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.

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.

Somatic mutations in a subset of growth hormone (GH)-secreting pituitary tumors convert the gene for the alpha polypeptide chain (alpha s) of Gs into a putative oncogene, termed gsp. These mutations, which activate alpha s by inhibiting its guanosine triphosphatase (GTPase) activity, are found in codons for either of two amino acids, each of which is completely conserved in all known G protein alpha chains. The likelihood that similar mutations would activate other G proteins prompted a survey of human tumors for mutations that replace either of these two amino acids in other G protein alpha chain genes. The first gene so far tested, which encodes the alpha chain of Gi2, showed mutations that replaced arginine-179 with either cysteine or histidine in 3 of 11 tumors of the adrenal cortex and 3 of 10 endocrine tumors of the ovary. The mutant alpha i2 gene is a putative oncogene, referred to as gip2. In addition, gsp mutations were found in 18 of 42 GH-secreting pituitary tumors and in an autonomously functioning thyroid adenoma. These findings suggest that human tumors may harbor oncogenic mutations in various G protein alpha chain genes.

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.

Immunohistochemically detected p53 protein accumulation was an independent marker of shortened survival and was seen more often in familial than in sporadic carcinomas. Our findings also suggest a correlation between p53 protein accumulation and p53 gene mutation.

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The Philadelphia chromosome is present in more than 95% of chronic myeloid leukemia patients and 13% of acute lymphocytic leukemia patients. The Philadelphia translocation, t(9;22), fuses the BCR and ABL genes resulting in the expression of leukemia-specific, chimeric BCR-ABL messenger RNAs. To facilitate diagnosis of these leukemias, we have developed a method of amplifying and detecting only the unique mRNA sequences, using an extension of the polymerase chain reaction technique. Diagnosis of chronic myeloid and acute lymphocytic leukemias by this procedure is rapid, much more sensitive than existing protocols, and independent of the presence or absence of an identifiable Philadelphia chromosome.

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.