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Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

We describe cell type-specific significance analysis of microarrays (csSAM) for analyzing differential gene expression for each cell type in a biological sample from microarray data and relative cell-type frequencies. First, we validated csSAM with predesigned mixtures and then applied it to whole-blood gene expression datasets from stable post-transplant kidney transplant recipients and those experiencing acute transplant rejection, which revealed hundreds of differentially expressed genes that were otherwise undetectable.

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.

Hexachlorobenzene (HCB) is a persistent environmental pollutant with toxic effects in man and rat. Reported adverse effects are hepatic porphyria, neurotoxicity, and adverse effects on the reproductive and immune system. To obtain more insight into HCB-induced mechanisms of toxicity, we studied gene expression levels using DNA microarrays. For 4 weeks, Brown Norway rats were fed a diet supplemented with 0, 150, or 450 mg HCB/kg. Spleen, mesenteric lymph nodes (MLN), thymus, blood, liver, and kidney were collected and analyzed using the Affymetrix rat RGU-34A GeneChip microarray. Most significant (p < 0.001) changes, compared to the control group, occurred in spleen, followed by liver, kidney, blood, and MLN, but only a few genes were affected in thymus. This was to be expected, as the thymus is not a target organ of HCB. Transcriptome profiles confirmed known effects of HCB such as stimulatory effects on the immune system and induction of enzymes involved in drug metabolism, porphyria, and the reproductive system. In line with previous histopathological findings were increased transcript levels of markers for granulocytes and macrophages. New findings include the upregulation of genes encoding proinflammatory cytokines, antioxidants, acute phase proteins, mast cell markers, complements, chemokines, and cell adhesion molecules. Generally, gene expression data provide evidence that HCB induces a systemic inflammatory response, accompanied by oxidative stress and an acute phase response. In conclusion, this study confirms previously observed (immuno)toxicological effects of HCB but also reveals several new and mechanistically relevant gene products. Thus, transcriptome profiles can be used as markers for several of the processes that occur after HCB exposure.

Several studies using microarrays have shown that changes in gene expression provide information about the mechanism of toxicity induced by xenobiotic agents. Nevertheless, the issue of whether gene expression profiles are reproducible across different laboratories remains to be determined. To address this question, several members of the Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute evaluated the liver gene expression profiles of rats treated with methapyrilene (MP). Animals were treated at one facility, and RNA was distributed to five different sites for gene expression analysis. A preliminary evaluation of the number of modulated genes uncovered striking differences between the five different sites. However, additional data analysis demonstrated that these differences had an effect on the absolute gene expression results but not on the outcome of the study. For all users, unsupervised algorithms showed that gene expression allows the distinction of the high dose of MP from controls and low dose. In addition, the use of a supervised analysis method (support vector machines) made it possible to correctly classify samples. In conclusion, the results show that, despite some variability, robust gene expression changes were consistent between sites. In addition, key expression changes related to the mechanism of MP-induced hepatotoxicity were identified. These results provide critical information regarding the consistency of microarray results across different laboratories and shed light on the strengths and limitations of expression profiling in drug safety analysis.