Biomedical subjects

Séverine Lair

Publications and source records attributed to Séverine Lair.

4 recordsLinked to original sources

PubMed2006

VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles.

MOTIVATION: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses. RESULTS: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at http://bioinfo.curie.fr/vamp AVAILABILITY: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/doc.

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PubMed2006

ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis.

Transcriptome microarrays have become one of the tools of choice for investigating the genes involved in tumorigenesis and tumor progression, as well as finding new biomarkers and gene expression signatures for the diagnosis and prognosis of cancer. Here, we describe a new database for Integrated Tumor Transcriptome Array and Clinical data Analysis (ITTACA). ITTACA centralizes public datasets containing both gene expression and clinical data. ITTACA currently focuses on the types of cancer that are of particular interest to research teams at Institut Curie: breast carcinoma, bladder carcinoma and uveal melanoma. A web interface allows users to carry out different class comparison analyses, including the comparison of expression distribution profiles, tests for differential expression and patient survival analyses. ITTACA is complementary to other databases, such as GEO and SMD, because it offers a better integration of clinical data and different functionalities. It also offers more options for class comparison analyses when compared with similar projects such as Oncomine. For example, users can define their own patient groups according to clinical data or gene expression levels. This added flexibility and the user-friendly web interface makes ITTACA especially useful for comparing personal results with the results in the existing literature. ITTACA is accessible online at http://bioinfo.curie.fr/ittaca.

Breast Neoplasms↗

PubMed2004

Inhibition of human bladder tumour cell growth by fibroblast growth factor receptor 2b is independent of its kinase activity. Involvement of the carboxy-terminal region of the receptor.

The b isoform of fibroblast growth factor receptor 2, FGFR2b/FGFR2-IIIb/Ksam-IIC1/KGFR, a tyrosine kinase receptor, is expressed in a wide variety of epithelia and is downregulated in several human carcinomas including prostate, salivary and urothelial cell carcinomas. FGFR2b has been shown to inhibit growth in tumour cell lines derived from these carcinomas. Here, we investigated the molecular mechanisms underlying the inhibition of human urothelial carcinoma cell growth following FGFR2b expression. Using a nylon DNA array, we analysed the gene expression profile of the T24 bladder tumour cell line, transfected or not with a construct encoding FGFR2b. The expression of FGFR2b in T24 cells decreased insulin-like growth factor (IGF)-II mRNA levels. This decrease was correlated with a decrease in IGF-II secretion and may have been responsible for the observed inhibition of cell growth because the addition of exogenous IGF-II restored growth rates to normal levels. Using SU5402, an inhibitor of FGFR tyrosine kinase activity, and a kinase dead mutant of the receptor, FGFR2b Y659F/Y660F, we also demonstrated that the growth inhibition and decrease in IGF-II secretion induced by FGFR2b did not require tyrosine kinase activity. Finally, we demonstrated the involvement of the distal carboxy-terminal domain of the receptor in decreasing IGF-II expression and inhibiting T24 cell growth, as Ksam-IIC3, a variant of FGFR2b carrying a short carboxy-terminus, neither downregulated IGF-II nor inhibited cell proliferation. Our data suggest that FGFR2b inhibits the growth of bladder carcinoma cells by reducing IGF-II levels via its carboxy-terminal domain, independent of its tyrosine kinase activity.

Base Sequence↗

PubMed2004

Selecting biomedical data sources according to user preferences.

MOTIVATION: Biologists are now faced with the problem of integrating information from multiple heterogeneous public sources with their own experimental data contained in individual sources. The selection of the sources to be considered is thus critically important. RESULTS: Our aim is to support biologists by developing a module based on an algorithm that presents a selection of sources relevant to their query and matched to their own preferences. We approached this task by investigating the characteristics of biomedical data and introducing several preference criteria useful for bioinformaticians. This work was carried out in the framework of a project which aims to develop an integrative platform for the multiple parametric analysis of cancer. We illustrate our study through an elementary biomedical query occurring in a CGH analysis scenario. AVAILABILITY: http://www.lri.fr/~cohen/dss/dss.html

Algorithms↗