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

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

<p>Abstract</p> <p>Background</p> <p>In high grade gliomas, 1p19q codeletion and <it>EGFR </it>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 <it>EGFR </it>amplification, identified by CGH-array.</p> <p>Results</p> <p>The two groups of gliomas exhibited very different gene expression profiles and were consistently distinguished by unsupervised clustering analysis. One of the most striking differences was the expression of normal brain genes by oligodendrogliomas with 1p19q codeletion. These gliomas harbored a gene expression profile that partially resembled the gene expression of normal brain samples, whereas gliomas with <it>EGFR </it>amplification expressed many genes in common with glioblastoma cancer stem cells. The differences between the two types of gliomas and the expression of neuronal genes in gliomas with 1p19q codeletion were both validated in an independent series of 16 gliomas using real-time RT-PCR with a set of 22 genes differentiating the two groups of gliomas (<it>AKR1C3</it>, <it>ATOH8</it>, <it>BMP2</it>, <it>C20orf42</it>, <it>CCNB1</it>, <it>CDK2</it>, <it>CHI3L1</it>, <it>CTTNBP2</it>, <it>DCX, EGFR, GALNT13, GBP1, IGFBP2, IQGAP1, L1CAM, NCAM1, NOG, OLIG2, PDPN, PLAT, POSTN, RNF135</it>). Immunohistochemical study of the most differentially expressed neuronal gene, alpha-internexin, clearly differentiated the two groups of gliomas, with 1p19q codeletion gliomas showing specific staining in tumor cells.</p> <p>Conclusion</p> <p>These findings provide evidence for neuronal differentiation in oligodendrogliomas with 1p19q codeletion and support the hypothesis that the cell of origin for gliomas with 1p19q codeletion could be a bi-potential progenitor cell, able to give rise to both neurons and oligodendrocytes.</p

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes

Standardized analysis and sharing of genome-phenome data for neuromuscular and rare disease research through the RD-Connect platform

<b>Abstract: </b><div>RD-Connect (rd-connect.eu) is an EU-funded project building an integrated platform to narrow the gaps in rare disease research, where patient populations, clinical expertise and research communities are small in number and highly fragmented. Guided by the needs of rare disease researchers and with neuromuscular and neurodegenerative researchers as its original collaborators, the RD-Connect platform securely integrates multiple types of omics data (genomics, proteomics and transcriptomics) with biosample and clinical information – at the level of an individual patient, a family or a whole cohort, providing not only a centralized data repository but also a sophisticated and user-friendly online analysis system. Whole-genome, exome or gene panel NGS datasets from individuals with rare diseases and family members are deposited at the European Genome-phenome Archive, a longstanding archiving system designed for long-term storage of these large datasets. The raw data is then processed by RD-Connect's standardised analysis and annotation pipeline to make data from different sequencing providers more comparable. The corresponding clinical information from each individual is recorded in a connected PhenoTips instance, a software solution that simplifies the capture of clinical data using the Human Phenotype Ontology, OMIM and Orpha codes. The results are made available to authorised users through the highly configurable online platform (platform.rd-connect.eu), which runs on a Hadoop cluster and uses ElasticSearch – technologies designed to handle big data at high speeds. The user-friendly interface enables filtering and prioritization of variants using the most common quality, genomic location, effect, pathogenicity and population frequency annotations, enabling users from clinical labs without extensive bioinformatics support to do their primary genomic analysis of their own patients online and compare them with other submitted cohorts. Additional tools, such as Exomiser, DiseaseCard, Alamut Functional Annotation (ALFA) and UMD Predictor (umd-predictor.eu) are integrated at several levels. The RD-Connect platform is designed to enable data sharing at various levels depending on user permissions. At the most basic level (“does this specific variant exist in any individual in this cohort?”) it has lit a Beacon within the Global Alliance for Genomics and Health’s Beacon Network (www.beacon-network.org). At the next stage of sharing – finding similarities between patients in different databases with a matching phenotype and a candidate variant in the same gene – it is actively involved in the development of Matchmaker Exchange (www.matchmakerexchange.org), allowing users of different systems to securely exchange information to find confirmatory cases. And finally, since all patients within the system have been consented for data sharing, users of the system, after validation and authorization, are able to access datasets from other centres, providing an instant means of gathering cohorts for cross-validation and further study. Although open to any rare disease, the platform is currently enriched for neuromuscular and neurodegenerative phenotypes and includes almost 1000 genomic datasets from the NeurOmics project (www.rd-neuromics.eu) with several other contributions in the pipeline, including 1000 limb-girdle muscular dystrophy index cases from the Myo-Seq project (www.myo-seq.org) and more. The platform is free of charge to use and is open for contributions of NGS and phenotypic data from research labs worldwide via [email protected] <p></p></div

The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes