Fibromatoses desmoïdes: étude immunohistochimique de 14 cas sur tissue microarray (TMA)

Background/aim

Desmoid fibromatosis are rare, benign but locally aggressive tumors, characterized by an infiltrative growth and a tendency towards local recurrence, but an inability to metastasise. The morphological diagnosis may be difficult, requiring immunohistochemistry. The aim of our study is to determine the im munohistochemical phenotypes of these tumours to evaluate if they are helpful and to define a diagnostic strategy.

Methods

Immunohistochemistry was used to examine the expression of β-catenin, APC protein, in archival material derived from fourteen cases of extraabdominal desmoid tumors. Desmoids specimens were assembled into a clinical data-linked tissue micro — array. Nuclear β-catenin expression was observed in 100% of the specimens. Positive cytoplasmic staining for APC protein was found in 11 of 14 (78,6%). But all samples were negative for oestrogen and progesterone receptors, c-KIT and WT1.

Results

Our results regarding β-catenin and APC confirm the previous findings that those proteins play a crucial role in the pathogenesis of sporadic aggressive fibromatosis.     

Overexpression of α2,3sialyl T-antigen in breast cancer determined by miniaturized glycosyltransferase assays and confirmed using tissue microarray immunohistochemical analysis

Glycan structure alterations during cancer regulate disease progression and represent clinical biomarkers. The study determined the degree to which changes in glycosyltransferase activities during cancer can be related to aberrant cell-surface tumor associated carbohydrate structures (TACA). To this end, changes in sialyltransferase (sialylT), fucosyltransferase (fucT) and galactosyltransferase (galT) activity were measured in normal and tumor tissue using a miniaturized enzyme activity assay and synthetic glycoconjugates bearing terminal LacNAc Type-I (Galβ1-3GlcNAc), LacNAc Type-II (Galβ1-4GlcNAc), and mucin core-1/Type-III (Galβ1-3GalNAc) structures. These data were related to TACA using tissue microarrays containing 115 breast and 26 colon cancer specimen. The results show that primary human breast and colon tumors, but not adjacent normal tissue, express elevated β1,3GalT and α2,3SialylT activity that can form α2,3SialylatedType-IIIglycans (Siaα2-3Galβ1-3GalNAc). Prostate tumors did not exhibit such elevated enzymatic activities. α1,3/4FucT activity was higher in breast, but not in colon tissue. The enzymology based prediction of enhanced α2,3sialylated Type-III structures in breast tumors was verified using histochemical analysis of tissue sections and tissue microarrays. Here, the binding of two markers that recognize Galβ1-3GalNAc (peanut lectin and mAb A78-G/A7) was elevated in breast tumor, but not in normal control, only upon sialidase treatment. These antigens were also upregulated in colon tumors though to a lesser extent. α2,3sialylatedType-III expression correlated inversely with patient HER2 expression and breast metastatic potential. Overall, enzymology measurements of glycoT activity predict truncated O-glycan structures in tumors. High expression of the α2,3sialylated T-antigen O-glycans occur in breast tumors. A transformation from linear core-1 glycan to other epitopes may accompany metastasis.     

Confirming microarray data--is it really necessary?

The generation of corroborative data has become a commonly used approach for ensuring the veracity of microarray data. Indeed, the need to conduct corroborative studies has now become official editorial policy for at least 2 journals, and several more are considering introducing such a policy. The issue of corroborating microarray data is a challenging one-there are good arguments for and against conducting such experiments. However, we believe that the introduction of a fixed requirement to corroborate microarray data, especially if adopted by more journals, is overly burdensome and may, in at least several applications of microarray technology, be inappropriate. We also believe that, in cases in which corroborative studies are deemed essential, a lack of clear guidance leaves researchers unclear as to what constitutes an acceptable corroborative study. Guidelines have already been outlined regarding the details of conducting microarray experiments. We propose that all stakeholders, including journal editorial boards, reviewers, and researchers, should undertake concerted and inclusive efforts to address properly and clarify the specific issue of corroborative data. In this article we highlight some of the thorny and vague areas for discussion surrounding this issue. We also report the results of a poll in which 76 life science journals were asked about their current or intended policies on the inclusion of corroborative studies in papers containing microarray data.     

Is cross-validation valid for small-sample microarray classification?

MOTIVATION: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS: An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).     

从microarray时序表达数据识别周期表达基因

根据周期表达基因的周期性和峰值特点,提出了一种将microarray时序表达数据划分为若干个基因表达周期,并对周期内的峰值特点进行评估以识别周期表达基因的方法,能有效减小microarray实验时的噪声干扰。选取了三组广泛使用的时序表达数据和一组可靠的周期表达基因集合对该方法的效果进行了测试,并与三种典型的周期表达基因识别方法的效果进行了比较。该方法能有效地从各种microarray时序表达数据中识别周期表达基因。     

Mowat–Wilson syndrome detected by using high resolution microarray

Individuals with Mowat–Wilson syndrome (MWS; OMIM#235730) have characteristic facial features, a variety of congenital anomalies such as Hirschsprung disease, and intellectual disabilities caused by mutation or deletion of ZEB2 gene. This deletion or cytogenetic abnormality has been reported primarily from Europe, Australia and the United States, but not in Korea. Here we report a patient with characteristic facial features of MWS, developmental delay and spasticity. High resolution microarray analysis revealed 0.9 Mb deletion of 2q22.3 involving two genes: ZEB2 and GTDC1. This case shows the important role of high resolution microarray in patients with unexplained psychomotor retardation and/or facial dysmorphism. Knowledge about the most striking clinical signs and implementation of effective molecular tests like microarray could significantly increase the detection rate of new cases of MWS in Korea. This is the first reported case of MWS in Korea.     

Locating tissue collections in tissue economies—deriving value from biomedical research

Abstract

This paper examines diverging notions of value in the use of tissue sample collections and other information resources using a case study of hereditary colorectal cancer research in Finland. Recent science and technology policies that emphasize the production of commercial value derived from tissue sample collections are challenged by varying conceptions of value, as well as structural factors that relate to the combination of different public population information systems in the Finnish research system. Such challenges reflect a tension in the economic aspirations of the ideology of the knowledge society in relation to the goals of national health care policies, as well as the role of the state as a mediator of knowledge production and commercial development.     

What have we learnt about microarray analyses of atherogenesis?

PURPOSE OF REVIEW: This review covers recent developments in microarray studies in the field of atherogenesis research. RECENT FINDINGS: During the past year microarrays have been applied to the analysis of pathogenic mechanisms of several atherosclerosis risk factors, including ageing, hypertension, obesity, and cytomegalovirus infection. In addition, gene expression patterns during the development of in-stent restenosis have been examined. Several studies have also explored the pleiotropic effects of statin therapy. As a technical improvement, the combination of laser microdissection with microarrays in human samples has been reported. SUMMARY: Microarray analyses have given important new information about atherogenesis. It is anticipated that microarray studies will significantly contribute to further discoveries in the field.     

Towards microbial tissue engineering?

Tissue engineering involves the creation of multicellular tissues from individual cells. It was previously perceived that tissues were only formed by higher organisms such as plants and animals. However, it is now known that multicellular systems of microorganisms, such as microbial colonies, biofilms, flocs and aggregates, can also show extensive spatial organization. Here, we discuss methods that can be used to spatially organize microorganisms--bacteria, in particular--into tissue-like materials with defined internal architectures. Some potential uses of such "microbial tissues" are covered.     

Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.     

MILANO – custom annotation of microarray results using automatic literature searches

Background  

High-throughput genomic research tools are becoming standard in the biologist's toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining – the process of representing literature data in a fashion that is easy to relate to genomic data – is one solution to this problem.     

ILOOP – a web application for two-channel microarray interwoven loop design

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues on how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from http://mcbc.usm.edu/iloop.     

