Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

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 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.

Effect of various normalization methods on Applied Biosystems expression array system data.

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System.

Hierarchical cluster analysis of myoepithelial/basal cell markers in adenoid cystic carcinoma and polymorphous low-grade adenocarcinoma.

Distinguishing adenoid cystic carcinoma from polymorphous low-grade adenocarcinoma of the salivary glands is important for their management. We studied the expression of several myoepithelial and basal/stem cell markers (smooth muscle actin, calponin, smooth muscle myosin heavy chain, metallothionein, maspin, and p63) by immunohistochemistry in 23 adenoid cystic carcinoma and 24 polymorphous low-grade adenocarcinoma, to identify the most useful marker or combination of markers that may help their diagnoses. The results were analyzed using hierarchical cluster analysis and chi(2) test for trend. We noted diffuse expression of smooth muscle actin in 20 adenoid cystic carcinoma vs one polymorphous low-grade adenocarcinoma (P<0.0001), calponin in 15 adenoid cystic carcinoma vs one polymorphous low-grade adenocarcinoma (P<0.0001), smooth muscle myosin heavy chain in 15 adenoid cystic carcinoma vs one polymorphous low-grade adenocarcinoma (P=0.001), metallothionein in 22 adenoid cystic carcinoma vs eight polymorphous low-grade adenocarcinoma (P<0.001), maspin in 22 adenoid cystic carcinoma vs 14 polymorphous low-grade adenocarcinoma, and p63 in 21 adenoid cystic carcinoma vs 14 polymorphous low-grade adenocarcinoma. Hierarchical clustering of smooth muscle actin, calponin, smooth muscle myosin heavy chain, and metallothionein was virtually identical (kappa< or =0.0035), suggesting no significant advantage to their use in combination than individually. Diffuse smooth muscle actin expression showed the highest accuracy (91.5%) and positive predictive value (95.2%) for adenoid cystic carcinoma. Thus, diffuse expression of smooth muscle actin, calponin, smooth muscle myosin heavy chain, and metallothionein was highly predictive of adenoid cystic carcinoma, whereas maspin and p63 were frequently expressed in both tumors. In differentiating adenoid cystic carcinoma from polymorphous low-grade adenocarcinoma, smooth muscle actin as a single ancillary test in support of the histological findings, appears to be as efficient as multiple immunohistochemical tests.

Double immunohistochemical staining with MUC4/p53 is useful in the distinction of pancreatic adenocarcinoma from chronic pancreatitis: a tissue microarray-based study.

CONTEXT: Immunohistochemical stains have been used for the distinction of pancreatic adenocarcinoma from chronic pancreatitis. OBJECTIVE: To determine if a double stain for MUC/p53 improved specificity and sensitivity for distinction of pancreatic adenocarcinoma from chronic pancreatitis by comparing maspin, mucin 4 (MUC4), p53, Smad4, and the double stain MUC4/p53. DESIGN: Seventy-four pancreatic adenocarcinomas and 19 chronic pancreatitis cases were retrieved from archival files. Tissue cores were arrayed to create a tissue microarray of 2-mm cores. Sections were stained with antibodies against maspin, MUC4, p53, and Smad4. Additionally, a 2-color, double stain for MUC4 and p53 was developed and evaluated. Five percent or greater staining in either of the cores was considered positive. Intensity (0, 1, 2) and extent (%) of tumor cells staining was also determined. RESULTS: The sensitivity for distinction of pancreatic adenocarcinoma from chronic pancreatitis with maspin, MUC4, p53, and Smad4 was 90%, 77%, 60%, and 63%, respectively; the specificity was 67%, 78%, 88%, and 88%, respectively. When MUC4 and p53 were combined in a double stain, and positive staining for either considered a positive result, the sensitivity increased to 96% but specificity was 73%. When immunoreactivity for both antibodies was necessary for a positive result, sensitivity fell to 39% but specificity was 100%. No correlation was found between intensity or extent of staining with any of the individual stains and tumor differentiation. CONCLUSION: The double immunohistochemical stain for MUC4/p53 can be a useful diagnostic tool in conjunction with the hematoxylin-eosin-stained section for pancreatic adenocarcinoma, particularly when limited tumor is available for multiple stains.

Gene expression signature in peripheral blood detects thoracic aortic aneurysm.

BACKGROUND: Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA. METHODS AND FINDINGS: Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set. CONCLUSIONS: This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA.