Mining spatial and functional immune cell patterns to develop and clinically validate novel prognostic tissue markers
The goal of this consortium is to investigate and exploit the full prognostic potential of inflammation reactions related to tumor cells in hereditary breast cancer as well as for renal allografts after transplantation and make it available for translational research. In subproject SP2 modules for robust image and data analysis are developed so that the spatial context of immune-cell distributions are characterized. These modules are based on work performed within the first funding period. Analysis results are stored in a unified way in databases to make them available for further use of project partners. Based on these data and by developing and applying methods of data mining and systems immunology diagnostic algorithms with high prognostic or predictive power are created.
Using an iterative process and in close collaboration with project partners (especially with SP4) we will discover and validate diagnostically relevant “Phenes”. “Phenes” are mathematically descriptions of spatial and functional entities derived from immune-cell distributions in tissue. Due to the possibly large number of Phene candidates but a restricted availability of patient data, new and robust data mining algorithms have to be developed. Those ensure the successful translation of our findings to prospective studies.
Keywords: Image, analysis, Tissue Diagnostics, Image Mining