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PubMed2006

Identification of genes differentially expressed in glioblastoma versus pilocytic astrocytoma using Suppression Subtractive Hybridization.

Glioblastoma (GBM) is a highly malignant glioma, which has the propensity to infiltrate throughout the brain in contrast to pilocytic astrocytoma (PA) of the posterior fossa, which does not spread and can be cured by surgery. We have used Suppression Subtractive Hybridization to define markers that better delineate the molecular basis of brain invasion and distinguish these tumor groups. We have identified 106 genes expressed in PA versus GBM and 80 genes expressed in GBM versus PA. Subsequent analysis identified a subset of 20 transcripts showing a common differential expression pattern for the two groups. GBM differs from PA by the expression of five genes involved in invasion and angiogenesis: fibronectin, osteopontin, chitinase-3-like-1 (YKL-40), keratoepithelin and fibromodulin. PA differs from GBM by the expression of genes related to metabolism (apolipoprotein D), proteolysis (protease-serine-11), receptor and signal transduction (PLEKHB1 for Pleckstrin-Homology-domain-containing-protein-family-B-member-1), transcription/translation (eukaryotic-translation-elongation-factor-1-alpha1) processes and cell adhesion (SPOCK1 for SPARC/Osteonectin-CWCV-kazal-like-domains-proteoglycan). The expression of these genes was confirmed by real-time quantitative RT-PCR and immunohistochemistry. This study highlights the crucial role of brain invasion in GBM and identifies specific molecules involved in this process. In addition, it offers a restricted list of markers that accurately distinguish PA from GBM.

Aged↗
PubMed2004

Improving patient care through molecular diagnostics.

Traditional cancer diagnostic techniques include assessment of histologic appearance, identification of specific tumor subtypes, tumor grading, assessment of lymph node status, and presence of metastasis. These are useful for initial evaluation, but are limited in their ability to predict response to treatment and/or risk of experiencing adverse events to particular treatments. Recently, there has been considerable progress in molecular diagnostics in this area. Using molecular-based technologies, it is now possible to identify some patients with an inherited risk of developing breast cancer, and to more accurately assess the prognosis of those who already have developed the disease. Various techniques are also available or under investigation to help select patients who are most likely to respond to certain types of treatment. At present, this includes diagnostic tests to identify single tumor markers for which specific therapies are available, such as estrogen receptor and HER2. Diagnostic systems that allow simultaneous assessment of multiple markers, such as DNA microarrays and proteomics, are currently being validated. The use of these systems will allow further development of customized therapies directed toward the particular molecular defects in a given cancer.

Biomarkers, Tumor↗
PubMed2004

Molecular diversity of astrocytes with implications for neurological disorders.

The astrocyte represents the most abundant yet least understood cell type of the CNS. Here, we use a stringent experimental strategy to molecularly define the astrocyte lineage by integrating microarray datasets across several in vitro model systems of astrocyte differentiation, primary astrocyte cultures, and various astrocyterich CNS structures. The intersection of astrocyte data sets, coupled with the application of nonastrocytic exclusion filters, yielded many astrocyte-specific genes possessing strikingly varied patterns of regional CNS expression. Annotation of these astrocyte-specific genes provides direct molecular documentation of the diverse physiological roles of the astrocyte lineage. This global perspective in the normal brain also provides a framework for how astrocytes may participate in the pathogenesis of common neurological disorders like Alzheimer's disease, Parkinson's disease, stroke, epilepsy, and primary brain tumors.

Animals↗
PubMed2006

Lysosomal storage disease upon disruption of the neuronal chloride transport protein ClC-6.

