Making cancer care more effective and individualized is a central aim for cancer researchers worldwide. This is usually being pursued via DNA sequencing based efforts and the identification of oncogenic driver mutations. We believe and have emerging evidence that this approach will miss many therapeutic opportunities. In contrast, functional individualized systems medicine (ISM) may help to uncover targeted drugs and drug combinations with unexpected cancer-specific therapeutic potential.
We continuously develop new methods for standardized assays for functional drug testing and understanding subclonal heterogeneity with FIMM Technology Center Core Units. The drug response and prognosis of cancer patients may be strongly dependent on specific cancer subclones e.g cancer heterogeneity, as well as on the diversity and interactions of multiple cell types in cancer (microenvironment). Our aim is to develop and utilize the multiplexed high-content imaging of cancer tissues and patient -derived ex vivo cells in order to understand the cancer biology as well as identify drug responses at the single cell level in ex vivo cell cultures. In collaborative project with FIMM PI Peter Horvath, we have developed machine learning -based image analysis methods for high-content imaging of cancer cells and tissues, and computer-aided laser microdissection method. In 2017, we have also set up a new FIMM High Content Imaging and Analysis Core, for both academy and companies.
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