|в Technische Universität München|
Cross-country post-doctoral fellowship under the EuroTechPostdoc Programme
Identification of imaging phenotypes indicative of cancer mutation profiles
Joint project between the Biomedical Diagnostics Lab (Eindhoven University of Technology) and the Bioinformatics Lab (Technical University of Munich)
Cancer is a leading cause of death and a major healthcare burden worldwide. Selecting the right treatment for the right patient is essential to reduce costs, improve outcome, and spare the patients the severe side effects of ineffective treatment. Genomic analysis has revealed that the response to therapy is dictated by the tumor-specific genetic and molecular landscape, which may change unpredictably in response to treatment. Optimal selection and adjustment of the therapeutic strategy requires predictive biomarkers that can be easily monitored during treatment.
Gold-standard biomarkers, obtained by immuno-histological analysis of surgical or biopsy tissue specimens, quantify microvascular density, hypoxia, cellular proliferation, and the expression of several molecules involved in cancer growth. With the recent advances in DNA sequencing technologies, analysis of tumor gene-expression and mutations profiles has also become possible. However, biopsy-based molecular assays are inherently invasive, they represent only a snapshot of tumor progression in time, and they provide limited spatial localization. This evidences an urgent need for imaging biomarkers able to provide spatial and temporal characterization of cancer in-vivo and non-invasively.
Imaging, and in particular functional and molecular imaging, has shown promise for in-vivo, non-invasive assessment of cancer therapy response. Among all modalities, ultrasound imaging has unique advantages especially for therapy monitoring, whereby frequent measurements are needed. Ultrasound is portable, relatively cheap, widely available (also at the bedside), and has no ionizing radiation.
In this context, we are looking for a highly motivated post-doc candidate to investigate novel methods for in-vivo, non-invasive tumor profiling by multi-parametric ultrasound imaging. By model-based quantification, multiple ultrasound biomarkers reflecting structural, functional, and molecular properties of cancer tissue can be extracted. Probabilistic frameworks (e.g., machine learning) can then be used to map the obtained imaging biomarkers to the underlying tumor genetic/molecular landscape. In-vivo tumor profiling by identification of unique imaging signatures that correlate to specific therapy response and resistance profiles can thus be achieved, possibly enabling optimal selection, monitoring and tailoring of the therapeutic strategy.
Interested candidates should contact Prof. Massimo Mischi (e-mail: email@example.com), dr. Simona Turco (firstname.lastname@example.org), or Prof. Dmitrij Frishman (email@example.com). Further information about the programme is available at http://postdoc.eurotech-universities.eu.