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Use case: Interoperability of large scale image data sets from different biological scales | BioMedBridges

WP6 - Use case: Interoperability of large scale image data sets from different biological scales

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This work package will demonstrate the utility of the interoperability of large scale image data sets from different biological scales (cell – tissue – organism) to enable drug target and biomarker discovery for human disease, with cancer as an example. Based on the standards and services developed in WP2 and 3, WP6 will use integrated access to systematic imaging data of disease gene function in cultured human cells and systematic imaging data available from tissue microarrays of diseased tissue from both human patients and mouse models. This use case will thus link the four BMS Research Infrastructures Euro-BioImaging, BBMRI, EATRIS and Infrafrontier with the standards and services to be provided by ELIXIR. The comparison of morphological image data on cellular phenotypes of individual genes with morphological image data of the diseased tissues in mouse models and human patients could create a powerful predictor of optimized biomarkers as well as drug targets in cancer. Linking these imaging data with molecular data, including the cancer genome sequence and cancer expression data, will allow in silico validation of the predictions and prioritization of biomarkers for validation in clinical research.


Task 1.1. Implementation of interoperability standards and ontologies for reference image data sets. The first task to make integrated image data access usable is to map the (meta)data standards and ontologies present within each image data domain (cell, human and mouse tumor tissue) onto each other to enable correlative analysis. In line with the standards and services developed in WP2, 3 and where applicable respecting the secure access to medical data developed in WP4, we will implement unambiguous maps between the respective metadata. For this we will select high throughput imaging reference data sets with cancer related assays (e.g., as well as tumor tissue and clinical data (we will make these available at:

Task 1.2. Prediction of novel cancer biomarkers (e.g. breast and prostate cancer). Correlative analysis of interoperable cell and tissue image datasets with their associated annotation and metadata will be mined with state of the art bioinformatic tools to predict novel biomarker candidates. A particular focus will be on genes with a function in cell cycle and cell division control as well as invasive behaviour, for which comprehensive molecular and cellular datasets are available. An initial set of predicted biomarkers will then be further cross-validated against current biomolecular databases, drawing on cancer genome and expression data, as well as general sequence and structural properties of the identified genes to also explore their potential as drug targets.

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