Cancer is the second leading cause of mortality in EU member states with ~90% of all cancer deaths caused by metastatic spread. Despite its significance, measuring metastatic potential as well as potential indicators of therapy efficacy remain unmet clinical challenges. Recently, it has been demonstrated in vitro, that aggressive metastatic cells pull on their surroundings suggesting that metastatic potential could be gauged by measuring the forces exert by tumours. Furthermore, many solid tumours show a significantly increased interstitial fluid pressure (IFP) which prevents the efficient uptake of therapeutic agents. As a result, a reduction in IFP is recognized as a hallmark of therapeutic efficacy. Currently, there is no non-invasive modality that can directly image these forces in vivo.
Our objective is the non-invasive measurement of both IFP within tumours as well as the forces they exert on their surrounding environment. This will be used to predict a tumour’s metastatic potential and importantly, changes in these forces will be used to predict the therapeutic efficacy of drug therapy. To attain this goal, the biomechanical properties of the tumour and its neighbouring tissue will be measured via MR-elastography at various measured deformation states. Resultant images will be used to reconstruct images of the internal and external forces acting on the tumour. We call this novel imaging modality Magnetic Resonance Force (MRF) imaging .
We will calibrate MRF via cell cultures and pre-clinical models, and then test the method in breast, liver, and brain cancer patients. Thereby, we will investigate whether MRF data can predict metastatic spread and measure IFP in patients. We will also investigate the potential to non-invasively modulate the force environment of cancer cells via externally applied shear forces with the aim of impacting cell motility and proliferation. This can provide novel mechanism for anticancer therapeutic agents via mechanotransduction.
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FinanzierungsplanRIA - Research and Innovation action
TN23 6NF Ashford
5684 PC Best