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.

Identification of new Rel/NFkappaB regulatory networks by focused genome location analysis.

NFkappaB is an inducible transcription factor that controls kinetically complex patterns of gene expression. Several studies reveal multiple pathways linking NFkappaB to the promotion and progression of various cancers. Despite extensive interest and characterization, many NFkappaB controlled genes still remain to be identified. We used chromatin immunoprecipitation combined with microarray technology (ChIP/chip) to investigate the dynamic interaction of NFkappaB with the promoter regions of 100 genes known to be expressed in mitogen-induced T-cells. Six previously unrecognized NFkappaB controlled genes (ATM, EP300, TGFbeta, Selectin, MMP-1 and SFN) were identified. Each gene is induced in mitogen-stimulated T-cells, repressed by pharmacological NFkappaB blockade, reduced in cells deficient in the p50 NFkappaB subunit and dramatically repressed by RNAi specifically designed against cRel. A coregulatory role for Ets transcription factors in the expression of the NFkappaB controlled genes was predicted by comparative promoter analysis and confirmed by ChIP and by functional disruption of Ets. NFkappaB deficiency produces a deficit in ATM function and DNA repair indicating an active role for NFkappaB in maintaining DNA integrity. These results define new potential targets and transcriptional networks governed by NFkappaB and provide novel functional insights for the role of NFkappaB in genomic stability, cell cycle control, cell-matrix and cell-cell interactions during tumor progression.

T7-based linear amplification of low concentration mRNA samples using beads and microfluidics for global gene expression measurements.

We have demonstrated in vitro transcription (IVT) of cDNA sequences from purified Jurkat T-cell mRNA immobilized on microfluidic packed beds down to single-cell quantities. The microfluidically amplified antisense-RNA (aRNA) was nearly identical in length and quantity compared with benchtop reactions using the same starting sample quantities. Microarrays were used to characterize the number and population of genes in each sample, allowing comparison of the microfluidic and benchtop processes. For both benchtop and microfluidic assays, we measured the expression of approximately 4000 to 9000 genes for sample amounts ranging from 20 pg to 10 ng (2 to 1000 cell equivalents), corresponding to 41 to 93% of the absolute number of genes detected from a 100 ng total RNA control sample. Concordance of genes detected between methods (benchtop vs. microfluidic) and repeats (microfluidic vs. microfluidic) typically exceeded 90%. Validation of microarray by Real-time PCR of a panel of five genes suggests transcription of genes present is approximately six times more efficient with the microfluidic IVT compared with benchtop processing. Microfluidic IVT introduces no bias to the gene expression profile of the sample and provides more efficient transcription of mRNA sequences present at the single-cell level.

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.

Cyclin-dependent kinase 5 differentially regulates the transcriptional activity of the glucocorticoid receptor through phosphorylation: clinical implications for the nervous system response to glucocorticoids and stress.

Glucocorticoids, major end effectors of the stress response, play an essential role in the homeostasis of the central nervous system and influence diverse functions of neuronal cells. We found that cyclin-dependent kinase 5 (CDK5), which plays important roles in the morphogenesis and functions of the nervous system and whose aberrant activation is associated with development of neurodegenerative disorders, interacted with the ligand-binding domain of the glucocorticoid receptor (GR) through its activator p35 or its active proteolytic fragment p25. CDK5 phosphorylated GR at multiple serines, including Ser203 and Ser211 of its N-terminal domain, and suppressed the transcriptional activity of this receptor on glucocorticoid-responsive promoters by attenuating attraction of transcriptional cofactors to DNA. In microarray analyses using rat cortical neuronal cells, the CDK5 inhibitor roscovitine differentially regulated the transcriptional activity of the GR on more than 90% of the endogenous glucocorticoid-responsive genes tested. Thus, CDK5 exerts some of its biological activities in neuronal cells through the GR, dynamically modulating GR transcriptional activity in a target promoter-dependent fashion.

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.

Gene expression analysis of RNA purified from embryonic stem cells and embryoid body-derived cells using a high-throughput microarray platform.

In this unit, starting with purified RNA, experimental protocols for performing microarray expression analysis of embryonic stem cell lines compared to their corresponding differentiated embryocidal bodies are described. Methods for data analysis are suggested, with the goal of determining which genes are differentially expressed between the preparations. As an example, the use of the Affymetrix microarray expression platform is described, but alternative experimental options for analysis of RNA transcript levels are also summarized. This unit suggests quality control metrics, summarizes the critical parameters necessary for obtaining reproducible experimental results, and outlines quantitative PCR methods for validating microarray results.

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.

Multiple-laboratory comparison of microarray platforms.

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

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.