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A. Domain User Stories
Search for all "pre-cancerous" biospecimens that are available for sharing at Washington University, Thomas Jefferson University, and
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Fox Chase Cancer Center.
Domain Description: A cancer researcher sits down to his console with the intention of ordering some biospecimens for use at his organization. He opens the caTissue website at his lab and begins performing the search. Unfortunately, there is currently a shortage at his hospital of suitable pre-cancerous tissue. Therefore, he expands his search to Washington University, Thomas Jefferson, and Fox Chase, all of which are in driving distance so he could send a post doc to pick them up. He hits the search button, and the result from all three cancer centers are displayed on his web page. He selects suitable biospecimens, hits the print button, and sends his trusty post doc on his way.
Technical Description: Biospecimen repositories are deployed locally, as well as Washington University, Thomas Jefferson University, and Fox Chase Cancer Center. Each has their information models registered in a metadata repository, as well as has standardized APIs exposed. The local instance of caTissue discovers services with compatible metadata and APIs, and performs the query. The data returned is aggregated based on standardized metadata, and presented to the user. caTissue uses CDE names, descriptions, and standard value sets to display data, help the user build the query, and issue the query.
Identify samples obtained for
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glioblastoma multiforme (GBM) and the corresponding CT image information.
Domain Description: a cancer researcher has developed a new image detection algorithm for identifying glioblastoma multiforme, which is the most common and most aggressive type of primary brain tumor in humans, involving glial cells and accounting for 52% of all parenchymal brain tumor cases and 20% of all intracranial tumors. When viewed with MRI, glioblastomas often appear as ring-enhancing lesions. The appearance is not specific, however, as other lesions such as abscess, metastasis, tumefactive multiple sclerosis, and other entities may have a similar appearance. The cancer researcher's algorithm should be able to differentiate between cancerous lesions and other lesions, but he needs additional tissues and images to make his testing statistically significant. The cancer researcher sits down to his laptop and loads Cancer Bench-to-Bedside (caB2B). He builds a search on all known tissues that have been identified as globlastoma multiforme via stereotactic biopsy and have corresponding CT images. He hits the search button, gets a cup of coffee, and a returns to a list of 74 tissues with 465 images. He hits the export button, which downloads all the images with associated pathology results.
Technical Description: a number of organizations have exposed pathology and image services with standardized metadata. caB2B uses CDE names, descriptions, and value sets to allow the user to construct a query across all of these services. The user selects the CDEs to filter on, which includes a join across information models (caTissue annotations to imaging annotations). A semantic relationship between the two models based on biospecimen identifier has previously been established. A distributed query is formulated and executed. The resulting data is aggregated based on semantic relationships and presented to the user using CDE names and descriptions.
Determine if each sample used in an expression profiling experiment is available for a SNP analysis experiment.
Repeat.
Search for a particular gene based on the Entrez Gene ID and its related
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information e.g. messenger RNA and protein information from GeneConnect.
Repeat.
Automatically discover analytical steps using Illumina bead array analysis using inference based on the semantic metadata of the parameters.
- Support patient to trial matching through the use of computable eligibility criteria
- Support the addition of data elements to an existing information model and automatically capture and publish the information about the extensions.
- When defining new datasets for caIntegrator's data-warehouse for biomedical data collection and analysis, automatically record these new datatypes in a well-defined and federated manner so that data can be shared.
Wiki Markup \[may replace or merge with 5\]Discover and orchestrate services to achieve LS research goals; e.g. start with a hypothesis, identify relevant services that provides the necessary analysis and data, create the worklow/pipeline, report findings. Workflow related requirements:
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