the same biological phenomena have, in many cases, led to contradictory results. In this commentary/review, some of the causes of this confusion will be summarized, and progress in overcoming these obstacles will be described, with the goal of providing an optimistic view of the future for the use of array technologies in global expression profiling and other applications.

Comparisons between transcriptional regulation and RNA expression in human embryonic stem cell lines.

Recent studies have focused on transcriptional regulation and gene expression profiling of human embryonic stem cells (hESCs). However, little information is available regarding the relationship between RNA expression and transcriptional regulation, which is critical in the complete understanding of pluripotency and differentiation of hESCs. In the current study, we determined RNA expression of three different hESC lines compared to Human universal reference RNA expression (HuU-RNA) using a full genome expression microarray, and compared our results to target genes previously identified using ChIP-on-chip analysis. The objective was to identify genes common between the two methods, and generate a more reliable list of embryonic signature genes. Even though hESCs were obtained from different sources and maintained under different conditions, a considerable number of genes could be identified as common between RNA expression and transcriptional regulation analyses. As an example, results from ChIP-on-chip studies show that OCT4, SOX2, and NANOG co-occupy SOX2, OCT4, TDGF1, GJA1, SET, and DPPA4 genes. The results are consistent with RNA expression analyses that demonstrate these genes as differently expressed in our hESC lines, further substantiating their role across cell types and confirming their importance as embryonic signatures. In addition, we report the differential expression of growth arrest-specific (GAS) family of genes in hESC. GAS2L1 and GAS3 members of this family appear to be transcriptionally regulated by OCT4, SOX2, or NANOG, whereas GAS5 and GAS6 are not; all of the genes are differentially expressed, as determined by microarray and validated via quantitative (Q)- PCR. Collectively, these data provide insight into the relationship between gene expression and transcriptional regulation, resulting in a reliable list of genes associated with hESCs.

Analysis of the quality of contact-pin fabricated oligonucleotide microarrays.

As the quality of microarrays is critical to successful experiments for data consistency and validity, a reliable and convenient quality control method is needed. We describe a systematic quality control method for large-scale genome oligonucleotide arrays. This method is comprised of three steps to assess the quality of printed arrays. The first step involves assessment of the autofluorescence property of DNA. This step is convenient, quick to perform, and allowed reuse of every array. The second step involves hybridization of arrays with Cy3-labeled 9-mer oligonucleotide target to assess the quality and stability of oligonucleotides. Because this step consumed arrays, one or two arrays from each batch were used to complement the quality control data from autofluorescence. The third step involves hybridization of arrays from every batch with transcripts derived from two cell lines to assess data consistency. These hybridizations were able to distinguish two closely related tissue samples by identifying a cluster of 20 genes that were differently expressed in U87MG and T98G glioblastoma cell lines. In addition, we standardized two parameters that significantly enhanced the quality of arrays. We found that longer pin contact time and crosslinking oligonucleotides at 400 mJ/cm(2) were optimal for the highest hybridization intensity. Taken together, these results indicate that the quality of spotted oligonucleotide arrays should be assessed by at least two methods, autofluorescence and 9-mer hybridization before arrays are used for hybridization experiments.

The External RNA Controls Consortium: a progress report.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

Three microarray platforms: an analysis of their concordance in profiling gene expression.

BACKGROUND: Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25-30 base), long oligonucleotide (50-80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard. RESULTS: The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation), scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values) between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of <0.05 and at expression threshold levels of 1.5 and 2-fold, the agreement among the platforms was very high, ranging from 93% to 100%. CONCLUSION: Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change.

Nanotechnology, nanomedicine, and the development of new, effective therapies for cancer.

Cancer is the leading cause of death in the United States among people younger than 85 years, and for the first time has surpassed heart disease as the number one killer. This worrisome statistic has resulted not from an increase in the incidence of cancer, but because deaths from heart disease have dropped nearly in half while the number of cancer-related deaths has remained about the same. This fact accentuates the need for a new generation of more effective therapies for cancer. In this review, the development of new therapies will be discussed in the context of advances in nanotechnologies related to cancer detection, analysis, diagnosis, and therapeutic intervention. First, several nanoanalytical methods, such as the use of quantum dots in detection and imaging of cancer, will be described. These techniques will be essential to the process of precisely describing cancer at the level of the cell and whole organism. Second, examples of how nanotechnologies can be used in the development of new therapies will be given, including methods that might allow for more efficient and accurate drug delivery and rationally designed, targeted drugs. Finally, a new initiative--the National Cancer Institute Alliance for Nanotechnology in Cancer--will be described and discussed with respect to the scientific issues, policies, and funding.

Laser capture microdissection, microarrays and the precise definition of a cancer cell.

Most expression profiling studies of solid tumors have used biopsy samples containing large numbers of contaminating stromal and other cell types, thereby complicating any precise delineation of gene expression in nontumor versus tumor cell types. Combining laser capture microdissection, RNA amplification protocols, microarray technologies and our knowledge of the human genome sequence, it is possible to isolate pure populations of cells or even a single cell and interrogate the expression of thousands of sequences for the purpose of more precisely defining the biology of the tumor cell. Although many of the studies that currently allow for characterization of small sample preparations and single cells were performed utilizing noncancer cell types, and in some cases isolation protocols other than laser capture microdissection, a list of protocols are described that could be used for the expression analysis of individual tumor cells. Application of these experimental approaches to cancer studies may permit a more accurate definition of the biology of the cancer cell, so that ultimately, more specific targeted therapies can be developed.

Microarrays and the gene expression profile of a single cell.

With completion of the human genome sequence, it is now possible to study the expression of the entire human gene complement of approximately 30,000-35,000 genes. To accomplish this goal, microarrays have become the leading methodology for the analysis of global gene expression. Improvements in technology have increased the sensitivity of microarrays to the point where it is feasible to study gene expression in a small number of cells and even at the single cell level. A summary of developments in the area of expression profiling in single cells will be described, and the rationale for these types of studies will be presented. In addition, from a biologist's point of view, some bioinformatic challenges of expression analysis of single cells will be discussed.

Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression.

Completion of the human genome sequence has made it possible to study the expression of the entire human gene complement (>30,000 estimated genes). Aiding in this remarkable feat, DNA microarrays have become the main technological workhorse for gene expression studies. To date, detection platforms for most microarrays have relied on short (25 base) oligonucleotides synthesized in situ, or longer, highly variable length DNAs from PCR amplification of cDNA libraries. A third choice, long (50-80 base) oligonucleotide arrays, is now available and might eventually eliminate the use of cDNA arrays. The technology has advanced to such a point that researchers now demand microarrays that are cost-effective and have flexibility and quality assurance. Short- and long-oligonucleotide technologies offer such advantages, and could possibly become the major competing platform in the near future.