Mammalian CLC proteins function as Cl(-) channels or as electrogenic Cl(-)/H(+) exchangers and are present in the plasma membrane and intracellular vesicles. We now show that the ClC-6 protein is almost exclusively expressed in neurons of the central and peripheral nervous systems, with a particularly high expression in dorsal root ganglia. ClC-6 colocalized with markers for late endosomes in neuronal cell bodies. The disruption of ClC-6 in mice reduced their pain sensitivity and caused moderate behavioral abnormalities. Neuronal tissues showed autofluorescence at initial axon segments. At these sites, electron microscopy revealed electron-dense storage material that caused a pathological enlargement of proximal axons. These deposits were positive for several lysosomal proteins and other marker proteins typical for neuronal ceroid lipofuscinosis (NCL), a lysosomal storage disease. However, the lysosomal pH of Clcn6(-/-) neurons appeared normal. CLCN6 is a candidate gene for mild forms of human NCL. Analysis of 75 NCL patients identified ClC-6 amino acid exchanges in two patients but failed to prove a causative role of CLCN6 in that disease.

Animals↗
PubMed2003

In vivo regeneration of murine prostate from dissociated cell populations of postnatal epithelia and urogenital sinus mesenchyme.

The existence of a postnatal prostate stem cell is supported by several types of evidence. Withdrawal of androgen leads to involution of the gland, but readdition can rapidly stimulate regeneration. Tissue fragments derived from mouse or rat prostatic epithelia from midgestation embryos or adult mice, when combined with tissue fragments from urogenital sinus mesenchyme and grafted under the kidney capsule, can regenerate prostatic structures. Indirect evidence supports that the stem cell population is contained within the basal layer. Purified prostatic stem cell preparations would be useful to define the physical and functional properties required for regeneration and to compare with cells that accumulate during abnormal growth states, like prostate cancer. We have developed a regeneration system using dissociated cell populations of postnatal prostate epithelia and embryonic urogenital sinus mesenchyme. Efficient in vivo regeneration of prostatic structures in the subcapsular space of the kidney was observed within 4-8 wk with as few as 103 epithelial cells from prostates derived from donors 10 d to 6 wk of age. The regenerated structures show a branching tubular epithelial morphology, with expression of a panel of markers consistent with prostate development. Donor epithelial populations can be readily infected with GFP expressing lentiviral vectors to provide integration markers and easy visualization. The cell preparations of urogenital sinus mesenchyme can be expanded in short-term in vitro culture while their inductive capabilities are retained. Further definition of the subpopulation of prostate epithelial cells containing the regeneration activity should be possible with such technologies.

Animals↗
PubMed2005

Procollagen alpha 1 type 1: a potential aide in histopathological grading of glioma.

Collagen type I production has been shown to play a role in malignant transformation. We examined procollagen type I expression in brain tumors and with histopathological grading. Expression levels of procollagen alpha 1 type 1 were determined in 5 glioma cell lines by RT-PCR, Northern, and Western blot analysis. In addition, 41 primary brain tumors and 2 metastatic lung cancers to the brain were examined by PCR. Of the 5 glioma cell line analyzed, 3 (glioma 1, SW-1783 and U-118) expressed procollagen alpha 1 type I and were sensitive to vitamin D3 (VD3). In contrast, 2 of the cell lines (U-373 and T-98G) lacked procollagen alpha 1 type 1 expression. In patients' samples, 14 of 15 anaplastic and low grade gliomas expressed procollagen alpha 1 type I, and 12 of the 14 expressed high levels. In contrast, only 12 of 21 high grade gliomas from patients expressed procollagen alpha 1 type1 and among these, only 4 of the 12 expressed high levels. Thus, there is an inversed correlation between procollagen alpha 1 type 1 expression and histopathological grading (R2=- 0.56, p=0.0005). Our data suggest that procollagen alpha 1 type I expression occurs more commonly in intermediate and low grade gliomas and may assist in histopathological grading.

Biomarkers, Tumor↗
PubMed2003

Biclustering microarray data by Gibbs sampling.

MOTIVATION: Gibbs sampling has become a method of choice for the discovery of noisy patterns, known as motifs, in DNA and protein sequences. Because handling noise in microarray data presents similar challenges, we have adapted this strategy to the biclustering of discretized microarray data. RESULTS: In contrast with standard clustering that reveals genes that behave similarly over all the conditions, biclustering groups genes over only a subset of conditions for which those genes have a sharp probability distribution. We have opted for a simple probabilistic model of the biclusters because it has the key advantage of providing a transparent probabilistic interpretation of the biclusters in the form of an easily interpretable fingerprint. Furthermore, Gibbs sampling does not suffer from the problem of local minima that often characterizes Expectation-Maximization. We demonstrate the effectiveness of our approach on two synthetic data sets as well as a data set from leukemia patients.

