Consistent gene expression profiles in MexTAg transgenic mouse and wild type mouse asbestos-induced mesothelioma.

BACKGROUND: The MexTAg transgenic mouse model of mesothelioma replicates many aspects of human mesothelioma, including induction by asbestos, pathogenicity and response to cytotoxic chemotherapy, despite high levels of the SV40 large T Antigen (TAg) in the mesothelial compartment. This model enables analysis of the molecular events associated with asbestos induced mesothelioma and is utilised here to investigate the molecular dynamics of tumours induced in these mice, using gene expression patterns as a read out. METHODS: Gene expression of MexTAg mesothelioma cell lines bearing a high or low number of copies of the TAg transgene were compared to wild type mouse mesotheliomas and normal mouse mesothelial cells using Affymetrix microarray. These data were then compared to a similar published human microarray study using the same platform. RESULTS: The main expression differences between transgenic mouse and wild type mouse mesotheliomas occurred for genes involved in cell cycle regulation and DNA replication, as would be expected from overexpression of the TAg oncogene. Quantitative PCR confirmed that E2F and E2F regulated genes were significantly more upregulated in MexTAg mesotheliomas and MexTAg mesothelial cells compared to wild type mesotheliomas. Like human mesothelioma, both MexTAg and wild type mesotheliomas had more genes underexpressed than overexpressed compared to normal mouse mesothelial cells. Most notably, the cdkn2 locus was deleted in the wild type mouse mesotheliomas, consistent with 80 % human mesotheliomas, however, this region was not deleted in MexTAg mesotheliomas. Regardless of the presence of TAg, all mouse mesotheliomas had a highly concordant set of deregulated genes compared to normal mesothelial cells that overlapped with the deregulated genes between human mesotheliomas and mesothelial cells. CONCLUSIONS: This investigation demonstrates that the MexTAg mesotheliomas are comparable with wild type mouse mesotheliomas in their representation of human mesothelioma at the molecular level, with some key gene expression differences that are attributable to the TAg transgene expression. Of particular note, MexTAg mesothelioma development was not dependent on cdkn2 deletion.

Gene expression analysis in absence epilepsy using a monozygotic twin design.

PURPOSE: To identify genes involved in idiopathic absence epilepsies by analyzing gene expression using a monozygotic (MZ) twin design. METHODS: Genome-wide gene expression in lymphoblastoid cell lines (LCLs) was determined using microarrays derived from five discordant and four concordant MZ twin pairs with idiopathic absence epilepsies and five unaffected MZ twin pairs. Gene expression was analyzed using three strategies: discordant MZ twins were compared as matched pairs, MZ twins concordant for epilepsy were compared to control MZ twins, and a singleton design of affected versus unaffected MZ twin individuals was used irrespective of twin pairing. An overlapping gene list was generated from these analyses. Dysregulation of genes recognized from the microarray experiment was validated using quantitative real time PCR (qRT-PCR) in the twin sample and in an independent sample of 18 sporadic absence cases and 24 healthy controls. RESULTS: Sixty-five probe sets were identified from the three combined microarray analysis strategies. Sixteen genes were chosen for validation and nine of these genes confirmed by qRT-PCR in the twin sample. Differential expression for EGR1 (an immediate early gene) and RCN2 (coding for the calcium-binding protein Reticulocalbin 2) were reconfirmed by qRT-PCR in the independent sample. DISCUSSION: Using a unique sample of discordant MZ twins, our study identified genes with altered expression, which suggests novel mechanisms in idiopathic absence epilepsy. Dysregulation of EGR1 and RCN2 is implicated in idiopathic absence epilepsy.

A comparison of background correction methods for two-colour microarrays.

MOTIVATION: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. RESULTS: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. AVAILABILITY: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.

Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis.

BACKGROUND: Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis. RESULTS: A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability. CONCLUSION: The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.

A molecular diagnostic test for distinguishing lung adenocarcinoma from malignant mesothelioma using cells collected from pleural effusions.

PURPOSE: Patients with malignant mesothelioma or adenocarcinoma of the lung often present with respiratory complications associated with a malignant pleural effusion. Distinguishing between these malignancies is frequently problematic, as many of the clinical, cytologic, and histologic features of the diseases overlap. Following cytologic analysis of pleural effusions, subsequent confirmatory tissue biopsies involve increased patient morbidity and expense. We have therefore designed a gene expression-based test to classify the primary tumor causing a malignant pleural effusion, using cells collected from the effusion itself. EXPERIMENTAL DESIGN: We have used microarray data for 190 lung adenocarcinomas and 33 malignant mesotheliomas to identify genes differentially expressed between the two diseases. Genes expressed in normal mesothelial cells were removed, allowing the development of a PCR-based test to measure the expression of genes that discriminate between mesothelioma and lung adenocarcinoma from cytology specimens. RESULTS: Applying an real-time PCR-based assay involving 17 genes to 13 independent samples from biopsy-proven malignant mesothelioma and lung adenocarcinomas resulted in the correct identification of all samples. CONCLUSIONS: We have developed a test that is able to distinguish between lung adenocarcinoma and mesothelioma in cells collected from pleural effusions.

Empirical array quality weights in the analysis of microarray data.

BACKGROUND: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. RESULTS: In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. CONCLUSION: This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.

Terminal osteoblast differentiation, mediated by runx2 and p27KIP1, is disrupted in osteosarcoma.

The molecular basis for the inverse relationship between differentiation and tumorigenesis is unknown. The function of runx2, a master regulator of osteoblast differentiation belonging to the runt family of tumor suppressor genes, is consistently disrupted in osteosarcoma cell lines. Ectopic expression of runx2 induces p27KIP1, thereby inhibiting the activity of S-phase cyclin complexes and leading to the dephosphorylation of the retinoblastoma tumor suppressor protein (pRb) and a G1 cell cycle arrest. Runx2 physically interacts with the hypophosphorylated form of pRb, a known coactivator of runx2, thereby completing a feed-forward loop in which progressive cell cycle exit promotes increased expression of the osteoblast phenotype. Loss of p27KIP1 perturbs transient and terminal cell cycle exit in osteoblasts. Consistent with the incompatibility of malignant transformation and permanent cell cycle exit, loss of p27KIP1 expression correlates with dedifferentiation in high-grade human osteosarcomas. Physiologic coupling of osteoblast differentiation to cell cycle withdrawal is mediated through runx2 and p27KIP1, and these processes are disrupted in osteosarcoma.