Peter Mac Translational Breast Cancer Genomics and Therapeutics Lab Data Access Committee
Ontology highlight
ABSTRACT:
PROVIDER: EGAC00001000837 | EGA |
REPOSITORIES: EGA
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Project description:In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete.In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it.We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium).We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC.
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Project description:Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, which accounts for approximately 3% of cases of breast malignancies. Diagnosis relies largely on its clinical presentation, and despite a characteristic phenotype, underlying molecular mechanisms are poorly understood. Unique clinical presentation indicates that IBC is a distinct clinical and biological entity when compared to non-IBC. Biological understanding of non-IBC has been extrapolated into IBC and targeted therapies for HER2 positive (HER2+) and hormonal receptor positive non-IBC led to improved patient outcomes in the recent years. This manuscript reviews recent discoveries related to the underlying biology of IBC, clinical progress to date and suggests rational approaches for investigational therapies.
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Project description:The Genetic Association Information Network (GAIN) Data Access Committee was established in June 2007 to provide prompt and fair access to data from six genome-wide association studies through the database of Genotypes and Phenotypes (dbGaP). Of 945 project requests received through 2011, 749 (79%) have been approved; median receipt-to-approval time decreased from 14 days in 2007 to 8 days in 2011. Over half (54%) of the proposed research uses were for GAIN-specific phenotypes; other uses were for method development (26%) and adding controls to other studies (17%). Eight data-management incidents, defined as compromises of any of the data-use conditions, occurred among nine approved users; most were procedural violations, and none violated participant confidentiality. Over 5 years of experience with GAIN data access has demonstrated substantial use of GAIN data by investigators from academic, nonprofit, and for-profit institutions with relatively few and contained policy violations. The availability of GAIN data has allowed for advances in both the understanding of the genetic underpinnings of mental-health disorders, diabetes, and psoriasis and the development and refinement of statistical methods for identifying genetic and environmental factors related to complex common diseases.
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Project description:The term "Translational Genomics" reflects both title and mission of this new journal. "Translational" has traditionally been understood as "applied research" or "development", different from or even opposed to "basic research". Recent scientific and societal developments have triggered a re-assessment of the connotation that "translational" and "basic" are either/or activities: translational research nowadays aims at feeding the best science into applications and solutions for human society. We therefore argue here basic science to be challenged and leveraged for its relevance to human health and societal benefits. This more recent approach and attitude are catalyzed by four trends or developments: evidence-based solutions; large-scale, high dimensional data; consumer/patient empowerment; and systems-level understanding.
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Project description:Next-generation sequencing has produced petabytes of data, but accessing and analyzing these data remain challenging. Traditionally, researchers investigating public datasets like The Cancer Genome Atlas (TCGA) would download the data to a high-performance cluster, which could take several weeks even with a highly optimized network connection. The National Cancer Institute (NCI) initiated the Cancer Genomics Cloud Pilots program to provide researchers with the resources to process data with cloud computational resources. We present protocols using one of these Cloud Pilots, the Seven Bridges Cancer Genomics Cloud (CGC), to find and query public datasets, bring your own data to the CGC, analyze data using standard or custom workflows, and benchmark tools for accuracy with interactive analysis features. These protocols demonstrate that the CGC is a data-analysis ecosystem that fully empowers researchers with a variety of areas of expertise and interests to collaborate in the analysis of petabytes of data. © 2017 by John Wiley & Sons, Inc.
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Project description:Data Access Committee EGAC00001000154
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Project description:BACKGROUND:Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. METHODS:In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. RESULTS:Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. CONCLUSIONS:Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.
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Project description:Cancer genomics projects employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Examples include projects carried out by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). A crucial step in the extraction of knowledge from the data is the exploration by experts of the different alterations, as well as the multiple relationships between them. To that end, the use of intuitive visualization tools that can integrate different types of alterations with clinical data is essential to the field of cancer genomics. Here, we review effective and common visualization techniques for exploring oncogenomics data and discuss a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. The review covers visualization methods employed by tools such as Circos, Gitools, the Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC Cancer Genomics Browser, the Regulome Explorer and the Cancer Genome Workbench.
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Project description:While a large and growing body of research has demonstrated that mesenchymal stem/stromal cells (MSCs) play a dual role in tumor growth and inhibition, studies exploring the capability of MSCs to contribute to tumorigenesis are rare. MSCs are key players during tumorigenesis and cancer development, evident in their faculty to increase cancer stem cells (CSCs) population, to generate the precursors of certain forms of cancer (e.g. sarcoma), and to induce epithelial-mesenchymal transition to create the CSC-like state. Indeed, the origin and localization of the native MSCs in their original tissues are not known. MSCs are identified in the primary tumor sites and the fetal and extraembryonic tissues. Acknowledging the developmental origin of MSCs and tissue-resident native MSCs is essential for better understanding of MSC contributions to the cellular origin of cancer. This review stresses that the plasticity of MSCs can therefore instigate further risk in select therapeutic strategies for some patients with certain forms of cancer. Towards this end, to explore the safe and effective MSC-based anti-cancer therapies requires a strong understanding of the cellular and molecular mechanisms of MSC action, ultimately guiding new strategies for delivering treatment. While clinical trial efforts using MSC products are currently underway, this review also provides new insights on the underlying mechanisms of MSCs to tumorigenesis and focuses on the approaches to develop MSC-based anti-cancer therapeutic applications.
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