In clinical practice, a critical necessity nowadays is the detection of the so-called “minimal residual disease” (MRD) after primary surgery in highly metastatic tumors like melanoma. The MRD consists of residual tumor cells and micro-metastases, which could be dormant for years and even decades before they eventually re-emerge as solid metastases.
In preliminary work, we discovered that certain markers in plasma-derived extracellular vesicles (pEVs) have the potential to predict and detect tumor relapse in melanoma.
The aim of the project is to develop, test and prepare for translation into clinical practice a systems-biology-based diagnostic tool for assessing the probability of tumor relapse in melanoma patients, based on the profiling of pEVs.
This project proposal puts together efforts from modelers, bioinformaticians, medical bioinformaticians, and biomedical researchers to develop, test and prepare for translation into clinical practice a data-driven, mathematical-model-based diagnostic tool for assessment of the probability of tumor relapse in melanoma patients. The results we expect will allow individualized diagnostics and treatment in melanoma patients.
Here, the three branches of the computational systems biology (high-throughput data analysis, bioinformatics and mathematical modeling) are merged into a systems biology workflow for the iterative integration of in vitro and clinical data.
Projects like this try to pave the way towards a systems-based translational and personalized medicine, in line with the economic and scientific strategy outlined by German and European scientific committees to promote a biotechnology- and knowledge-funded economy.