Time-resolved Förster-resonance-energy-transfer DNA assay on an active CMOS microarray

We present an active oligonucleotide microarray platform for time-resolved F?rster-resonance-energy-transfer (TR-FRET) assays. In these assays, immobilized probe is labeled with a donor fluorophore and analyte target is labeled with a fluorescence quencher. Changes in the fluorescence decay lifetime of the donor are measured to determine the extent of hybridization. In this work, we demonstrate that TR-FRET assays have reduced sensitivity to variances in probe surface density compared with standard fluorescence-based microarray assays. Use of an active array substrate, fabricated in a standard complementary metal-oxide-semiconductor (CMOS) process, provides the additional benefits of reduced system complexity and cost. The array consists of 4096 independent single-photon avalanche diode (SPAD) pixel sites and features on-chip time-to-digital conversion. We demonstrate the functionality of our system by measuring a DNA target concentration series using TR-FRET with semiconductor quantum dot donors.     

How is a tissue built?

Tissues change in many ways in the period that they are part of a living organism. They are created in fairly repeatable structural patterns, and we know that the patterns are due to both the genes and the (mechanical) environment, but we do not know exactly what part or percentage of a particular pattern to consider the genes, or the environment, responsible for. We do not know much about the beginning of tissue construction (morphogenesis) and we do not know the methods of tissue construction. When the tissue structure is altered to accommodate a new loading, we do not know how the decision is made for the structural reconstruction. We do know that tissues grow or reconstruct themselves without ceasing to continue with their structural function, but we do not understand the processes that permit them to accomplish this. Tissues change their structures to altered mechanical environments, but we are not sure how. Tissues heal themselves and we understand little of the structural mechanics of the process. With the objective of describing the interesting unsolved mechanics problems associated with these biological processes, some aspects of the formation, growth, and adaptation of living tissues are reviewed. The emphasis is on ideas and models. Beyond the objective is the hope that the work will stimulate new ideas and new observations in the mechanical and chemical aspects of developmental biology.     

Microbial diagnostic microarray for food‐ and water‐borne pathogens

A microbial diagnostic microarray for the detection of the most relevant bacterial food- and water-borne pathogens and indicator organisms was developed and thoroughly validated. The microarray platform based on sequence-specific end labelling of oligonucleotides and the pyhylogenetically robust gyrB marker gene allowed a highly specific (resolution on genus/species level) and sensitive (0.1% relative and 10⁴ cfu absolute detection sensitivity) detection of the target pathogens. Validation was performed using a set of reference strains and a set of spiked environmental samples. Reliability of the obtained data was additionally verified by independent analysis of the samples via fluorescence in situ hybridization (FISH) and conventional microbiological reference methods. The applicability of this diagnostic system for food analysis was demonstrated through extensive validation using artificially and naturally contaminated spiked food samples. The microarray-based pathogen detection was compared with the corresponding microbiological reference methods (performed according to the ISO norm). Microarray results revealed high consistency with the reference microbiological data.     

How high is the level of technical noise in microarray data?

Background  

Microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data.     

How accurately can we control the FDR in analyzing microarray data?

SUMMARY: We want to evaluate the performance of two FDR-based multiple testing procedures by Benjamini and Hochberg (1995, J. R. Stat. Soc. Ser. B, 57, 289-300) and Storey (2002, J. R. Stat. Soc. Ser. B, 64, 479-498) in analyzing real microarray data. These procedures commonly require independence or weak dependence of the test statistics. However, expression levels of different genes from each array are usually correlated due to coexpressing genes and various sources of errors from experiment-specific and subject-specific conditions that are not adjusted for in data analysis. Because of high dimensionality of microarray data, it is usually impossible to check whether the weak dependence condition is met for a given dataset or not. We propose to generate a large number of test statistics from a simulation model which has asymptotically (in terms of the number of arrays) the same correlation structure as the test statistics that will be calculated from the given data and to investigate how accurately the FDR-based testing procedures control the FDR on the simulated data. Our approach is to directly check the performance of these procedures for a given dataset, rather than to check the weak dependency requirement. We illustrate the proposed method with real microarray datasets, one where the clinical endpoint is disease group and another where it is survival.