Against bRain cancEr: finding personalized therapies with in Silico and in vitro strategies
This project presents an innovative approach towards a personalized treatment and therapy of glioblastoma (GBM), the most common malignant primitive brain tumor. With currently available therapies (surgery followed by chemo and radio therapy) the average life expectancy of the affected patients is only one year. Surgery alone is not able to completely eradicate the tumor due to the infiltrative nature of GBM. Additionally, the cancer cells can rapidly develop resistance to currently available therapies.
In this scenario, our project will have two aims:
- to identify new therapies capable of blocking the proliferative and infiltrative capacity of GBM tumor cells, possibly extending these therapies to low-grade gliomas, to find an effective cure even in the initial phase of the disease;
- to develop new assays able to predict the response of patients to drugs, thus allowing a personalization of the therapy.
- The innovative aspects of this project consists in the development and integration of innovative experimental and computational techniques; namely in:
- the creation and maintenance of a bank of glioblastoma stem cells (GSC) obtained from glioma patients and on the study of different markers useful to select the most appropriate therapy (UNIUD);
- the creation of new 3 D in vitro models to study the invasiveness of GBM cells in an environment closely resembling the brain (SISSA);
- reposition of drugs already in use for different diseases and/or identification of new drug-candidates able to block the invasiveness of GBM via in silico studies. (CNR-IOM);
- assessment of the efficacy of the molecules identified by CNR-IOM, by using live-cell imaging on GSC cells (SISSA);
- and animal models (UNIUD);
- creation of an integrated system (ecosystem) for all the computational aspects of the project: advanced data management services, innovative pipeline for in silico screening of large databases of molecules, Machine Learning algorithms for image recognition and image content quantitative evaluation (eXact-Lab);
- development of commercial systems to predict in vitro the response to therapy (DOTT. DINO PALADIN). The Udine hospital will be the final user and will take care of the eventual clinical experimentation of the most promising molecules.