Biomarkers, Tumor↗
PubMed2004

An experimental evaluation of a loop versus a reference design for two-channel microarrays.

MOTIVATION: Despite theoretical arguments that so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists continue to use 'reference designs'. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency. RESULTS: The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice. AVAILABILITY: The data and R code are available at http://exgen.ma.umist.ac.uk CONTACT: veronica.vinciotti@brunel.ac.uk.

Algorithms↗
PubMed2004

An efficient Monte Carlo approach to assessing statistical significance in genomic studies.

MOTIVATION: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems. RESULTS: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.

Algorithms↗
PubMed2005

Robust estimation of protein expression ratios with lysate microarray technology.

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm

Algorithms↗
PubMed2005

Significance analysis of functional categories in gene expression studies: a structured permutation approach.

MOTIVATION: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists. METHOD: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. RESULTS: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times. AVAILABILITY: Code to implement SAFE in the statistical package R is available from the authors. SUPPLEMENTARY INFORMATION: http://www.bios.unc.edu/~fwright/SAFE.

Algorithms↗
PubMed2005

Estimating misclassification error with small samples via bootstrap cross-validation.

MOTIVATION: Estimation of misclassification error has received increasing attention in clinical diagnosis and bioinformatics studies, especially in small sample studies with microarray data. Current error estimation methods are not satisfactory because they either have large variability (such as leave-one-out cross-validation) or large bias (such as resubstitution and leave-one-out bootstrap). While small sample size remains one of the key features of costly clinical investigations or of microarray studies that have limited resources in funding, time and tissue materials, accurate and easy-to-implement error estimation methods for small samples are desirable and will be beneficial. RESULTS: A bootstrap cross-validation method is studied. It achieves accurate error estimation through a simple procedure with bootstrap resampling and only costs computer CPU time. Simulation studies and applications to microarray data demonstrate that it performs consistently better than its competitors. This method possesses several attractive properties: (1) it is implemented through a simple procedure; (2) it performs well for small samples with sample size, as small as 16; (3) it is not restricted to any particular classification rules and thus applies to many parametric or non-parametric methods.

Algorithms↗
PubMed2005

Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data.

MOTIVATION: High-throughput and high-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We have therefore developed a novel method for dimensionality reduction and tested on a published ovarian high-resolution SELDI-TOF dataset. RESULTS: We have developed a four-step strategy for data preprocessing based on: (1) binning, (2) Kolmogorov-Smirnov test, (3) restriction of coefficient of variation and (4) wavelet analysis. Subsequently, support vector machines were used for classification. The developed method achieves an average sensitivity of 97.38% (sd = 0.0125) and an average specificity of 93.30% (sd = 0.0174) in 1000 independent k-fold cross-validations, where k = 2, ..., 10. AVAILABILITY: The software is available for academic and non-commercial institutions.

Algorithms↗
PubMed2005

A protocol for building and evaluating predictors of disease state based on microarray data.

MOTIVATION: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction. RESULTS: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Algorithms↗
PubMed2005

Classification of microarray data with factor mixture models.

