Currently, diagnosis and therapy strategies for cancer patients strongly relies on cell- or tissue-based visual assessment of tumor markers. However, in most clinical, pathology and research centers the criteria used for their visual classification is merely qualitative and thus strongly operator-dependent. An inaccurate biomarker evaluation can be as harmful as an inappropriate drug choice in cancer treatment, leading to erroneous treatments with no benefit or even injury for the patient. Thus, there is an urgent need to introduce novel and quantitative methods to improve the assessment of these biomarkers.
In this project, our goal is to develop new bioimaging quantitative tools that will combine morphological characteristics, protein expression profiles and detailed spatial analysis at subcellular level of cells and tissues. Namely, we will focus on cell receptors, cell adhesion and cytoskeleton molecules associated to gastric cancer.

To accomplish this goal, we need (1) to develop novel tracers that improve pattern recognition and facilitate quantification of molecular and cellular/tissue features, and (2) to design new methods to automatically or semi-automatically compute and integrate cell phenotypic and molecular characteristics. Specifically, novel types of fluorescent probes as quantum dots will be used and combined with advanced optical image acquisition to attain accurate biomarker information. Alterations in tissue architecture, cell shape, polarity and size, nucleus-cytoplasm relative size and positioning, will be also numerically evaluated to discriminate cancer.


Principal Researcher: João Sanches