Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial.

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced.

Streamlined ion torrent PGM-based diagnostics: BRCA1 and BRCA2 genes as a model.

To meet challenges in terms of throughput and turnaround time, many diagnostic laboratories are shifting from Sanger sequencing to higher throughput next-generation sequencing (NGS) platforms. Bearing in mind that the performance and quality criteria expected from NGS in diagnostic or research settings are strikingly different, we have developed an Ion Torrent's PGM-based routine diagnostic procedure for BRCA1/2 sequencing. The procedure was first tested on a training set of 62 control samples, and then blindly validated on 77 samples in parallel with our routine technique.

Beta-catenin status in paediatric medulloblastomas: correlation of immunohistochemical expression with mutational status, genetic profiles, and clinical characteristics.

Medulloblastoma is the most frequent malignant paediatric brain tumour. The activation of the Wnt/beta-catenin pathway occurs in 10-15% of medulloblastomas and has been recently described as a marker for favourable patient outcome. We report a series of 72 paediatric medulloblastomas evaluated for beta-catenin protein expression, CTNNB1 mutations, and comparative genomic hybridization.

Anaplastic oligodendrogliomas with 1p19q codeletion have a proneural gene expression profile.

Background: In high grade gliomas, 1p19q codeletion and EGFR amplification are mutually exclusive and predictive of dramatically different outcomes. We performed a microarray gene expression study of four high grade gliomas with 1p19q codeletion and nine with EGFR amplification, identified by CGH-array.

Results: The two groups of gliomas exhibited very different gene expression profiles and were consistently distinguished by unsupervised clustering analysis.

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.

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.

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.

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.