MOTIVATION: The classification of few tissue samples on a very large number of genes represents a non-standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an 'absolute call', which represents a qualitative indication of whether or not a transcript is detected within a sample. The 'absolute call' is generally not taken in consideration in analyses. RESULTS: In contrast to frequently used cluster analysis methods to analyze gene expression data, we consider a problem of classification of tissues and of the variables selection. We adopted methodologies formulated by Ghahramani and Hinton and Rocci and Vichi for simultaneous dimensional reduction of genes and classification of tissues; trying to identify genes (denominated 'markers') that are able to distinguish between two known different classes of tissue samples. In this respect, we propose a generalization of the approach proposed by McLachlan et al. by advising to estimate the distribution of log LR statistic for testing one versus two component hypothesis in the mixture model for each gene considered individually, using a parametric bootstrap approach. We compare conditional (on 'absolute call') and unconditional analyses performed on dataset described in Golub et al. We show that the proposed techniques improve the results of classification of tissue samples with respect to known results on the same benchmark dataset. AVAILABILITY: The software of Ghahramani and Hinton is written in Matlab and available in 'Mixture of Factor Analyzers' on http://www.gatsby.ucl.ac.uk/~zoubin/software.html while the software of Rocci and Vichi is available upon request from the authors.

Algorithms↗
PubMed2006

Group testing for pathway analysis improves comparability of different microarray datasets.

MOTIVATION: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, e.g. metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis, functional class scoring (FCS), or global tests. RESULTS: Three public prostate cancer datasets obtained with the same microarray platform (HGU95A/HGU95Av2) were analyzed. Each dataset was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays was applied to the vsn-normalized data and mixed model analysis to the data normalized by MMN. For multiple testing adjustment the false discovery rate was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different datasets showed overlaps between 42 and 52%, while lists from different methods applied to the same dataset had between 63 and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three datasets was then subjected to group testing by Fisher's exact test. Group testing by GSEA and global test was applied to the three datasets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different datasets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single dataset.

Biomarkers, Tumor↗
PubMed2004

Expression of alpha-methylacyl-CoA racemase in papillary renal cell carcinoma.

Alpha-methylacyl-CoA racemase (AMACR) was first discovered by using cDNA microarray technology as a molecular marker for prostate cancer. Our recent microarray analysis of renal cell carcinomas showed a significant increase of AMACR mRNA levels in papillary renal cell carcinomas, but not in other subtypes. To investigate the value of this marker in pathologic diagnosis, we analyzed AMACR mRNA levels in cDNA microarrays from 70 kidney tumors. Furthermore, we evaluated the AMACR expression in 165 kidney tumors on tissue microarrays and 51 papillary carcinomas of other organs by immunohistochemistry. AMACR mRNA was significantly overexpressed in 7 of 8 papillary renal cell carcinomas with an average of 5.2-fold increase, and only in 2 of 62 nonpapillary kidney tumors. Immunohistochemistry demonstrated strong AMACR positivity in all cases of papillary renal cell carcinomas (41 of 41, 100%), including 6 metastatic papillary renal cell carcinomas, but only focal or weak reactivity in the minority (18 of 124, 15%) of other renal tumors including 13 of 52 clear cell renal cell carcinomas, 3 of 20 oncocytomas, and 2 of 17 urothelial carcinomas. All chromophobe (0 of 18) and sarcomatoid components of renal cell carcinomas (0 of 15) were negative for AMACR. Weak or focal AMACR immunoreactivity was detected in only 4 of 51 (8%) papillary carcinomas arising in other organs (2 of 14 thyroid, 2 of 13 lung, 0 of 6 breast, 0 of 6 endometrium, 0 of 6 ovary, and 0 of 6 pancreas). Using a combination of cDNA microarrays, tissue microarrays, and immunohistochemistry, we identified AMACR as a marker for papillary renal cell carcinoma, which could be valuable in subclassification of renal cell carcinomas and in the differential diagnosis of a metastatic papillary carcinoma.

Biomarkers, Tumor↗
PubMed2003

Gene expression in neoplasms of the pancreas: applications to diagnostic pathology.

It seems that, lately, every pathology journal has three or four articles documenting the discovery of another genetic alteration or describing global gene expression in a series of cancers. Although these discoveries provide insight into the biology of neoplasia, it is less clear how they can be quickly and efficiently translated to patient care. This review will use neoplasms of the pancreas as a model and show how recent discoveries of genetic alterations and gene expression patterns can have a significant impact on the diagnosis and even treatment of tumors. Emphasis will be placed on applications that are practical and useful to the daily practice of pathology.

Biomarkers, Tumor↗